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2020 – today
- 2024
[j9]Farman Ali, Majdi Khalid
, Abdullah Almuhaimeed, Atef Masmoudi, Wajdi Alghamdi, Ayman Yafoz:
IP-GCN: A deep learning model for prediction of insulin using graph convolutional network for diabetes drug design. J. Comput. Sci. 81: 102388 (2024)- [j8]Farman Ali, Majdi Khalid, Atef Masmoudi, Wajdi Alghamdi, Ayman Yafoz, Raed Alsini:
VEGF-ERCNN: A deep learning-based model for prediction of vascular endothelial growth factor using ensemble residual CNN. J. Comput. Sci. 83: 102448 (2024) - 2023
- [j7]Saddam Hussain Khan, Tahani Jaser Alahmadi, Wasi Ullah, Javed Iqbal, Azizur Rahim, Hend Khalid Alkahtani, Wajdi Alghamdi, Alaa Omran Almagrabi:
A new deep boosted CNN and ensemble learning based IoT malware detection. Comput. Secur. 133: 103385 (2023) - [j6]Adnan Khan, Jamal Uddin, Farman Ali, Harish Kumar, Wajdi Alghamdi, Aftab Ahmad:
AFP-SPTS: An Accurate Prediction of Antifreeze Proteins Using Sequential and Pseudo-Tri-Slicing Evolutionary Features with an Extremely Randomized Tree. J. Chem. Inf. Model. 63(3): 826-834 (2023) - 2022
- [j5]Mohammad Alahmadi, Wajdi Alghamdi:
Semi-Supervised Skin Lesion Segmentation With Coupling CNN and Transformer Features. IEEE Access 10: 122560-122569 (2022) - [j4]Wajdi Alghamdi, Muhammad Attique, Ebraheem Alzahrani, Malik Zaka Ullah, Yaser Daanial Khan:
LBCEPred: a machine learning model to predict linear B-cell epitopes. Briefings Bioinform. 23(3) (2022) - [j3]Naman Tuli, Suresh Chandra Raikwar, Mohammad Alahmadi, Wajdi Alghamdi, Harpreet Singh, Prashant Singh Rana:
Structure preserving loss function for single image super resolution. Displays 75: 102285 (2022) - [j2]Omar Barukab, Farman Ali, Wajdi Alghamdi, Yoosef Bassam, Sher Afzal Khan:
DBP-CNN: Deep learning-based prediction of DNA-binding proteins by coupling discrete cosine transform with two-dimensional convolutional neural network. Expert Syst. Appl. 197: 116729 (2022)
2010 – 2019
- 2018
- [b1]Wajdi Alghamdi:
Predictive modelling approach to data-driven computational psychiatry. University of London, Data Science and Soft Computing Lab and Department of Computing, Goldsmiths, UK, 2018 - [c7]Wajdi Alghamdi, Daniel Stamate, Daniel Stahl, Alexander Zamyatin, Robin M. Murray, Marta Di Forti:
A New Machine Learning Framework for Understanding the Link Between Cannabis Use and First-Episode Psychosis. eHealth 2018: 9-16 - [c6]Daniel Stamate, Wajdi Alghamdi, Jeremy Ogg, Richard Hoile, Fionn Murtagh:
A Machine Learning Framework for Predicting Dementia and Mild Cognitive Impairment. ICMLA 2018: 671-678 - [c5]Daniel Stamate, Wajdi Alghamdi, Daniel Stahl, Doina Logofatu, Alexander Zamyatin:
PIDT: A Novel Decision Tree Algorithm Based on Parameterised Impurities and Statistical Pruning Approaches. AIAI 2018: 273-284 - [c4]Daniel Stamate, Wajdi Alghamdi, Daniel Stahl, Alexander Zamyatin, Robin M. Murray, Marta Di Forti:
Can Artificial Neural Networks Predict Psychiatric Conditions Associated with Cannabis Use? AIAI 2018: 311-322 - [c3]Daniel Stamate, Wajdi Alghamdi, Daniel Stahl, Ida M. Pu, Fionn Murtagh, Danielle Belgrave, Robin M. Murray, Marta Di Forti:
Predicting First-Episode Psychosis Associated with Cannabis Use with Artificial Neural Networks and Deep Learning. IPMU (3) 2018: 691-702 - 2017
- [j1]Mohammad Ali Javaheri Javid, Wajdi Alghamdi, Anna Ursyn, Robert Zimmer, Mohammad Majid al-Rifaie:
Swarmic approach for symmetry detection of cellular automata behaviour. Soft Comput. 21(19): 5585-5599 (2017) - [c2]Daniel Stamate, Andrea Katrinecz, Wajdi Alghamdi, Daniel Stahl, Philippe Delespaul, Jim van Os, Sinan Guloksuz:
Predicting Psychosis Using the Experience Sampling Method with Mobile Apps. ICMLA 2017: 667-673 - 2016
- [c1]Wajdi Alghamdi, Daniel Stamate, Katherine Vang, Daniel Stahl, Marco Colizzi, Giada Tripoli, Diego Quattrone, Olesya Ajnakina, Robin M. Murray, Marta Di Forti:
A Prediction Modelling and Pattern Detection Approach for the First-Episode Psychosis Associated to Cannabis Use. ICMLA 2016: 825-830
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last updated on 2024-10-15 20:41 CEST by the dblp team
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