Category Archives: Uncategorized

Authors: Roshni.S, Sudarshana.K

Abstract: Face recognition technology has emerged as a powerful biometric solution capable of enhancing the security and efficiency of modern voting systems. Traditional voting mechanisms, including paper ballots and manual electronic verification, face numerous challenges such as voter impersonation, multiple voting, long verification times, and susceptibility to human error. In recent years, the rapid advancement of artificial intelligence and machine learning has enabled more accurate and scalable facial recognition systems, making them suitable for large-scale applications such as elections. This paper presents an in-depth study of a face recognition–based voting system, discussing its conceptual design, system architecture, methodology, security mechanisms, performance considerations, advantages, limitations, ethical implications, and future scope. The study concludes that while face recognition technology has significant potential to improve election integrity and voter convenience, successful implementation requires robust privacy protection, legal frameworks, and public trus.

Published by: vikaspatanker

Authors: Shrihari.G, Abilash.R

Abstract: Modern cloud infrastructures host large numbers of distributed services and microservices, where failures and attacks can propagate rapidly across virtual machines, containers, and orchestration layers. In this setting, static, signature-driven defenses are insufficient to maintain availability and resilience. Inspired by the biological immune system (BIS), this paper presents a self-healing cloud infrastructure framework that applies second-generation Digital Immune System (DIS) principles to detect, contain, and recover from process-level anomalies in real time. The approach treats cloud nodes and services as components of a larger artificial organism, embedding immune-like agents throughout the stack rather than relying solely on perimeter defense. At the core of the framework is a biologically plausible, multi-layered cellular signalling architecture for process anomaly detection. Building on Matzinger’s Danger Theory, the system moves beyond simple self/non-self discrimination by combining “danger signals” such as abnormal syscall patterns, privilege escalation attempts, and volatile resource usage with “safe signals” derived from stable workload and performance baselines. Specialized artificial cell populations—Dendritic Cells (aDCs), T-Helper Cells (T_H), and B-Cells—are instantiated as distributed agents within a cloud-aware middleware. aDCs aggregate local evidence on each node, T_H cells perform distributed consensus across nodes and services, and B-Cells maintain memory detectors that rapidly recognize previously observed attack strategies. These immune agents communicate over a virtual cytokine bus, enabling spatial-temporal correlation of signals across containers, virtual machines, and availability zones. When coordinated danger levels exceed adaptive thresholds, the framework triggers self-healing actions such as throttling or isolating compromised containers, rolling back affected service instances, or re-provisioning clean replicas through the underlying orchestration platform. Evaluation on syscall-level datasets and realistic exploit scenarios indicates that the proposed DIS-based controller can distinguish normal from attack behaviour with high accuracy while imposing minimal overhead, and that its coordinated responses significantly reduce both time-to-detection and time-to-recovery compared to baseline policies. The work demonstrates that biologically inspired, multi-agent immunity can provide a practical foundation for self-healing cloud infrastructure capable of adapting alongside evolving threats.

Published by: vikaspatanker

Authors: Deeksha M, Subhiksha N

Abstract: The rapid growth of healthcare data generated by electronic health records, medical imaging systems, wearable sensors, and telemedicine platforms has created unprecedented challenges for healthcare data management. Conventional on-premise infrastructures are increasingly unable to support the scalability, interoperability, and analytical intelligence required by modern healthcare ecosystems. Cloud computing has emerged as a promising alternative; however, its adoption in healthcare remains limited due to concerns regarding data security, regulatory compliance, interoperability, and performance reliability. This paper proposes a cloud-native intelligent healthcare data management framework that integrates secure data ingestion, standards-based interoperability, artificial intelligence–driven analytics, and automated compliance governance within a hybrid or multi-cloud environment. The framework is designed to support heterogeneous healthcare data sources while maintaining privacy, regulatory adherence, and real-time responsiveness. A detailed architectural design, data flow model, security mechanisms, and use-case-driven analysis are presented. The proposed solution demonstrates how cloud-native principles can enable scalable, secure, and intelligent healthcare data management suitable for next-generation digital health systems.

Published by: vikaspatanker

Authors: Rayapudi Gautam Kumar

Abstract: MO-NAS: Multi-Objective Neural Architecture Search Using NSGA-IIThis paper presents MO-NAS, a production-ready framework for automatically discovering optimal neural network architectures using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) multi-objective optimization ap‐ proach. Unlike traditional Neural Architecture Search (NAS) methods that optimize for a single objective (typically accuracy), MO-NAS simultaneously optimizes multiple competing objectives including accuracy, computational cost (FLOPs), model size (parameters), inference latency, and memory footprint. The framework supports multiple data modalities including image, text, sequence, and tabular data, making it a universal NAS solution. We incorporate advanced techniques such as zero-cost proxies for rapid evaluation, Bayesian guidance for search efficiency, and weight sharing to reduce training costs. Our approach produces a Pareto-optimal front of architectures, allowing practitioners to select the best trade-off for their specific deployment constraints.

 

Published by: vikaspatanker

Authors: Nitish Kumar Bharadwaj

Abstract: Decision-making in contemporary organizations is increasingly complex due to technological advances, volatile markets, and uncertainty in social, political, and economic environments. Relying solely on intuition or experience often leads to inefficient allocation of scarce resources and costly managerial errors. Operations Research (OR) provides a scientific and quantitative framework that supports rational decision-making through modeling, analysis, and optimization. This paper reviews the historical evolution of OR, clarifies conceptual foundations, and explains the processes through which OR transforms real-world problems into structured decision models. Different classes of models, deterministic, probabilistic, static, dynamic, descriptive, and normative are examined with reference to their applicability and limitations. The paper further highlights implementation challenges and discusses how computing advances have broadened the scope of OR in domains such as supply chains, healthcare, energy, and public policy. The study contributes by presenting a synthesized framework that links classical OR principles with contemporary decision environments, emphasizing how quantitative modeling can improve managerial effectiveness and support evidence-based decisions. Recommendations for future research and practice are also discussed.

DOI: https://doi.org/10.5281/zenodo.18081819

Published by: vikaspatanker

Authors: Nithish Kumar R, Gokul Kanna Sm

Abstract: Smart Healthcare represents a transformative shift in modern medical systems by integrating artificial intelligence (AI), machine learning (ML), deep learning, and Internet of Things (IoT) technologies into healthcare delivery. Early disease detection, accurate diagnosis, and personalized treatment remain critical challenges in healthcare systems worldwide. Traditional healthcare practices largely rely on manual diagnosis, clinician expertise, and time-consuming diagnostic procedures, which may lead to delayed detection, human error, and increased healthcare costs. With the rapid growth of AI and medical imaging technologies, automated disease detection and health monitoring systems have gained significant attention. Medical images such as X-rays, MRI scans, CT scans, ultrasound images, and skin lesion images contain rich visual information that can be effectively analyzed using machine learning and deep learning techniques. This paper presents an intelligent Smart Healthcare framework that utilizes AI-driven medical image analysis for early disease detection and clinical decision support. The proposed system includes image acquisition, preprocessing, feature extraction, disease classification, and result visualization. Experimental studies indicate that AI-based healthcare systems significantly improve diagnostic accuracy, reduce workload on healthcare professionals, and enhance patient outcomes. The system aims to support early diagnosis, reduce medical errors, optimize treatment planning, and promote efficient and patient-centric healthcare services.

 

 

Published by: vikaspatanker

Authors: Sukesh G, Sanjay S

Abstract: Early detection of crop diseases is a critical requirement for ensuring agricultural productivity, food security, and economic stability for farmers. Crop diseases caused by fungi, bacteria, viruses, and pests often spread rapidly and remain unnoticed during their initial stages, leading to severe yield loss and financial damage. Traditional crop disease detection methods rely on manual inspection by farmers or agricultural experts, which is time-consuming, subjective, and often inaccurate due to human limitations and environmental variations. Moreover, expert support is not always accessible to farmers in rural and remote areas. With the rapid advancement of machine learning and image processing technologies, automated crop disease detection systems have gained significant attention in recent years. Leaf images contain rich visual information such as color variation, texture patterns, and shape irregularities that can be effectively analyzed using computer vision techniques. This paper presents an automated crop disease detection framework using machine learning and image processing techniques to identify plant diseases at an early stage. The proposed system involves image preprocessing, feature extraction, and classification using both traditional machine learning algorithms and deep learning models. The system aims to reduce crop loss, minimize excessive pesticide usage, and assist farmers in making timely and informed decisions. Experimental evaluation demonstrates that the proposed approach achieves improved accuracy and reliability, making it suitable for real-world agricultural applications.

 

 

Published by: vikaspatanker

Authors: Dharunika G, Jack Robin J

Abstract: The increasing scale and dynamic complexity of modern cloud computing environments pose significant challenges for ensuring system reliability and availability, making traditional manual fault diagnosis and recovery insufficient. This paper explores advanced methodologies, contrasting established statistical monitoring techniques with emerging Artificial Intelligence (AI)-driven autonomous self-healing frameworks designed to manage faults, minimize downtime, and optimize resource utilization. Early methods employed correlation analysis using Canonical Correlation Analysis (CCA) and Exponentially Weighted Moving Average (EWMA) control charts for anomaly detection, followed by feature selection using ReliefF and SVM-RFE for problem location. The evolution toward AI-driven solutions leverages machine learning (ML), deep learning (DL), and reinforcement learning (RL) to achieve automated failure prediction and recovery. Recent advancements feature hybrid AI models and self- reflective multi-agent systems (SR-MACHA) that demonstrate enhanced accuracy and self- optimization capabilities, achieving significant reductions in Mean Time to Repair (MTTR) and improving system resilience. However, challenges remain regarding the scalability, computational cost of training complex models, and ensuring real-time performance in dynamic, heterogeneous cloud infrastructures.

Published by: vikaspatanker

Authors: Semran Ojha, Professor Rahul Patidar, Professor Jayshree Boaddh

Abstract: The rapid expansion of the Internet of Things (IoT) has created significant challenges in managing computation and data processing efficiently. As billions of interconnected devices generate massive workloads, traditional cloud infrastructures experience latency and performance bottlenecks. To address these limitations, this research introduces an intelligent edge-level load distribution model using Random Forest techniques. The proposed system leverages edge computing to process tasks closer to data sources, thereby reducing dependency on centralized cloud servers. The Random Forest algorithm is utilized to learn from historical task patterns and predict optimal job scheduling sequences. This is integrated with a wolf optimization strategy that dynamically adjusts load distribution across heterogeneous edge nodes without prior training requirements. Experimental evaluation conducted using MATLAB demonstrates that the proposed model effectively minimizes makespan by 0.79% and enhances edge utilization by 16.25% compared to the existing Preference-Based Stable Matching (PBSM) model. These improvements confirm that machine learning- driven edge load balancing can significantly improve resource allocation, task completion time, and overall network efficiency in large-scale IoT environments.

Published by: vikaspatanker

Authors: Gayatri dhasade, Soham vishe, Soham Bhintade, Darshan Bhamare, Prof.Poonam Chavan

Abstract: An academic event management system is A digital platform designed to plan, organize, manage and monitor academic and non-academic events held at educational institutions. College Event Management System is a digital platform designed to plan, organize, manage, and monitor academic and non-academic events conducted in educational institutions. The Colleges regularly organize seminars, workshops, cultural programs, sports events, technical competitions and guest lectures. competitions, and guest lectures. Handling these events manually using paperwork or scattered communications often results in inefficiency, communication issues, and data loss. Managing these events manually using paperwork or scattered communication often leads to inefficiency, miscommunication, and data loss. The proposed system provides a centralized solution to manage event creation, registration, scheduling, notifications and reporting. notifications, and reporting. It allows students, faculty and event coordinators to interact through a single platform, improving transparency and coordination. enables students, faculty, and event coordinators to interact through a single platform, improving transparency and coordination. By automating event workflows, the system reduces administrative workload, ensures timely communication and improves overall event execution. communication, and enhances overall event execution.

 

Published by: vikaspatanker