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All Journal Journal International of Lingua and Technology World Psychology Journal of Social Science Utilizing Technology Journal of World Future Medicine, Health and Nursing Journal of Social Science Utilizing Technology Journal of Computer Science Advancements Journal of Noesantara Islamic Studies Journal of Biomedical and Techno Nanomaterials Journal of Social Entrepreneurship and Creative Technology Journal of Multidisciplinary Sustainability Asean
Lim, Sofia
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Religion Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Education Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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Journal : Journal of Computer Science Advancements

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IMAGE PROCESSING AND COMPUTER VISION TECHNIQUES FOR AUTOMATED SMART SURVEILLANCE SYSTEMS Syahlan, Zainal; Lim, Sofia; Wong, Lucas
Journal of Computer Science Advancements Vol. 3 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i6.3323

Abstract

The rapid development of urbanization and security concerns has prompted the integration of automated smart surveillance systems to enhance public safety and operational efficiency. Traditional surveillance methods often rely on human monitoring, which is prone to errors and inefficiencies. Image processing and computer vision techniques provide a solution by automating object detection, tracking, and anomaly recognition. This study aims to investigate advanced image processing and computer vision techniques for improving the performance of automated smart surveillance systems. A hybrid approach combining convolutional neural networks (CNNs), attention mechanisms, and edge computing is proposed to enhance both detection accuracy and real-time processing speed. The research employed experimental design, utilizing a dataset of 12,000 annotated image frames and 85 hours of video footage from diverse environmental conditions. Performance metrics such as precision, recall, mean average precision (mAP), and processing speed were measured. Results demonstrate that the proposed model outperforms traditional CNN models, achieving higher detection accuracy and faster processing speed. The study concludes that integrating edge computing with adaptive image processing and attention-based neural networks significantly improves automated surveillance system performance in real-world settings. These findings offer valuable insights for the development of scalable and efficient smart surveillance technologies.
EMBEDDED SYSTEMS DESIGN FOR SMART PRODUCTS IN INDUSTRY FOUR POINT ZERO MANUFACTURING Sujana, Nana; Tan, Jaden; Lim, Sofia
Journal of Computer Science Advancements Vol. 4 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v4i1.3391

Abstract

Industry Four Point Zero manufacturing has transformed conventional production systems into intelligent, interconnected environments in which smart products play a central role. These smart products rely heavily on embedded systems to enable sensing, real-time control, communication, and autonomous decision-making under strict industrial constraints. This study aims to examine how embedded systems design influences the performance of smart products in Industry Four Point Zero manufacturing contexts, with particular attention to design attributes that support efficiency, adaptability, and reliability. A mixed-methods research design was employed, combining quantitative analysis of survey data collected from industrial practitioners with qualitative insights derived from case-based observations in manufacturing settings. The instruments focused on key embedded system design dimensions, including modularity, real-time responsiveness, communication efficiency, and system reliability, as well as corresponding smart product performance indicators. The results reveal that embedded systems design has a significant and positive effect on smart product performance, with communication efficiency and system reliability emerging as the strongest predictors of operational efficiency and fault tolerance. The findings demonstrate that smart manufacturing effectiveness is strongly determined by device-level design decisions rather than by higher-level digital infrastructures alone. In conclusion, the study highlights embedded systems design as a strategic foundation for smart products and underscores its critical role in achieving sustainable and resilient Industry Four Point Zero manufacturing.
Co-Authors Ajani, Anggra Trisna Akhtar, Shazia Ali Mufron, Ali Almeina Loebis, Iin Chan, Rachel I Putu Astawa Jasafat Nampira, Ardi Azhar Nuryanti, Titik Olsson, Ingrid Pande Ayu Naya Kasih Permatananda Prasetio, Rachmat Purnama, Yulian Sujana, Nana Syahlan, Zainal Tan, Ethan Tan, Jaden Tan, Marcus Thompson, Ethan Wong, Lucas Zaman, Khalil