Vahid Abolghasemi

Assistant Professor

University of Essex

United Kingdom

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Vahid Abolghasemi is an Assistant Professor at the University of Essex in the UK. His research interests encompass a wide range of areas including multi-disease detection in retinal imaging, signal processing for fault detection in BLDC motors, and advancements in glaucoma diagnosis through innovative imaging techniques. Additionally, he focuses on artificial intelligence applications in image classification, power management frameworks for renewable energy systems, and enhancing optical character recognition methods.

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Collaborators (3)

Ali Alameer

The Queen's University Belfast

UNITED KINGDOM

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Shahin Hedayati Kia

FRANCE

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Mohammad Hossein Anisi

Senior Lecturer (Associate Professor)

University of Essex

UNITED KINGDOM

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Vahid Abolghasemi

Research Interests

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About

Articles (22)

Multi‐Disease Detection in Retinal Imaging Using VNet with Image Processing Methods for Data Generation

Stator Interturn Fault Detection in BLDC Motors: A Signal-Processing-Based Method

Advancing Glaucoma Diagnosis Through Multi‐Scale Feature Extraction and Cross‐Attention Mechanisms in Optical Coherence Tomography Images

HybridBranchNetV2: Towards reliable artificial intelligence in image classification using reinforcement learning

A Power Control and Management Framework for Integration of PV-based DC Microgrids with Battery Storages and Supercapacitors

Enhanced Coronary Artery Disease Classification Through Feature Engineering and One-Dimensional Convolutional Neural Network

Enhancing optical character recognition: Efficient techniques for document layout analysis and text line detection

Advancing Multilingual Handwritten Numeral Recognition With Attention-Driven Transfer Learning

Enhanced Multi-Synchro-Squeezing Transform for Fault Diagnosis in Induction Machine Based on Third-Order Energy Operator of Stator Current Signature

Single Image Denoising via a New Lightweight Learning-Based Model

Strawberry Disease Detection Through an Advanced Squeeze-and-Excitation Deep Learning Model

Broken Rotor Bar Fault Detection of Induction Machine Based on Rectified Orthogonal Axes of Stator Current

Fuzzy Logic with Deep Learning for Detection of Skin Cancer

A Novel Mode Un-Mixing Approach in Variational Mode Decomposition for Fault Detection in Wound Rotor Induction Machines

Asymmetrical Fault Detection in Induction Motors Through Elimination of Load Torque Oscillations Effects in the Slight Speed Variations and Steady-State Conditions

A Dual Attention-Based Autoencoder Model for Fetal ECG Extraction From Abdominal Signals

Labeled projective dictionary pair learning: application to handwritten numbers recognition

Skin Cancer Diagnosis Based on Neutrosophic Features with a Deep Neural Network

Muscle force estimation from lower limb EMG signals using novel optimised machine learning techniques

High-Voltage Pulse Generators for Electroporation Applications: A Systematic Review

Recursive Singular Spectrum Analysis for Induction Machines Unbalanced Rotor Fault Diagnosis

Rotor Asymmetries Faults Detection in Induction Machines Under the Impacts of Low-Frequency Load Torque Oscillation

Collaborators (3)

Ali Alameer

Shahin Hedayati Kia

Mohammad Hossein Anisi

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