Wajdi Alghamdi

According to our database, Wajdi Alghamdi authored at least 16 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

IP-GCN: A deep learning model for prediction of insulin using graph convolutional network for diabetes drug design.

Farman Ali, Majdi Khalid, Abdullah Almuhaimeed, Atef Masmoudi, Wajdi Alghamdi, Ayman Yafoz

J. Comput. Sci., 2024

2023

A new deep boosted CNN and ensemble learning based IoT malware detection.

Saddam Hussain Khan, Tahani Jaser Alahmadi, Wasi Ullah, Javed Iqbal, Azizur Rahim, Hend Khalid Alkahtani, Wajdi Alghamdi, Alaa Omran Almagrabi

Comput. Secur., October, 2023

AFP-SPTS: An Accurate Prediction of Antifreeze Proteins Using Sequential and Pseudo-Tri-Slicing Evolutionary Features with an Extremely Randomized Tree.

Adnan Khan, Jamal Uddin, Farman Ali, Harish Kumar, Wajdi Alghamdi, Aftab Ahmad

J. Chem. Inf. Model., February, 2023

2022

DBP-CNN: Deep learning-based prediction of DNA-binding proteins by coupling discrete cosine transform with two-dimensional convolutional neural network.

Omar Barukab, Farman Ali, Wajdi Alghamdi, Yoosef Bassam, Sher Afzal Khan

Expert Syst. Appl., 2022

Structure preserving loss function for single image super resolution.

Naman Tuli, Suresh Chandra Raikwar, Mohammad Alahmadi, Wajdi Alghamdi, Harpreet Singh, Prashant Singh Rana

Displays, 2022

LBCEPred: a machine learning model to predict linear B-cell epitopes.

Wajdi Alghamdi, Muhammad Attique, Ebraheem Alzahrani, Malik Zaka Ullah, Yaser Daanial Khan

Briefings Bioinform., 2022

Semi-Supervised Skin Lesion Segmentation With Coupling CNN and Transformer Features.

Mohammad Alahmadi, Wajdi Alghamdi

IEEE Access, 2022

2018

Predictive modelling approach to data-driven computational psychiatry.

Wajdi Alghamdi

PhD thesis, 2018

Predicting First-Episode Psychosis Associated with Cannabis Use with Artificial Neural Networks and Deep Learning.

Daniel Stamate, Wajdi Alghamdi, Daniel Stahl, Ida M. Pu, Fionn Murtagh, Danielle Belgrave, Robin M. Murray, Marta Di Forti

Proceedings of the Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications, 2018

Can Artificial Neural Networks Predict Psychiatric Conditions Associated with Cannabis Use?

Daniel Stamate, Wajdi Alghamdi, Daniel Stahl, Alexander Zamyatin, Robin M. Murray, Marta Di Forti

Proceedings of the Artificial Intelligence Applications and Innovations, 2018

PIDT: A Novel Decision Tree Algorithm Based on Parameterised Impurities and Statistical Pruning Approaches.

Daniel Stamate, Wajdi Alghamdi, Daniel Stahl, Doina Logofatu, Alexander Zamyatin

Proceedings of the Artificial Intelligence Applications and Innovations, 2018

A Machine Learning Framework for Predicting Dementia and Mild Cognitive Impairment.

Daniel Stamate, Wajdi Alghamdi, Jeremy Ogg, Richard Hoile, Fionn Murtagh

Proceedings of the 17th IEEE International Conference on Machine Learning and Applications, 2018

A New Machine Learning Framework for Understanding the Link Between Cannabis Use and First-Episode Psychosis.

Wajdi Alghamdi, Daniel Stamate, Daniel Stahl, Alexander Zamyatin, Robin M. Murray, Marta Di Forti

Proceedings of the Health Informatics Meets eHealth - Biomedical Meets eHealth - From Sensors to Decisions, 2018

2017

Swarmic approach for symmetry detection of cellular automata behaviour.

Mohammad Ali Javaheri Javid, Wajdi Alghamdi, Anna Ursyn, Robert Zimmer, Mohammad Majid al-Rifaie

Soft Comput., 2017

Predicting Psychosis Using the Experience Sampling Method with Mobile Apps.

Daniel Stamate, Andrea Katrinecz, Wajdi Alghamdi, Daniel Stahl, Philippe Delespaul, Jim van Os, Sinan Guloksuz

Proceedings of the 16th IEEE International Conference on Machine Learning and Applications, 2017

2016

A Prediction Modelling and Pattern Detection Approach for the First-Episode Psychosis Associated to Cannabis Use.

Wajdi Alghamdi, Daniel Stamate, Katherine Vang, Daniel Stahl, Marco Colizzi, Giada Tripoli, Diego Quattrone, Olesya Ajnakina, Robin M. Murray, Marta Di Forti

Proceedings of the 15th IEEE International Conference on Machine Learning and Applications, 2016