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.
