Fahad Layth Malallah Data-verified

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

Researcher

Last publication 2026 Last refreshed 2026-05-16

faculty

9 h-index 34 pubs 302 cited

Biography and Research Information

OverviewAI-generated summary

Fahad Layth Malallah's research investigates the application of machine learning and deep learning techniques to address diverse challenges. He has explored the use of deep learning for classifying Iraqi banknotes to aid blind individuals and for remote face mask detection to limit COVID-19 transmission. Malallah has also worked on developing efficient gender classifiers for Arabic speech using convolutional neural networks (CNNs) and has investigated QR code encryption for enhancing bank information confidentiality. His work extends to detecting Distributed Denial-of-Service (DDoS) attacks in software-defined networks and improving COVID-19 diagnoses through deep learning and image processing. Additionally, Malallah has studied the prediction of problematic internet use in children using machine learning and the enhancement of EEG signals for emotional classification. His scholarship metrics include an h-index of 9, with 34 publications and 299 citations.

Metrics

  • h-index: 9
  • Publications: 34
  • Citations: 302

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)

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Collaboration Network

17 Collaborators 5 Institutions 3 Countries

Top Collaborators

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