Daily Archives: May 30, 2025

Authors: Research Scholar Aman Malik

Abstract: The rapid deployment of Artificial Intelligence (AI) across diverse sectors—including healthcare, transportation, finance, and governance—has prompted pressing legal and ethical concerns, especially in technologically emerging economies like India. This paper critically examines the legal and ethical challenges surrounding the integration of AI systems in practical applications, with a focus on the Indian regulatory landscape. While AI promises efficiency and innovation, it also raises fundamental questions of accountability, privacy, bias, and transparency. Key issues such as the attribution of liability for autonomous decisions, the ethical implications of algorithmic discrimination, and the lack of a clear legal framework for AI-generated data and actions are discussed. The paper further explores the limitations of existing Indian laws, including the Information Technology Act, 2000 and the absence of a dedicated AI or data protection statute (pre-GDPR adaptation). Drawing on global standards and domestic case studies, this study proposes a need for robust regulatory mechanisms, ethical design protocols, and sector-specific governance to ensure responsible AI deployment. The findings aim to contribute to the evolving discourse on AI governance and serve as a foundational reference for future legal reforms in India.

 

Published by: vikaspatanker

Authors: Raghu V Kaspa, Ramya K Cherukuvada

 

Abstract: Simulated Annealing (SA) is a probabilistic technique used for approximating the global optimum of a given function, with origins in statistical mechanics. It has found widespread utility in optimization problems central to machine learning (ML), particularly where the solution space is large and complex. This paper investigates the theoretical underpinnings of SA, explores its applications within ML domains, compares it with other optimization algorithms, and evaluates its performance. The work concludes with a discussion on SA's strengths and limitations in the context of modern ML challenges. The purpose of this research is to position SA as a viable tool in the ML optimization toolkit, particularly for tasks involving large, multi-modal search spaces where deterministic methods may falter.

DOI: 10.61137/ijsret.vol.11.issue3.124

 

Published by: vikaspatanker

Authors: Raghu V Kaspa, Ramya K Cherukuvada

Abstract: Simulated Annealing (SA) is a probabilistic technique used for approximating the global optimum of a given function, with origins in statistical mechanics. It has found widespread utility in optimization problems central to machine learning (ML), particularly where the solution space is large and complex. This paper investigates the theoretical underpinnings of SA, explores its applications within ML domains, compares it with other optimization algorithms, and evaluates its performance. The work concludes with a discussion on SA's strengths and limitations in the context of modern ML challenges. The purpose of this research is to position SA as a viable tool in the ML optimization toolkit, particularly for tasks involving large, multi-modal search spaces where deterministic methods may falter.

 

 

Published by: vikaspatanker

Authors: Raghu V. Kaspa, Kevin Camenzuli, Salai S

 

Abstract: The car rental industry faces increasingly complex challenges in optimizing fleet utilization, responding to demand variability, and managing operational costs. As competition intensifies and consumer expectations evolve, effective fleet management has become more vital than ever. Operations Research (OR) provides a suite of mathematical models, optimization techniques, and decision-making frameworks that help car rental firms streamline their operations. This paper explores how OR methodologies can be applied to key aspects of fleet management, including demand forecasting, fleet sizing and composition, vehicle allocation and relocation, and dynamic pricing. It also analyzes real-world case studies where OR tools have been successfully implemented and examines current challenges and future directions for the industry. By employing OR, car rental companies can improve operational efficiency, boost profitability, and offer better customer experiences.

DOI: 10.61137/ijsret.vol.11.issue3.123

 

Published by: vikaspatanker

Authors: Siddharth Gupta, Vineet Gupta, Muskan Jaiswal, Karishma Priya Dwivedi

Abstract: Artificial Intelligence (AI) is rapidly transforming healthcare by enhancing diagnostics, optimizing treatments, and improving operational efficiency. This paper presents a detailed analysis of AI applications in healthcare, case studies, data insights, challenges, risks, and future directions. It leverages clinical datasets and published research to examine the practical impact of AI on patient care and hospital systems.

 

 

Published by: vikaspatanker

Authors: Assistant Professor Ms. Shruti Rawat, Anmol Choudhary

Abstract: The primary objective of this research is to examine and analyze the impact of cultural diversity on the overall effectiveness and productivity of teams operating inside international organizations. The primary objective of this research is to bridge the existing knowledge deficit in the domain of cross-cultural management and organizational behavior. This is achieved by conducting an investigation into the intricate association between cultural diversity and team performance inside MNCs. In the context of this study, a mixed-methods approach is considered the most suitable methodology. This methodology enables a thorough examination of the correlation between cultural diversity and team performance, encompassing the utilization of both quantitative and qualitative data. The results emphasize the importance for MNCs to implement comprehensive strategies that take into account the specificities of leadership, organizational culture, and team dynamics in order to effectively use the benefits of cultural diversity. By engaging in such practices, firms can effectively tackle the complexities associated with cultural diversity and bolster their global competitive advantage. Through the implementation of these strategies, MNCs have the opportunity to effectively utilize the advantages presented by cultural diversity, hence enhancing their competitive advantage within the global market.

DOI: http://doi.org/

Published by: vikaspatanker

Authors: Shriram Deshpande, Shridhar Dhaduti, Rahul Meeshi, Daneshwari Jambagi

 

Abstract: Adequate quality and reliability of drinking water is vital for all inhabitants and for agriculture and industrial applications. Solar desalination is impactful method for getting potable water from brackish/wastewater in hot climatic condition and/or remote area where the scarcity of water as well as for electricity. In recent years, attention has been focused on development of various designs of solar still in order to overcome limitations possesses by conventional single basin single slope solar still. Pyramid solar still is one of the outcomes of such a development. This project reviews the development in the field of pyramid solar still as well as the various techniques to improve the performance of still. From the review on research carried out by the various researchers, it has been found that pyramid solar still is more efficient and economical in compare to conventional single slope single basin still.

DOI: http://doi.org/10.61137/ijsret.vol.11.issue3.122

 

Published by: vikaspatanker

Authors: Parash Pandey, Siddarth Gupta, Abhishek Kumar, Anmol Choudhary

Abstract: Artificial Intelligence (AI) is rapidly becoming a key part of modern cybersecurity. This paper explores how AI is changing digital security systems by automating threat detection, analyzing patterns, and helping respond to attacks faster than ever. AI helps detect cyber threats, protect data, and reduce the need for manual monitoring. But it also introduces new risks, such as biased algorithms, adversarial attacks, and privacy concerns. This paper highlights current applications, advantages, and challenges of AI in cybersecurity, and suggests ways to ensure responsible use of this powerful technology.

 

 

Published by: vikaspatanker

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DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: Girish Kishor Ingavale

 

Abstract: The exponential growth of various technologies, including artificial intelligence (AI), cloud computing, and big data analytics, has led to an unprecedented surge in the computational demands placed on data centers. This paper provides a detailed review of innovative zero-water cooling technologies that offer an alternative to traditional water-based cooling systems, ensuring optimal operating temperatures for AI hardware. We examine various waterless cooling methods, including immersion cooling, air-cooled heat sinks, and phase-change materials, assessing their effectiveness, energy efficiency, and environmental impact. Recent advancements in these technologies have significantly transformed thermal management practices in AI data centers, demonstrating a reduction of up to 50% in energy consumption while completely eliminating water usage in high-performance computing environments. We analyse recent innovations such as two-phase immersion cooling and advanced heat exchange systems, discussing their implementation in large-scale AI infrastructure. Additionally, the article examines the Closed Loop, Zero-Water Evaporation Design technique and its impact on Power Usage Effectiveness (PUE) and Water Usage Effectiveness (WUE). The findings highlight the potential of these technologies to enhance sustainability and operational efficiency in data center cooling, offering a promising solution to the thermal management challenges posed by the growing demand for AI workloads.

DOI: http://doi.org/10.61137/ijsret.vol.11.issue3.121

 

Published by: vikaspatanker