Muhammad Tahir

Work place: School of Software Technology, Dalian University of Technology, Dalian, China,116620



Research Interests: Network Security, Information Security, Application Security, Computational Learning Theory, Computational Game Theory, Artificial Intelligence


Muhammad Tahir received the B.S. degree in software engineering from the University of Sindh, Jamshoro Sindh, Pakistan, in 2008, and the M.S. degree in software engineering from the School of Software Engineering, Chongqing University, China, in 2014. He is currently pursuing the Ph.D. degree in software engineering with the School of Software Technology, Dalian University of Technology, China. He is on Ph.D. Study leave from Lecturer position with the Department of Computer Science, COMSATS University Islamabad, Sahiwal Campus, Pakistan. He has authored/coauthored publications in World renowned journals. His research interests include network security, web application performance tuning, mobile edge computing, game theory, artificial intelligence, and machine learning.

Author Articles
An Optimized Architecture of Image Classification Using Convolutional Neural Network

By Muhammad Aamir Ziaur Rahman Waheed Ahmed Abro Muhammad Tahir Syed Mustajar Ahmed

DOI:, Pub. Date: 8 Oct. 2019

The convolutional neural network (CNN) is the type of deep neural networks which has been widely used in visual recognition. Over the years, CNN has gained lots of attention due to its high capability to appropriately classifying the images and feature learning. However, there are many factors such as the number of layers and their depth, number of features map, kernel size, batch size, etc. They must be analyzed to determine how they influence the performance of network. In this paper, the performance evaluation of CNN is conducted by designing a simple architecture for image classification. We evaluated the performance of our proposed network on the most famous image repository name CIFAR-10 used for the detection and classification task. The experiment results show that the proposed network yields the best classification accuracy as compared to existing techniques. Besides, this paper will help the researchers to better understand the CNN models for a variety of image classification task. Moreover, this paper provides a brief introduction to CNN, their applications in image processing, and discuss recent advances in region-based CNN for the past few years.

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