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Usman Tariq
Researcher at Salman bin Abdulaziz University
Publications - 159
Citations - 2365
Usman Tariq is an academic researcher from Salman bin Abdulaziz University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 12, co-authored 95 publications receiving 662 citations. Previous affiliations of Usman Tariq include Ajou University & Islamic University.
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Journal ArticleDOI
A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU
Sweta Bhattacharya,Siva Rama Krishnan S,Praveen Kumar Reddy Maddikunta,Rajesh Kaluri,Saurabh Singh,Thippa Reddy Gadekallu,Mamoun Alazab,Usman Tariq +7 more
TL;DR: A hybrid principal component analysis (PCA)-firefly based machine learning model to classify intrusion detection system (IDS) datasets and experimental results confirm the fact that the proposed model performs better than the existing machine learning models.
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Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture
TL;DR: A new deep learning‐based method is proposed for microscopic brain tumor detection and tumor type classification and a comparison with existing techniques shows the proposed design yields comparable accuracy.
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Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion
Kiran Jabeen,Muhammad Attique Khan,Majed Alhaisoni,Usman Tariq,Yudong Zhang,Ameer Hamza,Arturas Mickus,Robertas Damaševičius +7 more
TL;DR: A new framework for breast cancer classification from ultrasound images that employs deep learning and the fusion of the best selected features is proposed, which outperforms recent techniques.
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Classification of stomach infections: A paradigm of convolutional neural network along with classical features fusion and selection
Abdul Majid,Muhammad Attique Khan,Mussarat Yasmin,Amjad Rehman,Abdullah Yousafzai,Usman Tariq +5 more
TL;DR: A new fully automated system is proposed for the recognition of gastric infections through multi‐type features extraction, fusion, and robust features selection that performs better as compared to existing methods and achieves an accuracy of 96.5%.
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An AI?based intelligent system for healthcare analysis using Ridge?Adaline Stochastic Gradient Descent Classifier
N. Deepa,B. Prabadevi,Praveen Kumar Reddy Maddikunta,Thippa Reddy Gadekallu,Thar Baker,M. Ajmal Khan,Usman Tariq +6 more
TL;DR: The proposed scheme RASGD improves the regularization of the classification model by using weight decay methods, namely least absolute shrinkage and selection operator and ridge regression methods, and attains an accuracy of 92%, which is better than the other selected classifiers.