Journal of Data Engineering and Knowledge Discovery

Development of an Adaptive E-Learning Model for Personalized Learning Path Recommendation Using Ant Colony Optimization and Hybrid Filtering

Oluwatoyin Catherine Agbonifo, Adeoye Samuel Adedara, Akindeji Ibrahim Makinde — Thu, 18 Jun 2026 00:00:00 +0000

The majority of traditional e-learning models often fail to cater to individual learners’ needs due to limited parameter flexibility across diverse populations and the scope of adaptive courses. To address these challenges, this research developed an adaptive e-learning model using Ant Colony Optimization (ACO), collaborative filtering, and content-based filtering. The ACO was used for adapting the learning contents and activities, while also dynamically adjusting the learning path. K-Nearest Neighbor (KNN) was used for e-learning styles and teaching strategies to generate courses for learners. The Felder-Silverman Learning Style Model is employed to identify and accommodate different learning styles, ensuring a tailored educational experience. The implementation of the model is done using Python and PHP frameworks. An online survey was conducted with 500 undergraduate students from various academic disciplines to evaluate the developed model. The KNN algorithm achieved the best performance with an R² of 0.986 and an MAE of 0.012, indicating both high accuracy and minimal prediction error. A direct comparison with the benchmark study reveals that our models outperform their counterparts, with the KNN model achieving (0.012, 0.986) against theirs (0.013, 0.875). It demonstrates significant improvements in learner satisfaction and academic performance. This research contributes to e-learning by offering a flexible, adaptive solution that enhances educational outcomes through personalized learning experiences.

An Intelligent Computer Vision and CNN Framework for Automated Plant Disease Detection and Precision Agriculture

Guruprasad Kulkarni, Shaik Kaleemullah — Thu, 18 Jun 2026 00:00:00 +0000

Plant diseases represent one of the most critical threats to global agricultural productivity, where maize crops are particularly susceptible to multiple fungal diseases that significantly reduce yield quality and quantity. Traditional disease identification methods depend on manual visual inspection by agricultural experts, which is time-consuming, inconsistent, and impractical for large-scale precision farming applications. Within the paradigm of intelligent agricultural systems, this study proposes an automated deep learning and computer vision framework for maize leaf disease identification and classification. The proposed system introduces a novel Multi-Resolution Weighted CNN (MRW-CNN) architecture that leverages multi-scale feature extraction to achieve superior classification performance across five disease categories, including Common Rust, Grey Leaf Spot, Healthy, Northern Leaf Blight, and Southern Rust. A comprehensive dataset of 2,500 annotated maize leaf images with disease stage annotations (early, advancing, and severe) is utilized for training and evaluation. Comparative analysis of nine fine-tuned CNN architectures demonstrates that MRW-CNN achieves the highest testing accuracy of 97.04% with validation accuracy of 96.56%, outperforming established architectures including Xception (95.80%), MobileNet (94.64%), and Inception V3 (94.48%). The experimental results confirm the effectiveness of the proposed approach in delivering accurate, scalable, and computationally efficient automated disease detection, contributing toward intelligent precision agriculture and early crop disease management systems.

PulseAI: A Real-Time Public Sentiment Analysis Framework Using Transformer-based Language Models on Customized Data

Mon, 29 Jun 2026 00:00:00 +0000

In today’s digital world, social media platforms generate massive amounts of real-time textual data that reflect public opinions, emotions, and trends. Analyzing this data efficiently has become essential for businesses, researchers, and decision-makers to understand customer behavior and public sentiment. This project presents PulseAI, a real-time public sentiment analysis framework that uses Transformer-based Natural Language Processing techniques for intelligent opinion mining across multiple online platforms. The system integrates data from sources such as Twitter/X, Reddit, YouTube, Instagram, and news platforms using asynchronous API-based data collection mechanisms. The proposed framework employs a fine-tuned RoBERTa/BERT model for sentiment classification and contextual understanding of textual data. Unlike traditional machine learning approaches, the transformer-based architecture effectively captures semantic relationships, contextual dependencies, and sarcasm in user-generated content. The system performs sentiment classification into Positive, Negative, and Neutral categories while also providing emotion detection and aspect-based sentiment analysis. A FastAPI backend processes the incoming data efficiently, while a React-based dashboard visualizes sentiment trends, platform-wise analysis, heatmaps, word clouds, and real-time analytics. Experimental evaluation demonstrates that the proposed PulseAI framework achieves high performance with an accuracy of approximately 94%, outperforming conventional models such as Naïve Bayes and LSTM. The framework provides scalable, accurate, and real-time sentiment intelligence that can support applications in business analytics, brand monitoring, market research, and public opinion analysis. Future enhancements may include multilingual sentiment analysis and multimodal AI techniques combining text and image understanding.