S
Sajid Anwar
Researcher at Information Technology Institute
Publications - 67
Citations - 2530
Sajid Anwar is an academic researcher from Information Technology Institute. The author has contributed to research in topics: Software system & Deep learning. The author has an hindex of 16, co-authored 67 publications receiving 1862 citations. Previous affiliations of Sajid Anwar include Ghulam Ishaq Khan Institute of Engineering Sciences and Technology & Seoul National University.
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Journal ArticleDOI
Structured Pruning of Deep Convolutional Neural Networks
TL;DR: The proposed work shows that when pruning granularities are applied in combination, the CIFAR-10 network can be pruned by more than 70% with less than a 1% loss in accuracy.
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Structured Pruning of Deep Convolutional Neural Networks
TL;DR: In this article, the importance weight of each particle is assigned by computing the misclassification rate with corresponding connectivity pattern, and the pruned network is re-trained to compensate for the losses due to pruning.
Proceedings ArticleDOI
Fixed point optimization of deep convolutional neural networks for object recognition
TL;DR: The results indicate that quantization induces sparsity in the network which reduces the effective number of network parameters and improves generalization, and reduces the required memory storage by a factor of 1/10 and achieves better classification results than the high precision networks.
Journal ArticleDOI
Comparing Oversampling Techniques to Handle the Class Imbalance Problem: A Customer Churn Prediction Case Study
Adnan Amin,Sajid Anwar,Awais Adnan,Muhammad Nawaz,Newton Howard,Junaid Qadir,Ahmad Hawalah,Amir Hussain +7 more
TL;DR: The empirical results demonstrate that the overall predictive performance of MTDF and rules-generation based on genetic algorithms performed the best as compared with the rest of the evaluated oversampling methods and rule-generation algorithms.
Journal ArticleDOI
Customer churn prediction in the telecommunication sector using a rough set approach
TL;DR: This study proposes an intelligent rule-based decision-making technique, based on rough set theory (RST), to extract important decision rules related to customer churn and non-churn, and shows that RST based on GA is the most efficient technique for extracting implicit knowledge in the form of decision rules from the publicly available, benchmark telecom dataset.