Authors: Prof. K.S. Tamboli, Javalekar Shubham Shivaji, Katkar Sanyukta Jivan, Solavane Shubham Bharat

Abstract: This paper presents the design and implementation of a hybrid solar-grid water pumping system with automatic power switching to ensure reliable irrigation. The system utilizes a photovoltaic (PV) array as the primary energy source and integrates grid supply as a backup during low solar conditions. An intelligent control algorithm is developed using MATLAB/Simulink to monitor system parameters and automatically switch between power sources. The system improves energy efficiency, reduces dependency on conventional electricity, and ensures uninterrupted water supply. Simulation and experimental results validate the effectiveness, reliability, and cost-efficiency of the proposed system.

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Authors: Yuvraj Singh, Mohd Wali Abbas, Shardool Vikram Singh, Raziya Siddiqui

Abstract: Explainable Artificial Intelligence (XAI) has emerged as a critical field in modern healthcare, addressing the limitations of traditional “black-box” AI systems that lack transparency and interpretability. Your project focuses on developing an interpretable AI framework to assist clinicians in diagnosis, treatment decision-making, and patient management. This review summarizes the motivation, existing literature, research gaps, methodological framework, and potential clinical impact, while also interpreting the conceptual diagrams provided. The work highlights how XAI can improve clinician trust, ensure accountability, reduce bias, and enhance patient outcomes by making AI decisions understandable and actionable.

DOI: https://doi.org/10.5281/zenodo.20020249

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Authors: Dr.K.N.Kazi, Phule Pragati Bhagwan, Kashid Mukta Rajendra, Rajmane Prajkta Pandurang

Abstract: An Integrated Solid Waste Management System for Power Generation and Agricultural Usage is a sustainable approach to manage waste effectively while producing useful outputs. The system focuses on collecting, segregating, and processing different types of solid waste such as organic, recyclable, and non-recyclable materials. This system helps in reducing environmental pollution, minimizing landfill usage, and promoting energy generation. It also supports farmers by providing low-cost organic manure, improving soil fertility and crop yield. By integrating waste management with energy production and agriculture, the project contributes to sustainable development and efficient resource utilization. Overall, the system offers an eco-friendly, cost-effective, and practical solution to address waste disposal problems while generating power and supporting agricultural activities.

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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

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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.

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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

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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

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Authors: Mahesh Ganji

Abstract: This research study examines incorporation of an AI based anomaly detection method of Oracle cloud Payroll to make the payroll more accurate, reduce risks, and enhance compliance. The performance of different machine learning models such as Isolation Forest, One-Class SVM, Neural Networks, and Logistic Regression is tested in terms of their performance in detecting payroll data anomalies. Findings indicate that models such as Logistic Regression performed moderately well but other models did not cope with false positives and poor anomaly detection. The future is to perfect the models, involve deep learning, and realize the real-time anomaly to make the payroll management in large organizations more efficient and accurate.

DOI: https://doi.org/10.5281/zenodo.19988211

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Authors: Associate Professor Dr. S. Thaiyalnayaki, Janga Kishore, Kareti Manoj, Jogu Ganesh, Kasaragadda Gopi Chand

Abstract: The rapid growth of urbanization in metropolitan cities has significantly influenced real estate markets and housing prices. Accurately estimating property values has become increasingly important for buyers, sellers, and real estate investors. This study presents a machine learning-based house price prediction system designed to analyze housing data and estimate property prices based on multiple influential factors. The dataset used in this research includes property attributes such as location, square footage, number of bedrooms, and number of bathrooms collected from metropolitan regions including Chennai and Bengaluru. The proposed system applies data preprocessing techniques to improve the quality of the dataset before model training. These preprocessing steps include handling missing values, encoding categorical variables, and performing feature scaling to ensure consistent data representation. After preprocessing, a predictive model based on Linear Regression is implemented to analyze the relationship

DOI: https://zenodo.org/records/19981757

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Authors: Prudvi Saisaran Ponduru

Abstract: Scalable artificial intelligence (AI) workflows increasingly fail not because individual models are weak, but because the surrounding architecture cannot process heterogeneous, bursty, high-stakes evidence at operational speed. This paper proposes ECHO-DR, an Event-Centric Hierarchical Orchestration architecture for real-time disaster response. The real-world problem addressed is the difficulty of turning social media, remote sensing, UAV imagery, weather alerts, seismic feeds, and incident reports into timely, auditable, and trustworthy operational intelligence during floods, earthquakes, wildfires, and storms. ECHO-DR introduces four core contributions: an event-centric memory plane that unifies vector retrieval, geospatial indexing, lakehouse lineage, and structured event graphs; a hierarchical routing policy that escalates only high-value or uncertain items to expensive multimodal reasoning; a stage-disaggregated serving design that independently scales encoders, prefill workers, decoders, and tool calls; and a governance plane that embeds auditability, human review, and zero-trust access control into the workflow. A formal utility-constrained routing model, event-linking algorithm, fusion rule, and capacity model are developed to show how the architecture scales under large workflows. The paper also provides an implementation blueprint, clean system diagrams, benchmarking methodology, ablations, and simulated evaluation results. Simulated trace-driven experiments indicate that the proposed gated architecture can reduce p95 provisional alert latency relative to a monolithic multimodal pipeline while maintaining evidence traceability and limiting deep-model cost. The work demonstrates that scalable AI for future big workflows should be designed as a compound, event-centered, policy-aware system rather than as a single model endpoint.

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