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 - 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 - Rabab A. Rasool, Muthana H. Hamd Abstract - This paper introduces a novel unimodal biometric system based on a derivative angle-based feature. Unlike traditional edge-based features, this approach leverages a one-to-one correlation between the angle and its corresponding edge, enabling robust and reliable recognition for a single-source trait biometric system. This unique property allows for performance comparable to multimodal systems, which typically rely on multi-source traits fusion techniques for improved accuracy. To evaluate the effectiveness of angle-based features, an extensive experiments is conducted on three diverse facial datasets (AR, FEI, and CK+) using 150 subjects. The facial features are extracted using seven distinct methods at varying levels, ensuring a comprehensive and fair comparison between edge and angle-based approaches. Recognition accuracy was assessed using various metrics, including False Acceptance Rate (FAR), False Rejection Rate (FRR), and three Error Distance Measures (EDMs): Euclidean, Manhattan, and Cosine distances. Results demonstrate that angle-based features achieve highly competitive performance compared to their edge-based counterparts. Across 210 recognition processes, edge-based features achieved an overall accuracy of 79%, while angle-based features recorded a closely comparable 73%. These findings highlight the potential of angle-based features as a promising approach for developing robust and reliable unimodal biometric systems.
Authors - Zakaria Kinda, Sadouanouan Malo Abstract - In Burkina Faso, farmers and plant pathologists remain with important challenges in treating cotton diseases. The development of natural language algorithms has advantages to the implementation of a tool for suggesting treatments for cotton diseases and the classification of cotton diseases using meteorological data. In order to propose a tool for suggesting appropriate treatments for diseases using large language models (LLM), this paper uses meteorological data collected by the National Agency of Meteorology of Burkina Faso (ANAM-BF) to 2014 and 2023 in addition to knowledge bases on cotton diseases. In this study, we compare the Llama2 model with the RAG system to the BERT model for classification and proposed of cotton disease treatments. The result of our approach obtains a 95.4% classification precision for cotton diseases with weather data. Users may interact with the tools to generate treatments for cotton diseases with the use of a console for Llama2 with RAG and a chatbot for BERT. The performance of Llama2 model with RAG to generate appropriate responses to cotton diseases in Burkina Faso was evaluated by comparing it with GPT.
Authors - Adarsh Varshney, Karthick Seshadri, Viswa Kiran Andraju Abstract - The Knowledge-Infused Policy Gradient with Upper Confidence Bound (KIPGUCB) strategy addresses contextual multi-armed bandit problems by balancing exploration and exploitation. This study evaluates the performance of a KIPGUCB-based agent in the partially observable environment of StarCraft II. Unlike traditional deep reinforcement learning models that rely on low-level atomic actions, our approach enhances decision-making by employing higher-level tactical strategies. A tactic manager dynamically selects optimal tactics based on game state and reward signals which improves resource management and structured tasks such as unit training. The agent’s performance is compared with a StarCraft II Grandmaster, a novice human player and DeepMind’s baseline RL agent across five mini-games. Experimental results show that the KIPGUCB-based agent outperforms the baseline model in resource focused and structured tasks but struggles in combat-oriented scenarios requiring adaptive responses.
Authors - Rajyashree H, Govindarajan J Abstract - The FMCW radar sensor technology has demonstrated remarkable effectiveness in various applications, both civilian and military, due to its affordability and efficiency in a wide range of operational conditions. Nonetheless, this technology faces challenges related to mutual interference during object detection, which can negatively impact its performance. Advanced signal processing techniques, combined with cutting-edge software tools, can help mitigate this issue. This study embarks on the innovative journey of developing a module within NS-3 a distinguished network simulator, that encompasses all essential components of FMCW radar, including transmission, reception, and processing units for the very first time. The prevailing challenges experienced by the researchers in asserting their protocols in real-time have motivated us to design and develop the radar module in NS-3 that fosters to validate the protocols for radars in heterogenous environment in an emulated setup. Two unique mitigation algorithms—Ti’sInterference Mitigation, and Frequency Hopping via Unslotted Aloha—have been meticulously implemented and assessed. This undertaking lays a firm groundwork for the enhancement of the radar module, facilitating better performance in intricate setups.
Authors - Vipin Bansal, Manisha Malhotra Abstract - Artificial Intelligence (AI) is transforming industries such as automotive, healthcare, insurance, and manufacturing through computer vision and Convolutional Neural Networks (CNNs) for image analysis. In medical imaging, AI enhances the interpretation of MRI, X-rays, and CT scans, reducing human error and improving diagnostic efficiency. Early detection of Diabetic Retinopathy (DR), a severe diabetes complication, is crucial to preventing vision loss. Traditionally, ophthalmologists manually analyzed retinal images to detect abnormalities like fluid leaks or lesions. AI now enables more precise and efficient analysis. This paper presents ViT-MADv2, an improved method for detecting DR-related abnormalities using Vision Transformer (ViT) generative models. As an extension of previous research [1], ViT-MADv2 enhances the base image generation module by incorporating diverse training data with variations in contrast, color, and lesion size. It also refines the similarity evaluator module to improve analysis. The model leverages a novel approach to compare embeddings from original and generated images, identifying DR-specific patterns. Experimental results demonstrate a 2% accuracy gain, reaching 96.5%, with improved sensitivity—a crucial factor in healthcare. These advancements strengthen AI-driven diagnostics, enhancing clinical confidence. Source code: https://github.com/vipinbansal1/vitmadv2.
Authors - Nuno J. P. Rodrigues, Tania S. S. Azevedo Abstract - The literature review on licensing and technology commercialization delves into the critical role that Intellectual Property (IP) management and effective licensing strategies play in the commercialization of technological innovations. Intellectual property, particularly patents, provides a competitive edge for firms, enabling them to appropriate financial returns from their innovations. Licensing emerges as a vital method for both leveraging technologies developed within organizations and universities and generating revenue streams. The review emphasizes the importance of robust IP management, which allows firms to secure their innovations and strategically negotiate licensing agreements to expand their market reach. The studies analyzed highlight the complex dynamics involved in technology transfer, with a particular focus on how universities play a pivotal role in transferring innovations to the market. University-based licensing officers often act as intermediaries between academic inventors and the commercial sector, facilitating the commercialization process. However, challenges such as bias in decision-making and the difficulties in securing timely licensing agreements, particularly in emerging fields like nanotechnology, are discussed. Additionally, the review explores how partnerships between companies and universities, through strategic alliances and well-structured contracts, can mitigate risks and enhance the successful commercialization of new technologies. Overall, this review synthesizes key insights into how companies and academic institutions can optimize their technology commercialization efforts through efficient IP management, strategic licensing, and collaborative partnerships.
Authors - VanVinh Le, HongGiang Nguyen Abstract - This study aims to use the 2025 Times Higher Education (THE) Asia University Rankings as a frame of reference to consider what drives and dictates university rankings in the ASEAN region. Their analysis looks at six major indicators – Teaching, Research, Citations, Industry Income, International Outlook and Rank.. Comparative analysis confirmed that while Vietnam universities are purely citation-dependent in their quest to enhance higher ranked tables, some universities of our Southeast Asian neighbors at least start from a more balanced and diverse evaluation. The study deployed three deep learning models BiLSTM, BiGRU, and a Hybrid BiGRU-BiLSTM to predict university rankings. Among all the models, the Hybrid model performed the best with the lowest RMSE (3.72) and MAE (2.61), which indicated the highest predictive accuracy and stability. The analysis additionally showed the power of higher citation scores to boost rankings, with the greatest gains accruing to institutions with the lowest ranking. Feature importance analysis indicated that collaboration between industry and research is very important for ASEAN universities. Quality of teaching was still a main focus in Vietnam universities. The results highlight the role of research impact and international collaboration to go up in ranking. The analysis cautioned that universities and colleges looking to secure their position on the world stage needed to prioritize the volume of research produced, degree of international engagement, and depth of industry collaboration. The findings guaranteed the suitability of deep learning models as a robust methodological toolbox for ranking prediction and data-driven strategic improvement of university performance.
Authors - HongGiang Nguyen, VanVinh Le, TrungKien Tran Abstract - This paper is an attempt to evaluate ASEAN universities’ research impact and more specifically that of Vietnam by analyzing research-oriented key performance indicators that drive rankings. According to data, these universities have large gaps between the average reputations for academia and employers, faculty-student ratios, and research output. The experiment's three models named LSTM, GRU, and Hybrid predicted rankings on citation performance. The results revealed that the Hybrid model produces the highest accuracy, reiterating that the citation per paper variable tends to most strongly drive the positive correlation between citations per paper and university ranking. In addition, the study also indicated that Hue University cited 175% more research opened up to gain below a rank of 55. This point emphasized that research output is a major driver of rankings and institutional prestige. At the same time, the study offered several recommendations for universities, particularly those looking to increase their rankings, including focusing on high-impact publications, developing global research partnerships, and investing in faculty development.
Authors - Jawad Mahmood, Muhammad Adil Raja, John Loane, Fergal McCaffery Abstract - This research focuses on developing a testbed for Artificial Intelligence (AI) enabled Unmanned Aerial Vehicles (UAVs), particularly addressing the challenges posed by delayed rewards in Reinforcement Learning (RL). The Multi-UAV testbed integrates a realistic flight simulator with a Flight Dynamics Model (FDM), creating a versatile environment for testing and training RL algorithms. Two primary models were implemented: the Advantage Actor-Critic (A2C) model controls the target UAV, while the Asynchronous Advantage Actor-Critic (A3C) model governs the tracking UAV, leveraging asynchronous updates for efficient exploration and faster learning. A significant obstacle in reinforcement learning is the issue of delayed rewards, where feedback for an agent’s actions is not immediately available, potentially leading to unstable learning and reduced performance. This project addresses this challenge by integrating the Intrinsic Curiosity Module (ICM) with the A3C model. The ICM generates intrinsic rewards encouraging the A3C-controlled tracking UAV to explore new states, even in the absence of external rewards. This approach mitigates the effects of delayed rewards, allowing the tracking UAV to maintain effective pursuit of the target UAV under dynamic conditions.
Authors - Ufuk Sanver, Mustafa Cem Kasapbasi Abstract - Psoriasis, a chronic autoimmune skin condition, is an abnormal proliferation of skin cells along with scaling and inflammation. Proper detection and identification of psoriasis early on are critical to avoid inappropriate treatment and management. Traditional methods of diagnosis heavily rely on prolonged and subjective to physician experience of clinical presentation and histopathological study. Deep learning algorithms have proved highly successful for medical image analysis in the past few years and offer computerized, accurate, and quick diagnosis features. Here the use of convolutional neural networks (CNNs) is explored for classifying and detecting psoriasis from dermatology images. In the current research a pre-trained deep learning models that are ResNet50, MobileNetV2 and EfficientNet-B0 with SGDM, and DenseNet201 with ADAM optimization techniques have been employed. In this paper a curated dataset of psoriasis skin images affected with psoriasis was curated, in order to discriminate psoriatic lesions. To increase the accuracy dataset is augmented with various im-age processing techniques. In the training The experimental results demonstrate that the developed model is very accurate, sensitive, and specific. Our work suggests that deep learning models might be employed as valuable diagnostic aids, reducing errors in diagnosis and improved patient outcomes. Future studies will focus on enhancing model explain ability and increasing datasets with varied skin types and disease severities.
Authors - Diego Perez-Lopez, Rodolfo Bojorque, Jorge Duenas-Lerin, Fernando Ortega Abstract - Recommender systems play a crucial role in personalized content delivery, with collaborative filtering (CF) being a widely used approach. However, traditional CF methods often struggle to fully capture complex user-item interactions. In this study, we propose neural stacking models that integrate multiple CF techniques to enhance predictive accuracy. Experimental results show that, among baseline matrix factorization (MF) models, Biased MF and BNMF achieve the best Mean Absolute Error (MAE), demonstrating their effectiveness in modeling user-item relationships. Nonetheless, the proposed neural stacking models outperform these approaches by dynamically weighting CF models based on contextual factors. Comparisons with deep learning-based CF models (GMF, MLP, and NeuMF) confirm that neural stacking provides a more personalized and adaptive recommendation strategy. Future research will focus on optimizing model architectures, incorporating additional contextual information, and evaluating scalability for large-scale applications.
Authors - Ujjwal MK, Sunil Parameswaran, V. Guna Chowdary, Varun Bharadwaj, Suresh Jamadagni Abstract - In today’s fast-paced world, mental wellness is as essential as physical health. EmoHealth is a new mobile application designed to help individuals harmonize their mind and body. By combining emotion recognition with health analytics, EmoHealth delivers a real-time, holistic snapshot of a person’s mental and physical health. Using Apple technologies like HealthKit and CoreML, EmoHealth continuously tracks your heart rate, breathing patterns, and vocal activity through your Apple Watch and iPhone. This advanced approach detects even subtle shifts in emotional and physical health, sending timely alerts that promote self-care and help prevent stress-related issues from escalating. The EmoHealth app empowers users to stay mindful of their mental health while providing essential information to assist with stress management and emotional well-being. It keeps users updated on physical and emotional health metrics, fostering greater self-awareness and offering a new path to well-being. With on-device data processing, EmoHealth ensures both convenience and peace of mind, bridging gaps in traditional healthcare and enhancing proactive mental health support. This innovative approach highlights the potential of technology to deliver early, personalized feedback that can make a meaningful difference in people’s lives.
Authors - Motsotua Confidence Hlatshwayo,Kayode Oyetade,Tranos Zuva Abstract - In today’s rapidly evolving digital landscape, the ethical adoption of Artificial Intelligence (AI) has become a critical concern for organizations seeking to innovate responsibly. This study explores the integration of AI within South African Small and Medium Enterprises (SMEs), emphasizing the ethical challenges and strategic considerations associated with adoption in resource-constrained environments. While AI offers transformative potential for enhancing innovation, efficiency, and competitiveness, its deployment raises significant ethical risks, including algorithmic bias, data privacy violations, and a lack of transparency and accountability. Grounded in the Technology-Organization-Environment (TOE) framework and Ethical Decision-Making (EDM) theory, this research adopts a literature review methodology to examine the drivers, barriers, and ethical dimensions of AI adoption in SMEs. The study reveals that ethical awareness, organizational readiness, and external regulatory support are crucial to enabling responsible innovation. It highlights that SMEs with stronger ethical intent and governance practices are better positioned to adopt AI in ways that promote trust, fairness, and long-term sustainability despite resource limitations. This study contributes a context-sensitive conceptual framework that integrates structural and ethical considerations, offering practical guidance for policymakers, business leaders, and technology developers. The findings underscore the importance of aligning digital transformation with ethical imperatives to ensure inclusive and equitable AI adoption in developing economies.
Authors - Elrasheed Ismail Mohommoud Zayid, Abdulmalik A. Humayed, Yagoub Abbker Adam Abstract - This study aims to test machine learning (ML) classification analysis across a rich network traffic dataset sample to figure out the patterns of Saudi cyberspace and alert the community for cybersecurity risks. The network topology used for generating a sample dataset was a kind of heterogeneous simultaneous photonic multiprocessor exchange bus architecture (SOME-Bus). First, the dirty and noisy dataset was cleaned using essential cleaning procedures. Dimensions of 22 characteristics and 1048575 datapoints were considered for the model/data evaluation procedures. Second, the top-ranked learning model candidates were nominated by using the LazyPredict technique. Third, the Saudi cyber domain's terra-Byte payload traffic is shaped and visualized using a potent supervised computing algorithm. Powerful performance measure criteria were employed to calculate the model’s final accuracy and error rates. The findings conclude that Saudi was in the ninth rank with respect to cybercrime source; and, decision tree (DT) was the highest-performing algorithm with respect to destination address.
Authors - Vindiya Wickramathunga, Sapna Kumarapathirage Abstract - Accurate cinnamon plantation disease detection is crucial for planters, especially those with limited expertise. Traditional manual inspection methods are time-consuming and subjective, risking delays or incorrect diagnoses. This paper proposes an automated detection system using a Convolutional Neural Network (CNN) integrated with Explainable Artificial Intelligence (XAI) techniques. A publicly available dataset comprising three disease classes and one healthy class was used for training. The CNN achieved 75–80% accuracy with a 0.78 F1-score. Visual explanations via HiRes-CAM provided higher resolution compared to GradCAM++, enhancing user trust. The system demonstrated fast inference times (1–2 seconds), supporting real-time field deployment.
Authors - Leila BENOUNISSA, Djilali BENABOU, Soumia TABETI Abstract - The mobile phone is the most widely used mass communication object in the world. It gives its user the feeling of being everywhere at the same time and at all times (Jauréguiberry, 2005). Calls, ringtones and button clicks can be a source of undesirable phenomena for others and disrupt social interactions in public spaces (Palen, Salzman and Youngs, 2001). This is how several studies have attempted to understand the attitudes of mobile phone users and their preferences in different social settings. The objective of this contribution is to understand how users perceive the impact of the mobile phone use in different public spaces and if there are differences in the use compared to the characteristics of the users. Keywords: Mobile Phone, Social Settings, Attitudes, Algeria.
