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.
Papers
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Journal ArticleDOI
Android malware detection through generative adversarial networks
TL;DR: The proposed technique to cater malware detection is by design a deep learning model making use of generative adversarial networks, which is responsible to detect the Android malware via famous two‐player game theory for a rock‐paper‐scissor problem.
Proceedings ArticleDOI
Value Based Fuzzy Requirement Prioritization and Its Evaluation Framework
TL;DR: This paper has proposed an intelligent fuzzy logic based technique for requirements prioritization based on the perceived value of each requirement, and proposed a framework for evaluation of existing as well as proposed requirement prioritization techniques.
Proceedings ArticleDOI
Just-in-time Customer Churn Prediction: With and Without Data Transformation
TL;DR: The objective of this paper is to provide an empirical comparison and effect of with and without state-of-the-art data transformation methods on the proposed JIT-CCP model and utilize Naive Bayes as an underlying classifier.
Book ChapterDOI
Customer Churn Prediction in Telecommunication Industry: With and without Counter-Example
TL;DR: Results show that rough set as a multi-class classifier provides more accurate results for binary/multi-class classification problems.
Journal ArticleDOI
Just-in-time customer churn prediction in the telecommunication sector
Adnan Amin,Feras N. Al-Obeidat,Babar Shah,May Al Tae,Changez Khan,Hamood Ur Rehman Durrani,Sajid Anwar +6 more
TL;DR: It is found from the empirical evaluation that it is possible to evaluate the performance of the predictive model using cross-company dataset for training purposes and it is evident that heterogeneous ensemble-based JIT-CCP model is more suitable approach to use as compared to individual classifier or homogeneous ensemble -based technique.