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Meta-heuristics for Feature Selection and Classification in Diagnostic Breast Cancer

Computers, Materials & Continua 2022年第10期73卷 749-765页

作者： Doaa Sami Khafaga Amel Ali Alhussan El-sayed M.El-kenawy Ali E.Takieldeen Tarek M.Hassan Ehab A.Hegazy Elsayed Abdel Fattah Eid Abdelhameed Ibrahim abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Communications and Computers Engineering Department Faculty of EngineeringDelta University for Science and TechnologyInternational Coastal RoadGamasa CityDakhliyaEgypt Faculty of Applied Health Science Technology Delta University for Science and TechnologyMansoura35712Egypt Faculty of Medicine Head of Internal Medicine DepartmentDelta University for Science and TechnologyMansouraEgypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

One of the most common kinds of cancer is breast *** early detection of it may help lower its overall rates of *** this paper,we robustly propose a novel approach for detecting and classifying breast cancer regions in thermal *** proposed approach starts with data preprocessing the input images and segmenting the significant regions of *** addition,to properly train the machine learning models,data augmentation is applied to increase the number of segmented regions using various scaling *** the other hand,to extract the relevant features from the breast cancer cases,a set of deep neural networks(VGGNet,ResNet-50,AlexNet,and GoogLeNet)are *** resulting set of features is processed using the binary dipper throated algorithm to select the most effective features that can realize high classification *** selected features are used to train a neural network to finally classify the thermal images of breast *** achieve accurate classification,the parameters of the employed neural network are optimized using the continuous dipper throated optimization *** results show the effectiveness of the proposed approach in classifying the breast cancer cases when compared to other recent approaches in the ***,several experiments were conducted to compare the performance of the proposed approach with the other *** results of these experiments emphasized the superiority of the proposed approach.

关键词： Breast cancer image segmentation dipper throated optimization feature selection meta-heuristics

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Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images

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Computers, Materials & Continua 2022年第11期73卷 4193-4210页

作者： Nagwan Abdel Samee El-sayed M.El-Kenawy Ghada Atteia Mona M.Jamjoom Abdelhameed Ibrahim abdelaziz A.Abdelhamid Noha E.El-Attar Tarek Gaber Adam Slowik Mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura 35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityEgypt School of Science Engineeringand EnvironmentUniversity of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailia41522Egypt Koszalin University of Technology Poland Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the *** of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of *** analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm *** lung region was segmented from the original chest X-ray images and augmented using various transformation ***,the augmented images were fed into the VGG19 deep network for feature *** the other hand,a feature selection method is proposed to select the most significant features that can boost the classification ***,the selected features were input into an optimized neural network for *** neural network is optimized using the proposed hybrid *** experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection *** addition,a deep statistical analysis is performed to study the performance and stability of the proposed *** results confirm the effectiveness and superiority of the proposed approach.

关键词： Covid-19 feature selection dipper throated optimization particle swarm optimization deep learning

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Improved Attentive Recurrent Network for Applied Linguistics-Based Offensive Speech Detection

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Computer Systems Science & Engineering 2023年第11期47卷 1691-1707页

作者： Manar Ahmed Hamza Hala J.Alshahrani Khaled Tarmissi Ayman Yafoz amira sayed A.Aziz Mohammad Mahzari Abu Sarwar Zamani Ishfaq Yaseen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Applied linguistics is one of the fields in the linguistics domain and deals with the practical applications of the language studies such as speech processing,language teaching,translation and speech *** ever-growing Online Social Networks(OSNs)experience a vital issue to confront,i.e.,hate *** the OSN-oriented security problems,the usage of offensive language is the most important threat that is prevalently found across the *** on the group targeted,the offensive language varies in terms of adult content,hate speech,racism,cyberbullying,abuse,trolling and *** these,hate speech is the most intimidating form of using offensive language in which the targeted groups or individuals are intimidated with the intent of creating harm,social chaos or *** Learning(ML)techniques have recently been applied to recognize hate speech-related *** current research article introduces a Grasshopper Optimization with an Attentive Recurrent Network for Offensive Speech Detection(GOARN-OSD)model for social *** GOARNOSD technique integrates the concepts of DL and metaheuristic algorithms for detecting hate *** the presented GOARN-OSD technique,the primary stage involves the data pre-processing and word embedding ***,this study utilizes the Attentive Recurrent Network(ARN)model for hate speech recognition and *** last,the Grasshopper Optimization Algorithm(GOA)is exploited as a hyperparameter optimizer to boost the performance of the hate speech recognition *** depict the promising performance of the proposed GOARN-OSD method,a widespread experimental analysis was *** comparison study outcomes demonstrate the superior performance of the proposed GOARN-OSD model over other state-of-the-art approaches.

关键词： Applied linguistics hate speech offensive language natural language processing deep learning grasshopper optimization algorithm

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Deep Learning Methods Used in Remote Sensing Images: A Review

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Journal of Environmental & Earth Sciences 2023年第1期5卷 33-64页

作者： Ekram M.Rewhel Jianqiang Li Amal A.Hamed Hatem M.Keshk amira S.Mahmoud sayed A.sayed Ehab Samir Hind H.Zeyada sayed A.Mohamed Marwa S.Moustafa Ayman H.Nasr Ashraf K.Helmy School of Software Engineering Beijing University of Science and TechnologyBeijing100083China Data Reception Analysis and Receiving Station DivisionNational Authority for Remote Sensing and Space ScienceCairo1564Egypt

Undeniably,Deep Learning(DL)has rapidly eroded traditional machine learning in Remote Sensing(RS)and geoscience domains with applications such as scene understanding,material identification,extreme weather detection,oil spill identification,among many *** machine learning algorithms are given less and less attention in the era of big ***,a substantial amount of work aimed at developing image classification approaches based on the DL model’s success in computer *** number of relevant articles has nearly doubled every year since *** in remote sensing technology,as well as the rapidly expanding volume of publicly available satellite imagery on a worldwide scale,have opened up the possibilities for a wide range of modern ***,there are some challenges related to the availability of annotated data,the complex nature of data,and model parameterization,which strongly impact *** this article,a comprehensive review of the literature encompassing a broad spectrum of pioneer work in remote sensing image classification is presented including network architectures(vintage Convolutional Neural Network,CNN;Fully Convolutional Networks,FCN;encoder-decoder,recurrent networks;attention models,and generative adversarial models).The characteristics,capabilities,and limitations of current DL models were examined,and potential research directions were discussed.

关键词： Deep Learning(DL) Satellite imaging Image classification Segmentation and object detection

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Phytochemical and biological studies of Butia capitata *** cultivated in Egypt

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Asian Pacific Journal of Tropical Biomedicine 2014年第6期4卷 456-462页

作者： Nagwa Mohamed Ammar Mohammed Said Hefnawy Sahar Youssef Al-Okbi Doha Abdou Mohamed Nabil Khamis El-sayed amira Ahmed El-Anssary Tom Mabry Pharmacognosy Department National Research Centre Pharmacognosy Department Faculty of PharmacyCairo University Food Sciences and Nutrition Department National Research Centre Tanning Materials and Protein Chemistry National Research Centre Molecular Cell and Developmental Biology The School of Biological ScienceBotany DepartmentTaxas State University

Objective:To study the antioxidant and anti-inflammatory activity of Butia capitata(***)leaf extracts along with phytochemical analysis of the proposed bioactive ***:Different successive extracts of *** *** were prepared with selective organic solvents and screened for their anti-inflammatory activities in tested animals and invitro antioxidant *** extensive phytochemical investigation of the bioactive extracts through paper chromatography,thin layer chromatography,column chromatography,gasliquid chromatography(GLC),high pressure liquid chromatography and spectral ***,ultraviolet,hydrogen and carbon nuclear magnetic resonance,electron ionization-mass spectrometry,heteronuclear multiple bond correlation and heteronuclear multiple quantum correlation were carried out Results:Results showed that different extracts possess promising antioxidant effect and significant anti-inflammatory activity with variable *** results of the phytochemical investigation of the bioactive extracts revealed the presence of volatile substances,lipoidal matter,α-tocopherol,free sugars,polysaccharides and flavonoidal ***:*** leaf extracts were shown to possess variable antioxidant effect,the most promising was methanol extract Both polar and non polar extracts were proved to have anti-inflammatory activity,the non polar extract was superior in this respect The bioactivity of the extracts was ascribed to the presence of flavonoids,sterols andα-tocopherol.

关键词： Butia capitata leaves antioxidant effect anti-inflammatory activity bioactive constituents rats.

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miR-29a Promotes Lipid Droplet and Triglyceride Formation in HCV Infection by Inducing Expression of SREBP-1c and CAV1

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Journal of Clinical and Translational Hepatology 2016年第4期4卷 293-299页

作者： Mennatallah Mamdouh Mahdy Nada Magdy El-Ekiaby Rana Mahmoud Hashish Radwa Ayman Salah Rasha sayed Hanafi Hassan Mohamed El-Said Azzazy Ahmed Ihab abdelaziz Department of Pharmaceutical Chemistry German University in Cairo New Cairo City Egypt Department of Pharmacology and Toxicology German University in Cairo New Cairo City Egypt School of Medicine New Giza University Cairo Egypt Department of Pharmaceutical Biology German University in Cairo New Cairo City Egypt Department of Stem Cells and Regenerative Medicine Zewail City of Science and TechnologyGiza Egypt Department of Chemistry Novel Diagnostic & Therapeutics American University in Cairo New Cairo City Egypt

Aims:To examine the regulation of SREBP-1c and CAV1 by microRNA-29a (miR-29a) in cells infected with hepatitis C virus (HCV) in an attempt to control HCV-induced nonalcoholic fatty liver ***:In order to examine the manipulation of SREBP-1c and CAV1 by miR-29a,oleic acid (OA)-treated JFH-I-infected Huh-7 cells were *** was added 24 h post-transfection and gene expression was investigated by qRT-PCR at 48 h post *** functional impact of the observed alteration in SREBP-1c and CAV1 expression was analyzed by examining lipid droplet (LD) and triglyceride (TG) content at 72 h post-OA treatment using light microscopy and spectrophotometry,*** load was quantified by qRT-PCR at 72 h ***:OA treatment induced the expression of miR-29a and SREBP-1c,as compared to untreated *** miR-29a expression led to a significant up-regulation of SREBP-1c as well as CAV1 compared to mock untransfected *** expression of miR-29a resulted in a marked increase in LDs and their respective TGs,while miR-29a antagomirs decreased both the LD and TG content compared to mock untransfected ***,forcing the expression of miR-29a in JFH-1 HCV-infected Huh-7 cells resulted in 53% reduction in viral titers compared to mock untransfected Huh-7 ***:Inducing miR-29a expression significantly induces SREBP-1c and CAV1 expression,thereby increasing LDs as well as their respective ***,forcing the expression of miR-29a resulted in reduction of HCV RNA levels in Huh-7 cells.

关键词： Caveolin-1 HCV Lipid droplets MicroRNA-29a SREBP-1c

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Efficient Deep CNN Model for COVID-19 Classification

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Computers, Materials & Continua 2022年第3期70卷 4373-4391页

作者： Walid El-Shafai amira A.Mahmoud El-sayed M.El-Rabaie Taha E.Taha Osama F.Zahran Adel S.El-Fishawy Mohammed Abd-Elnaby Fathi E.Abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Coronavirus(COVID-19)infection was initially acknowledged as a global pandemic in Wuhan in *** Health Organization(WHO)stated that the COVID-19 is an epidemic that causes a 3.4%death *** X-Ray(CXR)and Computerized Tomography(CT)screening of infected persons are essential in diagnosis *** are numerous ways to identify positive COVID-19 *** of the fundamental ways is radiology imaging through CXR,or CT *** comparison of CT and CXR scans revealed that CT scans are more effective in the diagnosis process due to their high ***,automated classification techniques are required to facilitate the diagnosis *** Learning(DL)is an effective tool that can be utilized for detection and classification this type of medical *** deep Convolutional Neural Networks(CNNs)can learn and extract essential features from different medical image *** this paper,a CNN architecture for automated COVID-19 detection from CXR and CT images is *** activation functions as well as three optimizers are tested and compared for this *** proposed architecture is built from scratch and the COVID-19 image datasets are directly fed to train *** performance is tested and investigated on the CT and CXR *** activation functions:Tanh,Sigmoid,and ReLU are compared using a constant learning rate and different batch *** optimizers are studied with different batch sizes and a constant learning ***,a comparison between different combinations of activation functions and optimizers is presented,and the optimal configuration is ***,the main objective is to improve the detection accuracy of COVID-19 from CXR and CT images using DL by employing CNNs to classify medical COVID-19 images in an early *** proposed model achieves a classification accuracy of 91.67%on CXR image dataset,and a classification accuracy of 100%on CT dataset with training times of 58 min and 46 min on CXR an

关键词： COVID-19 image classification CNN DL activation functions optimizers

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Efficient Segmentation Approach for Different Medical Image Modalities

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Computers, Materials & Continua 2022年第11期73卷 3119-3135页

作者： Walid El-Shafai amira A.Mahmoud El-sayed M.El-Rabaie Taha E.Taha Osama F.Zahran Adel S.El-Fishawy Naglaa F.Soliman Amel A.Alhussan Fathi E.Abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

This paper presents a study of the segmentation of medical *** paper provides a solid introduction to image enhancement along with image segmentation *** the first step,the morphological operations are employed to ensure image detail protection and *** objective of using morphological operations is to remove the defects in the texture of the ***,the Fuzzy C-Means(FCM)clustering algorithm is used to modify membership function based only on the spatial neighbors instead of the distance between pixels within local spatial neighbors and cluster *** proposed technique is very simple to implement and significantly fast since it is not necessary to compute the distance between the neighboring pixels and the cluster *** is also efficient when dealing with noisy images because of its ability to efficiently improve the membership partition *** results are performed on different medical image ***(Us),X-ray(Mammogram),Computed Tomography(CT),Positron Emission Tomography(PET),and Magnetic Resonance(MR)images are the main medical image modalities used in this *** obtained results illustrate that the proposed technique can achieve good results with a short time and efficient image *** results on different image modalities show that the proposed technique can achieve segmentation accuracies of 98.83%,99.71%,99.83%,99.85%,and 99.74%for Us,Mammogram,CT,PET,and MRI images,respectively.

关键词： Image segmentation ultrasonic mammogram CT PET MRI morphological operations FCM active contours

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Hybrid Segmentation Approach for Different Medical Image Modalities

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Computers, Materials & Continua 2022年第11期73卷 3454-3471页

作者： Walid El-Shafai amira A.Mahmoud El-sayed M.El-Rabaie Taha E.Taha Osama F.Zahran Adel S.El-Fishawy Naglaa F.Soliman Amel A.Alhussan Fathi E.Abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

The segmentation process requires separating the image region into sub-regions of similar *** sub-region has a group of pixels having the same characteristics,such as texture or *** paper suggests an efficient hybrid segmentation approach for different medical image modalities based on particle swarm optimization(PSO)and improved fast fuzzy C-means clustering(IFFCM)*** extensive comparative study on different medical images is presented between the proposed approach and other different previous segmentation *** existing medical image segmentation techniques incorporate clustering,thresholding,graph-based,edge-based,active contour,region-based,and watershed *** paper extensively analyzes and summarizes the comparative investigation of these ***,a prediction of the improvement involves the combination of these techniques is *** obtained results demonstrate that the proposed hybrid medical image segmentation approach provides superior outcomes in terms of the examined evaluation metrics compared to the preceding segmentation techniques.

关键词： Image segmentation ultrasonic images X-ray images CT images PET images MR images fuzzy c-mean morphological operations active contour

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Cross-Sectional Study of the Prevalence of Obesity Among Adults in Constantine

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Journal of Life Sciences 2012年第8期6卷 858-863页

作者： Souhai'la Dalichaouch-Benchaoui Leila Rouabah Nourredine Abadi amira sayed Fethi Tebbani Abdelkader Rouabah Laboratory of Molecular Biology and Genetic CHU Constantine 25000 Algeria Laboratory of Cellular and Molecular Biology University of Mentouri Constantine CHU Constantine 25000 Algeria

In Algeria, as in all countries in the developing world, obesity has become more and more common in adults, suggesting a worsening of risk factors for cardiovascular disease and diabetes for these generations. This study aims to assess the prevalence of obesity by anthropometry in adults in the city of Constantine over the year 2010-2011, and to explore its relationship with certain determining factors. A cross-sectional study with cluster sampling and stratification on sex allowed us to estimate the frequency of all categories of BMI. For this purpose, 320 adults living in Constantine were interviewed. In 2011, the prevalence of obesity was 35.84%. Women are more affected by obesity than men （40.83% vs. 22.08%）. The prevalence of abdominal obesity, according to IDF, was 81.0% for women vs. 56.2% for men; according to NCEP ATP III, it was 64.3% in women vs. 35.9% in men. Obesity is more common in families of low socioeconomic status. Similarly, level of education and obesity are inversely correlated. People who have a sedentary lifestyle are more likely to be obese than those who are physically active. Obesity is a serious condition that results in significant health care costs. There is a need to undertake epidemiological surveillance in childhood. Urgent preventive actions are required. Promotion of a healthy balanced diet and physical exercise is a priority in the prevention of obesity.

关键词： Obesity. prevalence adults socio-economic factors socio-cultural factors.

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