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Ali Khan
PeerJ Author & Reviewer
70 Points

Contributions by role

Reviewer 70

Contributions by subject area

Human-Computer Interaction
Artificial Intelligence
World Wide Web and Web Science
Algorithms and Analysis of Algorithms
Data Mining and Machine Learning
Data Science
Optimization Theory and Computation

Ali Haider Khan

PeerJ Author & Reviewer

Summary

Ali Haider Khan (Senior Member, IEEE) is currently a Data Science Expert with extensive experience in industry and academia. He is a renowned expert in artificial intelligence, healthcare informatics, and deep learning. He was a member of the AI Initiative Team. He has contributed more than 40 research papers to well-reputed international journals and conferences. He has over eight years of national and international academic experience. He has delivered guest talks, conducted seminars, and led training sessions at numerous national and international forums in the past. His research interests include image processing, social media analysis, medical image diagnosis, machine learning, and data mining. He aims to contribute to interdisciplinary research in generative neural networks.

Algorithms & Analysis of Algorithms Artificial Intelligence Data Mining & Machine Learning Data Science Optimization Theory & Computation

PeerJ Contributions

  • Articles 1
  • Reviewed 1
June 18, 2025
A new approach of anomaly detection in shopping center surveillance videos for theft prevention based on RLCNN model
Muhammad Sajid, Ali Haider Khan, Kaleem Razzaq Malik, Javed Ali Khan, Ayed Alwadain
https://doi.org/10.7717/peerj-cs.2944

Signed reviews submitted for articles published in PeerJ Note that some articles may not have the review itself made public unless authors have made them open as well.

May 27, 2025
Optimized deep learning approach for lung cancer detection using flying fox optimization and bidirectional generative adversarial networks
Manal Abdullah Alohali, Hamed Alqahtani, Shouki A. Ebad, Faiz Abdullah Alotaibi, Venkatachalam K., Jaehyuk Cho
https://doi.org/10.7717/peerj-cs.2853