Dr. Ali H. ABDULWAHHAB

Dr. Ali H. ABDULWAHHAB

Istanbul, TR.

About

Highly accomplished Ph.D. in Electrical and Computer Engineering specializing in advanced AI applications, machine learning, and deep learning. Expert in developing intelligent algorithms for medical imaging, brain signal processing, and Brain-Computer Interface (BCI) systems, with a strong focus on diagnosis, monitoring, and human-machine interaction. Proven research capabilities in cutting-edge neural networks and data analysis for complex engineering challenges.

Education

Altinbas University
Istanbul, Not Specified, Türkiye

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Doctorate (Ph.D.)

Electrical and Computer Engineering

Istanbul Gelişim University
Istanbul, Not Specified, Türkiye

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Master

Electrical and Electronic Engineering

Mustansiriyah University
Baghdad, Not Specified, Iraq

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Bachelor

Electrical Engineering

Publications

Handoff Performance Evaluation based on RSS Measurement and Threshold Distance.

Published by

International Journal of Computer Applications

Summary

Evaluated handoff performance in wireless networks using RSS measurements and threshold distance algorithms.

Harnessing Deep Learning for EEG Emotion Recognition: A Hybrid Approach with Attention Mechanisms.

Published by

Al-Iraqia Journal for Scientific Engineering Research

Summary

Developed a hybrid deep learning approach with attention mechanisms for enhanced EEG emotion recognition.

PAFWF-EEGC Net: parallel adaptive feature weight fusion based on EEG-dynamic characteristics using channels neural network for driver drowsiness detection.

Published by

Signal, Image and Video Processing

Summary

Introduced PAFWF-EEGC Net, a parallel adaptive feature weight fusion method using EEG-dynamic characteristics for driver drowsiness detection.

BCI-DRONE CONTROL BASED ON THE CONCENTRATION LEVEL AND EYE BLINK SIGNALS USING A NEUROSKY HEADSET.

Published by

Kufa Journal of Engineering

Summary

Implemented BCI-drone control utilizing NeuroSky headset data, focusing on concentration and eye blink signals.

HAFMAB-Net: hierarchical adaptive fusion based on multilevel attention-enhanced bottleneck neural network for breast histopathological cancer classification.

Published by

Signal, Image and Video Processing

Summary

Developed HAFMAB-Net, a hierarchical adaptive fusion network with multilevel attention for breast histopathological cancer classification.

MEMF-Net: A Mega-Ensemble of Multi-Feature CNNs for Classification of Breast Histopathological Images.

Published by

Iraqi Journal for Computer Science and Mathematics

Summary

Proposed MEMF-Net, a mega-ensemble of multi-feature CNNs for robust classification of breast histopathological images.

Detection Lung Nodules Using Medical CT Images Based on Deep learning techniques.

Published by

Baghdad Science Journal

Summary

Utilized deep learning techniques for the effective detection of lung nodules in medical CT images.

Unsupervised histopathological sub-image analysis for breast cancer diagnosis using variational autoencoders, clustering, and supervised learning.

Published by

Journal of Engineering and Sustainable Development

Summary

Conducted unsupervised histopathological sub-image analysis for breast cancer diagnosis, integrating VAEs, clustering, and supervised learning.

Cutting-Edge CNN approaches for breast histopathological classification: The impact of spatial attention mechanisms.

Published by

ShodhAI: Journal of Artificial Intelligence

Summary

Investigated cutting-edge CNN approaches and spatial attention mechanisms for improved breast histopathological classification.

Analysis of potential 5G transmission methods concerning Bit Error Rate.

Published by

AEU-International Journal of Electronics and Communications

Summary

Analyzed potential 5G transmission methods, focusing on their impact on Bit Error Rate performance.

Deep Learning-based Signal Identification in Wireless Communication Systems: a Comparative Analysis on 3G, LTE, and 5G Standards.

Published by

Al-Iraqia Journal for Scientific Engineering Research

Summary

Performed a comparative analysis of deep learning-based signal identification across 3G, LTE, and 5G wireless communication systems.

Detection of epileptic seizure using EEG signals analysis based on deep learning techniques.

Published by

Chaos, Solitons & Fractals

Summary

Applied deep learning techniques for the accurate detection of epileptic seizures using EEG signal analysis.

A review on medical image applications based on deep learning techniques.

Published by

Journal of Image and Graphics

Summary

Provided a comprehensive review of deep learning applications in various medical imaging contexts.

Drone Movement Control by Electroencephalography Signals Based on BCI System.

Published by

Advances in Electrical and Electronic Engineering

Summary

Demonstrated drone movement control via an EEG-based BCI system, leveraging electroencephalography signals.

FUSING STEREO IMAGES INTOITS EQUIVALENT CYCLOPEAN VIEW.

Published by

EPH -International Journal Of Science And Engineering

Summary

Explored techniques for fusing stereo images to create an equivalent cyclopean view.

Languages

Arabic
English
Turkish

Certificates

Convolutional Neural Networks for Medical Images Diagnosis

Issued By

Not Specified

Deep Learning for Image Classification in Python with CNN

Issued By

Not Specified

Image Processing with Python PIL

Issued By

Not Specified

Python for Deep learning: Build Neural Networking in Python

Issued By

Not Specified

Cisco Networking Academy® Get Connected

Issued By

Cisco

EEG/ERP Analysis with Python and MNE: An Introductory Course

Issued By

Not Specified

Certificates in Reviewing

Issued By

Not Specified

International Research Conference On Engineering And Applied Sciences

Issued By

Not Specified

Skills

Research Methodology

Research Design, Data Collection, Statistical Analysis, Academic Writing, Publication.

Deep Learning / Machine Learning

Convolutional Neural Networks (CNN), Neural Networks, Deep Learning, Machine Learning, AI Applications, Algorithm Development, Pattern Recognition.

BCI Systems

Brain-Computer Interface, EEG Signal Processing, Human-Machine Interaction, Neurosky Headset.

Signal Processing

EEG Signal Processing, Image Processing, Medical Imaging Analysis, Wireless Communication Signals.

Computational and Programming

Python, Arduino, Data Analysis, Algorithm Implementation.

Medical Imaging Analysis

Medical Imaging, Histopathological Image Classification, Lung Nodule Detection, Breast Cancer Diagnosis.

Microsoft Office

Word, Excel, PowerPoint.

Projects

MULTI-APPROACHED HISTOPATHOLOGY IMAGES CLASSIFICATION

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Summary

Ph.D. dissertation on advanced classification techniques for histopathology images.

Improved algorithm for EEG–based BCI application

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Summary

Master's thesis project aimed at enhancing Brain-Computer Interface algorithms.

Control Home Application using Arduino based on Temperature

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Summary

Bachelor's project focusing on developing a home automation system.