Hosameldin Ahmed
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View article: ASG-MammoNet: an attention-guided framework for streamlined and interpretable breast cancer classification from mammograms
ASG-MammoNet: an attention-guided framework for streamlined and interpretable breast cancer classification from mammograms Open
Introduction Breast cancer remains the most frequently diagnosed cancer and a leading cause of cancer-related death among women globally, emphasising the urgent need for early, accurate, and interpretable diagnostic tools. While digital ma…
View article: Token Mixing for Breast Cancer Diagnosis: Pre-Trained MLP-Mixer Models on Mammograms
Token Mixing for Breast Cancer Diagnosis: Pre-Trained MLP-Mixer Models on Mammograms Open
Data Access Statement: In this study, we use three publicly available datasets: MIAS (Mammographic Image Analysis Society database) (https://www.repository.cam.ac.uk/items/b6a97f0c-3b9b-40ad-8f18-3d121eef1459), CBIS-DDSM (Curated Breast Im…
View article: Improved Adversarial Transfer Network for Bearing Fault Diagnosis under Variable Working Conditions
Improved Adversarial Transfer Network for Bearing Fault Diagnosis under Variable Working Conditions Open
Bearings are one of the critical components of rotating machinery, and their failure can cause catastrophic consequences. In this regard, previous studies have proposed a variety of intelligent diagnosis methods. Most existing bearing faul…
View article: High Performance Breast Cancer Diagnosis From Mammograms Using Mixture of Experts With EfficientNet Features (MoEffNet)
High Performance Breast Cancer Diagnosis From Mammograms Using Mixture of Experts With EfficientNet Features (MoEffNet) Open
Data Statement: In this study, we use three publicly available datasets: MIAS (Mammographic Image Analysis Society database) (https://www.repository.cam.ac.uk/items/b6a97f0c-3b9b40ad-8f18-3d121eef1459 ), CBIS-DDSM (Curated Breast Imaging S…
View article: Convolutional-Transformer Model with Long-Range Temporal Dependencies for Bearing Fault Diagnosis Using Vibration Signals
Convolutional-Transformer Model with Long-Range Temporal Dependencies for Bearing Fault Diagnosis Using Vibration Signals Open
Fault diagnosis of bearings in rotating machinery is a critical task. Vibration signals are a valuable source of information, but they can be complex and noisy. A transformer model can capture distant relationships, which makes it a promis…
View article: Vibration Image Representations for Fault Diagnosis of Rotating Machines: A Review
Vibration Image Representations for Fault Diagnosis of Rotating Machines: A Review Open
Rotating machine vibration signals typically represent a large collection of responses from various sources in a machine, along with some background noise. This makes it challenging to precisely utilise the collected vibration signals for …
View article: Intrinsic Dimension Estimation-Based Feature Selection and Multinomial Logistic Regression for Classification of Bearing Faults Using Compressively Sampled Vibration Signals
Intrinsic Dimension Estimation-Based Feature Selection and Multinomial Logistic Regression for Classification of Bearing Faults Using Compressively Sampled Vibration Signals Open
As failures of rolling bearings lead to major failures in rotating machines, recent vibration-based rolling bearing fault diagnosis techniques are focused on obtaining useful fault features from the huge collection of raw data. However, to…
View article: Intelligent Fault Diagnosis Framework for Modular Multilevel Converters in HVDC Transmission
Intelligent Fault Diagnosis Framework for Modular Multilevel Converters in HVDC Transmission Open
Open circuit failure mode in insulated-gate bipolar transistors (IGBT) is one of the most common faults in modular multilevel converters (MMCs). Several techniques for MMC fault diagnosis based on threshold parameters have been proposed, b…
View article: Open-Circuit Fault Detection and Classification of Modular Multilevel Converters in High Voltage Direct Current Systems (MMC-HVDC) with Long Short-Term Memory (LSTM) Method
Open-Circuit Fault Detection and Classification of Modular Multilevel Converters in High Voltage Direct Current Systems (MMC-HVDC) with Long Short-Term Memory (LSTM) Method Open
Fault detection and classification are two of the challenging tasks in Modular Multilevel Converters in High Voltage Direct Current (MMC-HVDC) systems. To directly classify the raw sensor data without certain feature extraction and classif…
View article: Connected Components-based Colour Image Representations of Vibrations for a Two-stage Fault Diagnosis of Roller Bearings Using Convolutional Neural Networks
Connected Components-based Colour Image Representations of Vibrations for a Two-stage Fault Diagnosis of Roller Bearings Using Convolutional Neural Networks Open
View article: Fault Detection and Classification in MMC-HVDC Systems Using Learning Methods
Fault Detection and Classification in MMC-HVDC Systems Using Learning Methods Open
In this paper, we explore learning methods to improve the performance of the open-circuit fault diagnosis of modular multilevel converters (MMCs). Two deep learning methods, namely, convolutional neural networks (CNN) and auto encoder base…
View article: Machinery Vibration Data Resources and Analysis Algorithms
Machinery Vibration Data Resources and Analysis Algorithms Open
View article: Index
Index Open
2 (ART2) 246 aircraft engine health monitoring 6, 15 Akaike's information criterion (AIC) 47 amplitude demodulation 52 amplitude modulation (AM) 25, 49 analytic signal 72, 94-96, 105, 390 analytic wavelet 84, 114 ant colony optimisation 89…
View article: Internet addiction disorder detection of Chinese college students using several personality questionnaire data and support vector machine
Internet addiction disorder detection of Chinese college students using several personality questionnaire data and support vector machine Open
With the unprecedented development of the Internet, it also brings the challenge of Internet Addiction (IA), which is hard to diagnose and cure according to the state-of-art research. In this study, we explored the feasibility of machine l…
View article: Intelligent methods for condition monitoring of rolling bearings using vibration data
Intelligent methods for condition monitoring of rolling bearings using vibration data Open
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London
View article: Three-Stage Method for Rotating Machine Health Condition Monitoring Using Vibration Signals
Three-Stage Method for Rotating Machine Health Condition Monitoring Using Vibration Signals Open
This paper proposes a new three-stage method for rotating machines health condition monitoring. In the first stage of the proposed method, Multiple Measurement Vectors Compressive Sampling (MMV-CS) is used to obtain compressively-sampled s…
View article: Three-Stage Hybrid Fault Diagnosis for Rolling Bearings With Compressively Sampled Data and Subspace Learning Techniques
Three-Stage Hybrid Fault Diagnosis for Rolling Bearings With Compressively Sampled Data and Subspace Learning Techniques Open
To avoid the burden of much storage requirements and processing time, this paper proposes a three-stage hybrid method, Compressive Sampling with Correlated Principal and Discriminant Components (CSCPDC), for bearing faults diagnosis based …
View article: Compressive Sampling and Feature Ranking Framework for Bearing Fault Classification With Vibration Signals
Compressive Sampling and Feature Ranking Framework for Bearing Fault Classification With Vibration Signals Open
Failures of rolling element bearings are amongst the main causes of machines breakdowns. To
\nprevent such breakdowns, bearing health monitoring is performed by collecting data from rotating machines,
\nextracting features from the collect…
View article: Intelligent condition monitoring method for bearing faults from highly compressed measurements using sparse over-complete features
Intelligent condition monitoring method for bearing faults from highly compressed measurements using sparse over-complete features Open
Condition classification of rolling element bearings in rotating machines is important to prevent the breakdown of industrial machinery. A considerable amount of literature has been published on bearing faults classification. These studies…