Category Archives: Uncategorized

Authors: K.Satheesh, S.Chandana, V.Balaji, Sk.Yaseen, V.V.Bhanu Satya Sri

Abstract: The Medical Image Analysis Tool uses Google's Gemini 2.5 Flash multimodal model to analyze X-rays, MRIs, CT scans, and ultrasound images, generating comprehensive diagnostic reports with findings, diagnoses, and patient-friendly explanations. Integrated with DuckDuckGo search, it enables real- time retrieval of medical literature and treatment protocols for evidence-based recommendations. Built with Streamlit and the Agno framework, the tool delivers structured, medically accurate responses in markdown format for healthcare professionals and students. This AI-powered assistant reduces diagnostic uncertainty, empowers data- driven decision-making, and enhances medical image interpretation efficiency.

Published by: vikaspatanker

Authors: Mrs. Kalluri Jaya Sri Sai, Dheeravath Rajender, Bommapala Manideep, Arishe Pramod

Abstract: With the increasing demand for advanced digital security, efficient and scalable real-time monitoring has become essential. Traditional security evaluation methods often rely on manual oversight or delayed reporting, which lacks the immediate and personalized feedback necessary to thwart modern attacks. This project presents an Intelligent System for Real-Time Cyberthreat Information that leverages automated data streaming to evaluate the digital landscape for threats. The proposed system analyzes network logs and global threat feeds for syntax, logic, and patterns of malicious activity, providing instant alerts along with clear threat explanations and suggested mitigation strategies.

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Authors: Mr. Chitoor Venkat Rao Ajay Kumar, Amgothu Shivateja, Dhanavath Praveen, Banoth Bhaskar

Abstract: The “Semantic and Contextual Intelligence-Based Court Verdict Prediction” system uses AI to analyze legal case data and predict outcomes. It understands the meaning and context of legal documents using Natural Language Processing. The system studies past judgments, case facts, and legal patterns to make predictions. It provides structured insights that help legal professionals in decision-making. This improves the speed, accuracy, and efficiency of legal analysis.

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Authors: Ms. Gyara Monika, Banothu Malsoor, Mendu Balram Sai Abhishek, Mohammed Abdul Sameer

Abstract: Explainable Artificial Intelligence (XAI) is a system that helps humans understand how machine learning models make decisions. Traditional AI models often work like a “black box,” where the output is given without explaining the reason. XAI provides clear explanations for predictions by showing important features, rules, or visual insights. This improves transparency, trust, and fairness in AI systems, especially in critical areas like healthcare, finance, and education. By making AI decisions understandable, XAI helps users and developers detect errors, bias, and improve model performance.

Published by: vikaspatanker

Authors: S. Balaji, N. Poyyamozhi

Abstract: Currently, the field of project management faces increasing uncertainty as projects must deal with changing requirements, resource shortages, and the unpredictable effects of human actions, technical systems, and external events. However, existing data-driven models have failed to provide interpretable results, preventing project managers from identifying the factors that create risks. Thus, this research presents a lightweight and explainable data-driven decision support system that enables project risk prediction and risk management in complex project management environments. The devised methodology employs a Project Management Risk Dataset, which includes project demographics and operational metrics, human factors, organizational context, technical aspects, and external influences. Moreover, a comprehensive data reliability testing is conducted through pre-processing methods for categorical attributes, one-hot encoding, and Min-Max normalization of budget and timeline, and risk metrics. Advanced feature engineering uses graph-based feature relationships to identify hidden project attribute dependencies, Graph Signal Processing to create project attribute dependencies, and LASSO with polynomial feature expansion to achieve optimal results. The proposed TAM-Lite architecture integrates TabNet, a mini autoencoder, and a shallow multilayer for project risk prediction. Moreover, stage-wise training is conducted based on Gradient Boosted Rule Sets with Extreme Learning Machines and fuzzy logic classification. The model generates risk level probabilities, which are evaluated through Bayesian Networks and counterfactual explanations to deliver clear and actionable risk reduction recommendations.

DOI: https://doi.org/10.5281/zenodo.19483001

 

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Authors: Dr. Satish Kumar

 

Abstract: This study investigates the unsteady magneto hydrodynamic free convective flow of a viscous, incompressible, and electrically conducting fluid past an infinite vertical porous plate with porous medium and applied uniform magnetic field in the direction of the flow. The effect of injection/suction velocity and the magnetic field on the flow field, skin friction and heat transfer are reported and discussed in detail. The Hartmann number and porosity parameter influence the flow velocity, while the Prandtl and Grashof number govern the heat transfer characteristics. The governing partial differential equations for momentum and energy are transformed into a dimensionless form using appropriate similarity variables.

DOI: https://doi.org/10.5281/zenodo.19482335

 

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Authors: Mr. Durgunala Ranjith, Gaddam Abhinay Reddy, Guda Raja Krishna, Tejavath Nithin Nayak

Abstract: The “Smart and Intelligent Web Traffic Analytics and Monitoring System” is designed to track and analyze website traffic in a simple and effective way. It collects real-time data about users, page visits, and browsing behavior. The system helps website administrators understand how users interact with their website. It can identify traffic patterns and detect unusual or suspicious activities. Visual reports and dashboards make the data easy to read and interpret. This system supports better decision-making to improve website performance and security.

 

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Authors: Mrs. D. Srilatha, Shaik Umaiza Bhanu, Yanamala Lalith, Shaik Amin Sadik, Kamunuri Kasi Ganesh

Abstract: — In the digital era, managing lost and found items efficiently remains a challenge due to reliance on manual methods and unstructured reporting systems. Traditional approaches such as notice boards and text- based communication often result in disorganized data, delayed responses, and low matching accuracy. These limitations highlight the need for an intelligent and automated solution. This paper presents SMARTLOFO: AI Powered Lost and Found Platform, a full- stack web application designed to streamline the process of reporting, tracking, and retrieving lost items. The system is developed using React for the frontend and a Python-based FastAPI backend, with MongoDB/SQLite for data storage. It provides a user-friendly interface along with secure authentication using JWT and bcrypt. A key contribution of the system is the integration of an AI-powered smart matching algorithm. Using Google Gemini, the system performs image analysis to extract item descriptions, categories, and features. These attributes are processed using a scoring-based matching mechanism that evaluates similarity based on category, extracted features, location, and time proximity. Matches exceeding a defined threshold are automatically identified, and users are notified via an email notification system. The platform is deployed on a cloud environment, enabling real-time interaction and accessibility. Despite its advantages, the system depends on user participation and input accuracy. Future enhancements include improving scalability and incorporating advanced machine learning models. Overall, SMARTLOFO demonstrates an intelligent and scalable approach to modernizing lost-and-found systems using artificial intelligence and full-stack technologies.

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Authors: Shailja Jha

Abstract: The rapid proliferation of Artificial Intelligence (AI) technologies has exposed significant limitations in traditional state-centric regulatory frameworks, particularly in complex and diverse jurisdictions such as India. This paper advances the concept of a transition from a “regulatory state” to a “regulatory space,” emphasizing the distributed, multi-actor nature of AI governance. Drawing on comparative regulatory theory, the study analyzes how India’s AI governance is characterized by institutional fragmentation, overlapping mandates, and sector-specific regulatory interventions rather than a unified legal framework. By examining key regulatory bodies, policy instruments, and emerging guidelines across domains such as data protection, digital markets, and sectoral compliance, the paper maps the contours of India’s evolving AI governance ecosystem. It further compares India’s approach with global models, including the European Union’s risk-based regulatory regime and the United States’ market-driven governance structure, to highlight divergences and convergences in regulatory philosophy. The analysis demonstrates that India’s fragmented governance structure, while often viewed as a limitation, may also function as a flexible “regulatory space” that enables adaptive, context-sensitive oversight. However, this flexibility comes with challenges related to coordination, accountability, and enforcement consistency. The paper concludes by proposing a hybrid governance model that integrates centralized policy direction with decentralized regulatory innovation, thereby aligning India’s AI governance with both domestic priorities and global regulatory trends.

DOI: https://doi.org/10.5281/zenodo.19481225

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Authors: P. Saranya, Ms. E. Sheela

Abstract: The rapid expansion of digital payment platforms has significantly transformed financial transactions worldwide. In India, the Unified Payments Interface (UPI) has emerged as one of the most widely adopted real-time payment systems due to its speed, convenience, and low transaction cost. However, the increasing popularity of UPI has also led to a substantial rise in fraudulent activities, including phishing attacks, unauthorized fund transfers, identity theft, and account takeover incidents. Traditional rule-based fraud detection systems rely on static thresholds and predefined heuristics, which are often unable to adapt to evolving fraud patterns and complex transaction behaviors. Furthermore, fraud detection datasets are typically highly imbalanced, where fraudulent transactions represent only a small fraction of the total data, making accurate detection more challenging. To address these limitations, this study proposes FraudShield-UPI, a machine learning-based fraud detection framework designed to improve the accuracy and reliability of fraud identification in digital payment systems. The proposed framework integrates Synthetic Minority Oversampling Technique (SMOTE) to handle class imbalance, Principal Component Analysis (PCA) for dimensionality reduction, and Extreme Gradient Boosting (XGBoost) for high-performance classification of fraudulent transactions. The system is implemented as a web- based application using the Flask framework, enabling real-time fraud prediction and interactive transaction analysis. In addition to the proposed model, a comparative evaluation platform is developed to benchmark traditional machine learning algorithms including Decision Tree, Support Vector Machine (SVM), and Random Forest using the same dataset and evaluation metrics. Experimental evaluation on a simulated UPI transaction dataset demonstrates that the proposed SMOTE-PCA-XGBoost model significantly outperforms baseline models in terms of accuracy, precision, recall, and F1-score, while effectively reducing both false positives and false negatives. The results highlight the capability of the proposed framework to detect fraudulent transaction patterns with improved reliability. The modular architecture and web-based deployment further demonstrate the practical feasibility of integrating the system into real- world financial platforms for enhanced digital payment security.

DOI: https://doi.org/10.5281/zenodo.19483516

 

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