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BERT-CNN: A Deep Learning Model for Detecting Emotions from Text

computers, Materials & Continua 2022年第5期71卷 2943-2961页

作者： Ahmed R.Abas Ibrahim Elhenawy Mahinda Zidan Mahmoud Othman Department of Computer Science Faculty of Computer and InformaticsZagazig UniversityZagazig44519Egypt Department of Computer Science Faculty of Computers and Information TechnologyFuture University in Egypt11835Egypt

Due to the widespread usage of social media in our recent daily lifestyles,sentiment analysis becomes an important field in pattern recognition and Natural Language Processing(NLP).In this field,users’feedback data on a specific issue are evaluated and *** emotions within the text is therefore considered one of the important challenges of the current NLP *** have been widely studied in psychology and behavioral science as they are an integral part of the human *** describe a state of mind of distinct behaviors,feelings,thoughts and *** main objective of this paper is to propose a new model named BERT-CNN to detect emotions from *** model is formed by a combination of the Bidirectional Encoder Representations from Transformer(BERT)and the Convolutional Neural networks(CNN)for textual *** model embraces the BERT to train the word semantic representation language *** to the word context,the semantic vector is dynamically generated and then placed into the CNN to predict the *** of a comparative study proved that the BERT-CNN model overcomes the state-of-art baseline performance produced by different models in the literature using the semeval 2019 task3 dataset and ISEAR *** BERTCNN model achieves an accuracy of 94.7%and an F1-score of 94%for semeval2019 task3 dataset and an accuracy of 75.8%and an F1-score of 76%for ISEAR dataset.

关键词： BERT-CNN deep learning emotion detection semeval2019 text classification

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Leveraging EEG Signals and Machine Learning for Schizophrenia Classification 6

Leveraging EEG Signals and Machine Learning for Schizophreni...

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6th International Conference on Computing and informatics, ICCI 2024

作者： Elfarsy, Abdelrahman El-Metwally, Sara Medical Informatics Program Faculty of Computers and Information Mansoura University Mansoura Egypt Computer Science Department Faculty of Computers and Information Mansoura University Mansoura Egypt

ISBN: (纸本)9798350373875

Schizophrenia is a neurological disorder known for its potential to disrupt brain function and cause erratic behavior. Timely diagnosis and intervention are crucial for improving patient outcomes. This paper conducts a comprehensive comparative study of three machine learning models: Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) to classify EEG signals associated with schizophrenia. The dataset utilized in this study comprises EEG data obtained from 28 individuals, data preprocessing techniques, including artifact removal, filtering, and normalization, were applied to enhance data quality. A set of informative statistical features was extracted from the EEG signals to capture relevant information. The three machine learning models are trained and evaluated using various performance metrics, including accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). Random Forest achieved the highest accuracy (96%), while SVM demonstrated strong precision and recall (95%). These findings highlight the potential of machine learning in aiding early schizophrenia diagnosis through EEG signal analysis. © 2024 IEEE.

关键词： Support vector machines

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Early Parkinson's Disease Detection and Classification using Machine Learning and Neutrosophic Sets

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Neutrosophic Sets and Systems 2025年 82卷 873-886页

作者： Oudah, Hasan H. Metwaly, Ahmed A. Eassa, Mohamed Abdelhafeez, Ahmed Nagm, Ahmad M. Salama, Ahmed S. College of Arts Department of Journalism Ahl Al Bayt University Karbala Iraq Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Engineering and Electronics Cairo Higher Institute for Engineering Computer Science and Management New Cairo Egypt

Optimizing therapy and rehabilitation for Parkinson's disease (PD) requires early identification and precise evaluation of the illness's course. However, there is disagreement about the best way to use gait analysis to categorize the severity of motor symptoms and identify early-stage Parkinson's disease. The precision of machine learning (ML) models in identifying early and intermediate stages of Parkinson's disease was assessed in this study. Six ML models are used in this study for the prediction of PD. Different metrics are used to evaluate ML models such as accuracy, precision, recall, f1-score, and AUC score. Then we propose a multi-criteria decision-making methodology (MCDM) to evaluate the ML models and select the best one. Two MCDM methods are used such as CRITIC method to compute the criteria weights and the TOPSIS method to rank the alternatives. These methods are used under the bipolar neutrosophic sets (BNSs) to deal with uncertainty information. The results show the support vector machine is the best ML model for the prediction of PD. © 2025, University of New Mexico. All rights reserved.

关键词： Neurodegenerative diseases

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Residual Attention Deep SVDD for COVID-19 Diagnosis Using CT Scans

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computers, Materials & Continua 2023年第2期74卷 3333-3350页

作者： Akram Ali Alhadad Omar Tarawneh Reham R.Mostafa Hazem M.El-Bakry Information Systems Department Faculty of Computers and InformationMansoura UniversityMansoura35511Egypt Computer Science Department Ibb UniversityIbbYemen SE Department Faculty of Computer Sciences and InformaticsAmman Arab UniversityAmman11953Jordan

COVID-19 is the common name of the disease caused by the novel coronavirus(2019-nCoV)that appeared in Wuhan,China in *** the infected people is the most important factor in the fight against the *** gold-standard test to diagnose COVID-19 is polymerase chain reaction(PCR),but it takes 5–6 h and,in the early stages of infection,may produce false-negative *** Computed Tomography(CT)images to diagnose patients infected with COVID-19 has become an urgent *** this study,we propose a residual attention deep support vector data description SVDD(RADSVDD)approach to diagnose *** is a novel approach combining residual attention with deep support vector data description(DSVDD)to classify the CT *** the best of our knowledge,we are the first to combine residual attention with DSVDD in general,and specifically in the diagnosis of *** attention with DSVDD naively may cause model *** in the proposed RADSVDD guides the network during training and enables quick learning,residual connectivity prevents vanishing *** approach consists of three models,each model is devoted to recognizing one certain disease and classifying other diseases as *** models learn in an end-to-end *** proposed approach attained high performance in classifying CT images into intact,COVID-19,and non-COVID-19 *** evaluate the proposed approach,we created a dataset from published datasets and had it assessed by an experienced *** proposed approach achieved high performance,with the normal model attained sensitivity(0.96–0.98),specificity(0.97–0.99),F1-score(0.97–0.98),and area under the receiver operator curve(AUC)0.99;the COVID-19 model attained sensitivity(0.97–0.98),specificity(0.97–0.99),F1-score(0.97–0.99),and AUC 0.99;and the non-COVID pneumoniamodel attained sensitivity(0.97–1),specificity(0.98–0.99),F1-score(0.97–0.99),and AUC 0.99.

关键词： Deep learning deep SVDD residual attention anomaly detection COVID-19 Coronavirus

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ELCD-NSC2: a novel early lung cancer detection and non-small cell classification framework

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Neural Computing and Applications 2024年第24期36卷 15149-15164页

作者： Helaly, Hadeer A. Badawy, Mahmoud El-Gendy, Eman M. Haikal, Amira Y. Electrical Engineering Department Faculty of Engineering Damietta University Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Computer Science and Informatics Taibah University Medina Saudi Arabia

The survival rate of lung cancer relies significantly on how far the disease has spread when it is detected, how it reacts to the treatment, the patient’s overall health, and other factors. Therefore, the earlier the lung cancer diagnosis, the higher the survival rate. For radiologists, recognizing malignant lung nodules from computed tomography (CT) scans is a challenging and time-consuming process. As a result, computer-aided diagnosis (CAD) systems have been suggested to alleviate these burdens. Deep-learning approaches have demonstrated remarkable results in recent years, surpassing traditional methods in different fields. Researchers are currently experimenting with several deep-learning strategies to increase the effectiveness of CAD systems in lung cancer detection with CT. This work proposes a deep-learning framework for detecting and diagnosing lung cancer. The proposed framework used recent deep-learning techniques in all its layers. The autoencoder technique structure is tuned and used in the preprocessing stage to denoise and reconstruct the medical lung cancer dataset. Besides, it depends on the transfer learning pre-trained models to make multi-classification among different lung cancer cases such as benign, adenocarcinoma, and squamous cell carcinoma. The proposed model provides high performance while recognizing and differentiating between two types of datasets, including biopsy and CT scans. The Cancer Imaging Archive and Kaggle datasets are utilized to train and test the proposed model. The empirical results show that the proposed framework performs well according to various performance metrics. According to accuracy, precision, recall, F1-score, and AUC metrics, it achieves 99.60, 99.61, 99.62, 99.70, and 99.75%, respectively. Also, it depicts 0.0028, 0.0026, and 0.0507 in mean absolute error, mean squared error, and root mean square error metrics. Furthermore, it helps physicians effectively diagnose lung cancer in its early stages and allows spe

关键词： Image classification

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Logistic Services in Hajj: Survey 1

Logistic Services in Hajj: Survey

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1st International Conference on Logistics, ICL 2024

作者： Amin, Reham Elsherif, Samiha M. Suez Canal University Faculty of Computers and Informatics Computer Sciences Department Ismailia Egypt Suez Canal University Faculty of Computers and Informatics Information Systems Department Ismailia Egypt

ISBN: (纸本)9798331518844

The Hajj (or pilgrimage to Mecca) is one of the most well-known of these international mass-gathering occasions, that takes place every year furthermore attracts Muslim pilgrims who go to distinct locations on a pre-determined schedule to execute a set of specific Islamic rituals. Due to its broad international attendance, the Hajj has been subjected to numerous recorded hazards, whether security-related, health-related, or otherwise. These risks have been attributed to variables such as transportation, human error, interstate tensions, and miscommunication. Healthcare, accommodation, security threats, crowd management, waste management, transportation, and other logistic services are related to Hajj services, which became an essential requirement during the Hajj season for pilgrims and Umrah performers in Saudi Arabia. This survey paper has reviewed the literature that studies logistic services during Hajj according to seven main branches. The survey is deemed unique and vital since it affects the pilgrimage life cycle, i.e. pilgrims who try to be chosen to undertake Islamic pilgrimage rites annually. In the paper's conclusion, new research practical directions are recommended supported by a comprehensive overview of recent technologies and an analytical survey. © 2024 IEEE.

关键词： Waste management

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Fusion Strategy for Improving Medical Image Segmentation

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computers, Materials & Continua 2023年第2期74卷 3627-3646页

作者： Fahad Alraddady E.A.Zanaty Aida HAbu bakr Walaa M.Abd-Elhafiez Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Information System Department Faculty of Computers and InformationSohag UniversitySohagEgypt Mathematical and Computer Science Department Faculty of ScienceAswan UniversityAswanEgypt College of Computer Science&Information Technology Jazan UniversityJazanKingdom of Saudi Arabia Computer Science Department Faculty of Computers and Artificial IntelligenceSohag UniversitySohagEgypt

In this paper,we combine decision fusion methods with four metaheuristic algorithms(Particle Swarm Optimization(PSO)algorithm,Cuckoo search algorithm,modification of Cuckoo Search(CS McCulloch)algorithm and Genetic algorithm)in order to improve the image *** proposed technique based on fusing the data from Particle Swarm Optimization(PSO),Cuckoo search,modification of Cuckoo Search(CS McCulloch)and Genetic algorithms are obtained for improving magnetic resonance images(MRIs)*** algorithms are used to compute the accuracy of each method while the outputs are passed to fusion *** order to obtain parts of the points that determine similar membership values,we apply the different rules of incorporation for these *** proposed approach is applied to challenging applications:MRI images,gray matter/white matter of brain segmentations and original black/white images Behavior of the proposed algorithm is provided by applying to different medical *** is shown that the proposed method gives accurate results;due to the decision fusion produces the greatest improvement in classification accuracy.

关键词： Decision fusion particle swarmoptimization(PSO) cuckoo search algorithm CS McCulloch algorithm genetic algorithm CT and MRI

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Copy-Right protection of color videos using robust watermarking based geometrically invariant moments of fractional orders and logistic sine cosine chaotic map

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Multimedia Tools and Applications 2024年 1-26页

作者： Alsahafi, Yousef S. Darwish, Mohamed M. Hosny, Khalid M. Department of Information Technology Khulis College University of Jeddah Jeddah Saudi Arabia Department of Computer Science Faculty of Computers and Information Assiut University Assiut Egypt Department of Computer Science University College of Al Wajh University of Tabuk Tabuk Saudi Arabia Department of Information Technology Faculty of Computers and Informatics Zagazig University Zagazig Egypt

Digital video watermarking research has yielded numerous promising breakthroughs in recent years. As more people grow interested in videos, the copyright protection of videos has become an urgent issue that must be addressed. Digital video watermarking is a significant method for copyright protection of videos because of the growing need to safeguard video data’s intellectual property. Some existing works cannot resist geometric attacks effectively, while others fail to address the balance between robustness and visual imperceptibility. Significant progress has been made in studying fractional orthogonal moments due to their geometric invariance and beneficial image description capabilities. For these reasons, this paper proposes a robust watermarking algorithm for color video using accurate quaternion fractional Gegenbauer moments (QFrGMs), which are constructed relying on a combination of quaternion theory and accurate fractional Gegenbauer moments (FrGMs). The proposed algorithm is designed to address existing deficiencies in some related works and improve resilience. This algorithm is imperceptibly invisible and resistant to various kinds of attacks. In the proposed algorithm, the watermark information is embedded into accurately selected coefficients of QFrGMs for the selected frame of the cover video. This algorithm is robust to geometric attacks due to the outstanding geometric invariance of QFrGMs. To ensure security and improve the suggested algorithm’s security, we employed a chaotic map termed a one-dimensional Logistic Sine Cosine (LSC) map with superior chaotic characteristics to scramble the watermark. Numerical experiments were performed regarding imperceptibility and robustness to test the effectiveness. The experimental results demonstrated that the proposed method provides high visual quality and robustness against common signal processing, geometric, frame averaging, frame swapping, and frame dropping attacks. Furthermore, the proposed algorithm e

关键词： Image watermarking

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Evolutionary optimization of Yagi–Uda antenna design using grey wolf optimizer

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Neural Computing and Applications 2025年第6期37卷 4155-4183页

作者： Braik, Malik Sheta, Alaa Aljahdali, Sultan El-Hefnawi, Fatma Al-Hiary, Heba Elashmawi, Walaa H. Computer Science Department Al-Balqa Applied University Salt Jordan Department of Computer Science Southern Connecticut State University New HavenCT United States Computer Science Department Taif University Taif Saudi Arabia Microwave Department Electronics Research Institute Giza Egypt Department of Computer Information System Al-Balqa Applied University Salt Jordan Department of Computer Science Faculty of Computers & Informatics Suez Canal University Ismailia Egypt Department of Computer Science Faculty of Computer Science Misr International University Cairo Egypt

Meta-heuristic optimization algorithms have become widely used due to their outstanding features, such as gradient-free mechanisms, high flexibility, and great potential for avoiding local optimal solutions. This research explored the grey wolf optimizer (GWO) to find the ideal configuration for a six-element Yagi–Uda antenna. The GWO algorithm adjusted the lengths of the antenna wires and the spacings between them. The goal was to maximize the antenna’s ability to transmit signals (throughput gain). Optimal antenna selection relies on various parameters, including gain, bandwidth, impedance matching, frequency, side-lobe levels, etc. The optimization of a six-element Yagi–Uda antenna presents a challenging engineering design problem due to its multimodal and nonlinear nature. Achieving optimal performance hinges on the intricate interplay between the lengths of the constituent elements and the spacing configurations. To this end, a multiobjective function was adopted to design this antenna. The performance of several meta-heuristic algorithms, including genetic algorithms, biogeography-based optimization, simulated annealing, and grey wolf optimizer, was compared. The GWO-based approach has performed better than its competitors. This optimized antenna design based on GWO reported a gain of 14.21 decibel. Therefore, the GWO-based method optimizes antennas that can be further investigated for other antenna design problems. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Simulated annealing

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On thermoelastic problem based on four theories with the efficiency of the magnetic field and gravity

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Journal of Ocean Engineering and science 2024年第4期9卷 338-347页

作者： A.A.Kilany A.N.Abd-alla A.M.Abd-Alla S.M.Abo-Dahab Department of Mathematics Faculty of ScienceSohag UniversitySohag 82524 Egypt Department of Computer Science Faculty of Computers and InformationLuxor UniversityEgypt Department of mathematics Faculty of ScienceSouth Valley UniversityQena 83523Egypt

The fundamental objective of this paper is to study the effectiveness of magnetic field and gravity on an isotropic homogeneous thermoelastic structure based on four theories of generalized *** another meaning,the models of coupled dynamic theory(CDT),Lord-Shulman(LS),Green-Lindsay(GL)as well as Green-Naghdi(GN II)will be taken in the ***,applying the harmonic method(normal mode technique),the solution of the governing equations and the expressions for the components of the displacement,temperature and(Mechanical and Maxwell’s)stresses is taken into account and calculated *** impacts of the gravity and magnetic field are illustrated graphically which are pronounced on the different physical ***,the results of some research that others have previously obtained may be found some or all of them as special cases from this study.

关键词： Generalized thermoelasticity Gravity Magnetic field Green and Naghdi TheoryⅡ Normal mode approach

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