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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Chandre, Pankaj R.
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Predicting lung cancer risk using explainable artificial intelligence Shoukat Makubhai, Shahin; Pathak, Ganesh R.; Chandre, Pankaj R.
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.6280

Abstract

Lung cancer is a lethal disease that claims numerous lives annually, and early detection is essential for improving survival rates. Machine learning has shown promise in predicting lung cancer risk, but the lack of transparency and interpretability in black-box models impedes the understanding of factors that contribute to risk. Explainable artificial intelligence (XAI) can overcome this limitation by providing a clear and understandable approach to machine learning. In this study, we will use a large patient record dataset to train an XAI-based model that considers various patient information, including lifestyle factors, clinical data, and medical history, for predicting lung cancer risk. We will use different XAI techniques, including decision trees, partial dependence plots, and feature importance, to interpret the model’s predictions. These methods will provide healthcare professionals with a transparent and interpretable framework for screening and treatment decisions concerning lung cancer risk.
Survey and comparative analysis of phishing detection techniques: current trends, challenges, and future directions Jadhav, Ashvini; Chandre, Pankaj R.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp853-866

Abstract

In the age of digital communication, scams such as phishing continue to be a problem, necessitating the need for ever-more-advanced detection techniques to safeguard sensitive data. Examining several methods now in use, this review article groups them according to the application (email, web server, mail server, or browser-based). It explores the advantages and disadvantages of behavior-based, heuristic-based, machine learning (ML)-based, and signature-based techniques and offers a comparative evaluation of their efficacy. The essay delves deeper into the latest developments in phishing detection research, such as ML-powered social media exploration and real-time website analysis. The evaluation goes beyond just identifying detecting techniques; it also includes a data-driven analysis. In particular, random forest and support vector machines are ML algorithms that regularly produce results with high accuracy for detecting phishing attempts. Metrics like as recall, F1-score, and precision show how well these algorithms. Furthermore, specialised techniques such as heuristic-based and cantina-based approaches provide remarkable performance, underscoring the possibility of additional research in this field. Future research explores improved phishing detection through: better accuracy with ML, integrating new technologies, analyzing user behavior. A hybrid approach combining these techniques offers a stronger defense.
Co-Authors Jadhav, Ashvini Pathak, Ganesh R. Shoukat Makubhai, Shahin