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Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

Computer Systems Science & Engineering 2023年第5期45卷 1711-1726页

作者： Manar ahmed Hamza Aisha Hassan Abdalla Hashim Dalia H.Elkamchouchi Nadhem Nemri Jaber S.alzahrani Amira Sayed A.Aziz Mnahel ahmed Ibrahim Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt College of Science and Humanities Studies in Alquwayiyah Shaqra UniversitySaudi Arabia

Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the *** the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the *** node clustering is a popular approach for ***,the sensor nodes are grouped to form clusters in a cluster-based WSN *** battery performance of the sensor nodes is likewise *** a result,the energy efficiency of WSNs is *** specific,the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station(BS).Therefore,energy efficiency and load balancing are very essential in *** the proposed method,a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques(GW-IPSO-TS)was *** selection of Cluster Heads(CHs)and routing path of every CH from the base station is enhanced by the proposed *** provides the best routing path and increases the lifetime and energy efficiency of the ***-to-end delay and packet loss rate have also been *** proposed GW-IPSO-TS method enhances the evaluation of alive nodes,dead nodes,network survival index,convergence rate,and standard deviation of sensor *** to the existing algorithms,the proposed method outperforms better and improves the lifetime of the network.

关键词： Wireless sensor networks energy-efficient load balancing energy consumption network’s lifetime cluster heads grey wolf optimization tabu search particle swarm optimization

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Late brachial plexopathy after a mid-shaft clavicle fracture:A case report

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World Journal of Orthopedics 2023年第10期14卷 776-783页

作者： Mohammad M alzahrani Department of Orthopaedic Surgery College of MedicineImam Abdulrahman Bin Faisal UniversityDammam 31441Saudi Arabia

BACKGROUND Clavicle fractures can rarely be associated with brachial plexus *** are commonly caused by tractional injury at the time of trauma,but unfrequently can be caused by compression of the brachial plexus from excessive callus or granulation tissue *** SUMMARY We report a rare case of an adult male who sustained a mid-shaft clavicle fracture and had a late presentation of brachial plexus palsy due to compression from excessive callus *** We reported a case of a rare occurrence of delayed brachial plexus palsy due to compression from excessive callus formation in a midshaft clavicle *** diagnosis and surgical decompression of the brachial plexus is critical,as when managed efficiently,a full recovery of the brachial plexus palsy can be expected in these patients.

关键词： Clavicle Fracture Brachial plexus Palsy Case report

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Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition

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Computers, Materials & Continua 2023年第3期74卷 5467-5482页

作者： Mohammed Maray Badriyya B.Al-onazi Jaber S.alzahrani Saeed Masoud Alshahrani Najm Alotaibi Sana Alazwari Mahmoud Othman Manar ahmed Hamza Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The recognition of the Arabic characters is a crucial task incomputer vision and Natural Language Processing fields. Some major complicationsin recognizing handwritten texts include distortion and patternvariabilities. So, the feature extraction process is a significant task in NLPmodels. If the features are automatically selected, it might result in theunavailability of adequate data for accurately forecasting the character ***, many features usually create difficulties due to high dimensionality *** this background, the current study develops a Sailfish Optimizer withDeep Transfer Learning-Enabled Arabic Handwriting Character Recognition(SFODTL-AHCR) model. The projected SFODTL-AHCR model primarilyfocuses on identifying the handwritten Arabic characters in the inputimage. The proposed SFODTL-AHCR model pre-processes the input imageby following the Histogram Equalization approach to attain this *** Inception with ResNet-v2 model examines the pre-processed image toproduce the feature vectors. The Deep Wavelet Neural Network (DWNN)model is utilized to recognize the handwritten Arabic characters. At last,the SFO algorithm is utilized for fine-tuning the parameters involved in theDWNNmodel to attain better performance. The performance of the proposedSFODTL-AHCR model was validated using a series of images. Extensivecomparative analyses were conducted. The proposed method achieved a maximum accuracy of 99.73%. The outcomes inferred the supremacy of theproposed SFODTL-AHCR model over other approaches.

关键词： Arabic language handwritten character recognition deep learning feature extraction hyperparameter tuning

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Remarks on Blow-Up Phenomena in p-Laplacian Heat Equation with Inhomogeneous Nonlinearity

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Journal of Partial Differential Equations 2021年第1期34卷 42-50页

作者： alzahrani Eadah Ahma MAJDOUB Mohamed Deapartment of Mathematics College of ScienceImam Abdulrahman Bin Faisal UniversityP.O.Box 1982DammamSaudi Arabia&Basic and Applied Scientific Research CenterImam Abdulrahman Bin Faisal UniversityP.O.Box 198231441DammamSaudi Arabia

We investigate the p-Laplace heat equation ut-△_(p)u=ζ(t)f(u)in a bounded smooth *** differential-inequality arguments,we prove blow-up results under suitable conditions onζ,f,and the initial datum u_(0).We also gi... 详细信息

关键词： Parabolic problems p-Laplacian equation blow-up positive initial energy

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Cuckoo Optimized Convolution Support Vector Machine for Big Health Data Processing

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Computers, Materials & Continua 2022年第11期73卷 3039-3055页

作者： Eatedal Alabdulkreem Jaber S.alzahrani Majdy M.Eltahir Abdullah Mohamed Manar ahmed Hamza Abdelwahed Motwakel Mohamed I.Eldesouki Mohammed Rizwanullah Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMecca24382Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityAbha 62529Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharj16278Saudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharj16278Saudi Arabia

Big health data collection and storing for further analysis is a challenging task because this knowledge is big and has many *** cloud-based IoT health providers have been described in the literature ***,there are a number of issues related to time consumed and overall network performance when it comes to big data *** the existing method,less performed optimization algorithms were used for optimizing the *** the proposed method,the Chaotic Cuckoo Optimization algorithm was used for feature selection,and Convolutional Support Vector Machine(CSVM)was *** research presents a method for analyzing healthcare information that uses in future *** major goal is to take a variety of data while improving efficiency and minimizing process *** suggested method employs a hybrid method that is divided into two *** the first stage,it reduces the features by using the Chaotic Cuckoo Optimization algorithm with Levy flight,opposition-based learning,and distributor *** the second stage,CSVM is used which combines the benefits of convolutional neural network(CNN)and *** CSVM modifies CNN’s convolution product to learn hidden deep inside data *** improved economic flexibility,greater protection,greater analytics with confidentiality,and lower operating cost,the suggested approach is built on fog *** results of the experiments show that the suggested method can minimize the number of features in the datasets,enhances the accuracy by 82%,and decrease the time of the process.

关键词： Healthcare convolutional support vector machine feature selection chaotic cuckoo optimization accuracy processing time convolutional neural network

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Artificial Intelligence Enabled Apple Leaf Disease Classification for Precision Agriculture

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Computers, Materials & Continua 2022年第3期70卷 6223-6238页

作者： Fahd N.Al-Wesabi Amani abdulrahman Albraikan Anwer Mustafa Hilal Majdy M.Eltahir Manar ahmed Hamza Abu Sarwar Zamani Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversityYemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Precision agriculture enables the recent technological advancements in farming sector to observe,measure,and analyze the requirements of individual fields and *** recent developments of computer vision and artificial intelligence(AI)techniques find a way for effective detection of plants,diseases,weeds,pests,*** the other hand,the detection of plant diseases,particularly apple leaf diseases using AI techniques can improve productivity and reduce crop ***,earlier and precise apple leaf disease detection can minimize the spread of the *** works make use of traditional image processing techniques which cannot assure high detection rate on apple leaf *** this motivation,this paper introduces a novel AI enabled apple leaf disease classification(AIE-ALDC)technique for precision *** proposed AIE-ALDC technique involves orientation based data augmentation and Gaussian filtering based noise removal *** addition,the AIE-ALDC technique includes a Capsule Network(CapsNet)based feature extractor to generate a helpful set of feature ***,water wave optimization(WWO)technique is employed as a hyperparameter optimizer of the CapsNet ***,bidirectional long short term memory(BiLSTM)model is used as a classifier to determine the appropriate class labels of the apple leaf *** design of AIE-ALDC technique incorporating theWWO based CapsNetmodel with BiLSTM classifier shows the novelty of the *** range of experiments was performed to showcase the supremacy of the AIE-ALDC *** experimental results demonstrate the promising performance of the AIEALDC technique over the recent state of art methods.

关键词： Artificial intelligence apple leaf plant disease precision agriculture deep learning data augmentation

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Morphological Investigation of Genus <i>Ziziphus</i>Mill. (Rhamnaceae) in Saudi Arabia

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American Journal of Plant Sciences 2018年第13期9卷 2644-2658页

作者： Rabab Abdullah Almalki Dhafer ahmed alzahrani Department of Biological Sciences Faculty of Applied Sciences Umm Al-Qura University Makkah Saudi Arabia Department of Biological Sciences Faculty of Science King Abdulaziz University Jeddah Saudi Arabia

The genus Ziziphus Mill. is a member of the Family Rhamnaceae and comprises of ca. 170 species distributed in tropical and subtropical regions. The species in the genus are of economic and medicinal importance. This study was conducted to identify the members of the genus in Saudi Arabia as well as morphological features that show delimitation in the genus. Field survey was conducted across the study area where 74 individuals were collected and used in the study;additionally, herbarium specimens were also employed. Both floral and vegetative parts which include habit, leaf, fruit and flowers were examined. The result showed that there is variation in morphological features among the species within the genus and the genus is represented by three species namely Ziziphus nummularia, Z. mucronata, and Z. spina-christi and two varieties Z. spina-christi var. spina-christi, Z. spina-christi var. microphylla.

关键词： Morphological Features Rhamnaceae Saudi Arabia Taxonomy Ziziphus

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Cat and Mouse Optimizer with Artificial Intelligence Enabled Biomedical Data Classification

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Computer Systems Science & Engineering 2023年第3期44卷 2243-2257页

作者： B.Kalpana S.Dhanasekaran T.Abirami Ashit Kumar Dutta Marwa Obayya Jaber S.alzahrani Manar ahmed Hamza Department of Information Technology RMD Engineering CollegeChennai601206India Department of Information Technology Kalasalingam Academy of Research and EducationSrivilliputtur626126India Department of Information Technology Kongu Engineering CollegeErode638060India Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Biomedical data classification has become a hot research topic in recent years,thanks to the latest technological advancements made in ***-dical data is usually examined by physicians for decision making process in patient *** manual diagnosis is a tedious and time consuming task,numerous automated models,using Artificial Intelligence(AI)techniques,have been presented so *** this motivation,the current research work presents a novel Biomedical Data Classification using Cat and Mouse Based Optimizer with AI(BDC-CMBOAI)*** aim of the proposed BDC-CMBOAI technique is to determine the occurrence of diseases using biomedical ***,the proposed BDC-CMBOAI technique involves the design of Cat and Mouse Optimizer-based Feature Selection(CMBO-FS)technique to derive a useful subset of *** addition,Ridge Regression(RR)model is also utilized as a classifier to identify the existence of *** novelty of the current work is its designing of CMBO-FS model for data ***,CMBO-FS technique is used to get rid of unwanted features and boosts the classification *** results of the experimental analysis accomplished by BDC-CMBOAI technique on benchmark medical dataset established the supremacy of the proposed technique under different evaluation measures.

关键词： Artificial intelligence biomedical data feature selection cat and mouse optimizer ridge regression

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CoFe-LDH nanowire arrays on graphite felt: A high-performance oxygen evolution electrocatalyst in alkaline media

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Chinese Chemical Letters 2022年第2期33卷 890-892页

作者： Biao Deng Jie Liang Luchao Yue Tingshuai Li Qian Liu Yang Liu Shuyan Gao Abdulmohsen Ali Alshehri Khalid ahmed alzahrani Yonglan Luo Xuping Sun Chemical Synthesis and Pollution Control Key Laboratory of Sichuan Province School of Chemistry and Chemical EngineeringChina West Normal UniversityNanchong 637002China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 610054China Institute for Advanced Study Chengdu UniversityChengdu 610106China School of Materials Science and Engineering Henan Normal UniversityXinxiang 453007China Chemistry Department Faculty of ScienceKing Abdulaziz UniversityP.O.Box 80203Jeddah 21589Saudi Arabia

Developing non-noble-metal oxygen evolution reaction(OER) electrocatalysts with high performance is critical to electrocatalytic water splitting. In this work, we fabricated Co Fe-layered double hydroxide(LDH) nanowire arrays on graphite felt(Co Fe-LDH/GF) via a hydrothermal method. The Co Fe-LDH/GF, as a robust integrated 3 D OER anode, exhibits excellent catalytic activity with the need of low overpotential of 252 and 285 mV to drive current densities of 10 and 100 mA/cm^(2) in 1.0 mol/L KOH, respectively. In addition, it also maintains electrochemical durability for at least 24 h. This work would open up avenues for the development of GF like attractive catalyst supports for oxygen evolution applications.

关键词： LDH Graphite felt Electrocatalyst Oxygen evolution Alkaline media

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A Cross-Sectional Study on Current Practice of the Management of Pregnancy Induced Hypertension and Its Maternal and Foetal Complications and Outcome in the Western Part of the State of Libya

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Open Journal of Obstetrics and Gynecology 2022年第3期12卷 222-241页

作者： ahmed Elghoudi abdulrahman Alsharef Asma Elghoudi College of Medicine and Health Sciences United Arab Emirates University Al Ain The United Arab Emirates Faculty of Medicine University of Tripoli Tripoli Libya

Background: Maternal and perinatal mortality and morbidity are mainly affected by hypertension during pregnancy (pre-eclampsia). Haemorrhage and cerebral bleeding are the primary and significant complications of pre-eclampsia. Abruption of the placenta and renal failure are among the major complications caused by this disease. The perinatal complications include Intrauterine Growth Restriction (IUGR), Intrauterine Foetal Death (IUFD), neonatal death, and prematurity. Objective: The purpose of the present study was to examine the incidence of Pregnancy-Induced Hypertension (PIH), pre-eclampsia, the management of PIH, including the early diagnosis, the pharmacological drug management used, and the prevalence of maternal and perinatal complications. Methods and Patients: This cross-sectional study took place in Aljalaa maternity hospital, which represented the west of the state of Libya in the period from 1st January 2012 to 31st December 2012, with patients who were diagnosed, managed, and terminated according to protocol management of the hospital. Results: In the present study, from the total deliveries in Aljalaa Maternity hospital, the incidence of PIH is 8.4% and pre-eclampsia is 5.1%. No maternal mortality, CNS haemorrhaged, hepatic failure, or renal failure. The reported maternal complications included: Eclampsia, HELLP syndrome (Haemolysis, Elevated Liver enzymes and Low platelets), abruption placenta, and pulmonary oedema see in eighteen percent, six percent, nine percent, four percent and less than one percent, respectively. The foetal complications were preterm babies and Intrauterine Growth Restriction (IUGR) in nineteen percent, Low Birth Weight (LBW) in twenty-five percent, Intrauterine Foetal Death (IUFD) in five percent, neonatal death in two percent and, perinatal death in six percent. In the PIH patients, the presence of associated symptoms such as headache, blurred vision, irritability, and the presence of signs such as severe hypertension, generali

关键词： Preeclampsia Proteinuric Pregnancy Induced Hypertension

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