Category Archives: Uncategorized

Authors: Sonali Vidhate, Fuldeore Pritee, Jagtap Vaishnavi, Khairnar Vishakha, Aruba Kudai, Safa Madoo

Abstract: The rapid expansion of cloud computing, artificial intelligence (AI), and data-intensive applications has significantly increased the energy consumption of data centers, making sustainability a critical concern. Conventional energy optimization techniques such as virtualization, Dynamic Voltage and Frequency Scaling (DVFS), and static cooling mechanisms provide limited adaptability to modern, dynamic workloads. This research paper presents a comprehensive analysis of AI-driven green computing approaches for improving energy efficiency in data centers. Using insights from existing literature, this work proposes an intelligent framework that integrates machine learning, reinforcement learning, and predictive analytics to optimize workload distribution, cooling systems, and energy demand forecasting in real time. The proposed approach aims to reduce energy consumption, minimize carbon emissions, and improve Power Usage Effectiveness (PUE) while maintaining system performance. Additionally, novel innovations such as carbon-aware scheduling and renewable-energy-aware AI optimization are discussed to enhance sustainability. The findings indicate that AI-based energy management can achieve significant energy savings and support the development of future-ready green data centers.

DOI: http://doi.org/10.5281/zenodo.20849513

Published by: vikaspatanker

Authors: Dr. Harsha Sammangi, Poloju Pravalika

Abstract: Explainable artificial intelligence (XAI) is widely promoted as a remedy for algorithmic opacity, premised on the assumption that revealing a model's reasoning improves human oversight and decision quality. This study investigates the Explainable AI Paradox: the possibility that explanations simultaneously increase trust, reliance, and adoption while also producing overconfidence in flawed models — improving decision quality when models are valid but amplifying errors when models are not. Using a controlled experiment with 921 managers making pricing, lending, or inventory decisions assisted by AI systems of deliberately varied validity, participants were randomly assigned to one of five explanation conditions: no explanation, feature-importance, counterfactual, uncertainty-aware, or a combined feature-importance-plus-uncertainty condition. The study measured decision accuracy, confidence calibration, reliance behavior, override justification quality, and simulated financial outcomes. Results show that feature-importance explanations increased reliance (+9.8 percentage points, p < .001) and confidence (+0.61 scale points, p < .001) relative to no explanation, but produced the largest overconfidence increase (+0.084, p < .001) and the only negative financial outcome effect (–0.14 SD, p < .01) among all explanation types — concentrated specifically in the flawed-model conditions, where a significant Explanation × Model-Quality interaction (β = 0.047 to 0.089 across outcomes, all p < .001) confirms that feature-importance explanations' benefits accrue under valid models while their overconfidence costs accrue under flawed models. Uncertainty-aware explanations, by contrast, improved calibration (–0.058, p < .001), reduced overconfidence (–0.047, p < .001), and produced the only significant positive financial outcome (+0.29 SD, p < .001) relative to no explanation. A twelve-stage design intervention pilot demonstrates that combining five calibration-oriented design principles — feature reliability tagging, confidence-first ordering, disagreement prompts, active verification nudges, and explanation-accuracy feedback — reduces the Overconfidence Index by 83% (from 0.084 to 0.014, p < .001) relative to unmanaged feature-importance explanations. Thematic analysis of 40 participant interviews identifies six mechanisms underlying these patterns, including a 'plausibility heuristic substitution' through which surface-level explanation coherence substitutes for independent verification. The paper contributes a theory of the Explainable AI Paradox to behavioral information systems research, identifies model-quality and explanation-type interactions as the central moderating mechanism, and provides a five-level maturity roadmap and design decision framework for deploying explainable, uncertainty-aware managerial AI systems.

DOI: http://doi.org/10.5281/zenodo.20843984

Published by: vikaspatanker

Authors: Associate Professor Rajesh Kumar Jhaa, Assistant Professor Balu Maloth, Asib Ekramul Molla, Hridya Shabu, Aadhil Mohammed

Abstract: This study evaluates the ergonomic conditions of the library in an academic institution, focusing on workstation design, user posture, and environmental factors. Academic libraries involve prolonged activities such as reading, writing, and computer use, which increase the risk of work-related musculoskeletal disorders (WMSD). The goal of this study is to find ergonomic risk factors and suggest design changes that will help. A mixed-method approach was utilized, encompassing observational analysis, a questionnaire survey, and posture assessment employing RULA and REBA tools. The findings reveal that a majority of users experience discomfort in the neck, lower back, shoulders, and eyes due to non-adjustable furniture, improper desk height, and inadequate lighting conditions. The RULA and REBA scores indicate moderate to high ergonomic risk levels. The study concludes that ergonomic interventions such as adjustable furniture, improved lighting systems, and user awareness can significantly enhance comfort, reduce health risks, and improve productivity in academic library environments.

DOI: https://doi.org/10.5281/zenodo.21977121

Published by: vikaspatanker

Authors: Atul Dubey, Vikash, Ashish Juneja

Abstract: The paper is a study on the use of ISO shipping containers to build sustainable Urban Economical weaker section housing society for Slum living in Metropolitan cities in India. As per world bank report nearly 35.2 % of urban population of India living in Slums (2018). In Japan, China and Vietnam the use of container housing solved their housing problems. Shigeru Ban , a house hold name in architecture in Japan has designed high end homes, biennials and museums by using containers. Room heating is a primary concern of using container as housing material because steel boxes are good heat conductors. But by using passive or active cooling technology we provide insulation. Also it leaves a low carbon foot print too. As per right to shelter in U. P. Avas Vikas Parishad v. friends coop. Housing society limited, the right to shelter has been fundamental right to residence secured under article 19(1)e and the right to life guaranteed under article 21.The state has to provide facilities and opportunities to build houses to standardize life of poor people. Container housing is a new trend of green construction technology, also it is made up of a heavy, good engineering property steel which is corrosion free and has adequate life to use as shelter. Average cost of shipping container houses are ranging from 5-15 lakh INR. India uses about 4 to 5 % of total shipping containers used in the world. This paper covers the literature review, methodology adopted for research, challenges and opportunity to use container as housing material.

DOI: http://doi.org/10.5281/zenodo.20838604

Published by: vikaspatanker

Authors: Dr. Praveen B. Hulloli, Dr.Venugopal Jalihal

Abstract: This research focuses on determining the applicability of Bradford’s Law of Scattering within the corpus of Indian medical research on Artificial Intelligence (AI) in healthcare. The study analyzing 9,774 papers that amassed 123,043 citations, the data was retrieved from the Web of Science citation database covering the period from 2005 to 2024. The study examined annual research output trends, researcher communication preferences, and the productivity of journals. The results show that year 2023 recorded the highest volume of publications, totaling 2104 (21.53%. The year 2021 demonstrated the peak research influence, as measured by total citations 26356 (21.42%) and an h-index of 69. Furthermore, journal impact was not solely linked to publication volume. Despite publishing fewer papers than volume leaders, the highly specialized “Expert Systems with Applications” demonstrated superior quality, achieving the highest Average Citations per Paper (ACPP) of 36.03. The Bradford’s analysis strongly confirmed the law’s applicability, with the core zone consisting of 1.90% of journals contributing 33.22% of the literature. This clear stratification, supported by a negligible negative error (0.49%), confirms the concentration of key AI medical literature in a small core of highly productive journals.

DOI: http://doi.org/10.5281/zenodo.20837913

Published by: vikaspatanker

Authors: Krishna Prisad Bajgai, Dr. Bhojraj Ghimire, Niraj Kumar Shah, Netra Prasad Joshi

Abstract: Artificial Intelligence (AI) and Machine Learning (ML) technologies are increasingly applied in financial institutions for credit scoring, fraud detection, algorithmic trading, and risk management. Although these techniques offer high predictive performance, many models operate as complex “black-box” systems whose decision-making processes are difficult to interpret. This lack of transparency creates challenges related to trust, fairness, and regulatory compliance. Explainable Artificial Intelligence (XAI) aims to provide transparency and interpretability to AI-based models by offering explanations for their predictions. This paper explores the role of explainable AI in financial decision systems, focusing on its applications in credit risk assessment, fraud detection, and financial forecasting. The study reviews existing explainability techniques such as SHAP, LIME, and interpretable models, and proposes a conceptual framework for integrating explainable AI into financial decision-making systems. The findings highlight that integrating explainability mechanisms improves trust, transparency, and regulatory compliance while maintaining model performance. The paper concludes with future research directions for developing trustworthy AI-driven financial systems.

DOI: http://doi.org/10.5281/zenodo.20837802

Published by: vikaspatanker

Authors: Assistant Professor Dilip Badrinarayan Soni, Dr. Hariom Singh Tomar

Abstract: An inevitable issue with integrative alternative healthcare systems (IAHS) is that although clinical reasoning is fuzzy, qualitative, and dependent on the practitioner's expertise, current evidence-based requirements mandate a precise and quantifiable approach. This paper proposes a new framework for decision making within IAHS using fuzzy logic by considering the fuzzy nature of Ayurvedic doshas, TCM meridians, and constitutional types within naturopathy. In this regard, we propose a hierarchical structure of fuzzy inference systems (FISs) containing 27 rules that map qualitative input statements ("Vata moderately high," "Qi slightly weak") to recommendations of therapy along with an MCDM process using the Fuzzy TOPSIS algorithm for treatment prioritization. Applied to clinical information from 180 patients suffering from metabolic syndrome, our system yields a consensus with an expert panel of 86.4% (κ=0.82), while decreasing variability in prescription by 58% in comparison with unassisted practitioners.

DOI: https://doi.org/10.5281/zenodo.20829144

Published by: vikaspatanker

Authors: Assistant Professor Dilip Badrinarayan Soni, Dr. Hariom Singh Tomar

Abstract: Personalized alternative medicine holds substantial promise for holistic healthcare; however, systematic optimization of multi-herb, multi-target interventions is still an open problem in terms of computational difficulties associated with combinatorics, nonlinearity, and individual differences. This paper develops a comprehensive mathematical optimization approach to personalized alternative medicine interventions by combining three approaches: evolutionary algorithms to optimize prescription of herbs, reinforcement learning to adapt the therapy, and Bayesian multidimensional hierarchical models to characterize patients’ responses to the medication. The effectiveness of the proposed optimization framework is validated through experimental analysis utilizing clinical records from traditional Chinese medicine (n=5,216). It is found that the optimized prescriptions with the use of evolutionary algorithms result in 28.5% higher effectiveness than the conventional methods (95% CI: 18.7-37.3%).

DOI: https://doi.org/10.5281/zenodo.20828820

Published by: vikaspatanker

Authors: Gowda Yashvi Manjunath, Ranjitha H, Impana P.S, Assistant Professor Chaithra K

Abstract: Road safety is a very important problem in today’s transportation systems. Rear-end collisions are among the most frequent and dangerous accidents on highways and city streets. The paper proposes a MATLAB based simulation of an intelligent obstacle detection and autonomous lane changing system for collision avoidance. We consider a scenario where an ego vehicle drives on a two-lane road and continuously observes a slower vehicle in front and a fast-approaching vehicle in the adjacent lane. The system features front obstacle detection, blind spot monitoring, adaptive speed control, and smooth lane-change execution based on a rule-based decision-making algorithm. When the vehicle in front enters the pre-set detection range of 45 m, the system checks the side lane for safety before beginning an overtaking maneuver. In case of a detected speed-risk vehicle in the blind spot zone, the lane change is delayed, and a warning alert is issued. Simulation results show successful collision avoidance, safe gap maintenance, and smooth overtaking operations, validating the effectiveness of the system as a simplified Advanced Driver Assistance System (ADAS) prototype. Index Terms — Collision prevention, lane change, blind spot detection, ADAS, V2V communication, obstacle detection, MATLAB simulation.

Published by: vikaspatanker

Authors: Rajendra S. Dhake, Vinod S. Khairnar, Rajkumar D. Rajkuvar, Sanika Hingalkar

Abstract: The dielectric response of soils is influenced by multiple factors including electromagnetic frequency, volumetric moisture content, soil composition, internal geometry of components and electrochemical interactions. These effects were characterized using the infinite sample technique to determine the real (ε/) and imaginary (ε//) parameters of the complex permittivity (ε*) of soils with different moisture contents. Measurements were conducted using an X-band microwave test bench operating at 9.44 GHz in the TE10 mode with a crystal detector and a slotted section. This configuration enables precise determination of dielectric parameters for bulk soil specimens. The observed behavior of ε/ and ε// shows an initially modest rise with increasing moisture followed by a pronounced increase at higher water contents, reflecting the strong influence of water on dielectric properties at microwave frequencies. Derived quantities such as a.c. electrical conductivity and dielectric relaxation times were also extracted from the permittivity data. The results demonstrate significant alterations in the electrical properties between dry and moist soils, with notable dependence on soil texture. These observations aid in interpreting ground-penetrating radar signatures and in the calibration of both active and passive microwave remote sensing systems.

DOI: http://doi.org/10.5281/zenodo.20825704

Published by: vikaspatanker