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Menampilkan 1–2 dari 2 artikel
Android Malware Detection Using Machine Learning with SMOTE-Tomek Data Balancing
Masari, Maryam Sufiyanu
; Danladi, Maiauduga Abdullahi
; Onyinye, Ilori Loretta
; Tohomdet, Loreta Katok
Journal of Computing Theories and Applications
Vol 3
, No 3
(2026)
This study presents a comprehensive comparative analysis of four traditional machine learning algorithms Decision Tree, Random Forest, K-Nearest Neighbors, and Support Vector Machine for Android malware detection using the preprocessed TUANDROMD dataset comprising 4,465 instances and 241 features representing both static and dynamic application characteristics. Motivated by the limitations of conventional signature-based and hybrid detection methods, especially in managing imbalanced datasets an...
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The Llama–ARCS Adaptive Learning framework: AI–VR Integration System for Real-Time Motivational Feedback in Higher Education
Evwiekpaefe, Abraham Eseoghene
; Chinyio, Darius Tienhus
; Tohomdet, Loreta Katok
Journal of Computing Theories and Applications
Vol 3
, No 3
(2025)
This study developed and evaluated an AI-integrated Virtual Reality (VR) system designed to enhance personalized learning in higher education. While VR improves engagement, existing systems often lack adaptivity or experience high latency during AI interactions. To address these limitations, this research introduces a novel integration of a cache-optimized Llama 2 Large Language Model (LLM) that delivers real-time, motivationally grounded feedback. The system was implemented using Unity 3D and v...
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