Authors - Nelson Herrera Herrera, Estevan Gomez-Torres, Paul Baldeon-Egas, Renato Toasa Abstract - The Context-Aware Mobile Systems (CAMS) framework simplifies the development of context-aware mobile applications using Model-Driven Development (MDD). CAMS incorporates a Domain-Specific Language (DSL) built with Xtext, enabling developers to model contextual information, define business rules, and establish event-driven behaviors. The generation of Android applications is automated via Acceleo, supporting the seamless integration of cloud services such as Azure Maps for geolocation, IoT Hub for sensor data management, and Twilio for contextual notifications. To enhance scalability and deployment efficiency, CAMS leverages Infrastructure-as-Code (IaC) solutions through Pulumi and Terraform, automating cloud resource provisioning. This approach reduces development complexity, minimizes manual configurations, and accelerates deployment cycles. A case study on package tracking showcases CAMS' ability to dynamically adjust application behavior in real time based on contextual data from IoT sensors and geolocation services. Evaluation results demonstrate a 40% reduction in development time compared to traditional methods, alongside improved scalability, supporting up to 10,000 IoT devices simultaneously. CAMS offers an innovative framework for developing intelligent mobile applications across various sectors, including logistics, smart cities, and real-time monitoring. Its modular architecture and automated deployment processes enable rapid prototyping and efficient scaling, making it adaptable to diverse application scenarios. Future enhancements include expanding support for iOS applications, integrating additional IoT capabilities, and incorporating AI-driven decision-making tools to enhance real-time responsiveness. CAMS positions itself as a robust and flexible solution for next-generation mobile applications, bridging the gap between contextual awareness and scalable cloud-based infrastructures.
Authors - Thavaprakash Arulsivakumar, Senthil Veerasamy Abstract - The perspectives and insights of sentiments on destinations are important for tourism stakeholders. Purpose of this research work is to understand tourist behaviour with the help of Electronic Word of Mouth (eWOM) and to bring out the cognitive insights using TripAdvisor for Asian destinations. A well-established Stimulus Organism Response (SOR) theory is used as a thematic foundation in this research and ChatGPT based content analysis is conducted using traveler reviews. Sample size of 151872 TripAdvisor review comments from 90 Asian destinations are analyzed in this research. Logistic regression and Structural equation modelling are used to examine the sample data. Empirical findings highlighted that travelers have unique sentiments on each destination and moderate positive sentiments influence the intention to visit. Macro and micro analysis is used in this research, its findings are re-shaping the demand and supply and is helpful for the decision makers to promote destinations.
Authors - Tihomir Dovramadjiev, Petya Manolova, Rozalina Dimova, Dimo Dimov, Vasil Gatev Abstract - This article investigates the interplay between human factors and criminology in identifying both the techniques and motivations behind cyber threats. By analyzing the psychological, social, and environmental drivers of cybercriminals, as well as the methods they employ, this study proposes a comprehensive framework for detecting and mitigating cyber threats. The integration of criminological theories and human factors research offers a novel approach to understanding and countering the evolving tactics of cyber attackers. In the era of Society 5.0, where human-centric technologies and artificial intelligence (AI) are deeply integrated into daily life, the role of human factors becomes even more critical. Human factors research helps uncover how cognitive biases, emotional states, and social influences shape both the behavior of cybercriminals and the effectiveness of defenders. For instance, AI-driven cybersecurity systems, while powerful, still rely on human oversight and decision-making, making it essential to address human vulnerabilities such as fatigue, stress, and misjudgment. Furthermore, as Society 5.0 emphasizes the fusion of cyber and physical spaces, understanding human behavior is key to designing systems that are resilient to social engineering, phishing, and other human-exploitative techniques. By combining main cybersecurity layers insights with human factors, this study not only addresses the technical aspects of cyber threats but also highlights the importance of human-centered design in cybersecurity of data assets.
Authors - Yuki Shirahama, Kayoko Yamamoto Abstract - In order to help yourself, it is necessary for the general public to keep adequate stockpiles in their homes that will be useful at the disaster outbreak times. Additionally, in order to provide mutual aid from the normal times, it is important for local residents to accumulate and share necessary information in a local community without relying on the government to provide information. Against such a backdrop, the purpose of the present study is to design, develop, operate and evaluate an original disaster information system to support self-help and mutual aid from the normal times to the disaster outbreak times, by integrating a stockpiling improvement support system, a local social network service (SNS) and web-geographic information systems (Web-GIS). Chofu City, Tokyo, was selected as the operation target area for the system. During the operation period of the system, the number of users was 57, and the number of information submitted by users was 25. From the result of the online questionnaire survey for users, it is evident that the system was useful in terms of improving the stockpiling situations in their homes at normal times, and in supporting the accumulation and sharing of disaster information among them from the normal times to the disaster outbreak times.
Authors - Marwan Babiker, Eda Merisalu, Zenija Roja, Henrijs Kalkis Abstract - A Health Information System (HIS) is an electronic database of managerial and clinical information, which allows physicians to utilize quality improvement processes in clinical practice and support multiple services in healthcare organizations. According to research, physician satisfaction varies greatly and is frequently correlated with particular features and the ease of exchanging information. The study aimed to identify the traits of doctors' acceptability of using a hospital information system (HIS) and their degree of satisfaction with it. It is a cross-sectional study that used an online self-administered survey, which was conducted among all 56 physicians working in family medicine centers in the Royal Commission health service program, Saudi Arabia. The questionnaire was used to assess the physician’s satisfaction level about the two structures that will be examined: system quality and satisfaction level. The findings showed that the physicians in the family medicine centers have a high agreement proportion about the use and satisfaction of the use of HIS, which indicates a high quality of the HIS. The outcomes measure system excellence and user satisfaction when assessing the effectiveness of HIS in several domains. The participating physicians expressed satisfaction with the way HIS improved their clinical processes and how reliable it was for patient care. More longitudinal studies are required to understand the indicator of physician satisfaction with the HIS use.
Authors - Rehab Hassan Bader, Issa A. Abed, Bayadir A.Isaa Abstract - Multi-Robot Systems (MRS) have developed as a disruptive technology in robotics, allowing multiple autonomous robots to work together to execute complex tasks. Multi-Robot Task Allocation (MRTA) is an important component of MRS since it involves distributing tasks to robots in a way that maximizes performance, efficiency, and resource use. Effective scheduling is crucial in MRTA because it determines the order and timing of task execution, which has a significant impact on overall system performance. This paper investigates current MRTA and scheduling strategies and algorithms, with an emphasis on the challenges raised by dynamic environments and the requirement for flexibility in task allocation. They discuss numerous approaches, including centralized and decentralized methods, constraint-based models, and reinforcement learning techniques, emphasizing the benefits and drawbacks. The combination of reallocation and rescheduling approaches is also being researched as a means of boosting system responsiveness to unanticipated changes. The findings suggest that future research should focus on developing hybrid models that take advantage of existing approaches to create more resilient and efficient multi-robot systems capable of operating in real-world scenarios.
Authors - Victoria Orozco Arias, Ernesto Rivera Alvarado Abstract - Data protection on websites is a common objective for entities and organizations, so new security strategies are constantly being sought. This research evaluates the effectiveness of using security metadata in HTML structures to protect sensitive data. To achieve this, a website scraping experiment will be conducted on sites that include authentication, to verify on which sites this type of fraud can be performed and to identify the use of security metadata. Our results show that 25% of the websites selected for the experiment could be extracted from the database, and the remaining sites have different settings, such as metadata, robots.txt file, and others, to protect and prevent data extraction. This proves that metadata is the first step of website security.
Authors - Ossman Lopez, Joan Alvarado, Juan Diaz-Gonzalez, Elisa Rendon-Cadavid, Sara Lopez, Mikel Maiza, Juan Felipe Restrepo-Arias, David Velasquez Abstract - This study presents the design and development of a non-invasive sensor for measuring the capacitance of cocoa fruits to detect ripening stages and the onset of fungal disease caused by Moniliophthora roreri. Capacitance is recognized in the state of the art as an effective physiological indicator due to its correlation with moisture content, cell development, and glucose levels in fruit. Based on this premise, the hypothesis was that capacitance measurements are directly related to fruit ripening and fungal infection. The sensor system features an electronic circuit that generates a 10V peak output at a frequency of 1 kHz, integrated with a Sauty Bridge for real-time capacitance measurements without harming the fruit. The device is lightweight, user-friendly, and energy-efficient. The experiments were carried out under controlled conditions using the CNCH13 cocoa variety to standardize the measurements. The preliminary results confirm a strong correlation between capacitance levels, ripening stages in healthy fruits, and the presence of fungal disease. The prototype developed effectively measures capacitance, providing a reliable method for assessing the physiological state of cocoa fruits.
Authors - Sunday Adeola AJAGBE, Oluwatobi AKINLADE, Korede Israel ADEYANJU, Olajide KUTI, Ademola Olusola ADESINA, Matthew O. ADIGUN Abstract - Many suggested projects carried out using a cryptography key are meant to guarantee security and privacy of Health Information Systems (HIS). Still, HIS's weakest flaw depending on these cryptographic techniques is key management and resource consumption in computers. This work presents an experimental comparison of asymmetric and symmetric cryptography keys; specifically, RSA (asymmetric) and AES, (symmetric), to ascertain effectiveness and efficiency with respect to the level of consumption of computer resources viz-a-viz Processing Time, computing memory, and CPU consumption. Using the anonymised dataset at https://wiki.openmrs.org/display/RES/Demo+Data, which has been ad-judged to fit for study, was undertaken. The results reveal that through-out the experiment, HIS data increases the RSA and AES both in processing time although RSA indicates higher value compare to AES in all the HIS input. In the same vein, memory usage for the RSA and AES rises as HIS data rises across the trial. For processor consumption, while RSA consistently increased, AES increased only twice, the first one was when HIS input increased from 500 to 1000, AES increased from 0 to 0.01 respectively. Also, HIS input increased from 4500 to 5000, AES increased from 0.01 to 0.03 respectively.
Authors - Danilo Cuichan, Mishell Moromenacho, Freddy Tapia Abstract - According to the Food and Agriculture Organization of the United Nations (FAO), the poultry sector continues to grow and position itself in different parts of the world; this has increased the demand for technological solutions that help increase the productivity of the sector, especially in the specialized poultry farming focused on quail production due to the benefits it offers. In this sense, artificial incubation is a complex task, since the manual selection of fertile eggs is susceptible to errors. Currently, traditional incubation methods depend on the experience of the operator. This is associated with human error, which threatens the profitability and sustainability of the sector. Therefore, the incorporation of new technologies is key in optimizing and automating processes. The present study aims to optimize the pre-incubation selection of quail eggs, which will include the collection and analysis of data related to egg viability (color, pigmentation and physical characteristics), all with the aim of classifying eggs that are optimal for incubation, reducing the loss of time and resources in eggs not suitable for incubation. In addition, comparative tests were performed to quantify the performance of the prototype and the efficiency of the system.
Authors - Kai-Po Chan, Guan-Lin Wu, Yen-Jen Chen Abstract - With the rapid development of IoT technology, smart agriculture has become a key driver in modern agricultural transformation. Agriculture 4.0 emphasizes integrating technology with traditional farming to monitor and manage agricultural production processes, thereby improving efficiency. Sensors, as the front-end components of IoT, can monitor environmental changes in real time, such as temperature, humidity, and light intensity, which are crucial for agricultural production. However, as IoT technology becomes more widespread, data protection mechanisms have become essential, particularly in network transmission, where data security and privacy protection are critical concerns. This study aims to address security issues in the data transmission process. IoT devices can rapidly transmit large amounts of data, and without effective encryption and protection measures, this data is vulnerable to external attacks or hacking during transmission, leading to data leakage or tampering. Such incidents can impact agricultural production and result in the loss of business secrets and financial interests. Therefore, enhancing the security and reliability of data transmission is the core problem this study aims to solve. This research proposes a hybrid encryption mechanism based on RSA asymmetric encryption and random noise generation using the STM32 DAC. This approach enhances data transmission security and resistance to attacks. Using the MQTT protocol, the study achieves real-time data transmission and display, storing data in a database to help agricultural producers monitor environmental changes, promptly identify issues, and make necessary adjustments, thereby improving production efficiency and management convenience.
Authors - Maysha Fahmida, Mst Ridika Mesbahul, Reza Amini, M. A. Quaium, Md Kamruzzaman Sarker Abstract - Health insurance companies need to optimize their services and pricing while ensuring financial sustainability. This study aims to model health insurance cost by analyzing a person’s future health risk based on their historical health conditions and associated diseases, using mortality and cause-of-death data provided by the Centers for Disease Control and Prevention (CDC). By analyzing this data, we uncover patterns and trends that inform the risk assessment process. We then evaluate the performance of various machine learning models in predicting health risks and estimating insurance costs. The results highlight the effectiveness of data-driven approaches in enhancing risk prediction accuracy and cost estimation. Our findings provide actionable insights for health insurance companies to improve personalized pricing strategies and better understand the factors influencing health risks. Finally, we discuss potential improvements and future directions for leveraging advanced data analytics and machine learning in health risk modeling.
Authors - S. M Julkar Naeen Abir, Abdul Kadar Muhammad Masum, Angappa Gunasekaran, Mohammad Ashraful Ferdous Chowdhury, Md. Abul Kalam Azad Abstract - Blockchain technology is redefining the landscape of commercial transactions, ushering in a new era of paperless currency. Yet, when it comes to formulating corporate strategy, adoption is still in its infancy stage. This article aims to assess the essential elements that influence the choice to embrace Blockchain in the business world. Fifteen important criteria were selected through detailed analysis of existing studies and expert comments. The identified factors were divided into 3 categories; technological, social, and business. The cause-effect relationship was examined using the DEMATEL approach. The findings revealed five critical causal factors that influence Blockchain adoption: interoperability issues, lack of acceptability, trust issues, lack of security, and legal infrastructure concerns. The findings provide unique insights into the business sector, allowing companies to increase performance by overcoming significant difficulties. This research also includes a plan for adopting Blockchain in transactions.
Authors - Khoa Thi Minh Tran, Quang Huy Tran, Huu Quang Nguyen, Huu Dung Ngo Abstract - The increasing complexity and expansion demand in modern software applications have led to the emergence of various architectural approaches, among which Model-View-Controller (MVC) and Microservices (MS) are two of the most popular methods. The MVC architecture stands out for its simplicity and ease of implementation, often utilised in monolithic applications with a clearly organised layering system. In contrast, the microservices architecture divides applications into independent services, each focussing on a specific function, thus improving scalability, fault tolerance, and deployment flexibility. This paper focusses on analysing and evaluating the performance (load capacity, response time, scalability) of MVC and MS architectures when deployed on the NestJS platform for E-Commerce website development (specifically, a phone sales website). The results of the website performance evaluation are based on the number of simultaneous client requests each architecture can handle, providing developers with information to choose the most suitable architecture for hightraffic websites.
Authors - John Heland Jasper Ortega Abstract - Artificial intelligence is rapidly reshaping multimedia arts courses by enhancing creative workflows and expanding artistic possibilities. Its integration into creative platforms has allowed students to improve efficiency and explore new techniques, fostering a dynamic digital art environment. However, concerns persist regarding originality, artistic authenticity, and the potential decline of traditional skills. Using the Expectation Confirmation Model, this study examines students' perceptions of AI in multimedia arts. While students acknowledge its convenience and practical benefits, they remain cautious about its long-term impact on artistic development. An N-gram analysis of student feedback reveals a spectrum of opinions ranging from enthusiasm for technological advancements to ethical concerns about copyright, fair attribution, and the future of handcrafted art. Notably, despite recognizing AI’s advantages, students express reservations about its role in shaping creative expression. These findings highlight the need for educational frameworks that balance AI-driven innovation with the preservation of traditional artistic techniques. Future research should explore on how Philippine institutions can integrate AI with traditional arts while educating students on ethics and industry practices to ensure responsible and competitive creative careers.
Authors - John Heland Jasper Ortega Abstract - Artificial intelligence (AI) is revolutionizing digital marketing by enhancing targeting, personalization, and automation, leading to data-driven advertising strategies. AI-powered analytics optimize customer engagement, enabling businesses to deliver highly tailored ads that improve conversion rates and return on investment. Automated tools streamline content creation and campaign management, while chatbots enhance customer interactions on a scale. In the Philippines, AI adoption in marketing is expanding, with local retailers and online platforms leveraging AI for personalized recommendations and programmatic advertising. However, challenges remain, including data privacy concerns, algorithmic bias, and a widening skills gap, as AI proficiency becomes increasingly essential for marketers. While AI offers efficiency, a word cloud analysis highlights concerns about its impact on creativity and the human touch in branding. According to the Technology Acceptance Model, Filipino marketers and multimedia arts students must develop AI literacy and strategic thinking to remain competitive. Ethical considerations also require greater oversight in AI-powered advertising to ensure responsible consumer engagement. The future of digital marketing in the Philippines depends on balancing AI-driven efficiency with human creativity, storytelling, and cultural relevance. Businesses must invest in upskilling initiatives and ethical frameworks to maximize AI’s potential while mitigating risks. Further research should examine AI’s long-term impact on job roles, industry dynamics, and consumer trust. As AI becomes more integrated into marketing strategies, success will hinge on how well professionals merge automation with authentic, human-centric advertising practices.
Authors - Rehab Abdulmonem Ali Alshireef Abstract - The advent of large language models (LLMs) has marked a turning point in artificial intelligence applications within healthcare. Med-PaLM 2, developed by Google, stands out as a specialized model trained on medical data that has demonstrated expert-level performance on the USMLE. This literature review explores the educational potential of Med-PaLM 2 across different learner levels—medical students, residents, and practicing physicians. It evaluates the benefits, limitations, and contextual challenges of adopting such AI tools in the Arab world, particularly in remote education and clinical skills laboratories. While Med-PaLM 2 offers new opportunities for personalized learning and simulation-based training, its integration must be guided by ethical frameworks, policy development, and regional adaptation efforts to ensure equitable and effective implementation.
Authors - Aliou Ngor Diouf, Ibrahimma Fall, Lamine Diop Abstract - SenSCHOOL Ontology is an extension of the CIDOC-CRM (Conceptual Reference Model) designed to model and integrate information about Tariqas, Sufi religious brotherhoods present in West Africa in its diversity. CIDOC-CRM is a widely used generic data model for exchanging and integrating information from a variety of heterogeneous cultural heritage (CH) sources. The main objective of SenSCHOOL is to facilitate the management, preservation, and exchange of information about Tariqas in a structured and organized manner. We describe the methodology and steps we followed to design SenSCHOOL. We present the implementation of SenSCHOOL, which enables the integration and structuring of information from heterogeneous sources.
Authors - Toni Tani, Lasse Metso, Timo Karri Abstract - Digital transformation is reshaping business ecosystems through advances in artificial intelligence (AI), process automation, enhanced analytics, improved information visualization, and increased innovation. This study examines the impact of AI on ecosystems using traditional bibliometric analysis and a unique approach to processing large volumes of textual data. First, 232 documents published between 2014 and 2024 from the Scopus database were analyzed using Bibliometrix and Biblioshiny to identify influential authors, thematic clusters, and emerging research areas. In the second phase, a text network software called Infranodus was used to scan and analyze the 54 most relevant abstracts from 2023-2024, after which the extracted insights were refined using generative AI (genAI). Subsequently, the extracted information was further developed via prompt engineering from visual graphs and ChatGPT, revealing interesting results that demonstrated the potential of genAI in repeatedly conducting research and managing business ecosystems. Ultimately, this study shows a novel way of combining bibliometric data and visual prompt engineering to harness dynamic relations iteratively.
Authors - Syeda Sohail, Maurice van Keulen Abstract - Process mining enables organizations to gain actionable insights into their business processes by analyzing digital footprints extracted from information systems. These insights unravel inefficiencies and exude process enhancement through bottleneck detection and conformance checking. This paper presents a case study where process mining is applied to five real-world event logs of a Commerce Platform-as-a- Service provider to expedite the business process by reducing waiting times and minimizing multiple customer interactions. A comprehensive process mining project methodology was implemented to conduct the case study. The findings revealed key bottlenecks and underlying factors that contribute to delays and excessive customer interactions. In response, process enhancement recommendations were implemented with the organization’s template adjustments for an efficient business process optimization. The study also addresses the dilemma of privacy-utility tradeoff by ensuring that the event logs adhere to privacy-by-design requirements without compromising the utility of the data. Instead, the fulfilled requirements further refined process mining and data analysis by minimizing and abstracting event logs in this relatively less sensitive domain.
Authors - Salvatore Vella, Fatima Hussain, Salah Sharieh, Alex Ferworn Abstract - Policies and procedures coordinate the work of multiple knowledge workers. These are standardized workflows with specified inputs and outputs. AI agents can automate some or all of the steps in the workflow. The automation will greatly enhance efficiency, minimize human errors, enable the employees to focus on more strategic tasks and provide oversight for these more routine tasks. This paper examines the application of AI agents to understand and automate these workflows. We propose a framework where the policy or procedure is corrected via a large language model and translated to a simplified BPEL (Business Process Execution Language) form for later execution by AI agents. This two-step approach enables the creation of reusable policy and procedure libraries that the AI agents can reuse. We demonstrate that improved policies and procedures can be created from the code. Through case studies, we show the practical benefits in real-world office settings. Integrating AI agents in knowledge work professions is an important research topic; this framework shows how this can be done in a standardized way.We provide the source code and artifacts for these experiments.
Authors - Tsholofetso Taukobong, Audrey Naledi Masizana, George Anderson Abstract - The research contributes to the performance of X-Ray Transmission sensor-based sorting process during diamond sorting. The aim is to overcome the shortcomings of the current baseline methods for detection of small highly over-lapped and irregular shaped rock particles that could go undetected as part of the waste recovery process. Most methods work well when approximate shape and size is well known and particles are not highly overlapped. However due to challenges of over-segmentation or under-segmentation, several image segmentation techniques are explored in order to propose a new and improved segmentation process that aims to reduce false negatives and false positives, thus improving performance and efficiency of the waste recovery process. This reports on classical methods explorations and preliminary experiments on the ongoing research
Authors - Emilia-Loredana Pop, Augusta Ratiu, Daniela-Maria Cristea Abstract - In this article, we have performed an analysis related to the subjects Databases, Database Management Systems, and Web Programming for the students enrolled in Computer Science specializations. The data analyzed has been collected during one university year with the help of an anonymous survey. We have focused on students’ evaluation for these subjects, and comparisons related to gender and study lines (English and Romanian) have also been provided. The lectures and the labs were enjoyed for all the subjects, with small remarks, near to the interaction and communication. For databases, query optimization was harder, and for Web Programming, the solving of lab errors brought challenges. The evaluation was high for the subjects and exceeded 62%.
Authors - Ulrich Tedongmo Douanla, Jean Louis Kedieng Ebongue Fendji, Giquel Therance Sassa, Marcellin Atemkeng Abstract - The Internet has become an essential tool for modern activities and a fundamental right for digital inclusion. However, many regions, particularly in Africa, remain underserved, with limited or unstable Internet access. To address this issue, communities jointly with support organizations have deployed community networks, which are wireless infrastructures that provide connectivity to local populations. Despite their benefits, these networks frequently experience outages that impact both network infrastructure and associated services. Ensuring their reliability requires effective monitoring and supervision solutions. In this work, we propose a supervision platform that leverages the Simple Network Management Protocol (SNMP), log file analysis, and Big Data technologies to enable real-time monitoring of community networks. SNMP is employed to collect device status data, while log files provide insights into the performance of network applications. To facilitate scalable and real-time processing, we integrate Spark Structured Streaming, enabling continuous data analysis and proactive issue detection. The platform also includes an alerting system that delivers notifications via SMS, email, or other channels in case of failures. By providing a comprehensive view of network health and automating incident response, our solution enhances the availability and resilience of community networks, ultimately improving Internet access in underserved regions.
Authors - Monica Alonso, Hortensia Amaris, Maria Angeles Moreno, Farzaneh Abdollahi, Lucia Gauchia Abstract - This paper introduces a novel approach to applying artificial intelligence algorithms based on Reinforcement Learning (RL) for microgrid energy management. Two energy storage systems are considered: stationary battery storage and electric vehicle batteries with G2V/V2G capability. The proposed energy management algorithm considers (i) the uncertainty of photovoltaic energy production, (ii) fluctuations in electricity market prices, and (iii) driver anxiety concerning the vehicle’ range at departure time. The significance of specific parameters, such as time horizon selection and the constant value related to the electric vehicle driver’s anxiety, are examined to optimise the RL reward. Results demonstrate the algorithm's excellent performance under different scenarios.
Authors - Muhammad Al-Zafar Khan, Jamal Al-Karaki, Marwan Omar Abstract - In this paper, we present a multi-agent reinforcement learning (MARL) framework for optimizing tissue repair processes using engineered biological agents. Our approach integrates: (1) Stochastic reaction-diffusion systems modeling molecular signaling, (2) Neural-like electrochemical communication with Hebbian plasticity, and (3) A biologically informed reward function combining chemical gradient tracking, neural synchronization, and robust penalties. A curriculum learning scheme guides the agent through progressively complex repair scenarios. In silico experiments demonstrate emergent repair strategies, including dynamic secretion control and spatial coordination.
Authors - Arinta Athaya Kayana, Candiwan Candiwan Abstract - Rapid digital transformation in various sectors has changed how individuals interact with technology, including in essential services such as fuel purchases. While the MyPertamina app promises to revolutionize access to subsidized fuel, its adoption has fallen short of expectations, raising curiosity about the underlying factors influencing user uptake. This study aims to explore the factors that influence the adoption of the MyPertamina application using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. The methodology used is a quantitative approach through distributing questionnaires to MyPertamina users. The analysis results showed a significant relationship between performance expectations, government regulations, and the reliability of institutional sources on behavioral intentions and adoption. However, factors such as effort expectancy, awareness, perceived financial cost, technological infrastructure support, government regulation and institutional privacy concerns did not show a significant impact towards behavioral intention and adoption. The findings highlight the importance of improving user perceptions of the app’s performance and ensuring the reliability of institutional sources for increase behavioral intention and adoption. Additionally, refining regulatory and enhancing app features for a smoother refueling experience are crucial in encouraging broader usage. By addressing these factors, stakeholders can implement more effective strategies to increase the adoption.
Authors - Gloria Virginia, Maria Amanda, Budi Susanto, Umi Proboyekti Abstract - The digital preservation of traditional culinary knowledge is essential to sustaining cultural identity amid rapid technological change. This study focuses on improving the usability of a semantic web-based portal for Indonesian traditional food through a user-centered design approach. The redesign involved card sorting, tree testing, and prototype usability evaluation. Key improvements include enhanced navigation structures, clearer labeling, and the application of interface patterns such as fat menus, breadcrumbs, and carousels. Usability testing showed notable increases in success rates and navigation efficiency, with a System Usability Scale (SUS) score of 85.5, indicating excellent usability. The integration of information architecture principles significantly improved user interaction and content discoverability. This research demonstrates that combining cognitive-oriented design with semantic data models creates more intuitive and accessible cultural heritage platforms.
Authors - Shahin Doroudian, Mohsen Dorodchi Abstract - Firefighting operations in hazardous environments demand agile and adaptive strategies to effectively combat fires while safeguarding the lives of firefighters and civilians. Traditional approaches often rely on predetermined protocols, which may not adapt well to dynamic and unpredictable situations. This paper proposes a novel framework utilizing Reinforcement Learning (RL) to optimize rescue mission strategies. By harnessing the power of RL, this approach enables AI agents to learn and adapt their behavior based on environmental cues and feedback, leading to more effective and responsive rescue operations. The framework integrates various factors such as terrain and path complexity and the presence of hazards, like fire and smoke, into the decision-making process. Through iterative learning, agents evolve their strategies, identifying optimal paths and rescue tactics. Furthermore, this proposal discusses the potential benefits of employing DRL in rescue missions, including enhanced adaptability, scalability, and robustness in diverse and challenging environments. The adoption of RL to optimize strategies for rescue missions represents a significant opportunity to progress in the disaster response domain. At the end, our results show that the RL-driven method enhances rescue operation outcomes and safety by reducing rescue times, increasing the number of people rescued, optimizing resource utilization, and boosting overall efficiency.
Authors - Yijun Shao, Ying Yin, Debby Tsuang, Phillip Ma, Edward Zamrini, Ali Ahmed, Charles Faselis, Katherine Wilson, Karl Brown, Qing Zeng-Treitler Abstract - This study involved the development and evaluation of a novel Deep Neural Network (DNN) model for Alzheimer's disease and related dementias (ADRD) phenotyping. The model was initially trained on a large cohort of 100,000 cases and controls and subsequently fine-tuned using a smaller, expert reviewed dataset of 1,200 individuals. The final fine-tuned model achieved an Area Under the Receiver Operating Characteristic curve (AUC) of 0.832. For further validation, the model's predictive capability was assessed in a separate randomly selected patient cohort comprising individuals without an ADRD diagnosis from 2009 to 2018. The survival analysis shows that patients with higher predicted ADRD risk scores exhibited a significantly increased incidence of developing ADRD after their index date within five years.
Authors - Bat-Erdene Batsukh, Ariunbold Tsoodol Abstract - Agentic AI significantly enhance the efficiency and quality of news writing by generating engaging headlines and concise summaries while maintaining the accuracy and objectivity of the original content. These tools leverage advanced algorithms and large language models (LLMs) to streamline the content creation process, ultimately improving audience engagement and journalistic productivity. The approaches range from using neural network algorithms to LLMs and question-answering systems, each with its unique strengths and challenges. Agent, like News writers, operate in a dynamic and fast-paced environment where staying updated with current global events is crucial. The digital era has transformed how journalists gather, verify, and disseminate information. The integration of Natural Language Processing (NLP) into a Flutter-based news app offers an amazing way to deliver personalized news feeds tailored to individual user experiences. By leveraging NLP techniques, the app can analyse user behaviour, understand content, provide relevant news recommendations, neural machine translations and news content summarizations. Developing a centralized AI-powered news app using Flutter presents a unique set of challenges that span technical, ethical, and operational domains. We conducted this research to discover ways to overcome these challenges and spectacles. In particular, we used artificial intelligence to automatically translate news into the reader's native language and to provide a summary view instead of a large amount of text when reading in detail.
Authors - Dmitry A. Zaitsev, Alistair A. McEwan, Alexander A. Kostikov Abstract - Real-time embedded applications are normally viewed as continuous processes and are often specified using Partial Differential Equations (PDEs) and with certain boundary conditions. In this paper we present techniques for fast mass-parallel numerical solving of PDEs. We compose specialized lattices based on the integer number approximation specified with Sleptsov nets to be implemented as dedicated hardware, which we prototype on an FPGA. For mass-parallel solving of PDEs, we employ ad-hoc finite-difference schemes and iteration methods that allow us to recalculate the lattice values in a single time cycle with appropriate accuracy suitable for control of hypersonic objects and thermonuclear reactions.
Authors - Mazdak Zamani Abstract - As generative AI (GenAI) becomes increasingly accessible and integrated into software development and education, there is a growing tendency to apply AI-based solutions—even when traditional, deterministic algorithms would be more appropriate. This paper discusses the critical differences between AI-based heuristic methods and deterministic algorithmic approaches, and argues for responsible, context-aware deployment of AI in problem-solving. Misusing GenAI in domains that demand precision, efficiency, or guaranteed optimality can lead to inefficient or even incorrect solutions, undermining both technical integrity and academic rigor.
Authors - Mmatshipi Chaba, Khutso Lebea Abstract - Transforming communication, 5G technology delivers exceptional speed, low latency, and enhanced connectivity, making it essential for the future of various industries. This research highlights the evolution and key components of 5G. A case study demonstrates its effectiveness as the ultimate solution, supported by concrete evidence of its transformative impact. Furthermore, there will be an exploration of 5G in general, focusing on policies governing 5G deployment and emphasising their critical role in ensuring security, privacy, and equitable access. Additionally, the advantages of 5G in improving operational efficiency within physical systems are highlighted.
Authors - Marco Torres-Umana, Ernesto Rivera-Alvarado Abstract - This paper presents a reinforcement learning (RL) approach for validating graphical user interfaces (GUIs) on networking devices. Traditional methods, including manual and automated approaches, face challenges in scalability, efficiency, and adaptability. The proposed RL solution generates test cases dynamically, exploring diverse GUI states and behaviors without relying on pre-established models or extensive data. By leveraging internal and external device observations and encoding techniques, the RL agent effectively navigates GUIs. Results demonstrate high solution similarity and shorter convergence times across various configurations, enhancing test coverage while minimizing manual effort. Future work will refine reward definitions, tackle larger state spaces, and extend the system to support additional devices and vendor interfaces.
Authors - Hiep. L. Thi Abstract - The Elliptic Curve Digital Signature Algorithm (ECDSA) has become a cornerstone in modern cryptographic applications, offering efficient and secure digital signatures. This article reviews recent advancements and notable events related to ECDSA, highlighting its growing adoption and addressing security considerations.
Authors - Layan Sawalha, Jiamei Deng, Temitope Omotayo Abstract - The accurate analysis of hybrid medical datasets consisting of textual reports and diagnostic images plays an important role in the early detection and better outcomes of breast cancer patients. A novel deep learning framework is proposed in this paper that combines Bidirectional Encoder Representations from Transformers (BERT), Generative Pre-trained Transformer-2 (GPT-2), and Convolutional Neural Networks (CNN) to overcome the challenges of multimodal data in breast cancer. This framework combines the strengths of BERT and GPT-2 for extracting rich contextual features from text with CNNs for capturing complex patterns in diagnostic images. By integrating textual and visual features into unified latent representations, this fusion enables accurate classification of breast cancer, distinguishing malignant from benign cases using both text and imaging data. The proposed framework lessens the bottleneck in multimodal to achieve outstanding results with an accuracy of 1.00, hence remarkably improving the precision of breast cancer diagnostic.
Lecturer, Lecturer, University of Botswana, Botswana
Mrs Tsholofetso Taukobong is a Lecturer with the Department of Computer Science, University of Botswana. She hold an MSc and BSc in Computer Science, both from the University of Botswana and is currently pursuing her PhD studies in Computer Science specializing in Computer Vision... Read More →
Authors - Ernesto Rivera-Alvarado, Saul Guadamuz Abstract - Predatory journals are characterized by publishing research articles without the proper peer-review process. They are mainly focused on getting money from authors, regardless of the quality of the article being submitted, in a scenario where academics and researchers are usually incentivized to create a high volume of research to advance their career paths. While publishing in a predatory journal is easier than in a reputable publisher, the author’s credibility could be tarnished. The rise of large language models has provided a new danger for this scenario. It is now easier to create low-quality research papers using IA tools that could be sent to predatory publishers that fail to perform a rigorous peer-review process. A bad combination of fast and poorly created research papers with highly available pay-to-publish journals provides an unsafe scenario for science publishing and unaware scientists. This paper aims to warn researchers about the dangers of generating low-quality research and publishing in predatory journals.
Authors - Mohammadmahdi Ghobadi, Renee Bryce Abstract - Modern applications are built from many small services that work together. In these systems, an API gateway plays a key role in routing requests, balancing the load, and processing events. Most API gateways use fixed rules that do not change with shifting workloads, causing delays as high-priority events may be blocked by less critical tasks. In this paper, we propose a new method that uses reinforcement learning to create adaptive API gateways. Our system learns from real-time performance data such as CPU usage, memory usage, event latency, arrival rates of high-priority events, and throughput of low-priority events. By using multiple metrics, the system adjusts its settings to respond to changing conditions. It processes high-priority events immediately while grouping low-priority events into batches for efficient processing. We use the Proximal Policy Optimization (PPO) algorithm because it is stable and effective for learning the best settings. We evaluated our method using simulations that mimic real-world conditions. The results show that our approach reduces the delay for highpriority events by about 41% and significantly lowers the delay for lowpriority events. The system also uses fewer resources than rule-based gateways. These improvements demonstrate that considering multiple performance metrics can lead to smarter, more adaptive API gateways. Our approach adapts very quickly to varying workloads, ensuring remarkably reliable operation during sudden spikes. Our work shows that reinforcement learning can improve API gateway performance for modern applications. This is important for fields such as finance, healthcare, and emergency systems, where fast and reliable responses are critical.
Authors - Paula Poveda-Sotomayor, Henry N. Roa, Edison Loza-Aguirre, Javier Guana-Moya, Nelson Salgado-Reyes Abstract - Water treatment facilities require efficient and continuous monitoring to ensure safe drinking water. Traditional Supervisory Control and Data Acquisition (SCADA) systems often face limitations in scalability, cost-effectiveness, and real-time data accessibility, prompting interest in alternative technologies such as the Internet of Things (IoT). This study proposes a cloud-integrated IoT framework explicitly designed for real-time monitoring of key water quality parameters, including pH, turbidity, and flow rate. The framework securely transmits data using MQTT protocol to AWS IoT Core, leveraging AWS Timestream and Grafana dashboards for advanced analytics, visualization, and proactive anomaly detection. The system's feasibility and performance were validated through simulation, guided by the Design Science Research (DSR) methodology. Results indicate significant potential benefits, including improved real-time responsiveness, reduced manual monitoring requirements and enhanced operational decision-making. This framework provides a scalable and secure solution that addresses existing SCADA limitations, laying the groundwork for future integration into smart water management practices.
Authors - S. M. Mizanoor Rahman Abstract - We proposed and investigated a bidirectional trust-triggered cyber-physical-human (CPH) system framework for human-robot collaborative assembly in flexible manufacturing. For this purpose, we developed a one human-one robot hybrid cell where the human and the robot collaborated with each other to perform the assembly operation of different manufacturing components in a flexible manufacturing setup. In the proposed framework, we configured the human-robot collaborative system in three interconnected components of a CPH system: cyber system (software system), physical system (the robot, sensors and necessary hardware), and human system (the human co-worker, supervisor and work environment). We divided the functions of the CPH framework into three interconnected modules: computing or computation, communication and control. We used a model to compute the human and robot’s bidirectional trust in real-time to monitor the performance of the CPH framework. We evaluated the performance of the CPH framework implementing it on an actual human-robot collaborative assembly setup considering systematic variations in the complexity levels of the computing module. The overall results revealed that variations in computing complexity significantly impacted the performance of the CPH framework in terms of human-robot interactions and task efficiency and quality. The results thus proved that it became easy and effective to monitor the performance and interactional effectiveness of a human-robot collaborative system modularly when the system was configured in the form of a CPH system. The results can transform the design, development, analysis and control of human-robot collaborative systems for various applications such as industrial manufacturing and assembly, military operations, transportation, healthcare and rehabilitation, construction, social services, etc.
Authors - Oleksandr M. Khimich, Elena A. Nikolaevskaya, Pavlo S. Yershov Abstract - Virtual assistants (VAs) are becoming an increasingly popular tool that helps users find information faster and more accurately. This paper proposes a new AI-powered Virtual Travel Guide (GuideAI) system. The concept, architecture, and implementation of a microservice for intelligent travel search are explored in detail. The study focuses on technical aspects, particularly the integration of GPT for analysis and personalization. It highlights how geolocation and personalization can be combined to create more adaptive, user-responsive systems. The proposed approach enhances travel experiences by providing real-time, tailored recommendations based on user preferences and location. The scientific contribution of this research lies in the development of a novel AI-driven approach to travel assistance, demonstrating how advanced natural language processing and contextual data analysis can improve recommendation accuracy. By leveraging machine learning techniques, GuideAI refines search results, enhances decision-making processes, and optimizes user interactions. This work contributes to the field of intelligent information retrieval and personalized AI systems, offering insights for future applications in tourism and beyond.
Authors - Alvaro A. Casanova, Elvis E. Gaona, Oscar Acosta Abstract - This paper introduces a stochastic modeling framework for analyzing binaural urban noise in Bogotá, Colombia. A total of 4,200 five-minute audio recordings were collected using binaural sensors placed at seven urban locations over 47 days. To analyze the data, we utilized cloud-based tools, including Inferencer app for machine-learning-based sound classification and Soundmetrics app for extracting acoustic parameters such as LEQ (equivalent continuous sound level), IACC (inter-aural cross-correlation coefficient), and WIACC (weighted inter-aural cross-correlation coefficient). The probabilistic analysis revealed instances of extreme noise events exceeding 85 dB. We identified correlation patterns that informed the development of a predictive model based on discrete-time Markov chains. Which incorporated both noise intensity and sound source classification into composite acoustic states. The simulated trajectories effectively captured the temporal dynamics of urban acoustic conditions, successfully meeting the study’s objective of modeling future noise dynamics. This approach demonstrates a scalable solution for real-time monitoring, statistical characterization, and predictive modeling of complex urban soundscapes, providing actionable insights that support data-driven decision-making in smart city planning.
Authors - Ramiro Ramirez, Yasmany Garcia-Ramirez, Jandry Jaramillo Abstract - This research explores the potential of mobile applications in improving road safety education and driver behavior. Road traffic crashes, particularly those involving vulnerable road users, continue to represent a significant global public health issue. Traditional road safety education methods often lack engagement and immediate feedback, limiting their effectiveness in promoting long-term behavioral change. This study introduces SafeJourney, a mobile application developed to enhance road safety learning through real-time feedback, location-based alerts, and personalized progress tracking. The application integrates GPS technology and mobile sensors to monitor driving, cycling, and pedestrian behavior, providing users with timely feedback to reinforce safe practices. The development of SafeJourney followed a modified SCRUM methodology, ensuring iterative and efficient progress. Key features include real-time alerts, integration with traffic law databases, and a user-friendly interface designed with Figma and React Native. Testing and validation confirmed the application's functionality, including low-latency tracking, device compatibility, and seamless integration with a cloud-based database. SafeJourney represents a promising innovation in road safety education, offering an interactive and scalable solution to reduce traffic-related injuries and fatalities.
Authors - Gery Prasetyo, Tanty Oktavia, Mohammad Ichsan Abstract - This research presents the current state of development and implementation of AI predictive analytics for project management support through the lens of Indonesian financial projects, highlighting a practical implementation gap and its impacts towards the fulfilment of enhancements in the core project management aspects of risk prediction, budget and resource estimation, deliverables quality monitoring, and automation of project financial processes. A qualitative methodology is applied to explore the various field experiences and sentiments of five financial project professionals in individual financial firms through direct semi-structured interviews. Analysis of the findings affirms the implementation of machine learning predictive analytics in the four key project areas, with tasks in risk strategy implementation, budget estimation, and resource allocation management revealed as the prominent applications utilized by respondent financial companies. Benefits in streamlining tasks and improvements towards project efficiency are noted by the respondents, along with enhancing team awareness against unseen risks and resource issues, setting new standards for quality management, and reducing human errors in rapid financial processing. The findings further uncovered key barriers in predictive analytics implementation success stemming from system maturity and accuracy issues, and moreover the influence of intangible human factors towards the financial sector business environment and practices. Improving the synergy of the technical capabilities predictive AI systems with human subjectivity and intuition is paramount to ensure successful implementations of predictive project support tools in the Indonesian finance sector.
Authors - Omar Munoz, Adolfo Ruelas, Pedro F. Rosales-Escobedo, Jorge E. Ibarra-Esquer, Ruben A. Reyes-Zamora, C. Aguilar-Avelar Abstract - The increasing demand for electricity and the integration of smart grid technologies have highlighted the need for advanced energy monitoring solutions. Non-Intrusive Load Monitoring (NILM) is essential for breaking down total energy consumption into individual appliance-level data, enabling more efficient and sustainable energy management. This work introduces the development and deployment of an IoT-enabled NILM system tailored for residential spaces, leveraging deep learning techniques to enhance classification accuracy. The system integrates a smart meter for real-time event detection and utilizes WaveNet, a deep neural network originally designed for speech processing, to classify appliance ON/OFF events based on electrical parameters. A comparative analysis with 1D-Convolutional Neural Networks (1D-CNN) and Long Short-Term Memory (LSTM) demonstrates that theWaveNet model can also achieve high classification accuracy, reaching 98.84%. The system’s performance was validated using real-world residential data, showcasing its practicality and scalability for real-time energy monitoring. These findings contribute to advancing NILM research by demonstrating the potential of deep learning models in smart energy applications.
Authors - Pedro Filipe Oliveira, Paulo Matos Abstract - This paper proposes the implementation and evaluation of an intelligent environment system designed to enhance the management of comfort preferences at a campus residence setting. With the growing importance of personalized comfort in shared living spaces, the integration of smart technologies offers promising solutions to cater to individual needs while optimizing energy efficiency. Leveraging sensors, actuators, and machine learning algorithms, the proposed system aims to dynamically adapt environmental conditions such as temperature, lighting, and ventilation based on occupants’ preferences. Through a combination of user-centric design, data analytics, and automation, the intelligent environment offers a seamless and intuitive interface for residents to interact with and customize their living environment. Furthermore, the paper discusses the practical challenges and opportunities associated with deploying such a system in a campus residence, including privacy concerns, user acceptance, and scalability. The effectiveness of the proposed solution is evaluated through energy consumption analysis, and feedback mechanisms, highlighting its potential to enhance comfort, well-being, and sustainability in residential settings. Ultimately, this research contributes to the advancement of smart living technologies and informs the design of future intelligent environments tailored to the needs of campus residences and similar shared living spaces.
Authors - Telmo Sampaio, Pedro Filipe Oliveira, Paulo Matos Abstract - This paper proposes the implementation and evaluation of an intelligent environment system designed to enhance the management of comfort preferences in a residence setting on campus. With the growing importance of personalized comfort in shared living spaces, the integration of smart technologies offers promising solutions to meet individual needs while optimizing energy efficiency. Leveraging sensors, actuators, and machine learning algorithms, the proposed system aims to dynamically adapt environmental conditions such as temperature, lighting, and ventilation based on occupants’ preferences. Through a combination of user-centric design, data analytics, and automation, the intelligent environment offers a seamless and intuitive interface for residents to interact with and customize their living environment. Furthermore, the paper discusses the practical challenges and opportunities associated with deploying such a system in a campus residence, including privacy concerns, user acceptance, and scalability. The effectiveness of the proposed solution is evaluated through energy consumption analysis, and feedback mechanisms, highlighting its potential to enhance comfort, well-being, and sustainability in residential settings. Ultimately, this research contributes to the advancement of smart living technologies and informs the design of future intelligent environments tailored to the needs of campus residences and similar shared living spaces.
Authors - Kunihiko Takamatsu, Sayaka Matsumoto, Nobuko Miyairi, Kin-Leong Pey, Alison Elizabeth Lloyd, Roy Tan, Eng Hong Ong, Jingwen Mu, Fiona Rebecca Sutherland, Mun Heng Tsoi, Sin Yi Yap, Hidekazu Iwamoto, Tokuro Matsuo, Noriko Ito, Tsunenori Inakura, Shotaro Imai, Nobuhiko Seki, Ford Lumban Gaol, Takafumi Kirimura, Taion Kunisaki, Kenya Bannaka, Ikuhiro Noda, Ryosuke Kozaki, Aoi Kishida, Katsuhiko Murakami, Yasuo Nakata, Masao Mori Abstract - Contemporary higher education institutions face increasingly complex challenges—including hybrid teaching, governance reform, and digital transformation—that traditional divisions between academic and administrative roles struggle to address. In this context, new hybrid faculty roles are needed to support organizational learning and innovation across institutional boundaries. This study explores how Abduction-Driven Management Faculty can contribute to expanding Knowledge Networks in Higher Education through the Eduinformatics framework. Contemporary higher education faces multifaceted challenges requiring interdisciplinary approaches. Eduinformatics, integrating educational principles with informatics methodologies, offers a structured framework for addressing these complexities. The research examines knowledge creation through the Knowledge Network Tag Model, where "tags" function as catalysts connecting seemingly unrelated knowledge components. Abduction, as a creative inference process, complements this model by generating explanatory hypotheses from observed phenomena. Post-pandemic transformations have high-lighted the need for hybrid faculty roles that transcend traditional administrative-academic boundaries. The study presents innovative positions like "Professor for Institute Management" that enable boundary-spanning activities. By engaging in international forums and creating environments for "designed serendipity," management faculty can foster abductive reasoning and institutional innovation. This approach, structured through frameworks like ABDU-M, enhances universities' capacity to adapt to rapidly changing educational landscapes by identifying patterns and generating hypotheses from complex educational data.
Authors - Natalia Martinez–Rojas Abstract - Achieving sustainability and productivity in agriculture, particularly in water-scarce regions, relies on the optimal allocation of water resources. In this paper a fuzzy logic defining model is proposed to maximize water resources allocation. Based on environmental data, crop water requirements, and soil moisture levels, this model updates irrigation schedules. This approach can increase the efficiency of water use, decrease the waste and be generally more sustainable. The model uses a rule-based fuzzy inference system to assess irrigation needs in real time, adapting to changing weather and soil conditions. Refining fuzzy logic-based modelling to evaluate scenarios and design policies, the study is an extension of previous efforts that moved away from prescriptive decision-making methods. The results show potential water savings without compromising crop yields, highlighting the practical relevance of this methodology.
Authors - Michael Efren Sutanto, Tanty Oktavia, Mohammad Ichsan Abstract - This study aims to detail the extent of development and implementation of generative AI technology for project management support in the financial services sector, focusing on the impacts of the practical underdevelopment gap phenomena towards realization of benefits in key project tasks of risk management, budget and resource allocation, and product or deliverables quality management. Utilizing qualitative methodologies, five semi-structured interviews were conducted with various financial project experts to uncover experiences and sentiments towards generative driven project management AI support tools in project practice. The analysis of findings discovered notable practical impacts were realized in specific subtopics of the project management areas studied, namely project tasks of risk identification and budgeting estimations. Generative AI project tools are conventionally applied as comparison and visualization tools, aiding in project team awareness throughout planning and improving efficiency through automated generation of general risk registers and preliminary budget and resource requirement documents. The findings further validate the necessity of human subjectivity as the driving factor of the practical implementation and academic research gap of project management generative AI.
Authors - Santhi Bharath Punati, Venkata Akhil Kumar Gummadi, Sandeep Kanta, Praveen Damacharla Abstract - The rise of global e-commerce demands accurate sentiment analysis across multiple languages to enhance customer experience and decision-making. However, existing sentiment analysis models struggle with multilingual and code-mixed data, leading to inconsistencies in customer sentiment interpretation. This research presents an advanced deep learning framework that integrates Multilingual BERT (mBERT) embeddings with an Attention-Augmented Gated Recurrent Unit (GRU) network to improve sentiment classification across diverse linguistic contexts. A dataset of 13,000 customer reviews spanning English, Hindi, Hinglish, German, and Spanish was processed using mBERT for contextual embedding, addressing tokenization and syntactic variability challenges. The proposed hybrid model leverages transformer-based contextual understanding with the sequence modeling capabilities of GRU, while the attention mechanism enhances key sentiment features. Experimental evaluations demonstrate the superiority of our model, achieving 93.45% test accuracy and a test loss of 0.0974, outperforming conventional architectures such as LSTM, BiLSTM, and BiLSTM-GRU. The results confirm the model’s effectiveness in maintaining contextual integrity and sentiment accuracy across multilingual datasets. This framework offers a scalable and adaptable solution for e-commerce platforms, enabling businesses to derive precise sentiment insights from global customer reviews. By addressing challenges in multilingual sentiment analysis, our approach facilitates personalized customer engagement, improved product recommendations, and strategic business decisions. Future research may explore expanding sentiment analysis to low-resource languages and real-time feedback systems, further strengthening the inclusivity and intelligence of e-commerce analytics.
Authors - Janset Shawash, Mattia Thibault, Juho Hamari Abstract - This paper explores Spatial Augmented Reality (SAR) implementation for cultural heritage interpretation, focusing on built heritage and interactive storytelling. Using Finland's Finlayson Factory as a case study, we investigate how SAR bridges digital narratives with physical historical contexts. We propose a workflow for transforming virtual narratives into spatial experiences through Research through Design that covers narrative analysis, spatial selection, conceptual translation, and evaluation. Our approach emphasizes accessibility, intuitive interactions, collaborative engagement, and immersive storytelling. Practical considerations including budget planning and operational integration are addressed to assess feasibility. This concept contributes insights for museums adopting interactive technologies to enhance visitor engagement with historical content.
Authors - Vishnu Kumar Abstract - Heart disease remains a leading cause of mortality in the United States, responsible for approximately 1 in 5 deaths in 2022. Modifiable behavioral and lifestyle factors, such as smoking, physical activity, and diet, play a critical role in cardiovascular risk. This study applies a machine learning (ML) approach to predict heart disease risk in the U.S. using data from the 2022 Behavioral Risk Factor Surveillance System (BRFSS). Three ML based classification models were developed using ten key behavioral and lifestyle features: general health perception, days of poor physical and mental health, time since the last checkup, physical activity engagement, average sleep duration, smoking status, e-cigarette use, body mass index (BMI), and alcohol consumption. Among the three ML based classification models, XGBoost exhibited superior performance, achieving an F1-score of 0.92 with balanced precision and recall across both classes. SHAP (Shapley Additive Explanations) was then used to identify the impact of behavioral and lifestyle factors on heart disease risk. Global SHAP analysis revealed that general health, poor mental health, and BMI were the most influential features affecting heart disease risk. Local SHAP analysis showed that the importance of individual features varied across different observations, with factors such as: time since the last checkup, and smoking status significantly influencing heart disease risk for certain individuals. These findings demonstrate the potential of explainable ML techniques to identify actionable, personalized cardiovascular risk factors. The insights gained can help healthcare providers tailor interventions and prevention strategies, prioritize high-risk individuals for early detection, and allocate resources more effectively to reduce the burden of heart disease.
Authors - Fawzy Alsharif, Hasan Kaan Aldemir, Akay Deliorman Abstract - This paper presents image processing techniques for detecting eye diseases such as Vessel Tortuosity (VT), Glaucoma, Central Serous Retinopathy (CSR), and Diabetic Retinopathy (DR). The system supports early symptom detection, condition monitoring, and timely intervention. For VT, green channel extraction, Gaussian blurring, and Otsu thresholding isolate vessels, followed by morphological operations and thinning for curvature analysis. In Glaucoma, contrast enhancement and multi-level Otsu thresholding segment the optic disc and cup, enabling Cup-to-Disc Ratio calculation. For CSR, green channel processing and Gaussian blurring highlight fluid accumulation. In DR, lesion visibility is improved through green channel extraction, blurring, and morphological filtering. This integrated approach enhances image clarity and segmentation, achieving 97%–99% accuracy in early disease detection.
Authors - Doaa Abdelrahman, Heba Aslan, Mahmoud M. Nasreldin, Ghada Elkabbany, Mohamed Rasslan Abstract - The Bitcoin economy has grown significantly and rapidly, reaching an estimated market capitalization of around $1.87 trillion. As a type of cryptocurrency—essentially digital money—Bitcoin enables direct transactions between users without relying on a central authority or intermediary. These transactions are validated by network participants using cryptographic techniques and are permanently stored in a decentralized public ledger known as the blockchain. New Bitcoins are introduced into circulation through a process that is called mining, and they can be traded for conventional currencies, goods, or services. The dramatic increase in Bitcoin’s value has drawn the attention of both cybercriminals aiming to exploit system flaws for profit and researchers working to identify these vulnerabilities, devise protective measures, and anticipate future trends. It outlines the Bitcoin protocol by describing its main components, their functions, and how they interact. Moreover, it explores the foundational cryptographic concepts and existing weaknesses within the Bitcoin infrastructure and concludes by assessing the strength and effectiveness of current security approaches.
Authors - Faycal Fedouaki, Mouhsene Fri, Kaoutar Douaioui, Ayoub El Khairi Abstract - The merging of blockchain and the industrial internet of things (IIoT) will reshape how smart manufacturing systems operate. This paper proposes a conceptual framework for using blockchain's decentralization architecture, cryptographic integrity, and smart contract automation to improve process monitoring in industrial environments. With real-time data collection from IIoT devices and secure transparent Blockchain ledgers, the proposed model addresses important issues such as tampering, interoperability, and latency when it comes to decision making. It also supports real-time analytics through incorporating reduced latencies using edge processing and message queuing. Additional design principles will address scalability issues by layered Blockchain structures and fog computing nodes, allowing the framework to keep pace with rising data volumes and increasing device densities. Even though the model is built on the latest breakthroughs and conforms to the Industry 4.0 paradigms, a prototype and experimental simulations are planned to value its empirical viability. Notably, this work intends to establish a resilient and efficient digital infrastructure for Next Generation industrial process monitoring.
Authors - Davide Paglia, Lorenzo Rigatti, Andrea Sabatini, Fabrizio Venettoni Abstract - In the context of the new emerging trend in capital markets of tokenization of financial assets, the paper explains how financial security can be registered on a market blockchain settled in ECB central bank digital currency, in T+0 time and in compliance with the current European regulatory framework. A particular use case is explored through the design, implementation and use of a new market infrastructure DLT based, an enterprise application called DLT Bond Platform for the issuance of a digital bond settled in European Central Bank wholesale digital currency via a delivery versus payment process, using a layer 2 permissionless blockchain. After introducing the context and the problem statement in the first two sections, a general description of the solution proposed, and its novel contributions are provided in the third section. In the forth section the main components of the DLT Bond Platform are described in detail, both web2 and web3 as well as the related business processes, namely: (i) Management of the entire life cycle of a bond in digital form; (ii) Management of all the settlement phases envisaged by the bond also through atomic transactions for the simultaneous transfer of the securities and the corresponding cash flows (delivery versus payment or "DvP") through the use of the solution made available by the Bank of Italy as part of the European Central Bank initiative called New Technologies for Wholesale Settlement (iii) Identification, authorization and management of users, profiles and the respective roles on chain; (iv) real-time monitoring and audit trail. The final section focuses on the results obtained and on the completion of the validation process, ultimately dwelling on potential future developments.
Authors - Abdulrahman Azab, Paul De Geest, Sanjay K. Srikakulam, Tomas Vondrak, Mira Kuntz, Bjorn Gruning Abstract - Effective resource scheduling is critical in high-performance (HPC) and high-throughput computing (HTC) environments, where traditional scheduling systems struggle with resource contention, data locality, and fault tolerance. Meta-scheduling, which abstracts multiple schedulers for unified job allocation, addresses these challenges. Galaxy, a widely used platform for data-intensive computational analysis, employs the Total Perspective Vortex (TPV) system for resource scheduling. With over 550,000 users, Galaxy aims to optimize scheduling efficiency in large-scale environments. While TPV offers flexibility, its decision-making can be enhanced by incorporating real-time resource availability and job status. This paper introduces the TPV Broker, a meta-scheduling framework that integrates real-time resource data to enable dynamic, data-aware scheduling. TPV Broker enhances scalability, resource utilization, and scheduling efficiency in Galaxy, offering potential for further improvements in distributed computing environments.
Authors - Nina Valchkova, Vasil Tsvetkov Abstract - This paper investigates the dynamic characteristics of a collaborative robotic mobile platform with enhanced manipulability. It`s motion parameters, such as linear velocities and accelerations, and their influence on platform control are analyzed. The experiments performed include monitoring acceleration processes, constant lateral movement, and deceleration and braking phases. The presented graphical analyses demonstrate key features of the platform dynamics that can be used to optimize the control of a collaborative robotic mobile platform.
Authors - Sakshi Tiwari, Snigdha Bisht, Kanchan Sharma Abstract - Effective waste management is critical to achieving sustainability in urban regions like Delhi-NCR, where heterogeneous waste streams pose a classification challenge. In this research, we propose WasteIQNet, an intelligent deep hybrid model designed for precise waste classification across 18 categories under a well-defined hierarchy: Wet (Compostable, Special_Disposal) and Dry (Recycle, Reduce, Reuse). Leveraging the WEDR dataset, we first standardized over 1.75 lakh images via JPEG conversion, 256×256 resizing, and RGB formatting. SMOTE+ENN was applied to balance class distributions to 20,000 images each. Feature extraction was achieved through simulated DASC-like global vector embeddings using MobileNetV3. Our baseline hybrid model integrated MobileNetV3Large and GraphSAGE, achieving an initial accuracy of 80.56%. After optimizing the model for multi-label learning through sigmoid activation, threshold-based decoding, and hierarchical label interpretation, we conducted extensive enhancements. Hyperparameter tuning with Optuna, Feature-wise Attention (FWA), and Top-K Mixture of Experts (TopK-MoE) improved accuracy to 83.33%. Subsequent normalization and activation function experiments (Mish, Swish, GELU) led to a peak accuracy of 94.44% using GELU. We further introduced Dynamic Sparse Training (DST) and Model-Agnostic Meta-Learning (MAML), raising accuracy to 95.04%. Final enhancements included label smoothing and early stopping, culminating in a best-in-class accuracy of 97.87%. WasteIQNet demonstrates a scalable, interpretable, and high-performance solution for automated waste classification, supporting smart city initiatives and responsible environmental management.
Authors - Md Amirul Islam, Giovanni Stea Abstract - Recent advancements in industry underscore the growing demand for systems that provide both high computational performance and real-time assurances, despite these objectives traditionally being seen as conflicting. To support the complex task of designing such Network-on-Chip (NoC) systems with both performance and Quality of Service (QoS) requirements, frameworks such as ARM MPAM envisage systems with hardware support for resource partitioning and the observation of its effects. It enables new application areas for traditional QoS techniques. Network Calculus (NC) uses transformation operations to model traffic profiles through network nodes. It enables the estimation of the minimum service guaranteed to a flow as it moves through flow-controlled nodes. This paper proposes using Network Calculus to derive insights from traces of traffic samples and to evaluate service curves for QoS validation and worst-case delay analysis in NoC architectures. The derived worst-case performance bound is compared with existing works based on Queueing Theory (QT) and Network Calculus (NC). This comparison demonstrates a significant improvement in the accuracy of the delay bounds over the existing QT and NC approaches.
Authors - Khalid Jaber Almalki Abstract - Blockchain technology offers a new solution to address IoT data accuracy, security, and speed issues in healthcare. This paper details a secure way to communicate, verify, and evaluate healthcare data from IoT devices. The technology protects medical data with strong encryption and agreement. The suggested solution outperforms bitcoin and conventional systems in data security, integrity, scalability, latency, speed, and interoperability. User identification, energy efficiency, network stability, and regulatory compliance top the framework's goals. It's ideal for healthcare applications. The strategy uses statistical analysis and machine learning to make verified data meaningful. This improves healthcare choices. The system improves patient care and operational efficiency, making it a major advance in healthcare data management. This study highlights how blockchain-enhanced IoT devices will transform healthcare data processing, improving patient outcomes and business practices.
Authors - Altaf Raja, AKhil Pandey, Vishal Shrivastava, Mohit Mishra, Sangeeta Sharma Abstract - Grocery inventory control is an essential thing of every day existence that often leads to inefficiencies such as over-buying, forgetting critical objects, or meals wastage. traditional methods rely on human reminiscence and guide tracking, which might be at risk of mistakes. This studies paper introduces mind field, a clever jar ready with AI and IoT technology, designed to automate grocery monitoring. mind field is available in two versions: kind-1, which specializes in weight-based totally tracking and notifications, and type-2, which includes superior sensors to screen freshness, temperature, and humidity. The machine integrates with a cellular software to offer real-time updates, personalized indicators, and buying reminders. by leveraging AI-pushed facts analysis and cloud storage, mind field objectives to enhance grocery control performance for households and small agencies. This paper discusses the trouble, proposed solution, machine structure, advantages, and future scope of the brain container machine. The brain container device leverages cutting edge AI and IoT technology to provide an sensible and automatic answer for grocery management. not like conventional stock monitoring methods, it no longer only detects low stock tiers however additionally analyzes person intake patterns to optimize grocery purchases. The seamless integration with a cellular software guarantees actual-time tracking and proactive indicators, making it a noticeably handy and efficient device. This research explores the gadget's architecture, functionalities, and its impact on lowering meals wastage, optimizing family and enterprise inventory, and promoting sustainable intake practices.
Authors - Aishwarya Reehl Abstract - Predicting Crime is an integral part of keeping the community safe and harmonious. It provides valuable information to the respective authorities to anticipate concerns, prevent victims from being potential targets, and allocate their resources in the best possible way. This paper determines the use of a Machine learning algorithm to predict Crime in Montgomery County. We propose a new model designed to enhance the accuracy of crime data. We cover how descriptive models help understand and demonstrate the next potential move for various crimes. This research also shows how we can pre-process information as needed for prediction algorithms.
Authors - Mikhail Ola Adisa, Sonny Rosenthal, Ifeoma Adaji, Shola Oyedeji, Jari Porras Abstract - Civic campaigns and technology interventions are increasingly recognized as powerful drivers of sustainable waste management behaviors. This study investigates how civic campaigns and persuasive technologies interact to promote sustainable waste practices in Finland. Drawing on a mixed-methods approach, the study combines survey data from 255 residents with interviews from civic organizations to explore the role of engagement levels, campaign effectiveness, and ICT interventions in shaping pro-environmental behavior. Findings reveal that while residents generally exhibit strong recycling habits, sustained participation depends on continued civic outreach, awareness of environmental concerns, and value-based motivation. Digital persuasive tools, such as mobile/web apps, social media, and innovative technologies, were found to be effective for raising waste awareness and supporting sustainable efforts but do not independently drive behavioral change. The study highlights the importance of integrating civic strategies with persuasive technologies to bridge the intention-action gap, scale engagement, and reinforce sustainability norms. The paper contributes to the literature on ICT for sustainability by demonstrating how tailored civic-tech can complement grassroots efforts to foster long-term sustainable behavior change.
Authors - Md.Raza Sheikh, Md.Farid Hossain, Tapu Rayhan, Ehashan Ahmed, Md Zahidul Islam Abstract - This study explores the use of machine learning models, particularly Deep Neural Networks (DNN), for crop prediction in Bangladesh’s diverse agricultural context. A comparison of five models—Gaussian Naive Bayes, Logistic Regression, Decision Trees, Random Forests, and DNN—was conducted using a comprehensive agricultural dataset. The results revealed that while all models had strengths, the DNN outperformed the others, achieving an accuracy of 97.98% in training and 97.95% in validation, with near-perfect precision, recall, and F1 scores. The DNN’s performance, despite its interpretability challenges, underscores its potential in accurately predicting crops from complex, high-dimensional data, crucial for Bangladesh’s varied cropping patterns. This research emphasizes the need for robust agricultural data and suggests that DNNs could significantly improve crop planning, management, and food security, contributing to a sustainable future for precision agriculture in Bangladesh.
Authors - Saravana Kumari Shanmuga Sundaram, Shyam A V Abstract - e-Sanjeevani is India's national telemedicine system. It had its modest origin during the COVID lockdown and is now the biggest recorded platform worldwide for primary healthcare, especially for underprivileged communities. For the design, implementation, and assessment, this article investigates the two service models of the platform: e-Sanjeevani OPD (patient-to-doctor) and e-Sanjeevani HWC (doctor-to-doctor). This study assesses e-Sanjeevani's scalability, usage patterns, and integration with the more extensive health system, such as the Ayushman Bharat Health Accounts (ABHA), and National Digital Health Mission (NDHM) frameworks, based on research literature, secondary data from official sources, and policy documents. For those who do not have access to primary healthcare, the platform has provided several advantages. However, there have been difficulties reaching the system's maximum efficiency. By early 2025, the platform had helped over 342 million individuals throughout India, supporting several aspects of healthcare delivery like diabetic foot, caries in elderly persons, and so on. The research suggests a conceptual framework for incorporating Large Language Models (LLMs) into e-Sanjeevani, addressing the existing challenges and extending the solution’s possibilities. The framework includes LLM-driven features, including clinical decision support, real-time translation, automated documentation, and individualized patient education. It comprises a layered architecture effortlessly incorporated into telehealth, supporting artificial intelligence augmentation at pre-consultation, consultation, and post-consultation phases. This integration can significantly increase provider efficiency, lower workload, and raise the general quality of treatment. The results underline present achievements and the transforming opportunities of LLM-enabled fair telemedicine for India and other low- and middle-income nations.
Authors - Fredy Gavilanes-Sagnay, Edison Loza-Aguirre, Henry N. Roa, Narcisa de Jesus Salazar Alvarez Abstract - This study investigates the performance of various channel estimation and signal detection techniques, including Kalman Filtering, Convolutional Neural Net-works (CNNs), and Recurrent Neural Networks (RNNs); with a focus on their application in 5G/6G networks. We evaluate these methods based on key metrics, including Bit Error Rate (BER), Mean Squared Error (MSE), and computational complexity, under different Signal-to-Noise Ratio conditions. Our results demonstrate that Deep Learning models (CNNs and RNN) significantly outperform traditional methods in terms of accuracy, achieving lower BER and MSE values. However, these improvements come at the cost of increased computational complexity, making them less feasible for real-time applications in resource-constrained environments. Reinforcement Learning models also show promise, offering real-time adaptability for dynamic spectrum management and beam tracking but they also face challenges regarding computational efficiency. Despite some limitations, Kalman Filtering remains valuable for applications where low latency and computational efficiency are critical. Our findings highlight the importance of optimizing these models to balance accuracy and computational load for large-scale 5G/6G networks.
Authors - Diego Pucuji, Angel Cornejo, Paul Velasteguí Abstract - The intensive use of fossil fuels has led to increasing environmental degradation, driving the search for sustainable solutions such as photovoltaic systems applied to electric mobility. In this context, electric motorcycles stand out for their energy efficiency, low maintenance, and ease of recharging. The parish of Calderón, in Quito, provides an ideal setting to evaluate these technologies due to its varied topography and climatic variability, which directly impact the efficiency of solar systems. This study, conducted at the Instituto Superior Tecnológico Vida Nueva, analyzed the performance of an off-grid photovoltaic system used to charge electric motorcycles under real operating conditions between March and April 2025. The installed system consisted of four 550W solar panels, a 2,560Wh lithium-ion battery, and a 3,000W inverter. A controlled load was applied using heat guns, and key energy parameters, including autonomy, consumption, and efficiency, were monitored. Despite the high cloud cover and rainfall during the study period, the system achieved an average daily generation of 5.46 kWh. The tested motorcycles achieved ranges of 22 km and 25 km with energy consumption of 1111 Wh and 1259 Wh, respectively. The results demonstrate the feasibility of using solar energy in urban areas for light electric mobility.
Authors - Lorentz Jantschi Abstract - A multiple-choice assessment system was created and was used from 2006 to 2024 to assess over 3,000 students in topics of chemistry. The system was used in multiple assessments - each student had the opportunity to assess themselves several times. When determining the grade, for each set of assessments associated with a student, one assessment was eliminated - the weakest assessment - in the case of multiple assessments - and the average assessment was calculated with the remaining assessments. Each test contained 30 multiple-choice questions, and each correct answer was awarded 3 points. In a previous study, the observed distribution of students by the number of assessments was provided. In the present study, the sample of the number of student evaluations was analyzed under 3 theoretical distribution hypotheses: the classical Poisson and two of its generalizations, P´olya-Aeppli-Poisson and Snyder–Ord–Beaumont, respectively. The study was motivated by the fact that once identified the theoretical distribution, is possible to estimate the number of students who dropped out before the assessment. The study provided a negative result: all these distributions were rejected with a risk of being in error of 5%.
Authors - Humberto Merritt Abstract - Mobile applications have become the most important digital technology for social interaction, culture formation, and knowledge sharing in recent years. Electronic platforms such as search engines, social network sites (SNS), on-demand Internet streaming media, and smartphone apps swiftly shape our daily activities politically, culturally, and technologically. Although SNS impact how people interact with one another, their usage varies depending on the type of application. This research explores what social network sites are preferred in Mexico according to the application and platform used. In particular, it seeks to answer the following questions: 1) What SNS are preferred by Mexicans, and 2) What are the sociodemographic factors that determine their preferences? The methodology follows a quantitative approach that draws on the National Survey on the Availability and Use of Information Technologies in Households (ENDUTIH) carried out in 2023 by the National Institute of Statistics and Geography of Mexico (INEGI). Empirical evidence suggests that Mexicans favor SNS such as Facebook, WhatsApp, Instagram and YouTube. Average users are in their mid-thirties, and women are more intensive users of SNS.
Authors - Maen Hammad, Hamza Hanandeh, Ahmed Fawzi Otoom Abstract - For software development processes, software project management plays significant role in accomplishing successful projects. One major activity in project management is the forecasting of project expenses, time duration and resources that need to be determined and allocated in advance. This paper proposes an automated prediction model to predict the effort, in terms of months, required to complete a software project. The model applies different machine learning algorithms to predict the required effort in terms of months. A set of experiments has been applied on the ISBSG dataset. The result shows that both X-Gradient Boosting and Gradient Boosting algorithms produce the best classification results. While the Logistic Regression and SVM produce the lowest accuracy results. The results also show the positive impact of the feature selection process on the classifier’s accuracy. The goal is to minimize the project’s features to the most influential ones in the prediction process.
Authors - Guang-Jhe Lin, Chih-Heng Ke, Cheng-Feng Hung Abstract - The adequacy of the bandwidth is essential for delivering stable and high-quality transmission services. However, existing routing methods often rely on single transmission paths, which limit bandwidth utilization and place higher demands on routing decisions for bandwidth allocation. Small traffic flows may occupy high-capacity paths, leaving larger traffic flows unmet. To address these challenges, this paper proposes a novel routing algorithm that allocates paths based on bandwidth demands. By decomposing the routing problem through the overlay of multiple single paths, the algorithm reduces complexity. In addition, it integrates traffic splitting techniques and reinforcement learning to dynamically optimize path selection and improve performance in software-defined networks. The simulation results show that the proposed method achieves an average improvement of approximately 50.79% in system throughput compared to shortest path routing and 34.68% compared to maximum bandwidth path routing, demonstrating its effectiveness in optimizing network resource utilization.
Authors - Mustafa Hammad, Maleeha Ismail, Fadheela Hussain Abstract - Choosing risk-free loan applicants presents a significant challenge for the banking industry, as the process is lengthy, resource-intensive, and prone to human error. Machine learning (ML) offers a promising approach to predict creditworthiness by learning patterns from historical data. In this study, we implemented four machine learning classifiers—Logistic Regression, Random Forest, Multilayer Perceptron, and Naïve Bayes—to predict the creditworthiness of loan applicants. Our results demonstrated that the Naïve Bayes classifier achieved the highest performance, with a precision of 83.3% and an F-measure of 79.2%, making it the most effective among the tested models. Feature selection and stacking techniques were explored but showed minimal improvements, with accuracy gains of less than 1%. These findings suggest that simpler ML models, like Naïve Bayes, may effectively address creditworthiness prediction without the need for complex ensembles. This research supports the application of ML for faster, more accurate loan processing in the banking industry, with potential to reduce credit risk and streamline loan approval decisions.
Authors - Shahinul Hoque, Farhin Farhad Riya, Jinyuan Sun, Hairong Qi, Kevin Tomsovic Abstract - Machine learning (ML) models hosted on cloud platforms are increasingly susceptible to security vulnerabilities, particularly due to their exposure to external queries in untrusted environments. In this paper, we explore this specific vulnerability by leveraging fuzzing techniques to systematically generate diverse input samples (X) to query cloud-hosted ML models. By capturing the corresponding outputs (y), we attempted to train a shadow model that mimics the behavior of the target model. This methodology allows us to systematically assess the security risks associated with such models, including information leakage, extraction of decision boundaries, and model inversion. The core of our study is to determine the feasibility of mimicking cloud-hosted ML models using shadow models trained via various fuzzing attacks. We focus on computationally efficient fuzzing methods to evaluate the practicality of these attacks. Our findings demonstrate that fuzzing effectively creates a comprehensive dataset for training the shadow model, thereby minimizing the number of queries needed to mount successful attacks. Moreover, we discuss the broader implications of these security breaches on the confidentiality, integrity, and availability of the models, identifying significant security deficiencies in current deployment practices of cloud-hosted ML models. We conclude with proposed countermeasures designed to defend the security of these systems, underscoring the importance of implementing robust defensive strategies in cloud-based ML frameworks.
Authors - Phillip G. Bradford, Henry Orphys, Dmitry Udler, Nadia Udler Abstract - This paper discusses a bi-modal AI system applied to legal reasoning for tax law. The results given here are very general and they apply to similar systems beyond tax law. These results use the downward and upward L¨owenheim–Skolem theorems to contrast the two modalities of this AI approach to tax law. One modality focuses on the syntax of proofs and the other focuses on logical semantics. Particularly, one modality uses a rule-based theorem-proving system to perform legal reasoning. The objective of this theorem-proving system is to provide proofs as evidence of valid legal reasoning. These proofs are syntactic structures that can be presented in court. The second modality uses large language models (LLMs). An objective of our application of LLMs is to enhance and simplify user input and output for the theorem-proving system. In addition, the LLMs may help in the translation of the natural language tax law into logic for the theorem proving system. The combination of these two modalities empowers our vision. The LLMs leverage notions of semantics for tax law inputs and they may help translate tax law statutes. While the theorem proving system gives syntactic proof-trees for legal arguments for potential justifications of tax statements.
Authors - Julkar Nine, Akif Ahmed, Shibbir Ahmed, Wolfram Hardt Abstract - The push toward full autonomy in the automotive industry has been bolstered by advancements in Artificial Intelligence, particularly Deep Learning and Machine Learning techniques, which facilitate efficient processing of sensor data for environmental object detection and recognition. Detection of objects with precision and robustness becomes particularly critical on roads, where increased vehicle speeds require fast and reliable decision-making. German highways, known for their high average vehicle speeds, underscore the need for specialized object detection algorithms tailored for such environments. However, these technologies typically depend on large datasets for optimal performance. This research presents a novel approach to detecting and recognizing German highway traffic signs using YOLOv8 while addressing the challenge of limited data availability. By curating high-quality custom datasets for training, the model achieved approximately 90% accuracy on test data and 80% on real-world data, even with reduced dataset size, and demonstrated semi-real- time to real-time performance, highlighting its potential for practical deployment in autonomous driving systems.
Authors - Shahad Al-Tamimi, Qasem Abu Al-Haija, Abdullah AlShuaibi Abstract - Serverless computing (SC), specifically Function-as-a-Service (FaaS), provides scalability and operational flexibility while posing substantial security concerns. This study investigates enhanced detection and response strategies for SC contexts, focusing on real-time threat monitoring, anomaly identification, and automated incident response. The report emphasizes typical vulnerabilities, such as misconfigurations and unsecured Application Programming Interface (APIs), while emphasizing the need for complete, integrated security policies to combat emerging threats. The study examines these difficulties and proposes strong solutions to improve the resilience and security of serverless systems.
Authors - Wolf A. Kafka, Gunther Bernatzky, Pravir Malik Abstract - Chronic fatigue, sleep disturbances, and mental exhaustion are increasingly prevalent in modern societies, necessitating innovative, non-invasive therapeutic solutions. This study evaluates the impact of the Centropix KLOUD Pulsed Electromagnetic Activation (PEMA) system on various dimensions of human well-being. Conducted by an independent research group (SFU Vienna), the study involved 428 participants (aged 19–84) across Germany, Austria, Switzerland, and the USA. Participants used the KLOUD device—a full-body electromagnetic field mat delivering broadband pulsed stimulation—for 15 minutes daily over 42 days. Validated assessment tools, including the Veterans RAND- 12, Fatigue Assessment Scale (FAS), Jenkins Sleep Scale, and NRS-11 for discomfort, were used to measure outcomes at baseline (T-0) and after the intervention (T-1). Results revealed statistically significant reductions in exhaustion, and improvements in mental performance, energy levels, and sleep quality. In a key subgroup (n = 103) identified with severe exhaustion symptoms at baseline, only 14.9 % continued to report such symptoms after the intervention—an 85.1 % reduction. These findings suggest that the KLOUD-PEMA system offers clinically relevant benefits for enhancing vitality and resilience without side effects, supporting its role in primary and secondary preventive health strategies. The technology’s simplicity, accessibility, and safety profile make it a compelling candidate for broader integrative health applications.
Authors - Kelebogile Kgwadi, Timothy T Adeliyi Abstract - As technology advances rapidly, businesses increasingly rely on big data to drive digital transformation and gain a competitive edge. Big data enables organizations to understand customer needs better and enhance decision-making processes, delivering significant benefits. However, effective decision-making requires the meaningful processing of information, making the identification and application of critical success factors (CSFs) essential for the successful adoption of big data applications. These CSFs help organizations overcome challenges and leverage opportunities presented by big data adoption. This study aims to identify and analyze the critical success factors influencing the effective adoption of big data applications in business organizations. A systematic literature review was conducted, guided by the PRISMA framework, covering articles published between 2010 and 2023. The process involved thorough database searches, article screening, and selection based on predefined criteria. A total of 98 articles were meticulously analyzed using Principal Component Analysis, with R Studio and WEKA employed for statistical analysis. The findings identified privacy management, security, access control, data governance, and availability as the top five factors impacting big data adoption. These insights informed the development of a framework emphasizing the pivotal role of CSFs in the seamless deployment and utilization of big data technologies. This study provides valuable guidance for information technology experts in business organizations, highlighting the key factors necessary for successful big data implementation. Ultimately, it contributes to advancing knowledge in this critical field, offering actionable strategies for effective big data integration.
Authors - Tatavarthi Lakshmi Chandrasena, Arashdeep Kaur Abstract - The progress in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) has significantly enhanced the technologies used for predicting solar flares. These advancements enable better analysis and interpretation of complex solar data, leading to improved accuracy in forecasting potentially disruptive solar events. Since solar flares can impact global communication networks, power systems, and pose health risks to astronauts due to elevated radiation levels, there is a pressing need for precise and timely predictive models. This paper explores various modern approaches to solar flare prediction, highlighting the contribution of AI-based tools in enhancing their accuracy and lead time. The integration of ML and DL not only refines these models but also presents challenges related to their complexity and data requirements. By assessing the strengths and weaknesses of these techniques and proposing potential improvements, this paper aims to provide the extensive review of various existing techniques and methods that have been deployed in the literature for solar flare prediction. This paper also gives a comparative study of various solar flare prediction models.
Authors - Prawit Chumchu, Kailas Patil, Alfa Nyandoro Abstract - Automated cannabis cultivation faces considerable challenges, primarily due to complex environmental control requirements and timely disease detection. To address these issues, this study develops and evaluates a novel cannabis growing system integrated with Artificial Intelligence of Things (AIoT), aimed at automating environmental parameters and disease management. The proposed AIoT-based system autonomously regulates humidity, temperature, lighting, nutrients, water, and medicinal treatments to optimize cannabis plant health. Central to this automation is an intelligent disease detection module leveraging deep learning techniques capable of classifying cannabis leaf conditions into five categories: Healthy, Malnutrition, Red Spider Mites, Bacterial Spot, and High Temperature Stress. Initially, we assessed five pretrained convolutional neural networks (CNNs): InceptionV3, Xception, ResNet50, ResNet50V2, and ResNet152V2, which achieved accuracies up to 100% after 100 epochs. Subsequently, we developed simplified CNN models specifically optimized for deployment on low-cost edge devices, such as the Raspberry Pi, achieving the same high accuracy (100%) while significantly reducing computational complexity. These optimized models were integrated into our AIoT system, successfully automating real-time adjustments of critical growing conditions. Our findings underscore the potential of AIoT technologies in transforming cannabis agriculture by providing accurate, efficient, and scalable solutions for disease detection and cultivation management, thus enabling broader adoption of intelligent, automated agricultural practices.
Authors - Ha L Thu Abstract - The rapid advancement of Big Data has transformed business network analysis, enabling organizations to extract meaningful insights for decision-making and optimization. This paper explores key methodologies in business network analysis, focusing on how Big Data techniques enhance network visualization, relationship mapping, and performance optimization. We discuss applications in supply chain management, financial networks, and corporate strategy. Additionally, we address challenges such as data privacy, computational scalability, and real-time processing. Finally, we propose future research directions for integrating artificial intelligence and advanced analytics into business network optimization.
Authors - Carlos Roberto Franca Abstract - This paper presents the mathematical foundation for the main generative AIs currently available, both free and subscription-based. During the first two months of 2025 (January and February) and until March 9, challenges involving original and unpublished mathematical formulas titled Infinite Series with Multiple Ratios (SRMs) were presented. The proposer has been working on this research since 1996. The main objective of the paper is to present the developments of generative AI agents from OpenAI (free and paid ChatGPTs), DeepSeek R1 (a generative AI released in January 2025), Gemini Advanced 2.0 Flash (a subscription-based generative AI from Google), as well as the free generative AIs Grok 3 (from xAI) and Claude Sonnet 3.7 from Anthropic. Several challenges were posed, and this paper will focus on the challenge related to Computational Biology. The resolution occurs through original and unpublished formulas. It is believed that several fields can be impacted by SRMs. This article presents the performance of generative AIs when faced with specific questions that require advanced knowledge of mathematics and computational biology. There are three distinct stages. In the first, the work problem is presented with didactic support and an introduction to the conceptual part involved. In the second stage, the didactic part is removed and in the third stage, the full article published by Indus Foundations is presented, the questions are redone and it is verified how prepared they are to discern what is being asked and how they work with the available concepts autonomously and efficiently.
Authors - Armida Gonzalez-Lorence, Jose Alejandro Ascencio-Laguna, Cornelio Morales-Morales, Jose Gabriel Ayala-Landeros, Juan Emigdio Soto-Osornio Abstract - This research develops an integrated system that combines the Internet of Things with machine learning for the purpose of optimizing environmental conditions in Mexican poultry farms. A four-module architecture is implemented: IoT Module for real-time environmental data collection through various sensors (DHT22, MQ-7, MQ-137, MG-811), processing and storage module, multivariable machine learning module, and visualization module. Experimental validation was conducted over 62 days in a commercial poultry farm, continuously monitoring critical variables of temperature, humidity, CO₂, and NH₃. The data were processed using classification and regression algorithms, including Random Forest, neural networks, and Gradient Boosting, to generate real-time recommendations. Random Forest algorithms showed the best classification performance (68% accuracy), while Gradient Boosting achieved the lowest mean square error in regression (RMSE=1.32). Through variable importance analysis, it was identified that indoor temperature (37.5%), CO₂ levels (18.3%), and bird age (15.7%) are the most significant variables. Therefore, an Agglomerative Hierarchical Clustering analysis (k=5) was executed, which allowed categorize 5 specific microenvironments. The system implementation makes predictions about the trend of temperature, humidity, NH3, and CO2. The developed system establishes a significant evidence-based advancement for poultry farming in Mexico.
Authors - Omar N. Elayan, Qussai M. Yaseen, Ahmed S. Shatnawi Abstract - This paper proposes a machine-learning model using static and dynamic features to identify Windows malware. The paper uses a new dataset of 12158 Portable Executable PE files for the Windows operating system, 5936 malicious files belonging to nine malware families, and 6,222 benign files. The main features of the files were extracted based on Application Programming Interface (API) by three main known methods: Static using Python, Dynamic by Cuckoo Sandbox, and finally, Hybrid by combining them to check which way is more effective and accurate in detecting malicious files. The proposed model performs binary and multi-class classification to classify malicious files into nine types. The experiments show that Extra-Trees outperformed other classifiers, achieving an accuracy of 100% in binary classification and 97% in multiclass classification.
Authors - Leonardo Juan Ramirez Lopez, Juan Sebastian Orozco Duran Abstract - The Interoperable Electronic Records Platform (PIRE) is an interoperable solution designed to unify existing telemedicine systems and integrate Medical Internet of Things (IoMT) platforms into a single, secure, and scalable ecosystem. This approach enables real-time consolidation of clinical information, facilitating more agile and effective decision-making. From a project management perspective, PIRE represents an innovative strategy by centralizing data from multiple sources, thereby optimizing the planning, execution, and monitoring of digital health initiatives. The project combines exploratory, descriptive, and analytical methods within the Scrum framework, structured across four iterative cycles: (1) architecture definition and IoMT device integration, (2) security planning and clinical data protection, (3) graphical interface and relational model design, and (4) testing and validation in simulated and real-world environments. PIRE adopts international standards such as HL7 FHIR, DICOM, and SNOMED CT to ensure interoperability among heterogeneous healthcare systems. Preliminary results demonstrate a significant reduction in clinical data access times, enhanced information security, and increased operational efficiency through redundancy elimination. Overall, PIRE not only strengthens care continuity and patient safety but also enables a replicable model for digital health project management, grounded in interoperable platforms that unify processes, reduce costs, and improve care quality.
Lecturer, Lecturer, University of Botswana, Botswana
Mrs Tsholofetso Taukobong is a Lecturer with the Department of Computer Science, University of Botswana. She hold an MSc and BSc in Computer Science, both from the University of Botswana and is currently pursuing her PhD studies in Computer Science specializing in Computer Vision... Read More →
Authors - Ioannis Patias Abstract - The wide expansion of artificial intelligence (AI) agents and tools necessitates computational paradigms that can address the inherent limitations of centralized cloud-based architectures. Edge computing emerges as a critical enabler, providing distributed processing capabilities that are essential for real-time decision-making, reduced latency, and enhanced data privacy. This paper examines the fundamental reasons why edge architecture plays such a central role in powering AI agents and tools, and delves into the mechanisms through which such an integration occurs. The advantages of edge-based AI are analyzed, including localized inference, reduced bandwidth consumption, and improved resilience. Furthermore, we explore the architectural considerations and technological advancements that facilitate the deployment of AI models at the edge, such as optimized model compression, hardware acceleration, and federated learning. Through a synthesis of existing literature and an analysis of practical applications, this paper demonstrates the transformative potential of edge architecture in shaping the future of AI agents and tools, and proposes a simplified latency model. The rise in IoT devices, and the need for immediate localized decisions, makes edge AI a necessity.
Authors - Falk Pospischil, Yves Burkhardt, Oliver Klar, Johann Lichtblau Abstract - This paper presents the optimization problem of distributing traction forces among parallel group drives in railway vehicles to reduce energy consumption while considering constraints. The model requirements, including temperature-dependent losses, are deduced to precisely determine energy losses. The approach for online implementation of optimal traction force distribution using a Look-Up Table (LUT) is analyzed through simulation on a realistic drive cycle, resulting in an overall energy demand reduction of approximately 0.5 %. The effects of alternating driving strategies on wheel life and lifespan consumption of switching power electronics are examined, revealing that lifespans can be negatively affected by energy-efficient traction force distribution.
Authors - Hicham ESSAMRI, Abderrahim BAJIT, Khalid BOUALI, Hamza BENZZINE, Yasmine ACHOUR, Mohamed Nabil SRIFI, Rachid EL BOUAYADI Abstract - In IOT platform [1-4], most IOT nodes [4] include sensors to separately collect data and send them to the CLOUD for storage and specific processing. Among other things, if there are 5 sensors, the platform is supposed to send their values one after the other, which results in significant consumption in terms of execution time, energy consumption, communication conflict. This increases exponentially when the security and encryption of this data are necessary and arise to protect the integrity of the data. To reduce such consumption and avoid communication conflicts and all kinds of congestion at the IOT platform level and especially to compensate for the size of the IOT PAYLOAD [4] which results from its encryption, then we have designed a real-time IOT node, compact, optimal, and synchronous with the CLOUD. To do this, our idea is to store all the data from the different sensors, compact them into a single QrCode image file, visually compress this file, and ensure integrity protection using a light encryption before sending it on demand and synchronously to the CLOUD.
Authors - Nguyen T. Lan Huong Abstract - This paper explores the role of Data Science in modern economic anal- ysis. We discuss key methodologies, including machine learning, econometrics, and big data analytics, and their applications in economic forecasting, market analysis, and policy evaluation. Additionally, we highlight challenges such as data quality, ethical concerns, and computational complexity. The study concludes with future research directions and potential advancements in integrating Data Science with economic modeling.
Authors - Md. Omer Faruq, Mania Sultana, Taspia Akter Epou, Md. Tareq Hossain, Md. Motaharul Islam Abstract - The rapid growth of software systems has increased energy usage, creating environmental and financial problems. Poorly designed software, heavy algorithms, and unnecessary processes waste energy, raising costs and adding to pollution. To address this, our research focuses on making the software more energy-efficient without losing performance. We use tools like Intel Power Gadget to measure how much energy a software uses and suggest ways to improve, such as optimizing algorithms, managing memory efficiently, and cleaning up code. These techniques provide simple, practical steps for developers to create energy-saving software that supports sustainability and reduces costs.
Authors - Gopal S. Gawande, Virendra Shete, Maheshkumar Kolekar, Sachin Takale, Sanjaykumar Nipanikar Abstract - As a potent DSP technology, multirate DSP allows for inexpensive digital filter implementations, and it is extensively used for satisfying the sampling rates of various systems. An assortment of state-of-the-art digital signal processing (DSP) methods are at your disposal for optimizing multirate digital filters, including retiming, parallel processing, pipelining, folding, unfolding, and polyphase decomposition. This work presents new multirate filter designs that provide high computation rates, throughputs, and speeds by using a variety of optimization strategies. In order to reach the spartan-6 xc6slx150T-4fgg676 Field Programmable Gate Array device, decimation filters use multirate filter topologies and the Xilinx System Generator. It is observed that the speed is increased by 135 MHz for the transpose pipelined decimation filter as compared to simple decimation filter in average. Due to the parallelism property of polyphase decomposed structures, the throughput and computation rate are found maximum in case of efficient polyphase decimation filter structure. It has been observed that the polyphase decomposition technique enhances the average throughput and computation rate of the simple decimation filter by almost 14MSPS and 345 MMACPS respectively.
Authors - Hiep. L. Thi Abstract - The Edwards-curve Digital Signature Algorithm (EdDSA) is a modern cryptographic signature scheme that provides high security and efficiency. This paper reviews recent advancements in EdDSA, highlighting its mathematical foundations, implementation considerations, and its growing adoption in various applications such as blockchain, IoT, and secure communication. Security challenges and potential improvements are also discussed.
Authors - Youna Jung, Songyon Shin, Second Sujeong Park Abstract - Since Generative AI (GenAI) has introduced in 2022, it has transformed teaching and learning in higher education in diverse aspects. Students are increasingly using diverse GenAI tools to understand complex theories, generate ideas, draft work, create presentations with scripts, and obtain instant solutions to assignments. This dramatic shift in learning styles raises concerns on how to integrate this emerging technology while ensuring students are trained as problem- solvers who can effectively utilize AI without over-relying on it. As students’ needs vary across disciplines, it is critical to address this potential disparity in the ways in which different student groups learn, understand, and apply GenAI may differ, including in interdisciplinary courses. Towards this goal, this paper reviews the literature on GenAI in higher education, highlighting the lack of research addressing the differences among student groups. To bridge this gap, we propose the GAITE project, which aims to identify and address the unique needs of these groups through surveys and tailored solutions.
Authors - Hiba GAIZI, Abderrahim BAJIT, Hamza BENZZINE, Youness ZAHID, Hicham ESSAMRI, Mohamed Nabil SFRIFI, Rachid EL BOUAYADI Abstract - The growing demand for sustainable agricultural practices is driven by urgent global environmental issues. Greenhouses provide controlled settings that improve plant productivity through technological innovations. A promising approach to addressing these challenges is the integration of smart greenhouses with mobile IoT nodes equipped with autonomous navigation capabilities. This article introduces an advanced mobile node that autonomously follows a predefined path in the greenhouse using sophisticated computer vision techniques and deep learning models. The mobile node utilizes convolutional neural networks (CNN) to precisely track the path and strategically pause at each plant, collecting comprehensive subjective and objective data, thus enhancing the conventional functionality of IoT nodes. Agricultural IoT devices play a pivotal role in data presentation and connectivity via wired and wireless synchronized communication systems, though data security continues to be a persistent issue. By leveraging computational intelligence, the system compensates for lost and inaccurate sensor data, producing critical forecasts through advanced data analysis tools, allowing farmers to make informed decisions and enhance overall performance. The mobile node not only gathers data but also operates as the master I2C controller[1], overseeing communication with various I2C slaves and ensuring efficient data exchange between the Cloud and IoT nodes. The methodologies described here enable the optimization of both objective and subjective PAYLOADs, significantly improving data analysis, predictive accuracy, energy efficiency, and overall system performance. This article outlines these techniques, highlighting their impact on reducing data transmission time and improving system effectiveness in smart greenhouse environments.
Authors - S. M. Mizanoor Rahman Abstract - We developed a human-robot collaborative system in the form of a hybrid cell to perform a collaborative assembly task in a flexible manufacturing setup. We conducted an experiment where 20 human subjects separately performed the assembly task in collaboration with the robot. We observed the collaborative assembly task, reviewed related state-of-the-art research results, interviewed the subjects, relevant researchers and industry experts and thus derived a comprehensive set of assessment and benchmark metrics and methodologies for the collaborative manipulation (co-manipulation) task. The metrics included the core requirements of the task expressed as the key performance indicators (KPI) and the effectiveness of physical interactions between a human collaborator and the robot for the task. The proposed benchmark metrics and methods can help assess and benchmark the overall performance and interactional effectiveness of human-robot collaborative manipulation tasks and systems for various applications such as manufacturing and assembly, logistics and transport, military operations, construction, disaster operations, etc.
Authors - Hiep. L. Thi Abstract - Cryptocurrency wallets play a crucial role in the security, usability, and accessibility of digital assets. This paper explores different types of cryptocurrency wallets, their underlying cryptographic mechanisms, and their applications in financial transactions, decentralized finance (DeFi), and secure identity management. We also discuss security challenges and future developments in wallet technologies.
Authors - Kanyanut Homsapaya, Bonnyakorn Leelakarnsakul, Waraporn Aumarm, Orawan Watchanupaporn Abstract - Congestive heart failure (CHF) in cats is a serious condition characterized by the heart’s inability to pump blood effectively, resulting in fluid accumulation in the lungs (pulmonary edema), chest cavity (pleural effusion), or abdomen (ascites). This life-threatening disorder manifests with clinical signs such as respiratory distress, profound lethargy with progression often culminating in organ failure if left untreated. Early detection is critical, and the integration of artificial intelligence (AI) offers significant potential for improving outcomes by analyzing diagnostic imaging, physiological data, and medical records to predict CHF onset and facilitate timely, targeted interventions. In this experiment, the data consists of 181 cats diagnosed with pulmonary edema, collected from Kasetsart University Veterinary Medicine Hospital. It includes relevant features such as clinical signs, diagnostic test results, medical history, and physiological data, providing a comprehensive basis for building a predictive model for congestive heart failure. The research utilizes multiple algorithms, including Support Vector Machine (SVM), KNN etc. to develop predictive models for congestive heart failure. The performance of each model is evaluated, and the algorithm with the highest accuracy and reliability is selected as the optimal approach. This work demonstrates the superior performance of Support Vector Machine (SVM) in predicting congestive heart failure (CHF), underscoring their significant advantages in both accuracy and efficiency compared to other models.
Authors - Danilo Menegatti, Alessandro Giuseppi, Antonio Pietrabissa Abstract - To overcome one of the main limitations of federated learning, that is the non-negligible communication overhead between the clients and server, the present work proposed a novel federated scheme based on principles envisaged by semantic communications. The proposed semantic-based dimensionality reduction algorithm is employed to reduce the data exchanges by more than one order of magnitude and negligible performance loss. The effectiveness of the proposed approach is validated through a classification scenario leveraging transfer learning.
Authors - Sharukesh, Sai Sundarakrishna, Vignesh, Pradeep Abstract - Artificial Intelligence (AI) Technologies such as Generative AI prompting, Retrieval Augmented Generation (RAG) and Agentic AI have started to emerge in cybersecurity operations and use-cases independently. We propose an intelligent orchestration mechanism that sifts the requirements of contextual injection, smart autonomy, integrated use case and intent capture. It is capable of reducing hallucinations and capable of enhancing semantic reasoning using prompting, RAG and Agentic AI, smartly, simultaneously and on demand. We propose such an integrated novel suite of framework tools unifying System Integration and Events Management (SIEM) and security Orchestration Automation and Response (SOAR). We develop, discuss and demonstrate the system as a plug and play with popular cybersecurity platforms. We provide 2 real world case studies to demonstrate the efficiency over the current state of the art performing SIEM and SOAR tools. The plug-in is scalable for the Model context protocol (MCP) and Agent to Agent (A2A) ecosystems
Authors - Phillip Ma, Yijun Shao, Yan Cheng, Youxuan Ling, Qing Zeng-Treitler, Stuart J. Nelson Abstract - We explore the mental health of elderly in relation to social determinants of health (SDOH) during the COVID pandemic. Factors such as physical isolation and social disconnectedness, resulting from interventions such as home isolation and restricted visits to nursing homes, are considered as potential contributors to the decline in mental health. By leveraging data from the All of Us Research Program, we explore the relationship between mental health outcomes and SDOH among a large cohort of elderly participants. Male gender and reporting being African American appear to be associated with relatively better mental health outcomes, whereas being divorced, separated, or widowed is linked to poorer mental health. Income and education demonstrate inconsistent effects on mental health outcomes. The deep neural network models outperform regression models in the same outcomes, by modeling more complex relationships between variables. Predictive performance remains modest for COVID-related anxiety and general well-being.
Authors - Sunish Vengathattil, Shamnad Mohamed Shaffi Abstract - Citation resolution is essential for maintaining the accuracy and integrity of academic research. However, inconsistencies in citation formats, author name variations, and bibliographic errors make this process challenging. Traditional manual and rule-based methods are time-consuming and prone to errors, highlighting the need for advanced solutions. This study evaluates various machine learning models, including BERT, LSTM, and Random Forest, to improve citation resolution. Using AWS services for data storage, processing, and model training, the models were assessed based on accuracy and efficiency. Results show that deep learning models, particularly BERT, perform best in handling citation inconsistencies, though computational costs remain a concern. The findings emphasize the potential of machine learning in improving citation management for digital libraries and reference tools while suggesting future research for scalability and multilingual support.
Authors - Lethabo Mahlase, Daniel Ogwok Abstract - In this paper, we present a maze-solving system that integrates image preprocessing techniques, graph-based modelling, and pathfinding algorithms to identify the shortest paths through a maze. Preprocessing steps, including adaptive thresholding and median filtering, ensure accurate graph construction from images. The graph is built using a grid-based approach, balancing computational efficiency with structural accuracy. Three algorithms, Breadth-First Search, A*, and Ant Colony Optimization (ACO), are implemented and compared. Results show that Breadth-First Search offers the fastest solution for smaller, simple mazes, while A* minimises node exploration, providing the shortest paths and excelling in complex environments. ACO, though slower, demonstrates adaptability in scenarios where dynamic pathfinding is required. The system’s performance illustrates its potential for search and rescue applications, where fast and efficient pathfinding is essential. Future work will focus on optimising preprocessing times, refining algorithm performance, and exploring real-time data integration to enhance the system’s applicability in real life scenarios.
Authors - Kazunori Minetaki Abstract - This study analyzed care workers' fatigue and stress levels through facial expression analysis, specifically using Percentage of Eye Closure (PERCLOS) and action units. A weighted average of AU04 and AU07 for the action unit analysis was used as an index. The results showed that several participants exhibited relatively high values in both PERCLOS, measuring the percentage of time eyes are closed, indicating drowsiness or fatigue based on eyelid closure duration. and the weighted average of AU04 and AU07, indicating higher fatigue or stress levels. Follow-up interviews with these individuals revealed that they were experiencing significant mental and physical strain due to responsibilities such as overseeing the overall operation of the care facility or dealing with interpersonal issues. Facial expression analysis, being non-invasive and time-efficient, shows potential as a practical tool for monitoring the condition of care workers in real-world settings. The findings are expected to inform interventions that support caregiver well-being and foster healthier work environments.
Authors - Zarak Khan, Jiatong Yang, Rimshah Jawad, Ivania Martinez, Md Mozammel Hoque, Xinyi Zhao, Jim Samuel Abstract - The birth of a child brings immense joy to a mother’s life. However, the reality can be different for mothers experiencing Postpartum Depression (PPD). According to the World Health Organization (WHO), around 13% of women experience postpartum mental health disorders, with rates rising to nearly 20% in developing countries. PPD is a condition that affects many women worldwide, but because of the social stigma and the lack of accessible mental health support, it often goes undiagnosed or untreated. This paper presents MOMCare, a chatbot designed to support mothers navigating the challenges of PPD. MOMCare has a retrieval-augmented architecture with an end-to-end pipeline from data preprocessing to response generation. It employs hybrid classification, a dual embedding system, a dual verification guardrail, and a medical domain-specific reranking mechanism to generate empathetic and relevant PPD responses. This refined design of Retrieval Augmented Generation (RAG) ensures fast and factual response by reducing noise in retrieval and providing abundant context to gpt-3.5-turbo. MOMCare was evaluated using both automated and human metrics. Results show strong performance in both evaluations, which underlines the potential for chatbot interventions in the postpartum mental health domain. This system is robust enough to take new data and create a conversation generation pipeline that includes new information. Expanding the knowledge base using the conversation history with the users is also in development. The MOMCare chatbot and its features were built on sound ethical principles of healthcare and Artificial Intelligence (AI) and present a strong design emphasis on safety and fairness.
Authors - Christopher Agbonkhese, Omobola Gambo, Teslim Akande, Ishaya Gambo, Adebowale Adewuyi Abstract - This paper presents the design and development of a mobile application that serves as an AI-powered study assistant to support personalized learning and academic management. The app helps students create study plans, practice questions, track their progress, and receive feedback based on their unique learning styles and academic goals. The system uses a client-server architecture, making it scalable and efficient. It also includes useful features such as a scheduler, reminders, progress tracking, and integration with Google Calendar. Gamification elements are used to make studying more interactive and enjoyable for students. For the intelligent part of the system, we used TensorFlow and Scikit-learn libraries. These tools help the app understand each student’s learning patterns and recommend personalized study content. This way, each user gets a learning experience that fits their specific needs. In order to evaluate the system, we con-ducted user testing and analyzed student performance before and after using the app. We looked at metrics like academic improvement, task completion rates, and user satisfaction. The results showed that students became more focused, better at managing time, and more confident in their studies. Compared to existing apps like QANDA and SmartPal, our solution offers a more complete and personalized approach to learning. It combines AI, good design, and useful features to create a powerful tool for modern students
Authors - Serge Chaumette Abstract - Swarms of drones have been used for a few years in the military and civil domains, and their size has increased significantly. However, this has made them really challenging to manage. Among the issues, shape formation is one of the difficult operations to address. This increase in size is particularly significant for shows where large numbers (several hundreds to thousands) of drones are operated every day. These drones must obey precise flight plans to achieve the expected visual effects. Still, for these applications, there is no real need for dynamic shapes organization, since they can be computed offline prior to the mission. Shape formation is thus manageable. Even though military use cases are less visible to the general public, swarms of moderate yet significant sizes are used on theaters of operation, for instance, to achieve a saturation effect. There is little time in operation to prepare a mission, and the shape of the swarm is subject to changes that cannot be anticipated and that depend on the scenario and on its dynamic evolution, which is by nature unknown in advance. A process is thus required that can be initiated without major ground intervention (to ensure stealthiness and resilience), and that can be achieved in a collaborative manner within the swarms. In this paper, we address this issue of seamlessly organizing a swarm of drones in a given shape. We propose an innovative approach based on a DNA inspired code that circulates among the drones. This initial work raises a number of issues that relate to the nature of the target shape. These issues will be addressed in our future work.
Authors - Harnoor Kaur Khehra, Farnaz Sheikhi, Alan Bach, Elijah Mickelson, Farhad Maleki Abstract - Rice panicle segmentation plays a critical role in precision agriculture. Focusing on its indispensability, in this paper, we propose a multi-stage training pipeline that leverages the customized U-Net with EfficientNet-B3 and pseudo-labeling to improve segmentation accuracy. The datasets are prepared through the acquisition of video clips of rice crops, captured from different fields under diverse environmental conditions. The model is evaluated on internal and external test sets, demonstrating the effectiveness of the staged training and synthetic data augmentation. Comparing the performance of the proposed model with the strongly performed nnU-Net model shows the superiority of our proposed model in terms of Dice score and Intersection over Union. This highlights the impact of pseudo-labeling in improving the segmentation accuracy.
Authors - Sudharsan Vasudevan, Venkata Durga Kavya Bhatta, Krishna Mohan Bathula Abstract - New York City is experiencing continued population growth, driven by both documented and undocumented immigration. With over 8.8 million residents and a population density exceeding 29,000 individuals per square mile, this surge has intensified pressure on the city’s housing and shelter systems. The resulting overcrowding has contributed to a decline in living conditions and a rise in homelessness, especially among vulnerable and underserved populations. One particularly affected group is runaway and homeless youth (RHY), who face heightened risks such as trauma, exploitation, and limited access to essential services like education and healthcare. This study explores trends within the New York City Department of Homeless Services (DHS) shelter system, analysing key factors that influence shelter occupancy, exit patterns, and disparities based on demographics, education, and race or ethnicity. The research applies data science and machine learning techniques to forecast occupancy trends and identify variables associated with successful exits from shelters. It examines the operational efficiency, accessibility, and equity of the shelter system to uncover systemic challenges and areas for improvement. This study aims to provide actionable insights by utilizing a data-driven approach that can support informed decision-making, enhance service delivery, and guide long-term policy development. The findings are intended to help optimize resource allocation and promote more effective housing strategies. Overall, this work seeks to contribute to sustainable solutions for reducing homelessness in New York City and improving outcomes for those experiencing housing instability.
Authors - Arunima Saxena, Arindam Bhattacharyya, Lilian Molina, Savita Patil, Hussain Al-Asaad Abstract - Reliable interrupt handling is crucial in embedded systems, especially for real-time applications running on RISC-V architectures. This paper presents a modular and scalable verification methodology for evaluating external interrupt handling in the NEORV32 RISC-V processor. NEORV32 offers a rich trade-off between performance and resource usage, along with strong execution safety features and a flexible software framework - making it an ideal candidate for interrupt verification studies. To thoroughly test the processor’s interrupt logic, we built a custom simulation environment in ModelSim, encapsulating both the core and top-level architecture into reusable libraries. Our primary focus was the External Interrupt Controller (XIRQ), which supports 32 external interrupt channels with various triggering modes. Three targeted test scenarios were developed: a baseline interrupt case, an edge case stressing maximum bit values, and a rising edge-triggered case. Results from these simulations confirm consistent and reliable interrupt handling behavior across all test cases, with uniform response time and correct signal acknowledgment. This work provides practical insights for embedded system designers implementing interrupt-driven applications on RISC-V platforms and establishes a foundation for verifying other modules of NEORV32 in future work.
Authors - Jude Hardee, Ivana Strumberger, Eva Tuba Abstract - In this paper, the effectiveness of computer vision on the classification of visually similar minerals was evaluated. A dataset was created with special consideration to visually similar mineral groups, and was partitioned into three subsets, each increasing in number of similarity groups and number of mineral classes. A series of transfer learning models were tested on this dataset and on these subsets, and the results of each were analyzed. Each model was evaluated by the value of it’s training and validation accuracy, and how the number of mineral classes and similarity groups impacted that accuracy. Confusion matrices for each model were then analyzed to evaluate the per-class accuracy for the full dataset. Based on these accuracies, the effects of visual similarity on computer vision accuracy was determined. In every case, the transfer learning model’s accuracy decreased linearly as the number of total mineral classes increased, indicating that computer vision may be ineffective in classifying the 200 rock forming minerals [5]. Full dataset average accuracy for these models varied between 68.85% and 35.58%. The two models which proved most effective were EfficientNetB0, which had the highest accuracy (68.85%) and little overfitting, and VGG16, which had an accuracy of 43.60% with effectively no overfitting. Lastly, across every model tested, accuracy varied significantly per class, but this variance showed no connection to visual similarity, indicating both that computer vision is not effective in the consistent classification of minerals, and that visual similarity has no negative impact on the efficacy of computer vision. This information holds great significance to the geological community, the computer science community, and industries such as mineral exploration, oil and gas, green energy solutions, and construction.
Authors - Khalid BOUALI, Abderrahim BAJIT, Hamza BENZZINE, Hicham ESSAMRI, Yasmine ACHOUR, Hassan EL FADIL, Rachid EL BOUAYADI Abstract - Modern agriculture faces significant challenges from climate change, necessitating innovative strategies for sustainability. Precision agriculture has emerged as a transformative solution, leveraging advances in Artificial Intelligence (AI), IoT, and Cloud computing to transition traditional farming into intelligent systems. However, reliance on centralized architectures introduces new challenges related to response time and operational costs. This paper presents an optimized and intelligent IoT system architecture leveraging Edge computing to enhance agricultural sustainability, with a focus on greenhouse IoT platforms. The proposed architecture integrates AIoT and incorporates an intelligent edge computing systems that locally manages sensor nodes, processes data, and predicts critical agricultural parameters, including Temperature T, Humidity H, Air Quality CO₂, Light Intensity UV, and Soil Moisture pH. The study evaluates the performance of three Supervised Machine Learning Regression models, Linear Regression, Random Forest, and Extreme Gradient Boosting (XGBoost), in predicting missing sensor data using dataset contains the key agriculture parameters. Additionally, a Long Short-Term Memory (LSTM) neural network is trained on the same dataset and evaluated at the edge to forecast future variations in microclimatic parameters. To optimize system efficiency, a novel functionality is implemented, operating in ON and OFF modes based on node states. This functionality enables the system to predict data, minimizing unnecessary data collection, conserving energy, and improving the longevity of Static Edge Nodes distributed across the Greenhouse. This work highlights the potential of AIoT-driven precision agriculture to provide robust, intelligent, and sustainable farming practices, effectively managing IoT system components and delivering reliable monitoring of critical agricultural parameters amidst climate-related challenges.
Authors: Shreya Joshi, Vijay Ukani, Priyank Thakkar, Mrudangi Thakker, Dhruvang Thakker Abstract: This study introduces a hybrid malware detection approach that combines machine learning (ML) and deep learning (DL) techniques to enhance detection accuracy. By applying models like K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Gradient Boosting, we achieved 99.47% accuracy during training and 99.21% accuracy during testing, focusing on analyzing Portable Executable (PE) header data. Additionally, incorporating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) networks improved performance, achieving 99% accuracy, 97% precision, and 98% recall after 30 epochs. The proposed hybrid method reduces false positives and negatives while demonstrating scalability across various datasets, offering a reliable and efficient solution for contemporary malware detection.
Authors - Mark Alfred M. Nuguit, Elleicarjay C. Ramilo, Noel B. Linsangan Abstract - Urban noise events in busy metropolitan areas present significant challenges to urban planning and environmental management. This study introduces a cost-effective system integrating wireless sensor networks with deep learning to capture and classify urban noise events in Metro Manila. The prototype employs sensor nodes equipped with ESP32 microcontrollers and MAX9814 microphone amplifiers to record audio data, which is stored on SD cards and subsequently converted into 512×512 pixel spectrogram images using Python-based signal processing. These images serve as inputs to a MobileNet-based convolutional neural network, fine-tuned via transfer learning on a dataset of over 4,300 samples spanning two categories: civilian vehicles and human activities. The system, implemented on a Raspberry Pi with an interactive touchscreen interface, achieved an overall classification accuracy of 96.11%, as verified through confusion matrix analysis. This work demonstrates a scalable, low-cost framework for urban noise monitoring and provides valuable insights for environmental management and future urban planning strategies. The method’s efficiency and adaptability make it especially suitable for addressing the unique acoustic challenges of rapidly urbanizing regions.
