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Nehad M. Abdel Rahman Ibrahim
Researcher at University of Dammam
Publications - 14
Citations - 150
Nehad M. Abdel Rahman Ibrahim is an academic researcher from University of Dammam. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 2, co-authored 5 publications receiving 13 citations.
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Journal ArticleDOI
Applications of Big Data Analytics to Control COVID-19 Pandemic.
Shikah J. Alsunaidi,Abdullah M. Almuhaideb,Nehad M. Abdel Rahman Ibrahim,Fatema S. Shaikh,Kawther S. Alqudaihi,Fahd Alhaidari,Irfan Ullah Khan,Nida Aslam,Mohammed Alshahrani +8 more
TL;DR: In this article, the authors conduct a literature review to highlight the contributions of several studies in the domain of COVID-19-based big data analysis and present as a taxonomy several applications used to manage and control the pandemic.
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Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities
Kawther S. Alqudaihi,Nida Aslam,Irfan Ullah Khan,Abdullah M. Almuhaideb,Shikah J. Alsunaidi,Nehad M. Abdel Rahman Ibrahim,Fahd Alhaidari,Fatema S. Shaikh,Yasmine M. Alsenbel,Dima M. Alalharith,Hajar M. Alharthi,Wejdan M. Alghamdi,Mohammed Alshahrani +12 more
TL;DR: In this paper, the authors reviewed the latest proposed technologies that were used to control the impact of respiratory diseases and identified different techniques that produced the best results for diagnosing respiratory disease using cough samples.
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A deep learning approach to intelligent fruit identification and family classification
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Supervised machine learning-based prediction of COVID-19
Atta Ur, Rahman, Sultan, K.,Iftikhar Naseer,Rizwan Majeed,Dhiaa Musleh,Mohammed Gollapalli,S. Chabani,Nehad M. Abdel Rahman Ibrahim,Shahan Yamin Siddiqui,Muhammad Adnan Khan +8 more
TL;DR: This study aims to provide a comprehensive review of the role of AI & ML in investigating prediction techniques for the COVID-19 by proposing a cloud-based smart detection algorithm using support vector machine (CSDC-SVM) with cross-fold validation testing.
Journal ArticleDOI
A Neuro-Fuzzy Approach to Road Traffic Congestion Prediction
Mohammed Gollapalli,Atta-ur-Rahman,Dhiaa Musleh,Nehad M. Abdel Rahman Ibrahim,Muhammad Adnan Khan,Sagheer Abbas,Ayesha Atta,Muhammad Aftab Khan,Mehwash Farooqui,Tahir Iqbal,Mohammed Salih Ahmed,Mohammed Imran B. Ahmed,Dakheel Almoqbil,Majd Nabeel,Abdullah Ibrahim Salim Omer +14 more
TL;DR: In this article , a cloud-based intelligent road traffic congestion prediction model is proposed that is empowered with a hybrid Neuro-Fuzzy approach to reduce the delay in the queues, the vehicles experience at different road junctions across the city.