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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Ikram Ben Abdel Ouahab
University Abdelmalek Essaadi
Computer Science & IT Engineering

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Improve malware classifiers performance using cost-sensitive learning for imbalanced dataset Ikram Ben Abdel Ouahab; Lotfi Elaachak; Mohammed Bouhorma
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1836-1844

Abstract

In recent times, malware visualization has become very popular for malwareclassification in cybersecurity. Existing malware features can easily identifyknown malware that have been already detected, but they cannot identify newand infrequent malwares accurately. Moreover, deep learning algorithmsshow their power in term of malware classification topic. However, we foundthe use of imbalanced data; the Malimg database which contains 25 malwarefamilies don’t have same or near number of images per class. To address theseissues, this paper proposes an effective malware classifier, based on costsensitive deep learning. When performing classification on imbalanced data, some classes get less accuracy than others. Cost-sensitive is meant to solve this issue, however in our case of 25 classes, classical cost-sensitive weights wasn’t effective is giving equal attention to all classes. The proposed approach improves the performance of malware classification, and we demonstrate this improvement using two Convolutional Neural Network models using functional and subclassing programming techniques, based on loss, accuracy, recall and precision.
Deep learning for categorizing microsatellite stability in colorectal cancer Sofyan El Idrissi; Yassine Drider; Ikram Ben Abdel Ouahab; Mohammed Bouhorma; Fatiha El Ouaai
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3761-3769

Abstract

Cancer remains a significant global health challenge, with its incidence rising steadily in recent decades. In colorectal cancer (CRC), microsatellite instability (MSI), and microsatellite stability (MSS) are important biomarkers that influence treatment decisions and patient outcomes. Accurate MSI classification is critical but traditional methods can be costly and time-consuming. This study explores the potential of deep learning to classify MSI and MSS in CRC. A large dataset of CRC patients with confirmed MSI and MSS status was utilized, obtained through standard testing images. Deep learning models were applied to histopathological images, analyzing tissue features from digital slides. Convolutional neural network (CNN) and residual network (ResNet)-18 models demonstrated high accuracy in distinguishing between MSI and MSS CRCs. The best-performing model, which integrated genomic and histopathological data, achieved an area under the curve (AUC) receiver operating characteristic (ROC) of 0.85, indicating strong discrimination capability. The findings suggest that deep learning could be a valuable tool for clinical decision-making and personalized medicine in CRC.
Co-Authors Fatiha El Ouaai Lotfi Elaachak Mohammed Bouhorma Sofyan El Idrissi Yassine Drider