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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Parashiva Murthy Basavanapura Muddumadap
JSS Science and Technology University
Computer Science & IT Engineering

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Diabetic prediction using ridgelet process neural network with noise contrastive estimation Parashiva Murthy Basavanapura Muddumadap; Pruthvi Palahalli Ramachandra; Manjunatha Basavaraju; Nandeesh Hallimysore Devaraj
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4133-4143

Abstract

Globally, diabetes is the most prevalent disease and a major public health concern, often leading to serious complications like kidney disease, vision loss, and coronary issues. Deep learning (DL) and data mining techniques have emerged as reliable and highly effective methods for predicting diabetes, offering significant support to the medical field in accurate diagnosis. However, challenges like missing data and difficulties in handling high-dimensional data significantly impact diabetes prediction, resulting in poor performance. This research proposes a ridgelet process neural network (RPNN) with noise contrastive estimation (NCE) to mitigate the effects of noise and remove irrelevant features, enhancing prediction accuracy. In the pre-processing, standard scaling is involved to ensure data categorization, and missing data is handled using the interquartile range (IQR) method to improve prediction performance. The feature selection using partial least angle regression (PLAR) is utilized to handle high-dimensional data and minimize multicollinearity among features efficiently. The RPNN using a ridgelet transformer maps the data into higher-dimensional space, enabling capture of complex features and effective handling of linear patterns. The proposed method achieves better accuracy of 99.89%, 98.63%, and 99.37% on datasets like diabetes, PIMA, and diabetes 130-US when compared with existing methods like guided artificial neural network (G-ANN).
An improved weighted metric genetic algorithm based feature selection approach for classification of phishing URLs Nandeesha Hallimysore Devaraj; Prasanna Bantiganahalli Thimappa; Prajna Shivarajappa; Parashiva Murthy Basavanapura Muddumadap
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4144-4158

Abstract

In recent years, websites have been collecting information from users for various purposes. However, users are often unaware that the collected information is being used only for its intended purpose. Phishing is a common social engineering attack that seeks to deceive users into revealing sensitive information, such as bank credentials or personal details, which can be exploited for malicious activities. Protecting this sensitive data from phishing attacks is critical in data security. Many existing approaches effectively classify phishing attacks but fail to distinguish based on the intent of the webpage. This study addresses this problem by proposing a keyword-specific web crawler focusing on extracting a novel feature similarity index based on web page content. Further, the method employs a genetic algorithm (GA) to select highly discriminating optimized features for the classification of phishing uniform resource locator (URLs). The weighted combination of different metrics is used to enhance the model accuracy. As per the performance analysis, the proposed model achieves 98.69% accuracy and 98.75% fitness value with a low false positive rate (FPR). The model interpretability is measured through Shapley additive explanations (SHAP). Additionally, the proposed method is compared with existing works, and experimental results indicate that the proposed model performance is superior over the existing solutions.
Co-Authors Manjunatha Basavaraju Nandeesh Hallimysore Devaraj Nandeesha Hallimysore Devaraj Prajna Shivarajappa Prasanna Bantiganahalli Thimappa Pruthvi Palahalli Ramachandra