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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Selvaraj, Yoganand
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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.
Network load balancing and data categorization in cloud computing Komathi, Arunachalam; Kishore, Somala Rama; Velmurugan, Athiyoor Kannan; Pavithra, Maddipetlolu Rajendran; Selvaraj, Yoganand; Begum, Akbar Sumaiya; Muthukumaran, Dhakshnamoorthy
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1942-1951

Abstract

Cloud computing (CC) is rising quickly as a successful model presenting an on-demand structure. In the CC, the present investigation shows that load-balancing methods established on meta-heuristics offer better solutions for appropriate scheduling and allotment of resources. Conversely, several traditional approaches believe in only some quality of service (QoS) metrics and reject several significant components. Network load balancing and data categorization (NBDC) is proposed. This approach aims to enhance load balancing in the cloud field. This approach consists of two phases: the support vector machine (SVM) algorithm-based data categorization and the ant colony optimization (ACO) algorithm for distributing the network load on the virtual machine (VM). The SVM algorithm performs several data formats, such as text, image, audio, and video, resultant data class that offers high categorization accuracy in the cloud. The ACO algorithm reaches an efficient load balancing based on the time of execution (TE), time of throughput (TT), time of overhead (TO), time of optimization, and migration count (MC). Simulation results related to the baseline approach demonstrate an enhanced system function in terms of service level agreement violation, throughput, execution time, energy utilization, and execution time.
Co-Authors Begum, Akbar Sumaiya Kishore, Somala Rama Komathi, Arunachalam Muthukumaran, Dhakshnamoorthy Parivazhagan, Alaguchamy Pavithra, Maddipetlolu Rajendran Raja, Raju Sankar, Singaravel Senkamalavalli, Rajagopalan Srinivas, Porandla Varadarajan, Mageshkumar Naarayanasamy Velmurugan, Athiyoor Kannan