Journal of Electrical, Computer and Informatics
Journal of Electrical, Computer and Informatics is a scientific journal that publishes the latest research in electrical engineering, computer science, and informatics. It covers various topics, including power and energy systems, electronics, telecommunications, artificial intelligence, signal processing, IoT, cybersecurity, software engineering, big data, and cloud computing. Through a peer-review process, this journal publishes original research, literature reviews, and case studies that contribute to scientific and technological advancements, serving as a key reference for academics and practitioners in related fields and Published 4 times a year, in March, June, September and December.
International Forum of Researchers and Lecturers
Hybrid Subword: Character Representation for Robust Sentiment Classification on Multilingual and Code-Mixed Indonesian Text
User-generated Indonesian text frequently exhibits code-mixing with English (“Indonglish”), informal spelling, elongation, and keyboard typos. These phenomena break subword tokeniza tion assumptions and may degrade multilingual Transformer performance in deployment. This paper studies a hybrid representation that fuses XLM-R sentence features with a character-level CharCNN branch designed to capture orthographic patterns and mitigate character noise. We evaluate (i) a standard XLM-R fine-tuning baseline, (ii) an ablation that removes the character branch (NusaX only), and (iii) the proposed hybrid model on two datasets: NusaX-Senti (12 regional languages) and Indonglish (Indonesian–English code-mixed sentiment). Beyond clean test performance, we introduce a controlled robustness protocol by injecting character-level perturbations with probability p=0.18 and measuring performance drop. Results show that the XLM-R baseline achieves the best clean Macro-F1 on both datasets, while the hybrid model substantially improves robustness on Indonglish by reducing Macro-F1 drop from 0.030 to 0.007 under noise. We analyze common error confusions and discuss when character-aware features help or harm across languages.
Copyright (c) 2026 Journal of Electrical, Computer and Informatics
Hybrid XLM-R + Character-CNN Fusion for Robust Multilingual and Code-Mixed Sentiment Classification
Multilingual and code-mixed user-generated text (UGT) is noisy: spelling variants, elongations, and typos are common and can degrade transformer-only sentiment classifiers. This paper evaluates a hybrid architecture that fuses a subword transformer encoder (XLM R) with a character-level convolutional branch (CharCNN) to improve robustness under character-level perturbations. We benchmark on two test settings: (1) NusaX, a multilingual Southeast-Asian sentiment dataset, and (2) Indonglish, an Indonesian–English code-mixed sentiment dataset. We report standard clean-set metrics (Accuracy, Macro-F1) and a controlled robustness protocol that applies character-level noise with probability p = 0.18 (seed = 42) and measures performance drops. Results show the hybrid model reduces robustness degradation substantially on code-mixed text (Macro-F1 drop 0.007 vs. 0.030 for the XLM-R baseline), while incurring a modest clean-set performance trade-off. We provide an end-to-end pipeline, ablation analysis, and reproducible reporting artifacts (metrics JSON, confusion matrices, and error samples).
Reconceptualizing Community Innovation Pathways for Strengthening Entrepreneurial Sustainability and Social Value Creation in Local Development Settings
This study introduces an integrative model that links community innovation processes with entrepreneurial capacity building to promote long term sustainability within local communities. The model combines both technological and social aspects of innovation with entrepreneurial skills and resources to address the interconnected challenges of social, economic, and environmental sustainability. By synthesizing insights from existing literature and developing a conceptual framework, the study emphasizes the importance of creating a cohesive approach that treats innovation and entrepreneurship as interdependent components rather than isolated efforts. Through a series of community based case studies, the model is validated, demonstrating its applicability in diverse contexts and its ability to generate measurable social and economic outcomes. The findings reveal that communities adopting this integrative model are better equipped to address sustainability challenges through scalable, low cost, and culturally accepted innovations. Case studies from rural and marginalized areas show how local entrepreneurs, empowered by the model’s focus on capacity building, develop sustainable solutions that contribute to economic resilience and social equity. Additionally, the study highlights the significance of community engagement and participatory decision making, ensuring that innovation efforts are aligned with local needs and priorities. This approach fosters a sense of ownership and responsibility among community members, enhancing the long term success and sustainability of the initiatives. The study concludes by offering recommendations for practitioners and policymakers to adopt the integrative model in community development projects, emphasizing the need for policies that support both innovation and entrepreneurship. It also suggests areas for future research, including the role of digital technologies in enhancing innovation, the impact of stakeholder engagement, and the long term effects of the model on community sustainability.
Community Driven Entrepreneurial Transformation Models for Enhancing Economic Inclusion and Adaptive Capacity in Sustainable Development Programs
This study explores the effectiveness of community driven entrepreneurial transformation models in enhancing economic inclusion and adaptive capacity within sustainable development programs. The research utilizes a qualitative approach, incorporating multiple case studies from marginalized and rural communities actively engaged in entrepreneurial initiatives such as self help groups, indigenous entrepreneurship, and rural social entrepreneurship. Through participatory assessment methods, data were collected via interviews, focus group discussions, and workshops involving community members and local stakeholders. Thematic analysis was employed to identify recurring themes, patterns, and strategies that contribute to the success and sustainability of community driven models. The findings reveal that community driven models significantly improve local economic participation, particularly for marginalized groups such as women and youth. These models foster access to finance, market knowledge, and social capital, thus contributing to the development of resilient local economies capable of adapting to economic and environmental challenges. In contrast to traditional top down approaches, which often fail to address local needs, community driven initiatives ensure greater alignment with the realities of the communities they aim to serve. The study emphasizes the importance of community engagement, as it leads to higher levels of ownership, accountability, and long term sustainability. Despite their success, community driven models face challenges, including resource constraints, centralized decision making, and cultural barriers. Overcoming these barriers requires the development of policies that support local entrepreneurship and integrate local knowledge into development processes. The research suggests that hybrid models, combining both top down and bottom up approaches, may offer a promising solution to address these challenges and enhance the scalability of community driven initiatives. Future research should focus on scaling successful models, exploring the role of technology in supporting entrepreneurial ecosystems, and investigating the long term impacts of these initiatives on local economic and social resilience.
Strategic Integration of Community Innovation and Entrepreneurship for Building Resilient Local Economies in Development Oriented Ecosystems
This study explores the strategic integration of community innovation and entrepreneurship as a mechanism for enhancing local economic resilience. By examining the benefits of combining entrepreneurial efforts with community driven innovation, the research highlights how integrated approaches can drive economic growth, create jobs, and foster long term sustainability. The findings indicate that such integration not only strengthens local economies but also promotes social inclusion, adaptive capacity, and innovation diffusion. The study emphasizes the importance of cross sector partnerships and collaboration among stakeholders, including local entrepreneurs, government agencies, and community organizations, in achieving these outcomes. Furthermore, the integration of digital tools and business intelligence systems plays a pivotal role in enhancing the efficiency and adaptability of local businesses, ensuring their sustainability and competitiveness. The study also discusses the barriers encountered during the integration process, such as inadequate infrastructure, policy fragmentation, and resistance to change, and offers practical recommendations for policymakers to overcome these challenges. These include developing supportive policy frameworks, investing in infrastructure, and promoting entrepreneurship education. Finally, the study suggests that future research should focus on exploring specific mechanisms for collaboration, the long term impacts of integration on economic resilience, and the role of emerging technologies in enhancing local economic development. The results of this research reaffirm the critical importance of strategic integration in fostering resilient, sustainable, and inclusive local economies, providing valuable insights for policymakers, researchers, and community stakeholders.
Examining Innovation Led Community Empowerment Approaches to Support Entrepreneurial Growth and Long Term Socioeconomic Stability
This study examines the intersection of empowerment and innovation in community development, focusing on how innovation-led empowerment models contribute to sustainable entrepreneurship and long-term socioeconomic stability. Traditional empowerment frameworks often face challenges in providing sufficient resources, infrastructure, and institutional support, particularly in underserved and rural communities. The integration of innovation—especially through digital technologies and online platforms—offers significant potential to overcome these structural barriers by enabling entrepreneurs to expand market access, improve operational efficiency, and enhance economic resilience. The research identifies key drivers of entrepreneurial success, including digital platform utilization, access to financial and structural resources, and the encouragement of youth-driven and employee-led innovation. It also emphasizes the importance of community participation, social capital, and collaborative ecosystems in sustaining inclusive growth. However, barriers such as limited digital literacy, inadequate technological infrastructure, and regulatory constraints continue to hinder the full potential of innovation-led empowerment. The findings suggest that targeted policies, digital capacity-building initiatives, and cross-sector partnerships are essential to maximize impact. Overall, integrating innovation into empowerment strategies is crucial for promoting economic independence, particularly among marginalized groups, and for fostering inclusive, innovation-driven community development
A Systemic Perspective on Community Innovation Practices in Advancing Inclusive Entrepreneurship and Sustainable Livelihood Outcomes
This study explores the systemic perspective on community innovation practices and their impact on inclusive entrepreneurship and sustainable livelihood outcomes. By adopting systems thinking, this research provides a comprehensive framework for understanding the interdependencies among various actors, practices, and outcomes within community-driven innovation systems. The study highlights how local innovation practices, driven by collaboration and participatory methods, contribute to addressing social, economic, and environmental challenges in rural communities. Key findings reveal that community innovation practices foster inclusivity by empowering marginalized groups such as women, youth, and persons with disabilities, creating opportunities for entrepreneurial activities and economic growth. Furthermore, the interconnections between social, economic, and environmental dimensions ensure that these practices contribute to sustainable development. Through empirical data collected from community-based innovation initiatives, the study showcases successful examples of grassroots innovation, such as sustainable farming projects and eco-tourism initiatives, which promote long-term resilience, resource management, and socio-economic development. This research offers valuable insights for policymakers, community leaders, and development practitioners, emphasizing the importance of adopting a systemic approach to foster inclusive entrepreneurship and sustainable livelihoods. It also calls for further research on the integration of grassroots and social innovation and the exploration of cultural and regional differences in innovation practices. Overall, the study contributes to the growing body of literature on community innovation, providing a holistic understanding of its role in achieving sustainable development goals.
Transforming Community Capabilities Through Entrepreneurial Innovation Models: Evidence from Participatory and Impact Oriented Development Programs
This study explores the transformative impact of entrepreneurial innovation models in participatory and impact oriented development programs on community capabilities. By employing a qualitative dominant mixed methods approach, the research investigates how these models contribute to long term community empowerment through the enhancement of entrepreneurial skills, autonomy, and social capital. The study integrates both qualitative and quantitative data collected through participatory observation, interviews, and case studies, offering a comprehensive evaluation of multiple development initiatives. Key findings reveal that entrepreneurial innovation models significantly improve community members’ entrepreneurial skills, allowing them to start and sustain their own businesses. Moreover, the study demonstrates that these models foster greater autonomy in decision making and enterprise management, leading to increased self reliance within communities. Through a combination of program evaluation methods, the research highlights the long term impacts of these models on community based entrepreneurial ventures, emphasizing not only the creation of new businesses but also the sustainability of these ventures over time. The capability based approach, which focuses on what individuals can achieve rather than what they possess, is identified as a key framework for assessing the effectiveness of these programs. This approach provides a deeper understanding of how community members are empowered and how their capabilities evolve over time. In comparison with conventional development programs that primarily focus on short term economic outcomes such as income generation and job creation, this study underscores the value of a capability based evaluation. It demonstrates that fostering a culture of innovation, collaboration, and leadership leads to more sustainable and self reliant communities. Based on these findings, the study recommends that future development initiatives integrate entrepreneurial innovation models to enhance community resilience and ensure long term sustainability. This approach, which emphasizes capability building over output delivery, will contribute to the creation of empowered communities capable of managing their own development in a rapidly evolving socio economic landscape.
Mapping Community Innovation Dynamics in Strengthening Entrepreneurial Mindsets and Economic Self Reliance Across Local Development Initiatives
Community innovation plays a vital role in fostering sustainable development and economic self reliance within local communities. This study explores the dynamics of community driven innovation, focusing on how it contributes to the development of entrepreneurial mindsets and the empowerment of local populations. The research examines the key factors influencing innovation in community contexts, including collaborative learning, social networks, and localized innovation practices. These elements are critical in enhancing entrepreneurial capabilities, providing access to resources, and fostering economic resilience. The study highlights the importance of integrating entrepreneurial mindset development into local development programs to enhance the sustainability and adaptability of community driven initiatives. It also compares the community innovation model with traditional linear development models, emphasizing the added value of innovation driven, dynamic approaches to addressing complex socio economic challenges. The findings suggest that community driven innovation leads to long term self reliance and economic empowerment, as it nurtures a culture of entrepreneurship that adapts to local needs and conditions. The study calls for the adoption of holistic, innovation oriented strategies in community development, which consider not only economic growth but also social inclusion and environmental sustainability. Additionally, the paper recommends further research on the intersection of innovation, entrepreneurship, and economic self reliance, focusing on how local innovation initiatives can be scaled and integrated into broader development policies.
Institutional Synergy and Grassroots Innovation as Drivers of Inclusive Community Based Entrepreneurship in Sustainable Development Contexts
This study explores the synergy between institutional support and grassroots innovation in driving inclusive community based entrepreneurship (CBE) within the context of sustainable development. By examining the collaborative dynamics between local communities and institutional actors, such as government bodies, NGOs, and educational institutions, the research highlights how these collaborations foster sustainable economic growth, social inclusion, and poverty reduction. The study adopts a qualitative research design, utilizing case studies and stakeholder analysis to identify the key actors involved in CBE initiatives and assess their roles in achieving sustainable development goals (SDGs). The findings reveal that institutional support provides essential resources, such as financial assistance, technical expertise, and market access, enabling grassroots entrepreneurs to scale their initiatives. On the other hand, grassroots innovation, grounded in local knowledge and needs, ensures that the solutions implemented are both effective and sustainable in the long term. Moreover, the study highlights the importance of institutional collaboration in facilitating the diffusion of grassroots innovations, which often face challenges in scaling without adequate support. The research also underscores the long term impact of the synergy between institutions and grassroots actors on the sustainability of community based enterprises, contributing to broader goals such as poverty alleviation, economic inclusion, and environmental sustainability. Finally, the study offers recommendations for policymakers, institutions, and grassroots actors to foster collaboration and innovation to drive inclusive entrepreneurship and sustainable development. Future research directions are also suggested, focusing on the role of digital technologies in supporting institutional synergy and grassroots innovation, and exploring how emerging technologies can further enhance community based entrepreneurship models.
Designing Adaptive Community Innovation Frameworks to Foster Sustainable Micro Entrepreneurship and Socio Economic Resilience in Emerging Local Economies
This study explores the development of an adaptive community innovation framework designed to foster sustainable micro entrepreneurship and enhance socio economic resilience in emerging local economies. Emerging economies face numerous challenges, including financial constraints, infrastructural deficiencies, and limited community engagement, all of which hinder the growth of micro entrepreneurial ventures. These challenges are further exacerbated by rapid socio economic changes and global market integration. The main objective of this research is to design a framework that is both flexible and context sensitive, aligning with local needs and enabling long term sustainability and resilience. To achieve this, the study adopts a mixed methods approach, combining both qualitative and quantitative methods. A participatory community analysis is conducted to identify local challenges and opportunities for innovation, ensuring the framework is grounded in the community’s unique context. The design of the framework is iterative, incorporating continuous feedback from local stakeholders, and empirical data from pilot implementations. The empirical validation of the framework in selected local economic initiatives reveals that the adaptive model significantly improves entrepreneurial capacity, market access, and resource utilization. Additionally, the framework strengthens community engagement and enhances the adaptive capacity of local economies, leading to increased resilience and long term sustainability. The study concludes that the adaptive framework provides a practical, scalable model for addressing the structural limitations of emerging local economies and promoting inclusive, sustainable economic growth. The research findings suggest that adopting such adaptive, community driven frameworks can facilitate effective responses to local challenges and foster resilient micro enterprise ecosystems. Policymakers, NGOs, and community organizations are encouraged to incorporate adaptive approaches in their efforts to support sustainable development. Future research should test the framework in diverse cultural and sectoral contexts to further refine its applicability and effectiveness.
An Integrative Model of Community Innovation and Entrepreneurial Capacity Building for Long Term Social and Economic Sustainability
This study explores the development and application of an integrative model that links community innovation and entrepreneurial capacity building to promote sustainable development in local communities. The model aims to address the interdependence between social, economic, and environmental dimensions of sustainability by integrating grassroots innovation processes with entrepreneurial efforts. The research adopts a conceptual empirical approach, synthesizing existing literature on community innovation, entrepreneurship, and sustainability, and applies these insights to create a framework that combines these elements in a cohesive way. The model is validated through community based case studies that provide real world context, highlighting the practical application of the framework in diverse community settings. The findings from the case studies show that integrating innovation and entrepreneurship results in positive social and economic impacts, such as increased local entrepreneurship, job creation, improved social cohesion, and enhanced community resilience. Communities that implemented the integrative model were able to address local challenges more effectively, fostering long term sustainability. These results underscore the importance of community engagement, stakeholder collaboration, and resource accessibility in creating robust entrepreneurial ecosystems. By focusing on both economic and social sustainability, the model demonstrates that communities can achieve resilience and adaptability, ensuring the long term success of development efforts. The study also highlights several gaps in the existing literature, particularly the fragmentation between innovation and entrepreneurship in previous models. This research advances prior studies by offering a holistic model that treats innovation and entrepreneurship as interdependent components of sustainable community development. The integrative model offers superior outcomes compared to traditional fragmented approaches, showing that combined efforts can yield more sustainable and resilient results. The study concludes by recommending that policymakers and practitioners adopt this integrative approach to community development, ensuring more inclusive and effective long term sustainability. Future research should explore the adaptation of this model across different socio economic and geographic contexts to refine and expand its applicability.
Optimization of Parallel Computing Algorithms for Electromagnetic Field Simulation in Next-Generation Antenna Design
Modern antenna design faces significant challenges in terms of simulation time and accuracy for electromagnetic (EM) field simulations. These simulations, especially those involving large-scale or complex antenna designs, are heavily reliant on intensive computations that require substantial computational resources. To address this, parallel computing approaches using GPUs (Graphics Processing Units) through the CUDA (Compute Unified Device Architecture) platform have shown significant results in accelerating EM simulations. In this study, we implemented CUDA-based parallel processing to improve the efficiency of electromagnetic field simulations in antenna design. By utilizing simulation methods such as FDTD (Finite Difference Time Domain) and MoM (Method of Moments), we achieved a reduction in simulation time by up to 40%, with speedups of up to 40.16× for FDTD simulations and approximately 10 times faster for MoM simulations. Additionally, memory optimization and algorithm improvements, such as memory coalescing and shared memory usage, ensured that the speedup did not sacrifice simulation accuracy. While there were challenges in adapting sequential algorithms to parallel execution and managing memory on the GPU, the use of profiling tools helped identify and resolve performance bottlenecks. The findings of this research demonstrate the effectiveness of CUDA-based processing in accelerating EM field simulations, which has significant implications for antenna design cycles. Future research could focus on further improving parallel processing algorithms and expanding the use of GPU acceleration in other antenna design simulation areas.
Copyright (c) 2025 Journal of Electrical, Computer and Informatics
Hybrid Deep Learning and Fuzzy Logic Intrusion Detection Model for Modern Telecommunication Networks
The increasing sophistication of cyberattacks has made traditional security systems less effective, particularly in the context of modern telecommunication networks. These evolving threats require more advanced, adaptive intrusion detection systems (IDS) to provide reliable protection. This study proposes a Hybrid IDS Model that combines deep learning, specifically Convolutional Neural Networks (CNN), with fuzzy logic to enhance detection accuracy and adaptability. The objective of this research is to develop an intelligent system capable of detecting both known and unknown cyber threats by leveraging the strengths of CNNs for feature extraction and fuzzy logic for handling imprecision in network data. The hybrid model introduces CNN to automatically extract critical features from network traffic, enabling the system to learn complex attack patterns. The fuzzy logic component processes the CNN outputs by applying fuzzy rules to classify network behavior as normal or anomalous, thus addressing the uncertainty inherent in network data. The model achieves 93% detection accuracy, outperforming traditional signature-based IDS systems, which are less effective at detecting zero-day and evolving threats. The proposed IDS is also evaluated for real-time applicability, showing strong performance in large-scale telecommunication networks. This study’s findings emphasize the system’s ability to adapt to new and evolving attacks, providing a more robust and scalable solution compared to conventional IDS. The research highlights the effectiveness of combining deep learning with fuzzy logic in cybersecurity, offering promising results for the future of telecommunication network protection. Future work will explore integrating advanced fuzzy systems and experimenting with other deep learning techniques to further enhance detection capabilities in the face of ever-evolving threats.
Design of a Web-Based Digital Library Management System with Biometric Authentication
The rapid digitalization of academic resources has necessitated the development of more secure and efficient library management systems. Conventional password-based authentication methods are increasingly vulnerable to misuse, unauthorized access, and administrative inefficiencies. This study aims to design and implement a web-based digital library management system integrated with biometric fingerprint authentication to enhance security, usability, and management efficiency. The research employed a system development approach using web technologies such as PHP, MySQL, and biometric SDK integration. Evaluation was conducted by comparing the proposed system with traditional password-based models in terms of authentication accuracy, login speed, user satisfaction, and administrative efficiency. The findings revealed significant improvements: authentication accuracy increased from 89.5% to 98.7%, login time decreased from 7.8 to 2.9 seconds, and user satisfaction rose from 3.4 to 4.7 on the Likert scale. Additionally, unauthorized access attempts were completely eliminated, and administrative efficiency improved by 21.7%. These results demonstrate that biometric fingerprint authentication provides a highly reliable and user-friendly security mechanism, outperforming conventional authentication methods. The study concludes that integrating biometric technology into digital library systems not only strengthens data protection but also enhances operational performance and user experience. Future research should explore multi-modal biometrics, cloud-based scalability, and advanced encryption to further optimize system effectiveness.
Evaluation of Campus Wi-Fi Network Security Against Man-in-the-Middle Attacks Using AI-Based Intrusion Detection Sistems
This research focuses on the effectiveness of an AI-based Intrusion Detection System (IDS) in detecting Man-in-the-Middle (MITM) attacks within campus Wi-Fi networks. MITM attacks are a significant threat to network security, as they allow attackers to intercept and manipulate communications between users and network services. The objective of this study is to evaluate the AI-based IDS’s ability to detect MITM attacks with higher accuracy and faster detection times compared to traditional signature-based IDS. The research employs machine learning (ML) and deep learning (DL) techniques, such as Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN), to analyze Wi-Fi traffic for MITM attack detection. The study also investigates the effectiveness of hybrid models, combining multiple AI algorithms, to improve detection rates and reduce false positives. The main findings indicate that the AI-based IDS achieved 98% accuracy, significantly outperforming traditional IDS, which only reached 85%. The AI-based system also demonstrated low false positive (1.5%) and false negative (2%) rates, with an average detection time of 0.5 seconds per packet. These results highlight the superiority of AI-based IDS in terms of detection speed, accuracy, and adaptability to evolving attack methods. The study concludes that AI-powered IDS offer a more reliable and efficient solution for protecting campus networks against MITM attacks, with recommendations for future improvements, including integrating advanced AI models and expanding the dataset.
Development of an Adaptive Control System for Synchronous Electric Motors Using Neural Networks to Improve Industrial Energy Efficiency
This study presents the development and performance evaluation of a Deep Neural Network (DNN)-based adaptive control system for a Permanent Magnet Synchronous Motor (PMSM). The main objective is to enhance the dynamic response, steady-state accuracy, and energy efficiency of the PMSM drive compared to a conventional Proportional–Integral (PI) controller. The proposed control architecture integrates a DNN within the speed and torque control loop, enabling online adaptation to system nonlinearities and varying load conditions. The neural network structure utilizes speed error, current, and torque feedback as inputs, while training data are obtained from motor dynamics in MATLAB/Simulink simulations. Both the DNN and PI controllers are implemented and tested under multiple scenarios with different load torques (0%, 50%, and 100% rated load) and reference speeds (500–1500 rpm). Simulation results demonstrate that the DNN-based adaptive controller significantly improves performance metrics. The settling time is reduced by over 50%, maximum overshoot by 72%, and steady-state error by 83% compared to the PI controller. Additionally, torque ripple and energy consumption are decreased by approximately 60% and 19%, respectively, showing enhanced smoothness and efficiency. The findings confirm that the DNN controller provides robust adaptability, precise tracking, and lower energy use, making it a promising alternative for intelligent PMSM drive applications in industrial and electric vehicle systems.
Medical Image Identification System Using Convolutional Neural Networks for Digital Radiology-Based Disease Diagnosis
Medical imaging is one of the primary tools in disease diagnosis, but the manual process is often hindered by human factors such as fatigue, cognitive bias, and observational limitations. This study aims to explore the use of a pretrained Convolutional Neural Network (CNN) model, ResNet50, to improve diagnostic accuracy in medical imaging. ResNet50 was selected due to its efficient architecture, which addresses the vanishing gradient problem through the use of skip connections, making it ideal for complex medical image classification tasks. The dataset used includes medical images from various open-source datasets, including chest X-rays, brain MRIs, and retinal histopathology images, which were preprocessed using image normalization and augmentation techniques to enhance data quality. The model was trained with hyperparameters such as a learning rate of 0.001 and batch size of 32, over 50 epochs using the Adam optimizer. The results showed that the model achieved 95% accuracy, with 99% precision and 98% recall in detecting pneumonia from chest X-rays, and 95.44% accuracy in classifying brain tumors. While the model showed excellent performance, challenges such as varying data quality, limited computational resources, and potential overfitting remain. This study demonstrates the significant potential of AI in medical imaging to reduce human diagnostic errors, with promising prospects for wider implementation in clinics and hospitals. However, broader adoption requires integration with clinical workflows and training for healthcare professionals on the use of AI-based systems.
Blockchain Implementation to Enhance Medical Data Security in Electronic Health Record Systems
Blockchain technology has emerged as a revolutionary solution for enhancing the security, transparency, and efficiency of Electronic Health Record (EHR) systems. Traditional database-driven EHRs face persistent challenges such as data breaches, unauthorized access, and limited auditability. This study explores the implementation of a private blockchain framework integrated with smart contracts to ensure the confidentiality, integrity, and availability of medical data. Through a comparative analysis between conventional and blockchain-based EHR systems, the research demonstrates that blockchain provides superior data protection by leveraging cryptographic hashing, decentralized validation, and immutable ledgers. Smart contracts further improve automation by securely managing access control, consent authorization, and inter-institutional data sharing without intermediary involvement. The results indicate that while blockchain-based EHRs slightly reduce throughput and increase latency due to distributed consensus mechanisms, they significantly enhance auditability and traceability, achieving up to 98.7% improvement over traditional systems. This ensures that patient data remains tamper-proof and verifiable across multiple healthcare entities. The findings confirm blockchain’s potential as a secure and scalable foundation for next-generation health information systems. The study concludes that adopting private or hybrid blockchain models, supported by smart contract automation and regulatory alignment, can transform healthcare data management, promote interoperability, and strengthen patient trust in digital health infrastructures.
Implementation of Smart Classroom with IoT and Cloud Computing Integration to Support Digital Learning Environments
The integration of Internet of Things (IoT) and cloud computing in educational environments has the potential to revolutionize the learning experience by creating smart classrooms that enhance engagement, adaptability, and resource management. Traditional classrooms have faced significant challenges in supporting online and hybrid learning environments due to limitations such as fixed schedules, physical presence requirements, and a teacher-centered approach. These constraints make it difficult to meet the growing need for flexible and scalable learning solutions that support both in-person and remote students. This study aims to design and implement an IoT-based smart classroom integrated with cloud computing to address these challenges and enhance the learning experience. The IoT sensor integration involves the use of sensors to automate classroom management tasks such as lighting, temperature control, and attendance tracking. The cloud computing infrastructure provides scalable and efficient storage and processing of classroom data, enabling real-time monitoring and data analysis. The mobile application developed for students and teachers facilitates interaction with the classroom systems, offering features like remote control of classroom settings and access to learning materials. The implementation of this smart classroom system showed significant improvements in classroom management efficiency, including energy savings, time efficiency, and high user satisfaction. Moreover, the smart classroom system positively impacted student engagement and interaction by providing real-time feedback and facilitating collaborative learning. Teachers reported improvements in teaching effectiveness due to the availability of real-time data, while students benefited from personalized learning experiences. Comparing smart classrooms with traditional classrooms highlights the enhanced interactivity, adaptability, and resource management provided by IoT and cloud computing. This study emphasizes the importance of integrating these technologies to create flexible, scalable, and efficient learning environments.
Development of an Optimal Public Transportation Route Planning System Using Dijkstra Algorithm and Analytical Hierarchy Process
This study develops a decision support system (DSS) for public transportation route optimization by integrating the Analytical Hierarchy Process (AHP) method and the Dijkstra algorithm. The main objective of this study is to produce a route planning model that is not only efficient in terms of distance and time, but also considers qualitative factors such as comfort, safety, and user satisfaction. The AHP method is used to determine the importance weight of each criterion based on expert evaluation through a pairwise comparison matrix, while the Dijkstra algorithm is utilized to calculate the path with the lowest total cost based on the integrated weights. Simulation results show that the AHP–Dijkstra hybrid model is able to reduce the average travel time by up to 20.8% compared to the standard Dijkstra algorithm, and increase the user satisfaction level from 68.4% to 83.2%. These findings indicate that the multi-criteria approach produces routes that are more adaptive to real-world conditions, while supporting the operational efficiency and sustainability of urban transportation. Thus, this system has the potential to be an effective tool for transportation planners and managers in designing optimal, environmentally friendly, and user-oriented route networks.
Development of an Emotion Recognition System Based on Deep Learning for Human-Computer Interaction Applications
This study presents the development of an emotion recognition system based on deep learning, designed to enhance human-computer interaction by enabling machines to interpret human emotions through facial expressions. Traditional systems often rely on handcrafted features that lack adaptability to diverse environments, leading to reduced accuracy and efficiency. To overcome these limitations, a Convolutional Neural Network (CNN) was implemented and trained using the FER2013 dataset. The proposed model achieved an accuracy of approximately 90%, significantly outperforming conventional feature-based approaches. Experimental results demonstrated that the CNN effectively recognized various emotional states, such as happiness, sadness, anger, surprise, and disgust, even under variations in lighting and facial pose. The system’s robustness and scalability make it suitable for real-world applications, including virtual assistants, healthcare systems, and affective computing environments. Overall, this research highlights the potential of deep learning in building intelligent, emotion-aware technologies that improve interaction quality between humans and machines, providing a solid foundation for future advancements in emotion recognition and adaptive user interfaces