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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Annemneedi Lakshmanarao
Aditya Engineering College

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An efficient smart grid stability prediction system based on machine learning and deep learning fusion model Annemneedi Lakshmanarao; Ampalam Srisaila; Tummala Srinivasa Ravi Kiran; Kamathamu Vasanth Kumar; Chandra Sekhar Koppireddy
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp1293-1301

Abstract

A smart grid is a modern power system that allows for bidirectional communication, driven mostly by the idea of demand responsiveness. Predicting the stability of the smart grid is necessary for improving its dependability and maximizing the efficacy and regularity of electricity delivery. Predicting smart grid stability is difficult owing to the various elements that impact it, including consumer and producer engagement, which may contribute to smart grid stability. This research work proposes machine learning (ML) and deep learning (DL) approaches for predicting smart grid sustainability. Five ML algorithms, namely support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), random forest (RF), and logistic regression (LR), were applied for the prediction of smart grid stability. Later, the stacking ensemble and voting ensemble of ML algorithms were also applied for prediction. To further increase accuracy, a novel fusion model with DL artifical neural networks (ANN) and ML SVM was applied and achieved an accuracy of 98.92%. The experiment results show that the proposed model outperformed existing models for smart grid stability prediction.
Heart disease prediction using ML through enhanced feature engineering with association and correlation analysis Annemneedi Lakshmanarao; Thotakura Venkata Sai Krishna; Tummala Srinivasa Ravi Kiran; Chinta Venkata Murali krishna; Samsani Ushanag; Nandikolla Supriya
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1122-1130

Abstract

Heart disease remains a prevalent and critical health concern globally. This paper addresses the critical task of heart disease prediction through the utilization of advanced machine learning techniques. Our approach focuses on the enhancement of feature engineering by incorporating a novel integration of association and correlation analyses. A heart disease dataset from Kaggle was used for the experiments. Association analysis was applied to the categorical and binary features in the dataset. Correlation analysis was applied to the numerical features in the dataset. Based on the insights from association analysis and correlation analysis, a new dataset was created with combinations of features. Later, newly created features are integrated with the original dataset, and classification algorithms are applied. Five machine learning (ML) classifiers, namely decision tree, k-nearest neighbors (KNN), random forest, XG-Boost, and support vector machine (SVM), were applied to the final dataset and achieved a good accuracy rate for heart disease detection. By systematically exploring associations and relationships with categorical, binary, and numerical features, this paper unveils innovative insights that contribute to a more comprehensive understanding of the heart disease dataset.
Co-Authors Ampalam Srisaila Chandra Sekhar Koppireddy Chinta Venkata Murali krishna Kamathamu Vasanth Kumar Nandikolla Supriya Samsani Ushanag Thotakura Venkata Sai Krishna Tummala Srinivasa Ravi Kiran Tummala Srinivasa Ravi Kiran