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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Sanwar Hosen, A. S. M.
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Computer Science & IT Engineering

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A systematic analysis on machine learning classifiers with data pre-processing to detect anti-pattern from source code Akhter, Nazneen; Khatun, Afrina; Rahman, Md. Sazzadur; Sanwar Hosen, A. S. M.; Shahidul Islam, Mohammad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp376-384

Abstract

Automatic detection of anti-patterns from source code can reduce software maintenance costs massively. Nowadays, machine learning approaches are very commonly used to identify anti-patterns. Hence, it is very crucial to choose a classifier that can be useful for detecting anti-patterns. This work aims to help practitioners to choose a suitable classifier to detect anti-patterns. In this paper, we highlight 16 classifiers in four different categories to detect anti-patterns. Furthermore, the performance of these classifiers is identified with the data pre-processing (DPP) to detect four commonly occurring anti-patterns from the three commonly used open-source Java projects’ source code. The accuracy of Dagging classifiers is 98.4%. Kernel logistic regression (KLR) also performs well i.e., 97%. In the case of time complexity, naive Bayes (NB), decision trees (DT), support vector machines (SVM), library for support vector machines (LibSVM), logistic, and LightGBM (LB) have less time complexity to build a model in all the projects.
Multilayer stacking for polycystic ovary syndrome diagnosis Abu Taher, Kazi; Ahmed, Samia; Ferdous Esha, Jannatul; Rahman, Md. Sazzadur; Sanwar Hosen, A. S. M.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp1968-1975

Abstract

Polycystic ovary syndrome (PCOS) is a complicated hormonal condition that is experienced by women. Despite extensive research, the precise reason be hind PCOS remains unknown, and effective treatments are still lacking. Thus, early diagnosis and treatment have a significant positive impact on the health of women. Recently, there has been remarkable performance demonstrated by machine learning (ML)-based detection models for PCOS identification. They are fast and low cost compared to the traditional processes. In this work, a multi stacking PCOS detection model is proposed using K-fold cross validation. The model uses three different ML algorithms namely: na¨ıve Bayes (NB), ran dom forest (RF), and logistic regression (LR) as base classifiers and a neural network, multi-layer perception (MLP) as meta model. This approach utilizes two feature selection techniques and compares the performances on the stack ing methods. Among the two feature selection techniques, Pearson correlation approach performed better with average 98.79% accuracy, 99.17% sensitivity, 98.40% specificity, and 98.79% f1-score.
Co-Authors Abu Taher, Kazi Ahmed, Samia Akhter, Nazneen Ferdous Esha, Jannatul Khatun, Afrina Rahman, Md. Sazzadur Shahidul Islam, Mohammad