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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Artificial Intelligence (IJ-AI)
Arumugam, Jayanthi
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Evaluation of sequential feature selection in improving the K-nearest neighbor classifier for diabetes prediction Govindarajan, Rajkumar; Balaji, Vidhyashree; Arumugam, Jayanthi; Admassu Assegie, Tsehay; Mothukuri, Radha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1567-1573

Abstract

The K-nearest neighbor (KNN) classifier employs distance metrics to measure the distance between the test instance and the samples used in training. With smaller samples, the KNN classifier achieves higher accuracy with low computational time. However, computing the distance between the test instance and all training samples to determine the class of the test instance requires higher computational time for a high-dimensional dataset. This research employs sequential feature selection (SFS) to select the optimal feature for diabetes prediction while reducing the computational time complexity of the KNN classifier. The KNN classifier showed effectiveness with an accuracy rate of 84.41% with nine features. The performance of the KNN improves by 2.6% when trained on the optimal features selected with the SFS. The result revealed glucose level, blood pressure (BP), skin thickness (ST), diabetes pedigree function (DPF), age, and body mass index (BMI) as the most representative features in diabetes prediction. The KNN classifier gives higher accuracy with these features. However, insulin and the number of times a woman is pregnant do not show a significant effect on the KNN classifier.
Comparative analysis of wind speed prediction: enhancing accuracy using PCA and linear regression vs. GPR, SVR, and RNN Deepa, Somasundaram; Arumugam, Jayanthi; Purushothaman, Raguraman; Nageswari, D.; Babu, L. Rajasekhara
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i1.pp538-545

Abstract

For power systems with significant wind power integration to operate in an efficient and dependable manner, wind speed prediction accuracy is crucial. Factors such as temperature, humidity, air pressure, and wind intensity heavily influence wind speed, adding complexity to the prediction process. This paper introduces a method for wind speed forecasting that utilizes principal component analysis (PCA) to reduce dimensionality and linear regression for the prediction model. PCA is employed to identify key features from the extensive meteorological data, which are subsequently used as inputs for the Linear Regression model to estimate wind speed. The proposed approach is tested using publicly available meteorological data, focusing on variables such as temperature, air pressure, and humidity. Popular models like recurrent neural networks (RNN), support vector regression (SVR), and Gaussian process regression (GPR) are used to compare its performance. Evaluation metrics such as root mean square error (RMSE) and R² are used to measure effectiveness. Results show that the PCA combined with Linear Regression model yields more accurate predictions, with an RMSE of 94.11 and R² of 0.9755, surpassing the GPR, SVR, and RNN models.
Co-Authors Admassu Assegie, Tsehay Babu, L. Rajasekhara Balaji, Vidhyashree Govindarajan, Rajkumar Mothukuri, Radha Nageswari, D. Purushothaman, Raguraman Somasundaram Deepa