Tarek Abd El-Hafeez

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Verified

Tarek verified their affiliation via an institutional email.

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• PhD
• Professor (Full) at Minia University

Speech pathology is a multidisciplinary field that assesses, diagnoses, and manages communication disorders caused by neurological, developmental, structural, and degenerative conditions. Among these disorders, dysarthria is a motor speech impairment characterized by reduced speech intelligibility and abnormal articulatory patterns resulting from n...

Pomegranate is a fruit of substantial economic and nutritional importance worldwide. However, its production and quality are significantly threatened by a range of diseases, whose early and accurate detection remains a challenging task. Traditional disease identification relies primarily on manual inspection by agronomists, a process that is time-c...

Accurate classification of phonocardiogram (PCG) signals is vital for the early detection of cardiovascular diseases (CVDs), a leading cause of global mortality. However, conventional machine learning and deep learning approaches often suffer from suboptimal feature extraction, class imbalance, and inefficient hyperparameter tuning. This study prop...

Early and reliable detection of cardiac murmurs from phonocardiogram (PCG) recordings is essential for improving cardiovascular screening and supporting diagnosis in primary care. However, automated murmur classification remains challenging due to signal variability, class imbalance, and temporal dependence within heart-sound sequences. This study...

Automated liver lesion segmentation is constrained by a fundamental trade-off: architectures that deliver maximum accuracy (e.g., nnU-Net) are computationally prohibitive for real-time clinical use, while efficient frameworks capable of real-time performance (e.g., the YOLO family) have not been systematically optimized for the precise, multi-class...

Rapid decarbonisation of hard-to-electrify sectors requires low-emission hydrogen, but deployment is constrained by uncertainty in the Levelized Cost of Hydrogen (LCOH) across diverse national contexts. Using Africa as a case study, where green hydrogen planning spans highly heterogeneous conditions, this study develops a comparative machine learni...

While Deep Deterministic Policy Gradient (DDPG) reinforcement learning has demonstrated significant potential for optimizing aquaculture operations in laboratory and controlled environments, its practical deployment in commercial-scale Recirculating Aquaculture Systems (RAS) faces critical scalability and infrastructure challenges. This paper prese...

Recirculating Aquaculture Systems (RAS) have emerged as a sustainable approach to intensive fish production. In our previous work, we demonstrated the efficacy of a Deep Deterministic Policy Gradient (DDPG) reinforcement learning approach for optimizing feeding rates and maintaining water quality in RAS. However, the “black box” nature of deep rein...

Plant diseases severely threaten global agriculture, causing significant crop losses and jeopardizing food security. Traditional manual diagnostic methods are inefficient, time-consuming, and prone to human error, underscoring an urgent need for accurate, efficient, and scalable automated detection systems. While deep learning offers transformative...

The deployment of advanced reinforcement learning algorithms in edge computing environments presents significant challenges for real-time aquaculture management, particularly in resource-constrained recirculating aquaculture systems (RAS). Building upon our previous work demonstrating superior performance of DDPG controllers in commercial RAS opera...

Breast cancer diagnosis via histopathology image analysis is a complex and subjective process. While deep learning has emerged as a powerful tool for automation, achieving high accuracy across diverse cancer subtypes and magnification levels remains a significant challenge. This paper introduces a Novel-MultiScaleAttention model, an advanced archit...

Cervical cancer remains a significant global health concern, necessitating accurate and early diagnostic tools. This paper presents a robust deep learning pipeline for the multi-class classification of cervical cancer cells, introducing an optimized YOLO-based architecture enhanced with a novel Attention-Guided Multi-Scale Feature Fusion (AGMS-FF)...

Accurate and timely classification of rice paddy ripeness is critical for optimizing harvest decisions, improving grain quality, and strengthening global food security. Traditional manual assessments remain subjective, labor-intensive, and poorly scalable, underscoring the need for automated solutions. This study presents a rigorous comparative eva...

Early diagnosis of Parkinson’s disease (PD) is challenging due to subtle initial symptoms. This study introduces an advanced machine learning framework that leverages particle swarm optimization (PSO) to improve PD detection through vocal biomarker analysis. Our novel approach unifies the optimization of both acoustic feature selection and classifi...

Dates in Saudi Arabia hold immense cultural, economic, and nutritional importance, being a staple food and a symbol of heritage. Saudi Arabia produces approximately 1.5 million metric tons of dates annually, accounting for nearly 17% of global date production, underscoring its pivotal role in the global market. Precise and automated classification...

Musculoskeletal pain is a significant health concern affecting individuals across various demographics and professions, often leading to reduced productivity and impaired quality of life. This study proposes a framework leveraging Particle Swarm Optimization (PSO) to evaluate and assess musculoskeletal pain risk based on a comprehensive dataset enc...

This study aimed to develop digital citizenship skills among visually impaired university students.

Alzheimer’s disease (AD) involves complex genetic interactions that remain challenging to model computationally. We present a novel deep learning framework integrating Single Nucleotide Polymorphism (SNP) data with Graph Convolutional Networks (GCNs) to predict gene-disease relationships in AD. Our dual-pathway architecture combines: (1) linear SNP...

The American Sign Language Recognition Dataset is a pivotal resource for research in visual-gestural languages for American Sign Language and Sign-Language MNIST Dataset. The dataset contains over 64,000 images meticulously labeled with the corresponding ASL gesture or letter to recognize and classify ASL signs. Recent computer vision and deep lear...

Introduction Accurate and automated fruit classification plays a vital role in modern agriculture but remains challenging due to the wide variability in fruit appearances. Methods In this study, we propose a novel approach to image classification by integrating a DenseNet121 model pre-trained on ImageNet with a Squeeze-and-Excitation (SE) Attentio...

Dysarthria, a motor speech disorder commonly associated with neurological conditions such as cerebral palsy and ALS, poses significant challenges for early diagnosis and intervention. Traditional diagnostic methods often rely on subjective assessments, leading to delays in effective treatment. Recent advances in machine learning and deep learning o...

Super-resolution (SR) aims to reconstruct high-resolution images from low-resolution inputs, with deep learning advancements driving substantial improvements in SR performance. This paper presents a comprehensive review of single- and multi-image SR techniques, analyzing findings from 12,873 research papers published between 2015 and 2025 in the co...

This study investigates the effectiveness of Google Educational Apps in enhancing self-efficacy among education technology students at Minia University. Utilizing a quasi-experimental design, the research involved 28 graduate students from the Special Diploma in Education Technology, who engaged with an educational website developed on Google Sites...

This study investigates the potential of deep learning and computer vision for automated plant disease detection and classification. This study presents a novel approach to plant disease detection and classification using a modified YOLOv11 architecture. The methodology incorporates a custom classification head, dynamic validation splitting, and co...

Efficient cooling systems are critical for maximizing the electrical efficiency of Photovoltaic (PV) solar panels. However, conventional temperature probes often fail to capture the spatial variability in thermal patterns across panels, impeding accurate assessment of cooling system performance. Existing methods for quantifying cooling efficiency l...

Digital transformation has become a critical aspect of modern education, necessitating the development of digital skills among all students, including those with disabilities. Among these, blind students face unique challenges in acquiring the digital competencies needed for academic success and professional integration. This study aimed to enhance...

Non-specific shoulder pain is a common musculoskeletal condition, especially among college students, and it can have a negative impact on the patient’s life. Therapists have used scapular stabilization exercises (SSE) to enhance scapular control and mobility. This study investigates the prediction of the impact of scapular stability exercises in tr...

Cardiovascular diseases (CVD) have been found to be prevalent in society, frequently ending in death. According to the findings of a recent survey, the mortality rate is increasing due to the prevalence of adult cigarette consumption, elevated blood pressure, high cholesterol levels, and obesity. The previously mentioned causes are exacerbating the...

Background: The global healthcare system faces challenges in diagnosing and managing lung and colon cancers, which are significant health burdens. Traditional diagnostic methods are inefficient and prone to errors, while data privacy and security concerns persist. Objective: This study aims to develop a secure and transparent framework for remote c...

Background Hepatocellular Carcinoma (HCC) is a highly aggressive, prevalent, and deadly type of liver cancer. With the advent of deep learning techniques, significant advancements have been made in simplifying and optimizing the feature selection process. Objective Our scoping review presents an overview of the various deep learning models and alg...

This study introduces a novel deep learning-based approach for classifying poultry audio signals, incorporating a custom Burn Layer to enhance model robustness. The methodology integrates digital audio signal processing, convolutional neural networks (CNNs), and the innovative Burn Layer, which injects controlled random noise during training to rei...

This paper presents an analysis of trunk movement in women with postnatal low back pain using machine learning techniques. The study aims to identify the most important features related to low back pain and to develop accurate models for predicting low back pain. Machine learning approaches showed promise for analyzing biomechanical factors related...

This study aimed to investigate the impact of the Edmodo mobile learning environment on promoting psychological security among university students with visual impairments, at both the undergraduate and postgraduate levels. The researchers employed a combination of descriptive and quasiexperimental methodologies. The primary study sample consisted o...

Hepatocellular carcinoma (HCC) is a highly prevalent form of liver cancer that necessitates accurate prediction models for early diagnosis and effective treatment. Machine learning algorithms have demonstrated promising results in various medical domains, including cancer prediction. In this study, we propose a comprehensive approach for HCC predic...

Introduction Precise semantic segmentation of microbial alterations is paramount for their evaluation and treatment. This study focuses on harnessing the SegFormer segmentation model for precise semantic segmentation of strawberry diseases, aiming to improve disease detection accuracy under natural acquisition conditions. Methods Three distinct Mi...

This study investigates the application of cavitation in non-invasive abdominal fat reduction and body contouring, a topic of considerable interest in the medical and aesthetic fields. We explore the potential of cavitation to alter abdominal fat composition and delve into the optimization of fat prediction models using advanced hyperparameter opti...

This study presents an in-depth analysis of gasoline price forecasting using the adaptive network-based fuzzy inference system (ANFIS), with an emphasis on its implications for policy-making and strategic decisions in the energy sector. The model leverages a comprehensive dataset from the U.S. Energy Information Administration, spanning over 30 yea...

This study investigates the effectiveness of various deep learning and classical machine learning techniques in identifying instances of cyberbullying. The study compares the performance of five classical machine learning algorithms and three deep learning models. The data undergoes pre-processing, including text cleaning, tokenization, stemming, a...

This paper presents a novel deep learning model called Self-Attention Layer within a Convolutional Neural Network (SACNN), specifically designed for detecting acoustic data in extensive datasets containing emergency vehicle sirens and road noise. SACNN leverages a combination of EfficientNet and One Dimensional Convolu-tional Neural Networks (1D_CN...

The purpose of this study is to investigate the role of core muscles in female sexual dysfunction (FSD) and develop comprehensive rehabilitation programs to address this issue. We aim to answer the following research questions: what are the roles of core muscles in FSD, and how can machine and deep learning models accurately predict changes in core...

Combination therapy is a fundamental strategy in cancer chemotherapy. It involves administering two or more anti-cancer agents to increase efficacy and overcome multidrug resistance compared to monotherapy. However, drug combinations can exhibit synergy, additivity, or antagonism. This study presents a machine learning framework to classify and pre...

This paper investigated the use of language models and deep learning techniques for automating disease prediction from symptoms. Specifically, we explored the use of two Medical Concept Normalization—Bidirectional Encoder Representations from Transformers (MCN-BERT) models and a Bidirectional Long Short-Term Memory (BiLSTM) model, each optimized wi...

Epilepsy is a widespread neurological disorder characterized by recurring seizures that have a significant impact on individuals' lives. Accurately recognizing epileptic seizures is crucial for proper diagnosis and treatment. Deep learning models have shown promise in improving seizure recognition accuracy. However, optimizing their performance for...

Cyberbullying detection systems rely increasingly on machine learning techniques. However, class imbalance in cyberbullying datasets, where the percentage of normal labeled classes is higher than that of abnormal labeled ones, presents a significant challenge for classification algorithms. This issue is particularly problematic in two-class dataset...

Blockchain is a revolutionary technology that has the potential to revolutionize various industries, including finance, supply chain management, healthcare, and education. Its decentralized, secure, and transparent nature makes it ideal for use in industries where trust, security, and efficiency are of paramount importance. The integration of block...

Social media platforms have become the primary means of communication and information sharing, facilitating interactive exchanges among users. Unfortunately, these platforms also witness the dissemination of inappropriate and toxic content, including hate speech and insults. While significant efforts have been made to classify toxic content in the...

Urinary incontinence (UI) is defined as any uncontrolled urine leakage. Pelvic floor muscles (PFM) appear to be a crucial aspect of trunk and lumbo-pelvic stability, and UI is one indication of pelvic floor dysfunction. The evaluation of pelvic tilt and lumbar angle is critical in assessing the alignment and posture of the spine in the lower back r...

With the increasing amount of digital data generated by Arabic speakers, the need for effective and efficient document classification techniques is more important than ever. In recent years, both quantum computing and machine learning have shown great promise in the field of document classification. However, there is a lack of research investigatin...

Monkeypox is a rare viral disease that can cause severe illness in humans, presenting with skin lesions and rashes. However, accurately diagnosing monkeypox based on visual inspection of the lesions can be challenging and time-consuming, especially in resource-limited settings where laboratory tests may not be available. In recent years, deep learn...

Since cyberbullying impacts both individual victims and entire society, research on abusive language and its detection has attracted attention in recent years. Because social media sites like Facebook, Instagram, Twitter, and others are so widely accessible, hate speech, bullying, sexism, racism, aggressive material, harassment, poisonous comments,...

Face masks are widely used in various industries and jobs, such as healthcare, food service, construction, manufacturing, retail, hospitality, transportation, education, and public safety. Masked face recognition is essential to accurately identify and authenticate individuals wearing masks. Masked face recognition has emerged as a vital technology...

The study proposes a novel model for DNA sequence classification that combines machine learning methods and a pattern-matching algorithm. This model aims to effectively categorize DNA sequences based on their features and enhance the accuracy and efficiency of DNA sequence classification. The performance of the proposed model is evaluated using var...

The feature selection problem is a significant challenge in pattern recognition, especially for classification tasks. The quality of the selected features plays a critical role in building effective models, and poor-quality data can make this process more difficult. This work explores the use of association analysis in data mining to select meaning...

Prediction and classification of diseases are essential in medical science, as it attempts to immune the spread of the disease and discover the infected regions from the early stages. Machine learning (ML) approaches are commonly used for predicting and classifying diseases that are precisely utilized as an efficient tool for doctors and specialist...

Update3_Webometrics Egyptian Universities Rank 2010_2023

With the emergence of the covid 19 pandemic, E-learning usage was the only way to solve the problem of study interruption in educational institutions and universities. Therefore, this field reserved significant attention in current times. In this paper, we used ten Machine Learning (ML) algorithms: Decision Tree(DT), Random Forest(RF), Logistic Reg...

The Earthquake is an essential problem in human life, by using machine learning techniques in earthquake prediction, we can save humankind. Using the successful application of machine learning techniques indicates that it would be possible to use them to make accurate forecasts to avoid short-term earthquake damage. In this paper, with the first ai...

Pattern matching is a highly useful procedure in several stages of the computational pipelines. Furthermore, some research trends in this research domain contributed to growing biological databases and updated them throughout time. This article proposes an comparison and analysis of different algorithms for match equivalent pattern matching like co...

Feature selection is one of the major issues in pattern recognition. The quality of selected features is important for classification as the low‐quality data can degrade the model construction performance. Due to the difficulty of dealing with the problem that selected features always contain redundant information, this article focuses on the assoc...

Update2_Webometrics Egyptian Universities Rank 2010_2022

At numerous phases of the computational process, pattern matching is essential. It enables users to search for specific DNA subsequences or DNA sequences in a database. In addition, some of these rapidly expanding biological databases are updated on a regular basis. Pattern searches can be improved by using high-speed pattern matching algorithms. R...

Smart Health care

With the emergence of the covid 19 pandemic, E-learning usage was the only way to solve the problem of study interruption in educational institutions and universities. Therefore, this field reserved significant attention in current times. In this paper, we used ten Machine Learning (ML) algorithms: Decision Tree(DT), Random Forest(RF), Logistic Reg...

Machine learning plays an increasingly significant role in the building of Cyberbullying detection systems. In cyberbullying datasets, the percentage of normal labeled classes is higher than the percentage of abnormal labeled ones, which is called as class imbalance problem in data mining. Class imbalance is a challenging problem in classification,...

In recent years, research on abusive language and cyberbullying detection have gained a great deal of interest since it affects both individual victims and societies. Hateful communications, bullying, sexism, racism, aggressive content, harassment, toxic comments, and other forms of abuse have all increased dramatically due to the ease of access to...

With the emergence of the covid 19 pandemic, E-learning usage was the only way to solve the problem of study interruption in educational institutions and universities. Therefore, this field has garnered significant attention in recent times. In this paper, we used ten machine-learning algorithms (Logistic Regression, Decision Tree, Random Forest, S...

Prediction and classification of the diseases are essential in medical science, as it attempts to immune the spread of the disease and discover the infected regions from the early stages. Machine Learning (ML) approaches are commonly used for predicting and classifying the diseases that precisely utilized as an efficient tool for the doctors and sp...

Humanity development through education is an important method of sustainable development. This guarantees community development at present time without any negative effects in the future and also provides prosperity for future generations. E-learning is a natural development of the educational tools in this era and current circumstances. Thanks to...

Feature selection is one of the major issues in pattern recognition. The quality of selected features is important for classification as the low-quality data can degrade the model construction performance. Due to the difficulty of dealing with the problem that selected features always contain redundant information, this paper focuses on the associa...

As a key player in today's world, online social networks are emerging, providing a platform for expression and content distribution. This technology enables users to communicate easily with each other and share their data instantly. However, the internet isn't generally protected; it can be a source for abusive and harmful content and causing harm...

The increasing deployment of public wireless access points (hotspots) and the rise of wireless computing devices such as tablets and mobiles have made it easier for people to access information on the Internet. Though convenient, such networks have potential security and privacy risks. However, most users are neglecting the privacy threats because...

Online Social Networks (OSNs) are the most popular interactive media for communicating, posting, and sharing indefinite amounts of personal information. However, along with interesting and attractive topics and contents, some users neither like the fact that certain topics that are not among their interests can fill their personal pages nor do they...

Online social networks (OSNs) are now the most common and interactive media for many users. Most people engaged with social media without stopping to think about these networks’ effects on our lives, whether positive or negative. However, along with useful and exciting content, sometimes unsuitable or abusive content can be published on these netwo...

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Dimensionality reduction is an interesting area of research in data mining. An effective way to reduce dimensions is feature selection that removes irrelevant information meanwhile helping to understand the learning model better and improving prediction accuracy. In this paper, we face a challenge of filter methods to determine number of significan...

Nowadays, Online Social Networks (OSNs) are the most popular and interactive media that used to express feelings, communicate and share information between people. However, along with useful and interesting content, sometimes unsuitable or abusive content can be published on these networks, such as hate speech and insults. Hate speech includes any...