Nowadays fire detection and recognition are one of the major securities and security region for saving human life. Concern to this we are going to propose a new model for early detection and recognition of the fire fo...
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Naval defense systems employ underwater mines as a critical measure to bolster maritime security. While these mines play a pivotal role in safeguarding vital waters, their deployment introduces a dual challenge: ensur...
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The Internet of Things (IoT), a rapidly expanding and constant technology today, enables the efficient exchange of information and control of electronic equipment. As technology grows more and more integrated into our...
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This paper presents KATH - a No-coding Data Processing system designed to assist genetic researchers at Harvard University in their work on mutations in the human genome related to eyesight pathologies. We aim to depl...
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Emotional well-being is a growing concern in Malaysia, and this study aims to address it by proposing the mobile application 'Crochet-Together' as a tool to promote positive emotional well-being among young ad...
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Emotion recognition based on physiological signals has become a crucial area of research in affective computing and human-computer interaction, with applications in smart homes, workplaces, educational institutions, h...
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ISBN:
(纸本)9791188428137
Emotion recognition based on physiological signals has become a crucial area of research in affective computing and human-computer interaction, with applications in smart homes, workplaces, educational institutions, healthcare, and entertainment. In this study, a real-time emotion recognition system utilizing fog computing architecture was developed by considering the challenges of latency, total response time, resource usage, and security in IoT environments. The random forest machine learning model was trained with time-based statistical features by using the DREAMER dataset. Even though the model achieved an accuracy of 84.21% with 104 features, to meet real-time performance requirements, the system was optimized to calculate 24 features, maintaining a commendable accuracy of 79.70%. Extensive experiments demonstrated the superior performance of fog computing compared to edge and cloud computing in terms of latency, queuing delay, jitter, and most importantly total response time. The results highlight the proposed system's ability to support multiple users simultaneously. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.
Digital image processing, also known as computer image processing, refers to the process of converting image signals into digital signals and processing them using computers. Digital image processing first appeared in...
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With the rapid development of blockchain, blockchain-based outsourcing computing has been widely used. Outsourcing computing allows users with limited resources to outsource complex computing to those with powerful re...
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Oral cancer is a widespread global issue, significantly impacting low- and middle-income countries. Early detection can drastically improve survival rates, but traditional methods have proven to be insufficient. This ...
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With the continuous development of new power technology, the number of new access equipment in the power system has shown a continuous growth trend, and the accompanying harmonic hazards have also become a necessary c...
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