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Adi Alhudhaif
Researcher at Salman bin Abdulaziz University
Publications - 52
Citations - 540
Adi Alhudhaif is an academic researcher from Salman bin Abdulaziz University. The author has contributed to research in topics: Computer science & Pattern recognition (psychology). The author has an hindex of 3, co-authored 28 publications receiving 34 citations. Previous affiliations of Adi Alhudhaif include George Washington University.
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Robust automated Parkinson disease detection based on voice signals with transfer learning
TL;DR: By integrating the developed model into smart electronic devices, it will be able to develop alternative pre-diagnosis methods and will assist the physicians for PD detection during the in-clinic assessment and imply an enhancement in the life quality of patients and a cost reduction for the national health system.
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Determination of COVID-19 Pneumonia based on Generalized Convolutional Neural Network Model from Chest X-Ray Images
TL;DR: A transfer learning-based CNN model was developed by using a sum of 1,218 chest X-ray images (CXIs) consisting of 368 COVID-19 pneumonia and 850 other pneumonia cases by pre-trained architectures, including DenseNet-201, ResNet-18 and SqueezeNet.
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Electric-vehicle energy management and charging scheduling system in sustainable cities and society
TL;DR: An Electric Vehicle-Intelligent Energy Management and Charging’s Scheduling System (EV-EMSS) for charging station and PEVs management system that provides convenient energy management services by using battery control units and communication with charging stations for charging decisions.
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An image encryption algorithm based on new generalized fusion fractal structure
Musheer Ahmad,Shafali Agarwal,Ahmed Alkhayyat,Adi Alhudhaif,Fayadh Alenezi,Amjad Hussain Zahid,Nojood O. Aljehane +6 more
TL;DR: In this paper , a generalized fusion fractal structure is proposed by combining two one-dimensional fractals as seed functions from a larger spectrum of fractal functions, and a novel image encryption algorithm based on the new fractal function is proposed which utilizes a generated pseudo-random number (PRN) sequence as secret key.
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Classification of imbalanced hyperspectral images using SMOTE-based deep learning methods
TL;DR: In this paper, the authors reveal the difference and effects on the classifier performance between the original imbalanced dataset and the data set modified by balancing methods including Smote, Adasyn, K-Means, and Cluster.