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All Journal International Journal of Electrical and Computer Engineering Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Sharma, Vivek
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Computer Science & IT Electrical & Electronics Engineering

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Naïve Bayes-Based Intelligent Model For Identification and Analysis of Learners' Intelligence Potential Jumarlis, Mila; Mirfan, Mirfan; Suardi M; Sharma, Vivek; R Mudalim, Nurhasana
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 1 (2025): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35585/inspir.v15i1.109

Abstract

Academic achievement, especially report card scores, is frequently the only factor used to evaluate a student's intelligence in education, ignoring other aspects, including kinesthetic, musical, interpersonal, and intrapersonal intelligence. This traditional method limits opportunities for students whose abilities are outside of typical academic disciplines, marginalizing them.  In order to close this gap, the current work is to: (1) develop a decision support system (DSS) that uses the Naïve Bayes approach to determine students' intelligence potential; and (2) integrate the method into the DSS to accomplish rigorous and precise identification. Using PHP and a MySQL database, the system was created utilizing the Unified Modeling Language (UML), which included use case, activity, sequence, and class diagrams. Because of its track record of handling labeled data and producing highly accurate probabilistic predictions, Naïve Bayes was chosen. To capture all of the learner characteristics, information was gathered through interviews and observation. Based on the results, the suggested method successfully recognizes various forms of intelligence and offers tailored learning suggestions that are in line with each student's abilities. The objective, effective, and data-driven approach to intelligence identification provided by this study helps educators create more inclusive and talent-oriented teaching methods. Ultimately, the model fortifies the paradigm of holistic education by guaranteeing that teaching practices are enriched by the recognition, cultivation, and integration of a variety of intelligences.
Deep convolutional network based real time fatigue detection and drowsiness alertness system Sharma, Vijay Prakash; Yadav, Jitendra Singh; Sharma, Vivek
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5493-5500

Abstract

Fatigue and drowsiness detection techniques based on the external features are under progress, and the methods of facial feature extraction require further development. This paper discusses the innovative processes, efficient methods, and recent advancements in the field of drowsiness and fatigue detection. In this proposed model, a wide application is planned in the field of artificial intelligence by defining the fundamentals of human-computer interaction, facial expression recognition and driver fatigue-sleepiness determination. This research outlines an efficient and effective three-phase strategy for detecting drowsiness. Viola Jones is used to detect facial traits in these three phases. Detection of yawning and tracking once the face has been identified, the segmenting the skin, the system becomes lighting invariant portion by itself, focusing on the chromatic components based on skin, and to reject most of non-face image backdrops. The color eye tracking and yawning detection are carried out by template matching with the correlation coefficient. The vectors of features based on each of the above phases is concatenated, and a binary result is obtained. The analysis of sound and successive frames into fatigue and non-fatigue states has been classified. If the time in fatigue state exceeds the threshold, the system will sound an alarm. 
Co-Authors JUMARLIS, MILA Kishnani, Namrata R Mudalim, Nurhasana Sharma, Vijay Prakash Suardi M Yadav, Jitendra Singh