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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Engineering, Science and Information Technology
Patra, Bichitrananda
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Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Accuracy based-stacked ensemble learning model for the prediction of coronary heart disease Bhutia, Santosini; Patra, Bichitrananda; Ray, Mitrabinda
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4516-4525

Abstract

Coronary heart disease (CHD) is the primary cause of silent and noncommunicable deaths. Early detection is essential for slowing the progression of death and saving lives. Medical researchers use machine learning techniques to predict CHD. This article proposes an accuracy based-stacked ensemble learning (AB-SEL) model to predict CHD while minimizing computational time (CT). The dataset undergoes the logistic regression recursive feature elimination (LR-RFE) method to identify the important features. The three strong classifiers, logistic regression (LR), random forest (RF), and AdaBoost, are chosen to build ensemble machine-learning models, including techniques like bagging, majority voting, and stacking, for the Cleveland dataset accessible from Kaggle. Data scaling was done using the normal scalar method, and hyperparameter optimization was done using random search and grid search. Effectiveness is measured by accuracy, precision, recall, F1 score, and CT is validated through 5-fold cross-validation. The suggested approach achieved a 90.16% accuracy rate, required only 0.2 seconds of CT, and yielded an area under the curve (AUC) of 0.892.
Low-Latency Edge Computing for Real-Time Applications in Wireless Sensor Networks Mishra, Awakash; Rathour, Abhinav; Raju, Dheeravath; Patil, Shashikant; Muthiah, M.A.; Patra, Bichitrananda; Kalidhas, Aravindan Munusamy
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.1490

Abstract

Real-time data processing with Edge Computing, such as Low Latency Edge Computing (LLEC), allows for functioning at the network boundary or edge, which enhances responsiveness and reduces latency in WSNs. This approach is helpful for most time-critical needs in innovative city applications, healthcare, industrial automation, and other areas where prompt actions are crucial. In contrast to conventional cloud models, LLEC processes data at the collection site to reduce the transmission time, improving bandwidth efficiency. Moreover, LLEC increases the height of scalable walls and energy efficiency by shifting the computational burden to the edge nodes. This document focuses on the most critical problems in WSNs, such as restricted resources, limited scalability, and security issues. We offer a distributed edge framework with real-time processing features and minimal security protocols to address these gaps. Localized computation at cluster heads diminishes network congestion while prolonging sensor life. This paper presents multiple case studies demonstrating LLEC's effectiveness in practical applications. Finally, the paper discusses the widening scope of research and the importance of LLEC in future distributed systems.
Co-Authors Bhutia, Santosini Kalidhas, Aravindan Munusamy Mishra, Awakash Muthiah, M.A. Patil, Shashikant Raju, Dheeravath Rathour, Abhinav Ray, Mitrabinda