Daily Archives: December 29, 2025

Authors: Vishal Ranjan, Dr. Jyoti Yadav

Abstract: High-Performance Concrete (HPC) has become an essential material in modern construction due to its superior mechanical properties, durability, and environmental benefits. This paper explores the use of HPC in building structures within India, with a focus on its performance, advantages, and the impact on the construction industry. By reviewing recent studies, case studies, and performance data, this research demonstrates the role of HPC in enhancing structural integrity, reducing maintenance costs, and contributing to sustainability. The paper also discusses the challenges and potential future directions for the use of HPC in India’s infrastructure development.

DOI: https://doi.org/10.5281/zenodo.18091902

Published by: vikaspatanker

Authors: Ms.Dhanushni.N, Ms.Vivisha Catherin.P

Abstract: Alzheimer’s disease (AD) is a pressing global issue, It’s known as the severe neuron disease. They Mainly damages the Brain cells, which leads to permanent lose of memory which is also called dementia. Many people die due to this disease every year because it is not curable but the early detection can prevent from spreading. Alzheimer’s are most commonly found in the elder peoples or from the age of (60 and above). It requires an efficient and automated system which can detect the disease and classify it in the basis of Alzheimer’s stages like Mild Demented(MD), Moderate Demented(MOD), Non Demented(ND), Very Mild Demented(VMD). For the prediction we use Machine learning and deep learning Algorithm’s like convolutional neural networks for imaging data(CNNs), Random forest and Gradient Boosting(XGBoost / LightGBM), Support Vector Machines(SVM) Which is much more efficient from the preexisting models of the Alzheimer Detection. Of relying on methods, like CNNs and SVM for our model design like Random Forest and XGBoost do typically with fixed structures and manual feature selection processes; we take a different approach thats more intricate and advanced by utilizing transfer learning through the InceptionV3 network already trained on ImageNet for its robust feature extraction abilities. To boost our models effectiveness in handling datasets adequately; we integrate various data augmentation methods such as adjusting image angles and proportions along, with mirroring techniques. Address the issue of class distribution by adjusting the weights for classes to focus more on identifying cases of Alzheimers disease accurately. In addition, to this adjustment in class weighting strategy consider implementing techniques like dropout regularization method and early stopping along with model checkpoint mechanism to prevent the model from learning noise and improve generalization. This holistic strategy leads to a model that's proficient in reducing both positives and false negatives which is crucial, in accurate medical diagnosis.

Published by: vikaspatanker

Authors: Mallesh Miryala

Abstract: Large organizations run on operational data that changes every hour: people join and leave, locations are renamed, incidents unfold, and responsibilities shift. In practice, the hardest part is not storing records; it is keeping the records consistent enough that policy decisions and workflows remain trustworthy. This paper proposes a practical design pattern called the policy- aware operational graph. The pattern treats people, organizational units, locations, requests, and tasks as a connected graph with explicit ownership and audit history. It combines three ideas that are often built separately: identity lifecycle management, rule-driven routing, and cross-system transaction safety. The design is informed by field experience maintaining a continuity platform at a large public university and building high-volume fulfillment workflows at a national telecom. The paper contributes a reference architecture, a repeatable identity hygiene loop for key contacts, and an efficient duplicate-detection method that routes only uncertain cases to human review. A small reference implementation is provided to demonstrate how blocking keys and union-find can scale to large datasets without excessive memory or quadratic comparisons.

Published by: vikaspatanker

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