International Journal of Information Technology, Research and Applications

Integrating Indian Knowledge Systems with AI for Personalized Learning to Improve Academic Performance: A Novel Framework

VIMALA S — Wed, 01 Jul 2026 00:00:00 +0000

Abstract: The increasing adoption of Artificial Intelligence (AI) in education has opened new pathways for personalized learning; however, many existing models overlook cultural and contextual relevance. In the Indian educational landscape, Indian Knowledge Systems (IKS) offer time-tested principles such as experiential learning, self-discipline, and holistic development. This study aims to design an intelligent learning framework that meaningfully combines AI technologies with IKS principles to enhance student learning outcomes. The primary objective of this research is to develop and assess an AI-enabled system that integrates traditional Indian pedagogical values with modern data-driven approaches. The proposed method employs a hybrid architecture that analyzes student data—including academic performance, behavioral patterns, and engagement levels—using machine learning techniques. Models such as Random Forest and Neural Networks are utilized to generate personalized learning recommendations, while IKS elements are embedded to ensure balance between cognitive, emotional, and ethical development. To validate the framework, a simulated dataset representing diverse learner profiles is used. The results demonstrate that the integrated model improves student engagement, supports better academic performance, and encourages a more holistic learning experience compared to conventional AI-based systems. The novelty of this work lies in its interdisciplinary approach, blending traditional Indian educational philosophies with advanced AI methodologies. By aligning with the vision of NEP 2020, this study presents a culturally adaptive and scalable framework that contributes to the evolution of personalized education in India.

A Hierarchical Spatial-Temporal CNN-BiLSTM Hybrid Model for Brute-Force Attack Detection in High-Speed Networks

Stephen Wanjau, Wanjiru Njuki — Wed, 01 Jul 2026 00:00:00 +0000

As computer networks become faster, cyberattacks – particularly SSH and FTP brute-force attacks – have become more sophisticated, exposing limitations in traditional detection systems, including high false positive rates. This study proposes a hierarchical hybrid deep learning model integrating Convolutional Neural Networks (CNN) for spatial feature extraction and Bi-directional Long Short-Term Memory (Bi-LSTM) for temporal analysis. Principal Component Analysis (PCA) reduced 82 features to 18 key attributes, improving computational efficiency. The model was implemented using a GPU-enabled TensorFlow framework and evaluated on CIC-IDS 2017 and CSE-CIC-IDS 2018 datasets. Results show that the hybrid CNN–Bi-LSTM model outperforms standalone approaches, achieving 99.27% accuracy, 99.89% precision, 98.19% F1-score, and 97.84% recall, with a low false positive rate of 0.018%. Reliability analysis using Monte Carlo Dropout yielded 92.3% predictive certainty, while a Dietterich 5x2cv paired t-test confirmed statistically significant improvement over the HAST-IDS baseline. These findings demonstrate a scalable and high-accuracy approach for detecting brute-force attacks in modern network environments.

A Attention-Enhanced Lightweight Object Detection for Rice Pest Identification Using YOLOv8n with CBAM and BiFPN

D. JERLIN SERAPHINA, Dr. R. Venkatesan, Dr. U. Srinivasulu Reddy — Tue, 30 Jun 2026 00:00:00 +0000

The agricultural crop of rice supports the food security of over half of the world but the infestations of pests are considered to be one of the major causes of the loss in yield with the worst line of loss up to 80 percent. The original method of detection, manual scouting, is subjective, time-intensive, and can hardly be applied to large farms. In this paper, a lightweight, real-time object detection model that identifies pests on the rice will be presented based on the addition of Convolutional Block Attention Module (CBAM) and Bidirectional Feature Pyramid Network (BiFPN) neck to the YOLOv8n framework. On a single 26-class rice pest dataset of 11,319 images collected under four Roboflow sources: YOLOv8n (baseline), YOLOv8n+CBAM, the proposed YOLOv8n+CBAM+BiFPN, RT-DETR, Faster R-CNN,

and Florence-2 in zero-shot mode, we compare six detection methods under the same conditions. The proposed model has a precision of 0.5888, a recall of 0.4957, mAP50 of 0.4694 and mAP5095 of 0.3143 at a

constant inference latency of 2.40 ms per image, more than 60.3 times faster than the YOLOv8n baseline with an almost identical mAP50 gap We also demonstrate that the depthwise separable convolutions of BiFPN counterintuitive reduce the inference latency below the baseline, and zero-shot inference on Florence-2 does not work at all without domain adaptation.