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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Varadarajan, Mageshkumar Naarayanasamy
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Computer Science & IT Electrical & Electronics Engineering

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Enhancing 5G network performance through effective resource management with network slicing Suganthi, Nagarajan; Narasimhan Ganesh, Enthrakandi; Guruva Reddy, Elangovan; Balakumar, Vijayaraman; Ilakkiya, Thangam; Varadarajan, Mageshkumar Naarayanasamy; Ramesh Babu, Venkatachalam
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4721-4731

Abstract

The immense growth of mobile networks leads to versatile applications and new demands. The improved concert, transferability, flexibility, and performance of innovative network services are applied in diversified fields. More unique networking concepts are incorporated into state-of-the-art mobile technologies to expand these dynamic features further. This paper presents a novel system architecture of slicing and pairing networks with intra-layer and inter-layer functionalities in 5th generation (5G) mobile networks. The radio access network layer slices and the core network layer slices are paired up using the network slicing pairing functionalities. The physical network elements of such network slices will be logically assigned entities called softwarization of the network. Such a novel system architecture called network sliced softwarization of 5G mobile networks (NSS-5G) has shown better performances in terms of end-to-end delay, total throughput, and resource utilization when compared to traditional mobile networks. Thus, effective resource management is achieved using NSS-5G. This study will pave the way for future softwarization of heterogeneous mobile applications.
Enhancing clinical decision-making with cloud-enabled integration of image-driven insights Senkamalavalli, Rajagopalan; Sankar, Singaravel; Parivazhagan, Alaguchamy; Raja, Raju; Selvaraj, Yoganand; Srinivas, Porandla; Varadarajan, Mageshkumar Naarayanasamy
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp338-346

Abstract

Using the complementary strengths of Bayesian networks, decision trees, artificial neural networks (ANNs), and Markov models, this endeavor intends to completely revamp clinical decision-making. In order to provide instantaneous access to image-driven insights and clinical decision support systems (CDSS), want to create a revolutionary framework that merges these cutting-edge methods with cloud-enabled technologies. The proposed framework gives a comprehensive perspective of patient data by merging the probabilistic reasoning of Bayesian networks with the interpretability of decision trees, the pattern recognition abilities of ANNs, and the temporal interdependence of Markov models. This helps doctors to make more educated judgments based on a larger spectrum of information, leading to better patient outcomes. Healthcare workers can get to vital data from any place because to the cloud-enabled architecture's seamless scalability and accessibility. This not only increases the efficiency of decision-making, but also improves communication and cooperation between different medical professionals. This uses cutting-edge modeling strategies and cloud computing to pave a new path in clinical decision-making. This system has the potential to greatly enhance healthcare by integrating image-driven insights with CDSS, to the advantage of both patients and healthcare practitioners.
Predictive modeling for healthcare worker well-being with cloud computing and machine learning for stress management Sudha, Muthukathan Rajendran; Malini, Gnanamuthu Bai Hema; Sankar, Rangasamy; Mythily, Murugaaboopathy; Kumaresh, Piskala Sathiyamurthy; Varadarajan, Mageshkumar Naarayanasamy; Sujatha, Shanmugam
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp1218-1228

Abstract

This paper provides a new method for stress management-focused predictive modeling of healthcare workers' well-being via cloud computing and machine learning. The need for proactive measures to track and assist healthcare workers' mental health is highlighted by the rising expectations placed on them. Using various data sources, our system compiles information from surveys, social media, electronic health records, and wearable devices into a single location for analysis. Predictive models that predict healthcare workers' stress levels and well-being are developed using gradient boosting, a strong machine learning (ML) technique. This work is suitable for gradient boosting due to its resilience to overfitting and capacity to process many kinds of data. Healthcare organizations may improve the health of their employees by using our technology to detect stress patterns and identify the causes of that stress. It can use specific treatments and support systems to alleviate that stress. Widespread adoption and real-time monitoring are made possible by the scalability, flexibility, and accessibility of cloud computing infrastructure. This method shows promise in the direction of proactive solutions driven by data for controlling the stress of healthcare workers and improving their general well-being.
Co-Authors Balakumar, Vijayaraman Guruva Reddy, Elangovan Ilakkiya, Thangam Kumaresh, Piskala Sathiyamurthy Malini, Gnanamuthu Bai Hema Mythily, Murugaaboopathy Narasimhan Ganesh, Enthrakandi Parivazhagan, Alaguchamy Raja, Raju Ramesh Babu, Venkatachalam Sankar, Rangasamy Sankar, Singaravel Selvaraj, Yoganand Senkamalavalli, Rajagopalan Srinivas, Porandla Sudha, Muthukathan Rajendran Suganthi, Nagarajan Sujatha, Shanmugam