检索结果-南通市图书馆

Assessment of Flood Hazard of Jeddah Area 2009, saudi Arabia

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Water Resource and Protection 2010年第9期2卷 839-847页

作者： mashael al saud 不详

Due climatic variability and anthropogenic changes, floods have been raised lately in several regions worldwide. The resulting impact from floods is often harmful. This can be applied to saudi Arabia, the country which is known by dry climatic conditions, and it became lately a typical region for such natural hazard. Hence, floods are observed as a yearly disaster with high magnitude of influence. Jeddah, a coastal saudi city on the Red Sea to the west, has witnessed severe event in November 2009, when flooded water and sediments (torrents) invaded the urban areas and resulted decease of many people and destroyed the infrastructure and civilized zones. The lack of mitigation implements exacerbated the problem. This study implies an assessment of flood hazard risk in Jeddah region. It aims to identify the zones subjected to flood and then inducing the influencing factors at different levels of effect. For this purpose space techniques were utilized, with a focus on IKONOS satellite images, which are characterized by high resolution in identifying terrain features. In addition Geographic Information System (GIS) was also used to support space techniques. Thus, damaged areas and the mechanism of flooding process were recognized. This helps avoiding further urban expansion in areas under flood risk and will aid decision maker to put new strategies for hazard management.

关键词： Disaster Flooded Areas Space Tools saudi Arabia

Morphological and Phylogenetic Studies of a Copepod Species, Irodes parupenei Ho and Lin (2007), Infecting Parupeneus rubescens in saudi Arabia

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Ocean University of China 2022年第2期21卷 457-464页

作者： DKHIL Mohamed A alHAFIDH Wejdan al-QURAISHY Saleh alOTAIBI mashael BANAEEM Manal alSalEH Thekra ABDEL-GABER Rewaida Department of Zoology College of ScienceKing Saud UniversityRiyadh 11451Saudi Arabia

Dammam City is one of the gorgeous coastal areas in the Arabian Gulf of saudi *** present study aimed to ex-amine one of the copepod species infecting the rosy goatfish that represents a highly consumed fish species by the local population in the Arabian *** copepod species isolated from the infected fish specimens belong to the family Taeniacanthidae and was iden-tified as Irodes parupenei Ho and Lin(2007),primarily based on its morphological,morphometric,and ultrastructural characteris-tics,especially the structures of the dorsal cephalic area,segmentation of the first antenna,the absence of the maxilliped claw in the fe-male specimens,and the setation and spinulation of the legs 2-4 for the adult females are of great significance in the taxonomic *** 18S rRNA gene sequence was analyzed to ensure the precise identity and exact taxonomic status of the copepod *** result showed that this copepod species belong to Taenicanthidae and closely related to Irodes sauridi(gb|JF781550.1)in the same *** details on the specificity of the goatfish for Irodes species and identifying these parasitic taxa using molecular analysis are given in the present study.

关键词： Taenicanthidae Irodes species goatfish parasite specificity 18S rRNA gene

Metaheuristics Enabled Clustering with Routing Scheme for Wireless Sensor Networks

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2022年第12期73卷 5491-5507页

作者： mashael M.Asiri saud S.alotaibi Dalia H.Elkamchouchi Amira Sayed A.Aziz Manar Ahmed Hamza Abdelwahed Motwakel Abu Sarwar Zamani Ishfaq Yaseen Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Wireless Sensor Network(WSN)is a vital element in Internet of Things(IoT)as the former enables the collection of huge quantities of data in energy-constrained *** offers independent access to the target region and performs data collection in an effective *** energy constraints remain a challenging issue in WSN since it operates on in-built *** studies conducted earlier recommended that the energy spent on communication processmust be considerably reduced to improve the efficiency of *** organization and optimal selection of the routes are considered as NP hard optimization problems which can be resolved with the help of metaheuristic *** and routing are considered as effective approaches in enhancing the energy effectiveness and lifespan of *** this background,the current study develops an Improved Duck and Traveller Optimization(IDTO)-enabled cluster-based Multi-Hop Routing(IDTOMHR)technique for ***,IDTO algorithm is exploited for the selection of Cluster Head(CH)and construction of ***,Artificial Gorilla Troops Optimization(ATGO)technique is also used to derive an optimal set of routes to the *** clustering and routing approaches derive a fitness function with the inclusion of multiple input *** proposed IDTOMHR model was experimentally validated for its performance under different *** extensive experimental results confirmed the better performance of IDTOMHR model over other recent approaches.

关键词： Wireless sensor network clustering routing metaheuristics energy efficiency lifetime

Clustered Single-Board Devices with Docker Container Big Stream Processing Architecture

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2022年第12期73卷 5349-5365页

作者： N.Penchalaiah Abeer S.al-Humaimeedy mashael Maashi J.Chinna Babu Osamah Ibrahim Khalaf Theyazn H.H.aldhyani Department of CSE AITSRajampet516126Andhra PradeshIndia Software Engineering Department College of Computer and Information SciencesKing Saud UniversityP.O.Box 103786Riyadh11616Saudi Arabia Software Engineering Department King Saud UniversityRiyadh11543Saudi Arabia Department of Electronics and Communication Engineering Annamacharya Institute of Technology and SciencesRajampet516126Andhra PradeshIndia al-Nahrain University Al-Nahrain Nanorenewable Energy Research CenterBaghdad10072Iraq Applied College in Abqaiq King Faisal UniversityP.O.Box 400Al-Ahsa31982Saudi Arabia

The expanding amounts of information created by Internet of Things(IoT)devices places a strain on cloud computing,which is often used for data analysis and *** paper investigates a different approach based on edge cloud applications,which involves data filtering and processing before being delivered to a backup cloud *** Paper suggest designing and implementing a low cost,low power cluster of Single Board Computers(SBC)for this purpose,reducing the amount of data that must be transmitted elsewhere,using Big Data ideas and *** Apache Hadoop and Spark Cluster that was used to run a test application was containerized and deployed using a Raspberry Pi cluster and *** obtain system data and analyze the setup’s performance a Prometheusbased stack monitoring and alerting solution in the cloud based market is *** Paper assesses the system’s complexity and demonstrates how containerization can improve fault tolerance and maintenance ease,allowing the suggested solution to be used in *** evaluation of the overall performance is presented to highlight the capabilities and limitations of the suggested architecture,taking into consideration the suggested solution’s resource use in respect to device restrictions.

关键词： Big data edge cloud cluster architecture performance engineering Raspberry pi dockers warm container technology data streaming

Leveraging Gradient-Based Optimizer and Deep Learning for Automated Soil Classification Model

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2023年第7期76卷 975-992页

作者： Hadeel alsolai Mohammed Rizwanullah mashael Maashi Mahmoud Othman Amani A.alneil Amgad Atta Abdelmageed Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityP.O.box 103786Riyadh11543Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Soil classification is one of the emanating topics and major concerns in many *** the population has been increasing at a rapid pace,the demand for food also increases *** approaches used by agriculturalists are inadequate to satisfy the rising demand,and thus they have hindered soil *** comes a demand for computer-related soil classification methods to support *** study introduces a Gradient-Based Optimizer and Deep Learning(DL)for Automated Soil Clas-sification(GBODL-ASC)*** presented GBODL-ASC technique identifies various kinds of soil using DL and computer vision *** the presented GBODL-ASC technique,three major processes are *** the initial stage,the presented GBODL-ASC technique applies the GBO algorithm with the EfficientNet prototype to generate feature *** soil categorization,the GBODL-ASC procedure uses an arithmetic optimization algorithm(AOA)with a Back Propagation Neural Network(BPNN)*** design of GBO and AOA algorithms assist in the proper selection of parameter values for the EfficientNet and BPNN models,*** demonstrate the significant soil classification outcomes of the GBODL-ASC methodology,a wide-ranging simulation analysis is performed on a soil dataset comprising 156 images and five *** simulation values show the betterment of the GBODL-ASC model through other models with maximum precision of 95.64%.

关键词： Soil classification earth sciences machine learning parameter optimization metaheuristics

A Comprehensive Investigation of Machine Learning Feature Extraction and ClassificationMethods for Automated Diagnosis of COVID-19 Based on X-ray Images

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2021年第3期66卷 3289-3310页

作者： Mazin Abed Mohammed Karrar Hameed Abdulkareem Begonya Garcia-Zapirain Salama A.Mostafa mashael S.Maashi alaa S.al-Waisy Mohammed Ahmed Subhi Ammar Awad Mutlag Dac-Nhuong Le College of Computer Science and Information Technology University of AnbarRamadi31001Iraq College of Agriculture Al-Muthanna UniversitySamawah66001Iraq eVIDA Lab University of DeustoBilbao48007Spain Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaJohor86400Malaysia Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia Faculty of Engineering and Built Environment Universiti Kebangsaan MalaysiaBangi43600Malaysia Pure Science Department Ministry of EducationGeneral Directorate of CurriculaBaghdad10Iraq Institute of Research and Development Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam

The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global *** biological structure and symptoms of COVID-19 are similar to other viral chest maladies,which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 *** this study,an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods(e.g.,artificial neural network(ANN),support vector machine(SVM),linear kernel and radial basis function(RBF),k-nearest neighbor(k-NN),Decision Tree(DT),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-ray dataset has been created and developed,namely the COVID-19 ***(400 healthy cases,and 400 COVID cases).To the best of our knowledge,it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection *** on the results obtained from the experiments,it can be concluded that all the models performed well,deep learning models had achieved the optimum accuracy of 98.8%in ResNet50 *** comparison,in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBFaccuracy 94% for the prediction of coronavirus disease 2019.

关键词： Coronavirus disease COVID-19 diagnosis machine learning convolutional neural networks resnet50 artificial neural network support vector machine X-ray images feature transfer learning

Cache Memory Design for Single Bit Architecture with Different Sense Amplifiers

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2022年第11期73卷 2313-2331页

作者： Reeya Agrawal Anjan Kumar Salman A.alQahtani mashael Maashi Osamah Ibrahim Khalaf Theyazn H.H.aldhyani VLSI Center of Excellence GLA UniversityMathura281406India Computer Engineering Department Research Chair of New Emerging Technologies and 5G Networks and BeyondCollege of Computer and Information SciencesKing Saud UniversitySaudi Arabia Software Engineering Department King Saud UniversityRiyadh11543Saudi Arabia al-Nahrain University Al-Nahrain Nano-Renewable Energy Research CenterBaghdadIraq Applied College in Abqaiq King Faisal UniversityAl-Ahsa31982Saudi Arabia

Most modern microprocessors have one or two levels of on-chip caches to make things run faster,but this is not always the *** of the time,these caches are made of static random access memory *** take up a lot of space on the chip and use a lot of electricity.A lot of the time,low power is more important than several *** is true for phones and *** memory design for single bit architecture consists of six transistors static random access memory cell,a circuit of write driver,and sense amplifiers(such as voltage differential sense amplifier,current differential sense amplifier,charge transfer differential sense amplifier,voltage latch sense amplifier,and current latch sense amplifier,all of which are compared on different resistance values in terms of a number of transistors,delay in sensing and consumption of *** conclusion arises that single bit six transistor static random access memory cell voltage differential sense amplifier architecture consumes 11.34μW of power which shows that power is reduced up to 83%,77.75%reduction in the case of the current differential sense amplifier,39.62%in case of charge transfer differential sense amplifier and 50%in case of voltage latch sense amplifier when compared to existing latch sense amplifier ***,power reduction techniques are applied over different blocks of cache memory architecture to optimize *** single-bit six transistors static random access memory cell with forced tack technique and voltage differential sense amplifier with dual sleep technique consumes 8.078μW of power,i.e.,reduce 28%more power that makes single bit six transistor static random access memory cell with forced tack technique and voltage differential sense amplifier with dual sleep technique more energy efficient.

关键词： Current differential sense amplifier(CDSA) voltage differential sense amplifier(VDSA) voltage latch sense amplifier(VLSA) current latch sense amplifier(CLSA) charge-transfer differential sense amplifier(CTDSA) new emerging technologies

Fully Automatic Segmentation of Gynaecological Abnormality Using a New Viola–Jones Model

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2021年第3期66卷 3161-3182页

作者： Ihsan Jasim Hussein M.A.Burhanuddin Mazin Abed Mohammed Mohamed Elhoseny Begonya Garcia-Zapirain Marwah Suliman Maashi mashael S.Maashi BIOCORE Research Group Universiti Teknikal Malaysia MelakaMelaka76100Malaysia Director of UTeM International Centre BIOCORE Research GroupUniversiti Teknikal Malaysia MelakaMelaka76100Malaysia College of Computer Science and Information Technology University of AnbarRamadi31001Iraq Department of Computer Science College of Computer Information TechnologyAmerican University in the Emirates503000United Arab Emirates eVIDA Laboratory University of DeustoBilbao48007Spain Medical Laboratory Science Department King Abdulaziz UniversityJeddah21589Saudi Arabia Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia

One of the most complex tasks for computer-aided diagnosis(Intelligent decision support system)is the segmentation of ***,this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound *** main contributions of this research is the development of a novel Viola–James model capable of segmenting the ultrasound images of breast and ovarian cancer *** addition,proposed an approach that can efficiently generate region-of-interest(ROI)and new features that can be used in characterizing lesion *** study uses two databases in training and testing the proposed segmentation *** breast cancer database contains 250 images,while that of the ovarian tumor has 100 images obtained from several hospitals in *** of the experiments showed that the proposed approach demonstrates better performance compared with those of other segmentation methods used for segmenting breast and ovarian ultrasound *** segmentation result of the proposed system compared with the other existing techniques in the breast cancer data set was 78.8%.By contrast,the segmentation result of the proposed system in the ovarian tumor data set was 79.2%.In the classification results,we achieved 95.43%accuracy,92.20%sensitivity,and 97.5%specificity when we used the breast cancer data *** the ovarian tumor data set,we achieved 94.84%accuracy,96.96%sensitivity,and 90.32%specificity.

关键词： Viola-Jones model breast cancer segmentation ovarian tumor ovarian tumor segmentation breast cancer ultrasound images active contour cascade model

A New Multi-Agent Feature Wrapper Machine Learning Approach for Heart Disease Diagnosis

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2021年第4期67卷 51-71页

作者： Mohamed Elhoseny Mazin Abed Mohammed Salama A.Mostafa Karrar Hameed Abdulkareem mashael S.Maashi Begonya Garcia-Zapirain Ammar Awad Mutlag Marwah Suliman Maashi Department of Computer Science College of Computer Information TechnologyAmerican University in the Emirates503000United Arab Emirates College of Computer Science and Information Technology University of AnbarRamadi31001Iraq Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaJohor86400Malaysia College of Agriculture Al-Muthanna UniversitySamawah66001Iraq Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia eVIDA Lab University of DeustoBilbao48007Spain Department of Pure Science Ministry of EducationGeneral Directorate of CurriculaBaghdadIraq Department of Medical Laboratory Science Faculty of Applied Medical ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia

Heart disease(HD)is a serious widespread life-threatening *** heart of patients with HD fails to pump sufcient amounts of blood to the entire *** the occurrence of HD early and efciently may prevent the manifestation of the debilitating effects of this disease and aid in its effective *** methods for diagnosing HD are sometimes unreliable and insufcient in analyzing the related *** an alternative,noninvasive medical procedures based on machine learning(ML)methods provide reliable HD diagnosis and efcient prediction of HD ***,the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classication features from patients with *** this study,we propose an automated heart disease diagnosis(AHDD)system that integrates a binary convolutional neural network(CNN)with a new multi-agent feature wrapper(MAFW)*** MAFW model consists of four software agents that operate a genetic algorithm(GA),a support vector machine(SVM),and Naïve Bayes(NB).The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classication.A nal tuning to CNN is then performed to ensure that the best set of features are included in HD *** CNN consists of ve layers that categorize patients as healthy or with HD according to the analysis of optimized HD *** evaluate the classication performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using across-validation technique and by assessing six evaluation *** AHDD system achieves the highest accuracy of 90.1%,whereas the other ML and conventional CNN models attain only 72.3%–83.8%accuracy on ***,the AHDD system proposed herein has the highest capability to identify patients with *** system can be used by medical

关键词： Heart disease machine learning multi-agent feature wrapper model heart disease diagnosis HD cleveland datasets convolutional neural network