Authors - Kantharaju, Srinidhi G A Abstract - Healthcare institutions worldwide are increasingly adopting digital im-aging solutions to replace traditional film-based radiological systems. This re-search presents the development of an advanced Picture Archiving and Communication System (PACS) designed to address current limitations in medical image management. Our proposed system focuses on seamless integration of legacy im-aging equipment through innovative format conversion capabilities, specifically transforming proprietary image formats into standardized DICOM files. The implementation demonstrates significant improvements in storage efficiency, trans-mission speed, and overall workflow optimization within hospital environments. Through comprehensive testing and validation, we established that our solution successfully bridges the gap between older imaging modalities and modern digital infrastructure requirements. The system architecture incorporates robust security measures, user-friendly interfaces, and scalable database management to support growing institutional needs.
Authors - Ronald L. Pancho, Maria Teresa N. Pancho, Roland M. Simbre Abstract - Using digital technologies to enhance government operations and e-service delivery, e-governance encompasses diverse components crucial for improving digital initiatives and government e-service performance. This study examines how various elements of e-governance influence the effectiveness and efficiency of e-government e-services. The Philippines has actively pursued e-governance initiatives to encourage citizen participation, provide access to information and service delivery, enhance transparency and accuracy, and promote communication and interaction. In this context, the government's e-service performance becomes pivotal in assessing the effectiveness and efficiency of digital initiatives undertaken by the government. There were six hundred eighty-three (683) government e-services users to whom the respondents replied through Google Forms. The study used a self-made survey questionnaire and employed Cronbach's Alpha to test reliability and internal consistency. The experts conducted content validation, and non-probability purposive sampling techniques were used in which respondents were directly involved. The finding reveals that e-governance components positively correlate with government e-services' performance in effectiveness and efficiency. The study relates how the use of e-governance components affects e-services performance. The idea suggests that increasing e-governance practices enhances government e-services performance. Therefore, optimizing the e-governance components and enhancing their performance are essential in improving digital public service initiatives.
Authors - Sabo Hermawan, Ryna Parlyna, Surya Anugrah, Inkreswari Retno Hardini, Ferry Setyadi Atmadja, Fajri Hamdani, Alifah Nur Rahmawati, Cornellius Seno Adriano Abstract - This study examines the effectiveness of combining AI-optimized mobile design with Bootstrap performance improvements and ChatGPT-driven text analysis for Micro, Small, and Medium Enterprise (MSME) websites. A mixed-methods approach was utilized to assess the effects of these interventions. Performance measures, particularly Largest Contentful Paint (LCP), exhibited a significant 40% enhancement following modification. Simultaneously, text analysis performed with ChatGPT-4 attained an accuracy of 86.7% in detecting significant content concerns. This research fills a notable vacuum in the literature by offering a comprehensive analysis of technical and AI-based solutions for enhancing the online presence of MSMEs. The results highlight the capability of AI to improve the technical performance and content quality of mobile-optimized websites, providing practical insights for MSME digital strategies. Subsequent research will investigate wider geographical contexts. These findings are further contextualized within the Technology Acceptance Model (TAM), emphasizing the perceived usefulness and ease of use of AI-enhanced mobile websites.
Authors - Veena N D, Anithadevi M D Abstract - This investigation examines contemporary satellite imaging method-ologies and their implementation in Earth observation applications. We analyze the operational principles of various sensing platforms, data acquisition techniques, and processing algorithms used in modern remote sensing applications. Our research focuses on spectral band optimization and image reconstruction methodologies using actual satellite datasets. A comprehensive case study utilizing Kilauea Volcano imagery from Hawaii demonstrates practical applications of multi-band data processing techniques. Through systematic analysis of seven spectral channels from Landsat-7 Enhanced Thematic Mapper Plus sensor, we evaluate the effectiveness of different wavelength combinations for terrain characterization. Our experimental results indicate optimal spectral configurations for various surface feature identification tasks. The study concludes with an assessment of current technological capabilities and operational constraints affecting satellite-based Earth observation systems.
Authors - Sanjay Yadav, Sanjay Kumar, Sakshi, Aaryan Abstract - Artificial Intelligence (AI) is revolutionizing healthcare by enhancing diagnostics, optimizing treatment plans, and enabling datadriven public health strategies. This paper focuses on the application of AI to combat the opioid overdose crisis in the United States, utilizing machine learning techniques—particularly reinforcement learning—to inform policy interventions. We explore the use of deep Q-networks (DQN) trained on CDC opioid mortality data to simulate optimal intervention strategies across states, demonstrating a potential reduction of 15% in projected deaths. Additionally, we apply clustering and trend analysis on multi-year overdose data to uncover regional dynamics and emerging hotspots. The study also examines key ethical considerations, data privacy solutions, and integration challenges within AI-driven healthcare systems. By combining algorithmic insights with epidemiological realities, this work offers a scalable and explainable AI framework to support equitable healthcare policy design.
Authors - Septi Nurmalita, Surya Anugrah, Gentiga Muhammad Zairin, Rochma Sudiati, Eka Septariana Puspa, Windy Permata Suyono, Hera Khairunnia Abstract - In the era of accelerated digital transformation, blockchain has emerged as a disruptive force in the auditing profession. This study presents a Systematic Literature Review (SLR) of 20 peer-reviewed articles from Scopusindexed journals (2020–2025), critically examining the opportunities and challenges of blockchain adoption in auditing. The review finds that blockchain enhances audit quality through real-time data access, immutable transaction records, and automation via smart contracts—leading to improved transparency, fraud detection, and operational efficiency. However, adoption remains limited due to regulatory uncertainty, high implementation costs, technical interoperability issues, and organizational resistance within traditional audit firms. Emerging concerns such as private key vulnerabilities, ethical ambiguities, and role conflicts in interdisciplinary audit teams further complicate adoption. Despite these barriers, recent literature highlights new opportunities, including collaborative audit ecosystems and the rise of Audit 4.0, where blockchain is integrated with AI and IoT for predictive, real-time assurance. The study calls for global regulatory harmonization, cross-platform audit standards, and empirical validation of blockchain-based audit systems. It also proposes a future research agenda centered on the convergence of blockchain and artificial intelligence to enhance risk detection, audit automation, and digital accountability. The findings offer actionable insights for regulators, auditors, and scholars navigating the evolving nexus of technology and assurance.
Authors - Sabo Hermawan, Surya Anugrah, Ryna Parlyna, Windy Permata Suyono, Eka Septariana Puspa Abstract - This study investigates the impact of integrating web-based Enterprise Resource Planning (ERP) systems, Big Data Analytics (BDA), and AI-powered dashboards to create a Smart Campus environment that enhance student engagement in international programs. Employing a mixed-methods approach over one academic semester, the research incorporates system log analysis, surveys, inter-views, and focus groups. The findings reveal a significant improvement in aca-demic engagement (mean score = 7.8, σ = 1.1), student satisfaction (68% positive feedback), and retention rates among international students. AI dashboards enabled early interventions for at-risk students, while ERP and BDA supported administrative efficiency and personalized services. These outcomes contribute to Sustainable Development Goal (SDG) 4: Quality Education, by promoting inclusivity, efficiency, and digital transformation in higher education.
Authors - Ketaki Patil, Om Nalagune, Shravani Patil, Sangita Bavkar Abstract - This paper presents the design and construction of a low-cost, Arduino Nano-based myoelectric prosthetic arm to increase access to assistive technology to underprivileged populations. The system leverages surface electromyography (sEMG) signals recorded from non- invasive Ag/AgCl electrodes to signal voluntary muscle contraction. The signals are interpreted by a simple threshold-based control algorithm, eschewing the use of advanced machine learning in favor of reliable and responsive performance. Upon exceeding a calibrated threshold, servo motors built into a 3D-printed PLA arm are actuated to mimic simple hand movements like opening and closing. The use of open-source hardware, low-cost materials, and additive manufacturing processes like FDM 3D printing guarantees a low-cost manufacturing process, permitting simple assembly, repair, and modification. The prosthesis is energy efficient, modular, and lightweight, ideal for educational use, rural hospitals, and prototyping. Experimental testing confirms consistent performance and real-time responsiveness to muscle activity. In summary, this paper demonstrates a low-cost, accessible, and scalable approach to functional prosthetic limb design, closing the gap between advanced biomedical solutions and those communities in need of them.
Authors - Unggul Purwohedi, Sabo Hermawan, Ria Rahma Nida, Yeti Lastuti Abstract - This study proposes a sustainable business model for BUMDes (Village-Owned Enterprises) Wukirsari, specifically examining a circular economy approach integrated with information systems to support Sustainable Development Goal 12 (Responsible Consumption and Production). The research ad-dresses the gap of limited circular economy adoption and digital integration in rural enterprises. Utilizing a mixed-methods approach, the study involved 18 respondents and employed tools such as Business Model Canvas, SWOT analysis, and Circular Mapping. Google Sheets, Forms, and Drive were integrated as the core information system for data tracking and management, demonstrating a practical application of information systems for business management in a rural context. Validation was achieved through triangulation of data sources and expert review. The results identify key circular units, including composting, recycled crafts, and eco-tourism, integrated into a comprehensive circular model with digital tracking mechanisms. The proposed model demonstrates improved efficiency, enhanced transparency, and local feasibility, offering a replicable frame-work for other rural enterprises. This research contributes to the novelty of tech-enabled circularity in rural contexts, providing a practical pathway for BUMDes to enhance sustainability and economic resilience through a robust business model. While the study's limitations include a small sample size and single-site focus, it lays a foundational understanding for future broader implementations of sustainable circular business models.
Authors - Rama Satish K V, Hemavathi J, Shrihari Kulkarni, V.V Sathya Sai Likhith, Balasubramani A, Shashaank Raj CS Abstract - The integration of AI-driven voice-controlled systems is transforming human-drone interaction, enabling intuitive and efficient UAV operation. This paper presents a novel approach for voice-commanded drone navigation using a fine-tuned T5 model, ensuring structured execution through predefined flight functions. The system incorporates Whisper for speech-to-text conversion and AirSim for high-fidelity simulation via its Python API. By leveraging NLP and real-time processing, the model accurately maps user commands to drone actions while maintaining execution safety. Experimental results demonstrate 97% accuracy in function mapping, highlighting the system’s reliability and responsive-ness. This work advances AI-powered UAV control, enabling applications in search-and-rescue, autonomous inspections, and hands-free drone operations.
