Daily Archives: August 27, 2025

Authors: Praveen bodana, Assistant Professor Khemraj Beragi

Abstract: Thermal power plants (TPPs) are a critical component of global electricity generation, yet they often suffer from efficiency loss and unplanned outages due to equipment faults. Traditional maintenance strategies (reactive or preventive) are often too slow or costly. In contrast, machine learning (ML) methods can analyze large historical and real-time sensor data to detect anomalies and predict failures early. This paper surveys supervised methods (SVM, random forests, neural networks), unsupervised models (autoencoders, clustering, PCA), and hybrid physics-ML approaches for TPP monitoring. It also examines sensor optimization and IoT-enabled real-time monitoring. Case examples from the literature show that ML-based predictive maintenance can greatly reduce unplanned downtime and maintenance costs (e.g., cutting costs by roughly 20–40%) while improving equipment availability. The findings indicate that optimized sensor networks, integrated IoT data, and advanced ML models can substantially enhance fault detection accuracy and overall plant efficiency.

DOI: http://doi.org/10.5281/zenodo.16964139

Published by: vikaspatanker

Authors: Uchenna Evans-Anoruo

Abstract: The escalating complexity of healthcare delivery in the United States, coupled with increasing costs and demand for services, necessitates sophisticated analytical approaches to optimize system performance. This article presents a comprehensive framework for implementing data-driven decision-making in healthcare systems through the integration of operations research techniques and statistical modeling. By leveraging queuing theory, simulation modeling, and decision analysis, healthcare organizations can significantly improve resource allocation, patient flow management, and service delivery efficiency. The integration of advanced IT systems enables real-time data collection and analysis, supporting continuous optimization of healthcare operations. This research demonstrates how systematic application of these methodologies can address critical challenges in US healthcare delivery while maintaining quality standards and improving patient outcomes.

DOI: https://doi.org/10.5281/zenodo.16964206

 

Published by: vikaspatanker

Authors: Bhupesh Paliwal, Professor Amit Thakur

 

Abstract: A Mobile Ad hoc Network (MANET) is a dynamic wireless network that can be formed infrastructure less connections in which each node can act as a router. The nodes in MANET themselves are responsible for dynamically discovering other nodes to communicate. Although the ongoing trend is to adopt ad hoc networks for commercial uses due to their certain unique properties, the main challenge is the vulnerability to security attacks. In the presence of malicious nodes, one of the main challenges in MANET is to design the robust security solution that can protect MANET from various routing attacks. Different mechanisms have been proposed using various cryptographic techniques to countermeasure the routing attacks against MANET. As a result, attacks with malicious intent have been and will be devised to exploit these vulnerabilities and to cripple the MANET operations. Attack prevention measures, such as authentication and encryption, can be used as the first line of defense for reducing the possibilities of attacks. However, these mechanisms are not suitable for MANET resource constraints, i.e., limited bandwidth and battery power, because they introduce heavy traffic load to exchange and verifying keys. In this paper, we identify the existent security threats an ad hoc network faces, the security services required to be achieved and the countermeasures for attacks in routing protocols. To accomplish our goal, we have done literature survey in gathering information related to various types of attacks and solutions. Finally, we have identified the challenges and proposed solutions to overcome them. In our survey, we focus on the findings and related works from which to provide secure protocols for MANETs. However, in short, we can say that the complete security solution requires the prevention, detection and reaction mechanisms applied in MANET.

DOI: https://doi.org/10.5281/zenodo.16962628

 

Published by: vikaspatanker

Authors: Dr. Jyoti, Associate Professor, Ms. Jyoti

Abstract: The rapid expansion of the Internet of Things (IoT) has led to a massive increase in the number of connected devices, generating large volumes of heterogeneous data. Managing this data and ensuring efficient device performance requires advanced computing infrastructures. Cloud computing offers a scalable and cost-effective platform to support IoT ecosystems by providing storage, processing, and analytics capabilities on demand. This study explores the integration of IoT ecosystems with cloud computing to optimize IoT performance. It examines IoT architecture, communication protocols, and data management strategies while highlighting the role of cloud-based services in reducing latency, improving scalability, and enhancing security. The research also emphasizes performance optimization through edge computing, load balancing, and intelligent resource allocation. The findings suggest that a well-structured IoT-cloud integration can significantly improve system efficiency, reduce operational costs, and enable real-time decision-making, paving the way for smarter and more sustainable IoT deployments.This paper lays the foundation for this research by introducing the concepts of cloud computing, IoT ecosystems, and deep learning, while highlighting their interdependencies and potential for performance optimization. The paper also outlines the motivation behind this study, identifies key challenges, and presents the significance and contributions of the research.

Published by: vikaspatanker