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Applied Linguistics with Mixed Leader Optimizer Based English Text Summarization Model

Intelligent Automation & Soft Computing 2023年第6期36卷 3203-3219页

作者： Hala J.Alshahrani Khaled Tarmissi Ayman Yafoz Abdullah Mohamed Manar ahmed Hamza Ishfaq Yaseen abu Sarwar Zamani Mohammad Mahzari 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 UniversitySaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The term‘executed linguistics’corresponds to an interdisciplinary domain in which the solutions are identified and provided for real-time language-related *** exponential generation of text data on the Internet must be leveraged to gain knowledgeable *** extraction of meaningful insights from text data is crucial since it can provide value-added solutions for business organizations and *** Automatic Text Summarization(ATS)process reduces the primary size of the text without losing any basic components of the *** current study introduces an Applied Linguistics-based English Text Summarization using a Mixed Leader-Based Optimizer with Deep Learning(ALTS-MLODL)*** presented ALTS-MLODL technique aims to summarize the text documents in the English *** accomplish this objective,the proposed ALTS-MLODL technique pre-processes the input documents and primarily extracts a set of ***,the MLO algorithm is used for the effectual selection of the extracted *** the text summarization process,the Cascaded Recurrent Neural Network(CRNN)model is exploited whereas the Whale Optimization Algorithm(WOA)is used as a hyperparameter *** exploitation of the MLO-based feature selection and the WOA-based hyper-parameter tuning enhanced the summarization *** validate the perfor-mance of the ALTS-MLODL technique,numerous simulation analyses were *** experimental results signify the superiority of the proposed ALTS-MLODL technique over other approaches.

关键词： Text summarization deep learning hyperparameter tuning applied linguistics multi-leader optimizer

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Phishing Attacks Detection Using EnsembleMachine Learning Algorithms

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Computers, Materials & Continua 2024年第7期80卷 1325-1345页

作者： Nisreen Innab ahmed Abdelgader Fadol Osman Mohammed Awad Mohammed Ataelfadiel Marwan abu-Zanona Bassam Mohammad Elzaghmouri Farah H.Zawaideh Mouiad Fadeil Alawneh Department of Computer Science and Information Systems College of Applied SciencesAlmaarefa UniversityDiriyahRiyadh13713Saudi Arabia Applied College King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Science Faculty of Computer Science and Information TechnologyJerash UniversityJerash26110Jordan Department of Business Intelligence and Data Analysis Faculty of Financial Sciences and BusinessIrbid National UniversityIrbid21110Jordan Faculty of Information Technology Ajloun National UniversityAjloun26767Jordan

Phishing,an Internet fraudwhere individuals are deceived into revealing critical personal and account information,poses a significant risk to both consumers and web-based *** indicates a persistent rise in phishing ***,these fraudulent schemes are progressively becoming more intricate,thereby rendering them more challenging to ***,it is imperative to utilize sophisticated algorithms to address this *** learning is a highly effective approach for identifying and uncovering these harmful *** learning(ML)approaches can identify common characteristics in most phishing *** this paper,we propose an ensemble approach and compare it with six machine learning techniques to determine the type of website and whether it is normal or not based on two phishing *** that,we used the normalization technique on the dataset to transform the range of all the features into the same *** findings of this paper for all algorithms are as follows in the first dataset based on accuracy,precision,recall,and F1-score,respectively:Decision Tree(DT)(0.964,0.961,0.976,0.968),Random Forest(RF)(0.970,0.964,0.984,0.974),Gradient Boosting(GB)(0.960,0.959,0.971,0.965),XGBoost(XGB)(0.973,0.976,0.976,0.976),AdaBoost(0.934,0.934,0.950,0.942),Multi Layer Perceptron(MLP)(0.970,0.971,0.976,0.974)and Voting(0.978,0.975,0.987,0.981).So,the Voting classifier gave the best *** in the second dataset,all the algorithms gave the same results in four evaluation metrics,which indicates that each of them can effectively accomplish the prediction ***,this approach outperformed the previous work in detecting phishing websites with high accuracy,a lower false negative rate,a shorter prediction time,and a lower false positive rate.

关键词： Social engineering attacks phishing attacks machine learning security artificial intelligence

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Gaussian Optimized Deep Learning-based Belief Classification Model for Breast Cancer Detection

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Computers, Materials & Continua 2022年第11期73卷 4123-4138页

作者： Areej A.Malibari Marwa Obayya Mohamed K.Nour Amal S.Mehanna Manar ahmed Hamza abu Sarwar Zamani Ishfaq Yaseen Abdelwahed Motwakel Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

With the rapid increase of new cases with an increased mortality rate,cancer is considered the second and most deadly disease *** cancer is the most widely affected cancer worldwide,with an increased death rate *** to radiologists’processing of mammogram images,many computer-aided diagnoses have been developed to detect breast *** detection of breast cancer will reduce the death rate *** early diagnosis of breast cancer using the developed computer-aided diagnosis(CAD)systems still needed to be enhanced by incorporating innovative deep learning technologies to improve the accuracy and sensitivity of the detection system with a reduced false positive *** paper proposed an efficient and optimized deep learning-based feature selection approach with this *** model selects the relevant features from the mammogram images that can improve the accuracy of malignant detection and reduce the false alarm *** learning is used in the extraction of features *** ext,a convolution neural network,is used to extract the *** two feature vectors are fused and optimized with enhanced Butterfly Optimization with Gaussian function(TL-CNN-EBOG)to select the final most relevant *** optimized features are applied to the classifier called Deep belief network(DBN)to classify the benign and malignant *** feature extraction and classification process used two datasets,breast,and *** to the existing methods,the optimized deep learning-based model secured 98.6%of improved accuracy on the breast dataset and 98.85%of improved accuracy on the MIAS dataset.

关键词： Breast cancer detection computer-aided diagnosis(CAD) deep learning CNN entropy butterfly optimization

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Cytokine profile in Egyptian hepatitis C virus genotype-4 in relation to liver disease progression

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World Journal of Gastroenterology 2005年第42期11卷 6624-6630页

作者： Abdel-Rahman N Zekri Mohammed S El-Din Ashour ahmed Hassan Hanaa M Alam El-Din Amal MR El-Shehaby Maha A abu-Shady Virology and Immunology Unit Cancer Biology Department National Cancer Institute Cairo University Egypt Microbiology Department Faculty of Pharmacy Azhar University Egypt Microbiology and Immunology Department Sohag Faculty of Medicine South Valley University Egypt Virology and Immunology Unit Cancer Biology Department National Cancer Institute Cairo University Egypt Biochemistry Department El-Kaser El-Aini School of Medicine Cairo University Egypt

AIM： To observe the imbalance between T helper cell Th1 and Th2 cytokines in several chronic hepatitis disease at different stages of disease progression. METHODS： We measured the cytokine levels of Thl （IL-2 and IL-2R）, Th2 （IL-10） and the pro-inflammatory cytokines （IL-6 and IL-6R and TNF and TNF-RI and II） by the ELISA technique in the sera of 33 hepatocellular carcinoma （HCC） patients and 20 chronic liver disease （CLD） patients. In addition, 20 asymptomatic hepatitis C virus carriers and 20 healthy subjects negative for hepatitis C virus（HCV） markers served as controls. RESULTS： Anti-HCV antibodies were found to be positive in 94% of HCC cases and 75% of CLD cases. On the other hand, HCV viremia was detected using RT- PCR in 67% of HCC cases and 65% of CLD cases. HBsAg was positive in 9% of HCC cases and 30% of CLD cases. Also bilharziaI-Ab was positive in 55% of HCC cases, 65% of CLD cases and in 70% of asymptomatic carriers （ASC）. HCC patients had significantly higher values of IL- 2R, TNF-RII （P〈0.001）, and TNF-RI （P〉0.05）, but lower TNFα （P〈0.001） and IL-6 （P = 0.032） in comparison to ASC. But, in comparison to non-cancer controls, HCC patients had higher values of IL-2R, IL-6R, TNF-RI and TNF-RII, but lower TNF-α （P〈0.001）. CLD patients had higher IL-2R, TNF-RI, and TNF-RII （P〈0.001） than ASC. But, in comparison to non-cancer controls, CLD patients had higher values of IL-2R, TNF-RI and TNF-RII, but lower TNF-α （P〈0.001）. IL-10 was higher （though not significantly） in HCC and CLD patients than in symptomatic carriers and non-cancer controls. CONCLUSION： Liver disease progression from CLD to HCC due to HCV genotype-4 infection is associated with an imbalance between Th1 and Th2 cytokines. IL-2R, TNF-RI, and TNF-RII could be used as potential markers.

关键词： Cytokine HCV Genotype-4 Liver disease

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Energy Aware Secure Cyber-Physical Systems with Clustered Wireless Sensor Networks

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Computers, Materials & Continua 2022年第9期72卷 5499-5513页

作者： Masoud Alajmi Mohamed K.Nour Siwar Ben Haj Hassine Mimouna Abdullah Alkhonaini Manar ahmed Hamza Ishfaq Yaseen abu Sarwar Zamani Mohammed Rizwanullah Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recently,cyber physical system(CPS)has gained significant attention which mainly depends upon an effective collaboration with computation and physical *** greatly interrelated and united characteristics of CPS resulting in the development of cyber physical energy systems(CPES).At the same time,the rising ubiquity of wireless sensor networks(WSN)in several application areas makes it a vital part of the design of *** security and energy efficiency are the major challenging issues in CPES,this study offers an energy aware secure cyber physical systems with clustered wireless sensor networks using metaheuristic algorithms(EASCPSMA).The presented EASCPS-MA technique intends to attain lower energy utilization via clustering and security using intrusion *** EASCPSMA technique encompasses two main stages namely improved fruit fly optimization algorithm(IFFOA)based clustering and optimal deep stacked autoencoder(OSAE)based intrusion ***,the optimal selection of stacked autoencoder(SAE)parameters takes place using root mean square propagation(RMSProp)*** extensive performance validation of the EASCPS-MA technique takes place and the results are inspected under varying *** simulation results reported the improved effectiveness of the EASCPS-MA technique over other recent approaches interms of several measures.

关键词： Intrusion detection system metaheuristics stacked autoencoder deep learning cyber physical energy systems clustering wsn

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Optimal Kernel Extreme Learning Machine for COVID-19 Classification on Epidemiology Dataset

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Computers, Materials & Continua 2022年第11期73卷 3305-3318页

作者： Saud S.Alotaibi Amal Al-Rasheed Sami Althahabi Manar ahmed Hamza Abdullah Mohamed abu Sarwar Zamani Abdelwahed Motwakel Mohamed I.Eldesouki Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Research Centre Future University in EgyptNew Cairo11745Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Artificial Intelligence(AI)encompasses various domains such as Machine Learning(ML),Deep Learning(DL),and other cognitive technologies which have been widely applied in healthcare *** models are utilized in healthcare sector in which the machines are used to investigate and make decisions based on prediction and classification of input *** this motivation,the current study involves the design of Metaheuristic Optimization with Kernel Extreme Learning Machine for COVID-19 Prediction Model on Epidemiology Dataset,named MOKELM-CPED *** primary aim of the presented MOKELM-CPED model is to accomplish effectual COVID-19 classification outcomes using epidemiology *** the proposed MOKELM-CPED model,the data first undergoes pre-processing to transform the medical data into useful *** by,data classification process is performed by following Kernel Extreme Learning Machine(KELM)***,Symbiotic Organism Search(SOS)optimization algorithm is utilized to fine tune the KELM parameters which consequently helps in achieving high detection *** order to investigate the improved classifier outcomes of MOKELM-CPED model in an effectual manner,a comprehensive experimental analysis was conducted and the results were inspected under diverse *** outcome of the experiments infer the enhanced performance of the proposed method over recent approaches under distinct measures.

关键词： COVID-19 epidemiology dataset machine learning artificial intelligence metaheuristics healthcare

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Moringa oleifera leaf ethanol extract ameliorates lead-induced hepato-nephrotoxicity in rabbits

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Asian Pacific Journal of Tropical Biomedicine 2020年第6期10卷 263-272页

作者： Nancy B.Mohamed Amira H.Mohamed Nashwa A.abu-Aita Soad M.Nasr Somia A.Nassar Kawkab A.ahmed Directorate of Veterinary Medicine 3 Teraet El Zomor St.Nasr El DinEl haramGizaEgypt Department of Clinical Pathology Faculty of Veterinary MedicineCairo UniversityGiza12211Egypt Department of Parasitology&Animal Diseases National Research Centre33 Bohouth St.DokkiGiza12622Egypt Department of Laboratory Medical Sciences College of Applied Medical SciencesPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Pathology Faculty of Veterinary MedicineCairo UniversityGiza12211Egypt

Objective: To evaluate the effect of Moringa oleifera leaf ethanol extract as an adjunct treatment on lead acetate induced hepatonephrotoxicity in rabbits. Methods: Thirty-six male New Zealand White rabbits were assigned into two main groups. The first group(14 rabbits) served as normal control. The second group(22 rabbits) was administered orally with lead acetate at a dose of 40 mg/kg/day, 5 days/week for 8 weeks. At the 4 th and the 8 th week of treatment, 6 animals(3 animals at each period) of the second group were sacrificed while the remaining animals(16 rabbits) were assigned randomly into 2 subgroups(8 rabbits each): treated and non-treated. The first subgroup was orally given 1 m L phosphate-buffered saline for further 4 weeks while the second subgroup was administered orally with Moringa oleifera leaf ethanol extract at a dose of 400 mg/kg/day for the same period. Blood samples were collected to determine hematological and serum biochemical indices. Tissue specimens were collected from the liver and kidney for evaluation of the oxidant/antioxidant markers and for histopathological ***: Lead acetate exposure decreased the mean body weight gain, hematocrit, hemoglobin, mean corpuscular volume, and lymphocytes count. Moreover, it markedly increased counts of monocytes and platelets, serum enzyme activity, levels of creatinine, total cholesterol, triglycerides, and low-density lipoprotein cholesterol. Malondialdehyde level was markedly increased while the reduced glutathione content was significantly decreased in liver tissue of lead intoxicated-rabbits. Histopathological alterations were also noticed in the liver and kidney of lead intoxicated rabbits. Moringa oleifera leaf ethanol extract significantly improved hematological and serum biochemical parameters and histopathological structure of the liver and kidney. Conclusions: Moringa oleifera leaf ethanol extract ameliorates hemato-biochemical and histopathological alterations caused by lead

关键词： Lead toxicity Moringa oleifera Rabbits Liver functions Kidney functions Histopathology

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Design of Intelligent Alzheimer Disease Diagnosis Model on CIoT Environment

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Computers, Materials & Continua 2022年第6期71卷 5979-5994页

作者： Anwer Mustafa Hilal Fahd NAl-Wesabi Mohamed Tahar Ben Othman Khaled Mohamad Almustafa Nadhem Nemri Mesfer Al Duhayyim Manar ahmed Hamza abu Sarwar Zamani Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Computer Science King King Khalid UniversityMuhayel Aseer62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a61101Yemen Department of Computer Science College of ComputerQassim UniversityAl-Bukairiyah52571Saudi Arabia Department of Information Systems College of Computer and Information SystemsPrince Sultan UniversitySaudi Arabia Department of Information Systems King King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

Presently,cognitive Internet of Things(CIoT)with cloud computing(CC)enabled intelligent healthcare models are developed,which enables communication with intelligent devices,sensor modules,and other stakeholders in the healthcare sector to avail effective decision *** the other hand,Alzheimer disease(AD)is an advanced and degenerative illness which injures the brain cells,and its earlier detection is necessary for suitable interference by healthcare *** this aspect,this paper presents a new Oriented Features from Accelerated Segment Test(FAST)with Rotated Binary Robust Independent Elementary Features(BRIEF)Detector(ORB)with optimal artificial neural network(ORB-OANN)model for AD diagnosis and classification on the CIoT based smart healthcare *** initial pre-processing,bilateral filtering(BLF)based noise removal and region of interest(RoI)detection processes are carried *** addition,the ORBOANN model includes ORB based feature extractor and principal component analysis(PCA)based feature ***,artificial neural network(ANN)model is utilized as a classifier and the parameters of the ANN are optimally chosen by the use of salp swarm algorithm(SSA).A comprehensive experimental analysis of the ORB-OANN model is carried out on the benchmark database and the obtained results pointed out the promising outcome of the ORB-OANN technique in terms of different measures.

关键词： Cognitive internet of things machine learning parameter tuning alzheimer’s disease healthcare decision making

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Machine Learning Enabled e-Learner Non-Verbal Behavior Detection in IoT Environment

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Computers, Materials & Continua 2022年第7期72卷 679-693页

作者： Abdelzahir Abdelmaboud Fahd N.Al-Wesabi Mesfer Al Duhayyim Taiseer Abdalla Elfadil Eisa Manar ahmed Hamza Mohammed Rizwanullah abu Serwar Zamani Radwa Marzouk Department of Information Systems College of Science and ArtsKing Khalid UniversityMahayil AsirSaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Science and Arts-Girls SectionKing Khalid UniversityMahayil AsirSaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia&Department of MathematicsFaculty of ScienceCairo UniversityGiza12613Egypt

Internet of Things(IoT)with e-learning is widely employed to collect data from various smart devices and share it with other ones for efficient e-learning *** the same time,machine learning(ML)and data mining approaches are presented for accomplishing prediction and classification *** this motivation,this study focuses on the design of intelligent machine learning enabled e-learner non-verbal behaviour detection(IML-ELNVBD)in IoT *** proposed IML-ELNVBD technique allows the IoT devices such as audio sensors,cameras,*** are then connected to the cloud server for further *** addition,the modelling and extraction of behaviour take ***,extreme learning machine sparse autoencoder(ELM-SAE)model is employed for the detection and classification of non-verbal ***,the Ant Colony Optimization(ACO)algorithm is utilized to properly tune the weight and bias parameters involved in the ELM-SAE *** order to ensure the improved performance of the IML-ELNVBD model,a comprehensive simulation analysis is carried out and the results highlighted the betterment compared to the recent models.

关键词： Internet of things e-learning machine learning non-verbal behavior extreme learning machine classification parameter tuning

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A Joint Resource Allocation Algorithm for D2D Communication

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Computers, Materials & Continua 2022年第2期70卷 3751-3762页

作者： Abdul Kadir Hamid Lamia Osman Widaa Fahd N.Al-Wesabi Imran Khan Anwer Mustafa Hilal Manar ahmed Hamza abu Sarwar Zaman Mohammed Rizwanullah Department of Electrical&Computer Engineering University of SharjahSharjahUnited Arab Emirates Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerKSA&Faculty of Computer and ITSana’a UniversityYemen Department of Electrical Engineering University of Engineering and Technology PeshawarPakistan Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The emergence of multimedia services has meant a substantial increase in the number of devices in mobile networks and driving the demand for higher data transmission *** result is that,cellular networks must technically evolve to support such higher rates,to be equipped with greater capacity,and to increase the spectral and energy *** with 4G technology,the 5G networks are being designed to transmit up to 100 times more data volume with devices whose battery life is 10 times ***,this new generation of networks has adopted a heterogeneous and ultra-dense architecture,where different technological advances are combined such as device-to-device(D2D)communication,which is one of the key elements of 5G *** has immediate applications such as the distribution of traffic load(data offloading),communications for emergency services,and the extension of cellular coverage,*** this communication model,two devices can communicate directly if they are close to each other without using a base station or a remote access ***,eliminating the interference between theD2Dand cellular communication in the *** interference management has become a hot issue in current *** order to address this problem,this paper proposes a joint resource allocation algorithm based on the idea of mode selection and resource *** results showthat the proposed algorithm effectively improves the systemperformance and reduces the interference as compared with existing algorithms.

关键词： D2D communication resource allocation wireless networks mobile communication

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