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http://hdl.handle.net/20.500.12519/633

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Browsing MITGov by Author "Khan, Muhammad Adnan"

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    Machine Learning Models for the Classification of Skin Cancer
    (Institute of Electrical and Electronics Engineers Inc., 2022) Arooj, Sahar; Khan, Muhammad Farhan; Khan, Muhammad Adnan; Khan, Muhammad Saleem; Taleb, Nasser
    Skin cancer is a serious illness that requires early identification in order to improve survival rates. Deep learning algorithms for computerized skin cancer detection have now become popular in recent years. These models may increase their performance by having access to additional data, and their prime objective is image categorization. This activity is extremely useful in the realm of health since it may help physicians and experts make the best decisions and accurately assess a patient's condition. Early detection of skin cancer helps patients to receive appropriate treatment and so enhance their survival rate. This proposed methodology is generated to detect and classify skin cancers. In this study, we employed four pre-trained deep learning models (Squeeze net, Alex net, Res net 101, VGG 19) for the classification of four types of skin cancers in more than 6000 skin images including actinic keratoses, intraepithelial carcinoma Bowen's disease (akiec), basal cell carcinoma (bcc), benign keratosis-like lesions (bkl) and melanocytic nevi (nv). The objective was the identification of the best model in the classification of these breast cancer images with highest accuracy. Experimental results reveal that the Squeeze net model achieved an accuracy of 92.5% which is highest when compared with all other models while Alex net, Res net 101, VGG 19 acquired 91.1%, 83.2%, and 90.4% respectively. © 2022 IEEE.
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    Software defect prediction using ensemble learning: A systematic literature review
    (Institute of Electrical and Electronics Engineers Inc., 2021) Matloob, Faseeha; Ghazal, Taher M.; Taleb, Nasser; Aftab, Shabib; Ahmad, Munir; Khan, Muhammad Adnan
    Recent advances in the domain of software defect prediction (SDP) include the integration of multiple classification techniques to create an ensemble or hybrid approach. This technique was introduced to improve the prediction performance by overcoming the limitations of any single classification technique. This research provides a systematic literature review on the use of the ensemble learning approach for software defect prediction. The review is conducted after critically analyzing research papers published since 2012 in four well-known online libraries: ACM, IEEE, Springer Link, and Science Direct. In this study, five research questions covering the different aspects of research progress on the use of ensemble learning for software defect prediction are addressed. To extract the answers to identified questions, 46 most relevant papers are shortlisted after a thorough systematic research process. This study will provide compact information regarding the latest trends and advances in ensemble learning for software defect prediction and provide a baseline for future innovations and further reviews. Through our study, we discovered that frequently employed ensemble methods by researchers are the random forest, boosting, and bagging. Less frequently employed methods include stacking, voting and Extra Trees. Researchers proposed many promising frameworks, such as EMKCA, SMOTE-Ensemble, MKEL, SDAEsTSE, TLEL, and LRCR, using ensemble learning methods. The AUC, accuracy, F-measure, Recall, Precision, and MCC were mostly utilized to measure the prediction performance of models. WEKA was widely adopted as a platform for machine learning. Many researchers showed through empirical analysis that features selection, and data sampling was necessary pre-processing steps that improve the performance of ensemble classifiers. © 2013 IEEE.