Category Archives: Uncategorized

Authors: Ahmad Rizal

Abstract: Insider threats represent one of the most challenging cybersecurity risks, as they originate from individuals with legitimate access to organizational systems and data. Traditional security mechanisms often fail to detect such threats due to their reliance on signature-based or rule-based approaches that lack contextual awareness. Behavioral analytics, powered by machine learning (ML), has emerged as a transformative approach for identifying anomalous patterns indicative of insider misuse, fraud, or sabotage. This review explores the integration of behavioral analytics and ML techniques to enhance insider threat detection capabilities. By leveraging user activity logs, network traffic data, and system interactions, ML models can establish baseline behavioral profiles and identify deviations in real time. The study examines supervised, unsupervised, and hybrid learning approaches, highlighting their effectiveness in detecting both known and unknown threats. Additionally, it discusses feature engineering, data preprocessing, and the role of contextual information in improving detection accuracy. Challenges such as data imbalance, privacy concerns, adversarial behavior, and model interpretability are also critically analyzed. The review further explores emerging trends, including deep learning, graph-based analytics, and explainable AI, which are shaping next-generation insider threat detection systems. Ultimately, behavioral analytics

DOI: https://doi.org/10.5281/zenodo.19491716

Published by: vikaspatanker

Authors: Muhammad Hakim

Abstract: The escalating complexity of global digital infrastructures, characterized by the convergence of 5G, massive IoT deployments, and hyperscale cloud-to-edge continuums, has rendered traditional linear network management models obsolete. At the heart of this complexity lies the network topology—the intricate map of nodes and interconnections that dictates the flow, latency, and resilience of data. This review article explores the paradigm shift toward Graph Analytics for Network Topology Optimization. Unlike traditional tabular data analysis, graph analytics treats the network as a native mathematical graph, where routers, switches, and endpoints are vertices, and the communication links are edges. This relational perspective allows for the discovery of structural properties—such as centrality, community clusters, and bottleneck bottlenecks—that are invisible to classical monitoring. We categorize the core methodologies of graph-driven optimization, including the use of Graph Neural Networks (GNNs) for predictive traffic steering and PageRank-inspired algorithms for identifying critical infrastructure vulnerabilities. The article examines how graph analytics enables "Topological Resilience," allowing networks to autonomously reconfigure their structure in response to failures or shifting demand. Furthermore, the review addresses the critical challenges of processing massive-scale dynamic graphs in real-time, the computational overhead of graph embeddings, and the necessity for explainable graph models in network operations. By synthesizing recent breakthroughs in spectral graph theory and combinatorial optimization, this paper provides a strategic roadmap for building "Self-Optimizing Topologies." The findings suggest that graph analytics is the foundational intelligence required to manage the "Relational Complexity" of the 6G era, ensuring that global networks are not just faster, but fundamentally more robust, efficient, and adaptive.

DOI: https://doi.org/10.5281/zenodo.19491714

Published by: vikaspatanker

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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Published by: vikaspatanker

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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Published by: vikaspatanker

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

 

Published by: vikaspatanker

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

 

Published by: vikaspatanker

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.

 

Published by: vikaspatanker

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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Published by: vikaspatanker