Fahad Layth Malallah
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
Also affiliated: University of Mosul (2019–2026); Cihan University-Erbil (2016–2017); Universiti Putra Malaysia (2013–2014); Cihan University Sulaimaniya (2017)
Faculty Researcher
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Fahad Layth Malallah's research focuses on the application of machine learning and deep learning techniques to address challenges in various domains. He has investigated the use of deep learning for classifying Iraqi banknotes to assist blind individuals and for remote monitoring and detection of face masks to limit COVID-19 infection. His work also includes developing efficient gender classifiers for Arabic speech using convolutional neural networks (CNNs) and exploring QR code encryption for enhancing bank information security. Malallah has studied DDoS attack detection in software-defined networks and the use of deep learning for improved COVID-19 diagnoses. His research extends to predicting problematic internet use in children using machine learning and enhancing EEG signal-based emotional classification through spectrogram contrast enhancement. Malallah has published 33 papers, with an h-index of 9 and 302 citations. He collaborates with Kamran Iqbal at the University of Arkansas at Little Rock, with whom he shares one publication.
Metrics
- h-index: 9
- Publications: 33
- Citations: 306
Selected Publications
-
Predicting Signs of Problematic Internet Use to Analyze Children’s Health Using Machine Learning (2026)
-
Spectrogram Contrast Enhancement Improves EEG Signal-Based Emotional Classification (2025)
Collaboration Network
Top Collaborators
- Spectrogram Contrast Enhancement Improves EEG Signal-Based Emotional Classification
- Predicting Signs of Problematic Internet Use to Analyze Children’s Health Using Machine Learning
Similar Researchers
Based on overlapping research topics