Category Archives: Uncategorized

Authors: P.Silpa Chaitanya, Ch. Lakshmi Mounika Priyadarsini, N. Sowmya, B. Pujitha, M. Lakshmi Triveni

Abstract: The rapid growth of Artificial Intelligence (AI) and machine learning has transformed automation in various fields, including education and corporate sectors. Traditional attendance systems that depend on manual entry or RFID cards are often time-consuming, inaccurate, and susceptible to proxy attendance. To address these issues, this paper proposes an AI-based smart attendance system that utilizes face recognition technology for real-time, contactless attendance marking. The system employs computer vision and deep learning models, particularly convolutional neural networks (CNN), to detect and recognize faces with high precision. Live camera input captures facial features, which are processed through image enhancement and feature extraction algorithms before being matched with a pre-trained dataset for identity verification. The system is scalable and capable of handling multiple users simultaneously. Experimental analysis shows that the model achieves over 95% accuracy under different lighting, facial poses, and occlusion conditions. This automated and secure approach reduces human intervention and offers a reliable, efficient, and intelligent alternative to traditional attendance methods.

DOI: https://doi.org/10.5281/zenodo.20065110

Published by: vikaspatanker

Authors: Mahantesh M. Ganganallimath, Dr. K. Vizayakumar, Dr. Umesh M. Bhushi

Abstract: By providing cast components to the automobile, aerospace, railroad, construction, defence, and heavy engineering industries, the Indian foundry sector is essential to the manufacturing sector. Casting flaws, process unpredictability, material waste, high rejection rates, energy inefficiency, and growing international competitiveness are some of the industry's major obstacles. In this regard, sustainable industrial growth now depends on quality assurance and quality-centric methods. The necessity of methodical quality assurance procedures, process control systems, and continuous improvement techniques in Indian foundries is examined in this study. The study highlights that quality-driven systems enhance customer satisfaction and product dependability while simultaneously lowering costs and promoting long-term competitiveness and environmental sustainability. The combination of Industry 4.0, automation, and statistical quality tools for stable growth is further supported by recent research on KPI-driven foundry quality systems and sustainable control models. An important part of the manufacturing sector, the Indian foundry industry greatly boosts employment and economic growth. This study looks into how top management influences quality management procedures in Indian foundries of different sizes. The study examines implementation difficulties, strategic quality efforts, and leadership commitment at various operational scales. The results show that whereas major foundries use organized quality management systems, small foundries encounter obstacles because of limited resources, ignorance, and opposition to change. The report suggests a framework to improve quality performance in the Indian foundry industry and emphasizes the necessity of a leadership-driven quality culture.

DOI: https://doi.org/10.5281/zenodo.20060718

Published by: vikaspatanker

Authors: Piyush Dewangan, Shivam Vishwakarma, Nikhil Yadav, Prahlad Yadav, Himanshu Mokashe, Deepak Sahu

Abstract: Global climate change poses severe threats to agricultural and forested ecosystems that underpin terrestrial carbon balance, biodiversity, and food security. This paper presents a comprehensive investigation into how Artificial Intelligence (AI)—encompassing machine learning, convolutional neural networks (CNNs), long short-term memory (LSTM) networks, transformers, and generative adversarial networks (GANs)—is transforming climate change responses across agriculture and forestry. Drawing on peer-reviewed literature and documented case studies, we examine AI applications including precision irrigation, crop disease detection, yield forecasting, satellite-based deforestation monitoring, wildfire risk prediction, acoustic biodiversity surveillance, and hydrological flood modeling. A three-tiered analytical framework maps causal pathways from technological deployment to environmental, economic, and social outcomes, while critically addressing structural barriers including data scarcity, algorithmic bias, computational inequity, and governance deficits. Principal findings confirm that AI delivers measurable gains in climate mitigation and adaptation efficiency; however, transformative societal potential remains contingent on equitable data access, open-source computational infrastructure, and coherent multilateral policy frameworks.

DOI: https://doi.org/10.5281/zenodo.20060622

Published by: vikaspatanker

Authors: Sujash Kumar Mandal

Abstract: Our present world is the world of AI, Machine learning, quantum physics where study has been shifted from bookish knowledge to online learning. Every sector has been growing and growing up rapidly; especially in higher education it shifted to online learning or virtual mood. Many courses and exams are conducted through online and also certificate is provided digitally. The present study focuses on about the attitude of digital literacy among post graduate students. Hundred samples has taken for the study; a self-made Likert scale used for the data collection by the researcher and t-test, SD, Mean deviation also used for statistical treatment. The researcher finds that there is no significant difference among post graduate students towards digital literacy according to their race, gender, locality basis.

DOI: https://doi.org/10.5281/zenodo.20056572

Published by: vikaspatanker

Authors: Dr. Harikesh Singh, Sunny Kumar, Suryanshu Singh, Shivam Singh, Smit Verma

Abstract: It is a time marked by increasing environmental degradation and disconnect from traditional knowledge systems, and the preservation and dissemination of information of medicinal plants has become critically important. The Vrikshveda project represents an attempt in creating such a comprehensive digital library documenting India’s rich botanical heritage, emphasising on medicinal plants and their therapeutic applications. This research paper presents the development and implementation of Vrikshveda, a modern web and app platform designed to distill knowledge to society about diverse plant reserves, their medicinal properties, traditional uses, and build a community around it. The system combines botanical information, traditional knowledge, scientific research, 3D AI detection and social features to create an accessible repository that serves researchers, healthcare practitioners, students, and the general public. We use web and app technologies including React, Node.js, Kotlin and MongoDB, to make Vrikshveda provide an interactive platform where users can explore detailed information about medicinal plants, including their taxonomic classification, morphological characteristics, therapeutic applications, and other content around it. The project is an attempt to answer the urgent need for documentation of indigenous botanical knowledge before we may lose it to modernization, and simultaneously promoting awareness about using them for medical benefits. Through comprehensive data collection from genuine sources, field research, and collaboration with traditional healers and botanists, Vrikshveda aims to fill the gap between centuries of wisdom and contemporary scientific understanding.

Published by: vikaspatanker

Authors: Dr. Jonathan Reed, Dr. Emily Carter, Michael Thompson, Dr. Sophia Reynolds, Andrew Richard

Abstract: The increasing deployment of machine learning (ML) systems in high-stakes domains such as healthcare, finance, criminal justice, and autonomous systems has significantly intensified concerns about transparency, accountability, reliability, and societal trust. While modern ML models particularly deep neural networks have demonstrated superior predictive performance, their complex, non-linear architectures often render them opaque, leading to criticism that they function as “black boxes” whose internal reasoning is difficult for humans to interpret, audit, or validate. This lack of interpretability poses risks in safety-critical and regulated environments, where stakeholders require clear, understandable justifications for automated decisions. In response to these challenges, Explainable Artificial Intelligence (XAI) has emerged as a crucial and rapidly evolving research area aimed at designing methods that make AI systems more interpretable, transparent, and aligned with human values, ethical principles, and legal requirements. This article presents a systematic review of Explainable AI techniques developed between 2000 and 2021, focusing on their role in enabling trustworthy machine learning systems by structuring the landscape of XAI into intrinsic (interpretable-by-design) and post-hoc (after-the-fact explanation) approaches, examining representative and widely adopted techniques such as LIME, SHAP, and Integrated Gradients, and critically discussing the methodological and practical challenges in evaluating explanation quality. Furthermore, the review analyzes how XAI intersects with broader principles of trustworthy AI including fairness, accountability, transparency, robustness, and human oversight while identifying key research gaps and outlining future directions for developing more reliable, human-centered, and socially responsible AI systems.

DOI: https://doi.org/10.5281/zenodo.20065857

Published by: vikaspatanker

Authors: Dr. Daniel Foster, Dr. Olivia Bennett, Ethan Clarke, Dr. Hannah Mitchell, Andrew Richard

Abstract: The rapid growth of deep learning has enabled state-of-the-art performance across vision, speech, and natural language processing tasks, driving widespread adoption in both academic research and industrial applications. However, this progress has been accompanied by a steady increase in model depth, parameter count, and computational complexity, which poses significant challenges for deployment in resource-constrained environments such as mobile devices, embedded systems, and edge computing platforms with limited memory, power, and latency budgets. To address these constraints, this article presents a comprehensive review of model compression and knowledge distillation techniques developed between 2000 and 2021, synthesizing foundational methods including network pruning, low-precision quantization, and entropy-based coding, as well as teacher–student learning paradigms that transfer representational and decision-level knowledge from large, overparameterized models to compact alternatives. Using representative architectural and training diagrams, we illustrate how these approaches systematically reduce memory footprint and computational cost while preserving, and in some cases improving, predictive accuracy. Finally, we examine key empirical findings across vision, speech, and language domains, identify persistent limitations related to generalization, hardware efficiency, and evaluation methodology, and outline future research directions toward scalable, energy-efficient, and deployable AI systems.

DOI: https://doi.org/10.5281/zenodo.20052481

Published by: vikaspatanker

Authors: Dr. Alexander Hayes, Dr. Natalie Brooks, Ryan Cooper, Dr. Victoria Simmons, Andrew Richard

Abstract: The rapid growth of deep neural networks (DNNs) in both model size and deployment scale has placed renewed emphasis on optimization techniques that balance convergence speed, numerical stability, computational efficiency, and resource utilization, particularly as training workloads increasingly span heterogeneous hardware platforms and distributed computing environments. This article presents a systematic analysis of optimization methods for large-scale deep learning, encompassing stochastic first-order approaches such as momentum-based gradient descent, adaptive optimizers that adjust learning rates based on gradient statistics, normalization strategies that stabilize internal representations and smooth optimization landscapes, curvature-aware methods that incorporate second-order information, and system-level techniques including large-batch, mixed-precision, and distributed training. Drawing on publicly available empirical evidence and widely cited foundational studies, we examine how optimization choices shape training dynamics, scalability characteristics, convergence behavior, and final model performance across diverse deep learning workloads. Using representative figures from prior work—including Batch Normalization training dynamics and adaptive optimization formulations—we synthesize practical guidance for selecting and tuning optimization strategies at scale while identifying persistent challenges related to generalization, communication efficiency, and the alignment of optimization algorithms with modern hardware and system architectures.

DOI: https://doi.org/10.5281/zenodo.20052352

Published by: vikaspatanker

Authors: Dr. Daniel Thompson, Dr. Olivia Bennett, James Walker, Dr. Hannah Collins, Andrew Richard

Abstract: Modern distributed systems operate at a scale and complexity that far exceed the limits of manual management and static fault-handling mechanisms, as they span geographically dispersed resources, heterogeneous hardware and software stacks, and dynamically changing workloads. In such environments, failures are not exceptional events but an inherent characteristic of normal operation, arising from partial outages, transient faults, software defects, and unpredictable interactions among system components. Autonomic computing emerged in the early 2000s as a response to these challenges, proposing self-managing systems capable of self-configuration, self-optimization, self-protection, and self-healing through continuous feedback and adaptation. Over the past two decades, advances in artificial intelligence and machine learning have substantially strengthened autonomic control loops, transforming them from rule-driven mechanisms into adaptive, data-driven decision systems that can learn from experience, generalize across failure scenarios, and operate effectively under uncertainty. This article presents a comprehensive overview of AI-driven autonomic control for self-healing distributed systems by synthesizing foundational autonomic computing architectures, closed-loop control models, and learning-based decision mechanisms. Leveraging established architectural diagrams from pre-2021 literature, we analyze how reinforcement learning, probabilistic reasoning, and hybrid AI techniques enhance fault detection, root-cause analysis, and recovery planning, and we conclude by highlighting key empirical studies and open research challenges that continue to motivate advances in intelligent, self-healing distributed systems.

DOI: https://doi.org/10.5281/zenodo.20064010

Published by: vikaspatanker

Authors: Dr. Jonathan Miller, Dr. Emily Carter, Michael Anderson, Dr. Sophia Reynolds, Andrew Richard

Abstract: Autonomous and intelligent systems are increasingly deployed in complex, real-world environments characterized by stochastic dynamics, partial observability, delayed feedback, and continual change, where classical model-based control strategies often struggle due to their reliance on accurate system identification, fixed assumptions, and limited scalability. In response to these challenges, Reinforcement Learning (RL) has emerged as a compelling control paradigm that enables agents to autonomously learn optimal or near-optimal control policies directly through interaction with their environment, leveraging reward-driven feedback rather than explicit system models. This article surveys and synthesizes reinforcement learning-based control mechanisms with a particular emphasis on actor-critic architectures and deep reinforcement learning approaches for continuous control, which have proven especially effective in high-dimensional and nonlinear domains. Drawing on foundational and influential studies published between 2000 and 2021, the discussion examines how RL frameworks facilitate adaptive decision-making, online policy improvement, and robust control under uncertainty, while also addressing critical issues related to convergence, stability, safety, and sample efficiency. Representative applications in robotics, autonomous navigation, and intelligent cyber-physical systems are highlighted to demonstrate practical impact, and publicly available architectural diagrams are integrated to clearly illustrate core learning loops, policy-value interactions, and control workflows, providing a cohesive and accessible reference for researchers and practitioners designing next-generation intelligent autonomous controllers.

DOI: https://doi.org/10.5281/zenodo.20052068

Published by: vikaspatanker