H
Hani Alquhayz
Researcher at Majmaah University
Publications - 51
Citations - 591
Hani Alquhayz is an academic researcher from Majmaah University. The author has contributed to research in topics: Computer science & Network packet. The author has an hindex of 8, co-authored 43 publications receiving 168 citations.
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Journal ArticleDOI
Multi-View Feature Fusion Based Four Views Model for Mammogram Classification Using Convolutional Neural Network
TL;DR: This study proposes Multi-View Feature Fusion (MVFF) based CADx system using feature fusion technique of four views for classification of mammogram and shows that the MVFF based system is performed better than a single view-based system for mammogram classification.
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An Optimally Configured and Improved Deep Belief Network (OCI-DBN) Approach for Heart Disease Prediction Based on Ruzzo–Tompa and Stacked Genetic Algorithm
Syed Arslan Ali,Basit Raza,Ahmad Kamran Malik,Ahmad Raza Shahid,Muhammad Faheem,Hani Alquhayz,Yogan Jaya Kumar +6 more
TL;DR: A stacked genetic algorithm that stacks two genetic algorithms to give an optimally configured and improved deep belief network named OCI-DBN to solve network configuration issues and optimization problems and improve the performance of the system.
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FFRP: Dynamic Firefly Mating Optimization Inspired Energy Efficient Routing Protocol for Internet of Underwater Wireless Sensor Networks
Muhammad Faheem,Rizwan Aslam Butt,Basit Raza,Hani Alquhayz,Muhammad Ashraf,Saleem Raza,Md. Asri Ngadi +6 more
TL;DR: The experimental outcomes show the better performance of the developed protocol in terms of high packets delivery ratio (PDR) and network throughput (NT) with low latency and energy consumption (EC) compared to existing routing protocols in UWSNs.
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Intelligent Cloud Based Heart Disease Prediction System Empowered with Supervised Machine Learning
Muhammad Adnan Khan,Sagheer Abbas,Ayesha Atta,Allah Ditta,Hani Alquhayz,Muhammad Farhan Khan,Atta-ur-Rahman,Rizwan Ali Naqvi +7 more
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Developing intelligent medical image modality classification system using deep transfer learning and LDA
TL;DR: An efficient and accurate approach for medical image modality classification that can used for retrieval of clinical cases from large medical repositories and may be deployed to diagnostic centers to assist the practitioners for accurate and efficient clinical case retrieval and disease diagnosis is proposed.