Daily Archives: May 3, 2026

Authors: Khushpreet Kaur, Anrika, Kashish, Ekta, Dr. Rajat Takkar

Abstract: The inability of existing online learning platforms to adapt to individual learner needs remains a fundamental and unresolved challenge in educational technology, contributing to persistently high dropout rates and poor knowledge retention across self-paced digital learning environments. This research proposes and evaluates a conversational AI assessment framework combined with dynamic personalised learning plan generation as a viable solution to this challenge. The study investigates whether natural, dialogue-based learner profiling yields more meaningful personalisation than conventional form-based or performance-data-driven approaches, and whether adaptive, quiz-based feedback integrated with multimodal resource matching improves learner engagement compared to uniform content delivery. A prototype platform named Flint was developed to implement and evaluate these research propositions, employing a dual-AI-engine architecture that addresses reliability and hallucination concerns identified in prior literature. Results demonstrate that conversational profiling successfully captures richer individual learner profiles, that dynamically generated plans align more closely with individual needs than static curricula, and that integrated gamification sustains motivation across extended learning engagements. The findings provide practical evidence to the growing body of research on AI-driven personalised education and demonstrate the feasibility of deploying large language model-powered individualised learning experiences at scale.

DOI: https://doi.org/10.5281/zenodo.20012543

Published by: vikaspatanker

Authors: Kodavati Ram Sanjay, Ruban Kumar S, Saran Raj S, Dr. Arun Kumar

Abstract: Automated detection of crop leaf diseases and classification of remote sensing land cover categories remain challenging owing to complex backgrounds, illumination vari-ability, spectral distortions, and high intra-class visual similarity. Existing frameworks provide strong baselines but commonly suf-fer from scale-sensitive segmentation, redundant feature fusion, limited contextual representation, and slow convergence in high-dimensional feature spaces. This paper proposes the Adaptive Recurrent FusionNet (ARFusionNet) framework — a Flask-based web application that integrates four coordinated inno-vations: Multi-Scale Adaptive Contrast Normalisation (MACN) for illumination-robust preprocessing; Graph-Based Superpixel Attention Segmentation (GSAS) for adaptive Region-of-Interest extraction; Bidirectional Gated Recurrent Units (BiGRU) embed-ded within Residual Efficient Convolution Blocks with Adaptive Weighted Feature Aggregation (AWFA); and Hybrid Binary Differential Evolution controlled Particle Swarm Optimisation (BDE-PSO) for efficient feature selection. DenseNet121 serves as the backbone feature extractor. We validate the system on the Plant Pathology 2020 dataset (1,821 high-resolution apple leaf im-ages; four disease classes). ARFusionNet achieves 98.2% classifi-cation accuracy, surpassing the state-of-the-art baseline (97.6%), while reducing training time by approximately 78 seconds and remaining fully executable on a standard CPU laptop without GPU dependency. The accompanying web application exposes eight interactive diagnostic modules including leaf visualisation, Canny edge display, convolved feature maps, neural network architecture visualisation, and real-time per-image prediction.

Published by: vikaspatanker

Authors: Dr.T.V.Deokar, Namrata Manoj Bandgar, Ankita Dilip Thombare, Bhagyashri Ashok Bhong

Abstract: A Realtimeloadflowmonitorindistributionsystemthisprojectdevelopsareal-timeloadflowmonitoring system for a distribution network using a bulb and a motor. The system continuously monitors electrical parameters to detect overload and fault conditions. A GSM module is used to send instant alerts to the user, while a buzzer provides local warning. This ensures quick response, improved safety, and reliable operation. The project also demonstrates the effect of different load types onsystem performance and serves as a simple, cost-effective model for smart power distribution

Published by: vikaspatanker

Authors: Ashwani Kumar, Dr. Sunil Maggu

Abstract: The intersection of machine learning and preventive healthcare offers transformative potential for earlydisease detection and personalized health guidance. However, most existing systems either producebinary classification outcomes without contextual risk stratification or provide static, non-adaptivehealthrecommendations disconnected from individual prediction confidence scores. This paper introducesHealthGuard AI, a novel multi-stage predictive framework that integrates supervised machinelearningclassification with probability-based risk stratification and a dynamically adaptivehealthrecommendation engine. The system simultaneously addresses three major chronic diseasedomains—Type 2 Diabetes Mellitus, Coronary Heart Disease, and Parkinson’s Disease — using clinicallyvalidatedfeature sets drawn from UCI Machine Learning Repository datasets. Beyond binary prediction, HealthGuard AI applies predict_proba() outputs to stratify individual disease risk into Low(≤0.40), Medium (0.41–0.70), and High (> 0.70) categories, each triggering a distinct, evidence-alignedhealthrecommendation profile. An additional Body Mass Index and Basal Metabolic Rate estimationmoduleemploying the Mifflin-St Jeor equation further extends the system’s scope into nutritional healthanalytics. Deployed as an interactive web application via Streamlit Community Cloud, HealthGuardAIachieves classification accuracies of 78.5%, 81.3%, and 87.2% for Diabetes, Heart Disease, andParkinson’s Disease respectively. The system demonstrates that probability-aware risk stratification, when combined with adaptive, risk-tiered recommendations, produces a meaningfully richer andmoreclinically actionable output than conventional binary prediction pipelines. Experimental results, systemarchitecture, and the clinical relevance of risk-tiered adaptive recommendations are discussedindetail.

DOI: https://doi.org/10.5281/zenodo.19993977

Published by: vikaspatanker