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All Journal International Journal of Electrical and Computer Engineering
Shadaksharappa, Harini
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Computer Science & IT Electrical & Electronics Engineering

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Classification of morphologically similar Indian rice variety using machine learning algorithms Shadaksharappa, Harini; Chakrasali, Saritha; Ningappa, Krishnamurthy Gorappa
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3202-3211

Abstract

India, among the agriculture-based economy grows wide variety of rice along with other crops. These varieties have different commercial values as they are different in their features. It becomes extremely challenging to classify rice varieties which have similar features but are different in their quality. This study considers four varieties of similar looking rice which conform to be Sona-Masuri. A total of 4180 images are considered to extract 56 features including textural, red, green, blue (RGB) and hue, saturation, value (HSV) color and wavelet decomposition. Machine learning (ML) models like support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT) and voting classifiers are developed for feature dataset and convolutional neural network (CNN) model for image dataset. The results obtained for every model are obtained using statistical methods and the results are expressed in a table for accuracy, precision, recall and F1-score. A classification accuracy of 72.48% is obtained for SVM using polynomial kernel trick by considering all 56 features. The customized CNN model is designed with three convolution layers has resulted in 97.13% of training accuracy and 87.5% of validation accuracy. Based on the results obtained, it is witnessed that the ML models employed in this study to classify rice types with similar appearances have practical applications.
Hybrid machine learning framework for chronic disease risk assessment Shadaksharappa, Harini; K. B., Rashmi; D. K., Shreyas; Mikali, Somanath; Gowda, Vishesh P.; C. A., Uday Shankar; Iyerr, Siddarth B.
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp321-332

Abstract

Chronic diseases like asthma, diabetes, stroke, and heart disease are the major causes of morbidity globally, which emphasizes the need for efficient predictive models to facilitate early detection and precautionary measures. Previous studies have used machine learning approaches for single-disease prediction, where models are designed for specific diseases, such as diabetes or heart disease. However, very few attempts have been made to develop unified frameworks for predicting multiple diseases simultaneously. This work presents a novel, unified framework using an ensemble of extreme gradient boosting classifier (XGBClassifier) and artificial neural networks (ANN) as individual classifiers to concurrently predict the risk of developing asthma, diabetes, stroke, and heart disease. This work follows a questionnaire-based approach that utilizes demographic, lifestyle, health metrics, symptoms and exposure-related data to create personalized risk assessments. The model achieves satisfactory accuracy rates of 95.82% for asthma, 96.68% for diabetes, 94.91% for stroke, and 94.52% for heart disease. The findings highlight how this novel hybrid model serves as an effective approach to tackle the intricate interactions between chronic ailments. The research also includes a user-friendly website that comprises a questionnaire and makes use of the best performing model to predict the probabilities of developing different diseases.
Co-Authors C. A., Uday Shankar Chakrasali, Saritha D. K., Shreyas Gowda, Vishesh P. Iyerr, Siddarth B. K. B., Rashmi Mikali, Somanath Ningappa, Krishnamurthy Gorappa