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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Jaswinder Singh
Guru Jambheshwar University of Science and Technology
Computer Science & IT Electrical & Electronics Engineering Engineering

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Comparative analysis of predictive machine learning algorithms for diabetes mellitus Kirti Kangra; Jaswinder Singh
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4412

Abstract

Diabetes mellitus (DM) is a serious worldwide health issue, and its prevalence is rapidly growing. It is a spectrum of metabolic illnesses defined by perpetually increased blood glucose levels. Undiagnosed diabetes can lead to a variety of problems, including retinopathy, nephropathy, neuropathy, and other vascular abnormalities. In this context, machine learning (ML) technologies may be particularly useful for early disease identification, diagnosis, and therapy monitoring. The core idea of this study is to identify the strong ML algorithm to predict it. For this several ML algorithms were chosen i.e., support vector machine (SVM), Naïve Bayes (NB), K nearest neighbor (KNN), random forest (RF), logistic regression (LR), and decision tree (DT), according to studied work. Two, Pima Indian diabetic (PID) and Germany diabetes datasets were used and the experiment was performed using Waikato environment for knowledge analysis (WEKA) 3.8.6 tool. This article discussed about performance matrices and error rates of classifiers for both datasets. The results showed that for PID database (PIDD), SVM works better with an accuracy of 74% whereas for Germany KNN and RF work better with 98.7% accuracy. This study can aid healthcare facilities and researchers in comprehending the value and application of ML algorithms in predicting diabetes at an early stage.
A novel approach to detect tomato leaf disease using vision transformer Sanjeela Sagar; Jaswinder Singh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1548-1565

Abstract

Tomatoes are one of the most widely consumed vegetables across the world. However, tomatoes are prone to diseases. Recognizing and classifying tomato leaf diseases is crucial task. Various deep learning (DL) methods have been developed by several researchers, but they have some complex issues like noise in images, high computational complexity, poor accuracy, and limited feature selection. The main goal of this research is to present novel DL based tomato leaf disease classification framework with neural network based gated vision transformer (G-ViT) model assisted attention mechanism. The proposed framework uses dilated convolution with bidirectional long short-term memory (Bi-DLSTM) used for efficient feature extraction to enhance the classification. An effective chaotic spider wasp optimization (CSWO) is used for feature selection. Further, novel attention based gated vision transformer (A-GVT) is used to classify tomato leaf diseases which integrates strengths of attention mechanism and G-ViT models. Further, to improve the generalizability of classification model, its parameters are tuned with black widow optimization (BWO) algorithm. The experimental findings shows that proposed framework outperformed previous studies on tomato leaf disease identification and classification models in terms of accuracy, precision, recall, F1-score, specificity, mean absolute error (MAE), and root mean square error (RMSE) with 99.7%, 98.29%, 98.22%, 98.25%, 99.19%, 0.03, and 0.25 respectively. The proposed study can pave a way for new agricultural revolution.
Co-Authors Kirti Kangra Sanjeela Sagar