Category Archives: Uncategorized

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

Authors: Anand Kumar Yadav

Abstract: Fortified Toward those extreme vitality emergency and the expanding consciousness around the necessity for Ecological protection, the proficient utilization of renewable vitality need turn into a heated point. The virtual control plant (VPP) will be a compelling method for coordination conveyed vitality frameworks (DES) successfully deploying them to force grid dispatching or power exchanging. In this paper, those working mode of the VPP with infiltration from claiming wind power, sun based force vitality stockpiling may be investigated. Firstly, the grid-connection prerequisites about VPP as stated by the current wind Also sun based photovoltaic (PV) grid-connection requirements, broke down its productivity need aid analyzed. Secondly, under a few average situations gathered a affiliation toward oneself guide (SOM) grouping calculation utilizing those VPP’s yield data, An benefit streamlining model may be created as An guideline for those VPP’s ideal operation. In light of this model, case investigations are performed and the effects show that this model may be both practical furthermore viable.

Published by: vikaspatanker

Authors: Sushil Panda

Abstract: Autonomous vehicles and Electric Vehicles are the new definitions of modern vehicles. Centralised control over the vehicle and data-driven decision-making are the main features of these systems. User experience is the product, where vehicle and software combined, are the main selling features in the business world for an Automaker. Neural networks play a crucial role in the backend, providing real-time updates to the user about the vehicle's status. The current study focuses on analysing the importance of the User interface and experience, as well as the important characteristic features of an infotainment system. This paper also presents a classic scenario of neural networks in State of Charge (SoC) monitoring for an Electric Vehicle, integrating real-time results updates with the User Interface and suggesting the user/driver through the dashboard, thereby enhancing data-driven communication and decision-making through an infotainment system in an automobile

Published by: vikaspatanker

Authors: Sushil Panda

Abstract: CAN bus in software-defined vehicles is vital to enhancing the vehicle's performance, safety, and cybersecurity. The CAN bus is the digital nervous system of modern cars, handling the communication stream between all the Electronic Control Units (ECUs) of a Software Defined Vehicle. This research aims to provide a comprehensive security-aware framework for CAN bus data prediction using advanced temporal neural networks, which are designed for a cybersecurity-aware framework for SDVs. The paper proposes a new hybrid architecture that combines a Transformer-based attention mechanism with novel Graph Neural Networks (GNNs) to capture both temporal dependencies and network topology patterns in bus communications. The approach aims to address the challenges associated with high-frequency, complex time series data while ensuring compliance with ISO/SAE 21434 cybersecurity standards and ISO 26262 functional safety requirements. This is achieved by preserving privacy while simultaneously involving multiple vehicle training and capabilities for detecting real-time intrusion. This paper aims to implement a hybrid architecture with transformers and GNNs together on data using random functions in Python. The results thus obtained demonstrate a significant improvement in prediction accuracy (96.3%), cybersecurity threat detection (98.1% precision), and energy efficiency (34% reduction in computational overhead). The proposed framework achieved ASIL-C compliance and reduced false alarm rates by 31% compared to existing methods while maintaining sub-millisecond inference latency suitable for safety-critical automotive applications.

Published by: vikaspatanker

Authors: Sushil Panda

Abstract: The proliferation of software-defined vehicles (SDVs) has necessitated the development of sophisticated fault detection mechanisms capable of processing high-dimensional, multimodal sensor data in real-time. This paper presents a comprehensive analysis of Convolutional Neural Network (CNN) architectures for fault detection in SDVs, examining their theoretical foundations, implementation strategies, and performance characteristics. Through extensive experimentation and comparative analysis, we demonstrate that CNN-based approaches achieve superior performance compared to traditional rule-based and statistical methods, with accuracy improvements of 15-25% and false positive rates reduced by up to 40%. Our technical contribution includes a novel ensemble architecture combining 1D-CNNs with attention mechanisms for temporal sensor data analysis, achieving 94.7% accuracy in fault classification. The paper provides detailed mathematical formulations, algorithmic implementations, and empirical validation across multiple vehicle subsystems, establishing CNNs as the state-of-the-art solution for fault detection in modern automotive systems.

Published by: vikaspatanker

Authors: Jitendra Sharma, Professor Amit Thakur

Abstract: Wireless Sensor Networks (WSNs) demand energy-efficient and reliable routing mechanisms due to the constrained resources of sensor nodes and the dynamic nature of wireless links. Traditional Dynamic Source Routing (DSR) operates efficiently in on-demand route discovery but fails to account for multiple correlated parameters such as residual energy, link stability, delay, and packet reception rate, which are critical in WSN environments. In this paper, we propose a Principal Component Analysis based Dynamic Source Routing (PCA-DSR) protocol that integrates statistical feature reduction with classical DSR. Multiple network and link parameters are periodically collected and transformed using PCA into a single principal component score that reflects overall route quality. This score is embedded in route discovery and maintenance phases to enable the selection of stable and energy-aware paths. Simulation results demonstrate that PCA-DSR achieves higher packet delivery ratio, reduced end-to-end delay, balanced energy consumption, and extended network lifetime compared to conventional DSR. The proposed approach highlights the effectiveness of dimensionality reduction techniques in enhancing routing decisions for resource-constrained WSNs.

DOI: http://doi.org/10.5281/zenodo.16948386

Published by: vikaspatanker

Authors: Dr. Shivani Budhkar, Chavan Krushna Rameshwar

 

 

Abstract: This paper investigates the role of Artificial Intelligence (AI) tools in modern workplaces, focusing on their potential to boost efficiency and overall productivity. AI-driven technologies provide organizations with advanced capabilities to streamline workflows, improve decision-making, and foster innovation. Drawing on existing research, industry reports, and case studies, the study highlights both the opportunities and challenges involved in adopting AI across different workplace settings. By analyzing real-world implementations and practical applications, this research offers actionable insights for organizations aiming to leverage AI as a means of achieving operational excellence and long-term strategic goals in the digital era.

DOI: http://doi.org/

Published by: vikaspatanker

Authors: Rahul Kumar Satbhaiya, Jitendra

 

 

Abstract: High-rise buildings are particularly susceptible to lateral forces in seismically active regions. The primary consideration in their design is ensuring adequate resistance to these lateral stresses, as insufficient stability may lead to excessive displacement, structural instability, or even collapse. To mitigate such risks, buildings must be designed with effective lateral load–resisting mechanisms that enhance overall stability and serviceability. Among the commonly employed methods, steel bracing and masonry infill within reinforced concrete (RC) frames are recognized for their efficiency in resisting lateral loads. Steel bracing systems are advantageous due to their ease of installation, minimal space requirements, and ability to provide significant stiffness and strength with considerable design flexibility. Similarly, masonry infill can be executed efficiently with skilled labor and contributes to the overall lateral resistance of the structure. This study investigates the seismic performance of a reinforced concrete high-rise building of configuration R+12 (13 stories in total). Three structural configurations are evaluated: (i) bare frame, (ii) in filled frame with solid masonry, and (iii) frame with X-braced corner supports. The building model was developed and analyzed using CSI ETABS software, considering a three-dimensional asymmetric layout with a floor height of 3 m. Dynamic analysis was carried out using the response spectrum method for seismic Zone V under soft soil conditions, as specified by Indian seismic design guidelines. The results demonstrate that external steel bracing provides superior stability and reduced displacement compared to masonry infill and bare frames. In terms of both resistance and moment capacity, the steel bracing system proved to be the most effective lateral load–resisting system. Furthermore, from a cost-performance perspective, the steel tie system was found to be the most economical, followed by solid masonry infill, while the bare frame exhibited the least efficiency.

DOI: http://doi.org/

 

 

Published by: vikaspatanker