检索结果-南通市图书馆

Weather Forecasting Prediction Using Ensemble Machine Learning for Big Data Applications

Computers, Materials & Continua 2022年第11期73卷 3367-3382页

作者： Hadil Shaiba Radwa Marzouk Mohamed K Nour Noha Negm Anwer Mustafa hilal Abdullah Mohamed Abdelwahed Motwakel Ishfaq Yaseen Abu Sarwar Zamani Mohammed Rizwanullah Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Science Mathematics and Computer Science DepartmentMenoufia UniversityEgypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt

The agricultural sector’s day-to-day operations,such as irrigation and sowing,are impacted by the ***,weather constitutes a key role in all regular human *** forecasting must be accurate and precise to plan our activities and safeguard ourselves as well as our property from ***,wind speed,humidity,wind direction,cloud,temperature,and other weather forecasting variables are used in this work for weather *** research works have been conducted on weather *** drawbacks of existing approaches are that they are less effective,inaccurate,and *** overcome these issues,this paper proposes an enhanced and reliable weather forecasting *** well as developing weather forecasting in remote *** data analysis and machine learning techniques,such as Gradient Boosting Decision Tree,Random Forest,Naive Bayes Bernoulli,and KNN Algorithm are deployed to anticipate weather conditions.A comparative analysis of result outcome said in determining the number of ensemble methods that may be utilized to improve the accuracy of prediction in weather *** aim of this study is to demonstrate its ability to predict weather forecasts as soon as *** evaluation shows our ensemble technique achieves 95%prediction ***,for 1000 nodes it is less than 10 s for prediction,and for 5000 nodes it takes less than 40 s for prediction.

关键词： Weather forecasting KNN random forest gradient boosting decision tree naive bayes bernoulli

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Feature Selection with Optimal Stacked Sparse Autoencoder for Data Mining

引用

Computers, Materials & Continua 2022年第8期72卷 2581-2596页

作者： Manar Ahmed Hamza Siwar Ben Haj Hassine Ibrahim Abunadi Fahd N.Al-Wesabi Hadeel Alsolai Anwer Mustafa hilal Ishfaq Yaseen Abdelwahed Motwakel Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Computer Science College of Science and Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a61101Yemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia

Data mining in the educational field can be used to optimize the teaching and learning performance among the *** recently developed machine learning(ML)and deep learning(DL)approaches can be utilized to mine the data *** study proposes an Improved Sailfish Optimizer-based Feature SelectionwithOptimal Stacked Sparse Autoencoder(ISOFS-OSSAE)for data mining and pattern recognition in the educational *** proposed ISOFS-OSSAE model aims to mine the educational data and derive decisions based on the feature selection and classification ***,the ISOFS-OSSAEmodel involves the design of the ISOFS technique to choose an optimal subset of ***,the swallow swarm optimization(SSO)with the SSAE model is derived to perform the classification *** showcase the enhanced outcomes of the ISOFSOSSAE model,a wide range of experiments were taken place on a benchmark dataset from the University of California Irvine(UCI)Machine Learning *** simulation results pointed out the improved classification performance of the ISOFS-OSSAE model over the recent state of art approaches interms of different performance measures.

关键词： Data mining pattern recognition feature selection data classification SSAE model

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Improved DHOA-Fuzzy Based Load Scheduling in IoT Cloud Environment

引用

Computers, Materials & Continua 2022年第5期71卷 4101-4114页

作者： R.Joshua Samuel Raj V.Ilango Prince Thomas V.R.Uma Fahd N.Al-Wesabi Radwa Marzouk Anwer Mustafa hilal Department of Information Science&Engineering CMR Institute of TechnologyBengaluru560037India Department of Computer Application CMR Institute of TechnologyBangalore560037India School of Computing Woldia Institute of TechnologyWoldia UniversityEthiopia Department of Commerce School of CommerceFinance and AccountancyChrist UniversityBangalore560029India Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi 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

Internet of things (IoT) has been significantly raised owing to thedevelopment of broadband access network, machine learning (ML), big dataanalytics (BDA), cloud computing (CC), and so on. The development of IoTtechnologies has resulted in a massive quantity of data due to the existenceof several people linking through distinct physical components, indicatingthe status of the CC environment. In the IoT, load scheduling is realistictechnique in distinct data center to guarantee the network suitability by fallingthe computer hardware and software catastrophe and with right utilize ofresource. The ideal load balancer improves many factors of Quality of Service(QoS) like resource performance, scalability, response time, error tolerance,and efficiency. The scholar is assumed as load scheduling a vital problem inIoT environment. There are many techniques accessible to load scheduling inIoT environments. With this motivation, this paper presents an improved deerhunting optimization algorithm with Type II fuzzy logic (IDHOA-T2F) modelfor load scheduling in IoT environment. The goal of the IDHOA-T2F is todiminish the energy utilization of integrated circuit of IoT node and enhancethe load scheduling in IoT environments. The IDHOA technique is derivedby integrating the concepts of Nelder Mead (NM) with the DHOA. Theproposed model also synthesized the T2L based on fuzzy logic (FL) systemsto counterbalance the load distribution. The proposed model finds usefulto improve the efficiency of IoT system. For validating the enhanced loadscheduling performance of the IDHOA-T2F technique, a series of simulationstake place to highlight the improved performance. The experimental outcomesdemonstrate the capable outcome of the IDHOA-T2F technique over therecent techniques.

关键词： Load scheduling internet of things cloud computing metaheuristics fuzzy logic

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Intelligent Optimization-Based Clustering with Encryption Technique for Internet of Drones Environment

引用

Computers, Materials & Continua 2022年第12期73卷 6617-6634页

作者： Dalia H.Elkamchouchi Jaber S.Alzahrani Hany Mahgoub Amal S.Mehanna Anwer Mustafa hilal Abdelwahed Motwakel Abu Sarwar Zamani Ishfaq Yaseen Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computers and Information Computer Science DepartmentMenoufia UniversityEgypt Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo 11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The recent technological developments have revolutionized the functioning of Wireless Sensor Network(WSN)-based industries with the development of Internet of Things(IoT).Internet of Drones(IoD)is a division under IoT and is utilized for communication amongst *** drones are naturally mobile,it undergoes frequent topological *** alterations in the topology cause route election,stability,and scalability problems in *** is considered as an effective method to transmit the images in IoD *** current study introduces an Atom Search Optimization basedClusteringwith Encryption Technique for Secure Internet of Drones(ASOCE-SIoD)*** key objective of the presented ASOCE-SIoD technique is to group the drones into clusters and encrypt the images captured by *** presented ASOCE-SIoD technique follows ASO-based Cluster Head(CH)and cluster construction *** addition,signcryption technique is also applied to effectually encrypt the images captured by drones in IoD *** process enables the secure transmission of images to the ground *** order to validate the efficiency of the proposed ASOCE-SIoD technique,several experimental analyses were conducted and the outcomes were inspected under different *** comprehensive comparative analysis results established the superiority of the proposed ASOCE-SIoD model over recent approaches.

关键词： Internet of drones atom search algorithm clustering encryption signcryption

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Deep Learning Enabled Microarray Gene Expression Classification for Data Science Applications

引用

Computers, Materials & Continua 2022年第11期73卷 4277-4290页

作者： Areej A.Malibari Reem M.Alshehri Fahd N.Al-Wesabi Noha Negm Mesfer Al Duhayyim Anwer Mustafa hilal Ishfaq Yaseen Abdelwahed Motwakel Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

In bioinformatics applications,examination of microarray data has received significant interest to diagnose *** gene expression data can be defined by a massive searching space that poses a primary challenge in the appropriate selection of *** data classification incorporates multiple disciplines such as bioinformatics,machine learning(ML),data science,and pattern *** paper designs an optimal deep neural network based microarray gene expression classification(ODNN-MGEC)model for bioinformatics *** proposed ODNN-MGEC technique performs data normalization process to normalize the data into a uniform ***,improved fruit fly optimization(IFFO)based feature selection technique is used to reduce the high dimensionality in the biomedical ***,deep neural network(DNN)model is applied for the classification of microarray gene expression data and the hyperparameter tuning of the DNN model is carried out using the Symbiotic Organisms Search(SOS)*** utilization of IFFO and SOS algorithms pave the way for accomplishing maximum gene expression classification *** examining the improved outcomes of the ODNN-MGEC technique,a wide ranging experimental analysis is made against benchmark *** extensive comparison study with recent approaches demonstrates the enhanced outcomes of the ODNN-MGEC technique in terms of different measures.

关键词： Bioinformatics data science microarray gene expression data classification deep learning metaheuristics

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Intelligent Deep Convolutional Neural Network Based Object DetectionModel for Visually Challenged People

引用

Computer Systems Science & Engineering 2023年第9期46卷 3191-3207页

作者： S.Kiruthika Devi Amani Abdulrahman Albraikan Fahd N.Al-Wesabi Mohamed K.Nour Ahmed Ashour Anwer Mustafa hilal Department of Computing Technology SRM Institute of Science and TechnologyIndia Department of Computer Sciences 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 Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Engineering Mathematics and Physics Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Artificial Intelligence(AI)and Computer Vision(CV)advancements have led to many useful methodologies in recent years,particularly to help visually-challenged *** detection includes a variety of challenges,for example,handlingmultiple class images,images that get augmented when captured by a camera and so *** test images include all these variants as *** detection models alert them about their surroundings when they want to walk *** study compares four CNN-based pre-trainedmodels:ResidualNetwork(ResNet-50),Inception v3,DenseConvolutional Network(DenseNet-121),and SqueezeNet,predominantly used in image recognition *** on the analysis performed on these test images,the study infers that Inception V3 outperformed other pre-trained models in terms of accuracy and *** further improve the performance of the Inception v3 model,the thermal exchange optimization(TEO)algorithm is applied to tune the hyperparameters(number of epochs,batch size,and learning rate)showing the novelty of the *** accuracy was achieved owing to the inclusion of an auxiliary classifier as a regularizer,hyperparameter optimizer,and factorization ***,Inception V3 can handle images of different *** makes Inception V3 the optimum model for assisting visually challenged people in real-world communication when integrated with Internet of Things(IoT)-based devices.

关键词： Pre-trained models object detection visually challenged people deep learning Inception V3 DenseNet-121

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Modeling of Explainable Artificial Intelligence for Biomedical Mental Disorder Diagnosis

引用

Computers, Materials & Continua 2022年第5期71卷 3853-3867页

作者： Anwer Mustafa hilal Imène ISSAOUI Marwa Obayya Fahd N.Al-Wesabi Nadhem NEMRI Manar Ahmed Hamza Mesfer Al Duhayyim Abu Sarwar Zamani Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlkharj16278Saudi Arabia Department of Natural and Applied Sciences Community CollegeQassim UniversityBuraydahSaudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’a31220Yemen Department of Information Systems King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

The abundant existence of both structured and unstructured data and rapid advancement of statistical models stressed the importance of introducing Explainable Artificial Intelligence(XAI),a process that explains how prediction is done in AI *** mental disorder,i.e.,Autism Spectral Disorder(ASD)needs to be identified and classified at early stage itself in order to reduce health *** this background,the current paper presents XAI-based ASD diagnosis(XAI-ASD)model to detect and classify ASD *** proposed XAI-ASD technique involves the design of Bacterial Foraging Optimization(BFO)-based Feature Selection(FS)*** addition,Whale Optimization Algorithm(WOA)with Deep Belief Network(DBN)model is also applied for ASD classification process in which the hyperparameters of DBN model are optimally tuned with the help of *** order to ensure a better ASD diagnostic outcome,a series of simulation process was conducted on ASD dataset.

关键词： Explainable artificial intelligence autism spectral disorder feature selection data classification machine learning metaheuristics

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Optimal Deep Learning-based Cyberattack Detection and Classification Technique on Social Networks

引用

Computers, Materials & Continua 2022年第7期72卷 907-923页

作者： Amani Abdulrahman Albraikan Siwar Ben Haj Hassine Suliman Mohamed Fati Fahd NAl-Wesabi Anwer Mustafa hilal Abdelwahed Motwakel Manar Ahmed Hamza Mesfer Al Duhayyim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science and ArtsKing Khalid UniversityMahayil AsirSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer and Self-Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Cyberbullying(CB)is a distressing online behavior that disturbs mental health *** studies have employed statistical and Machine Learning(ML)techniques for CB *** this motivation,the current paper presents an Optimal Deep Learning-based Cyberbullying Detection and Classification(ODL-CDC)technique for CB detection in social *** proposed ODL-CDC technique involves different processes such as pre-processing,prediction,and hyperparameter *** addition,GloVe approach is employed in the generation of word ***,the pre-processed data is fed into BidirectionalGated Recurrent Neural Network(BiGRNN)model for ***,hyperparameter tuning of BiGRNN model is carried out with the help of Search and Rescue Optimization(SRO)*** order to validate the improved classification performance of ODL-CDC technique,a comprehensive experimental analysis was carried out upon benchmark dataset and the results were inspected under varying aspects.A detailed comparative study portrayed the superiority of the proposed ODL-CDC technique over recent techniques,in terms of performance,with the maximum accuracy of 92.45%.

关键词： Cybersecurity cyberbullying social networks parameter tuning deep learning metaheuristics

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Diabetes self-management (DSM) in Omani with type-2 diabetes

引用

International Journal of Nursing Sciences 2014年第4期1卷 352-359页

作者： hilal Alrahbi School of Nursing College of Pharmacy and NursingUniversity of NizwaBirkat AlmoozOman

Objective:The aim of this study was to assess the status of diabetes self-management(DSM)among Omanis with type-2 diabetes and its relationship with glycemic control and demographic ***:A correlational descriptive design using questionnaire was conducted with a convenience sample of 266 Omani patients with type-2 diabetes to collect the ***:The findings indicated that DSM among the study subject is *** 1%of them were regular on SMBG;9.5%of them exercise regularly;and 18%of them maintain healthy diet *** significant relationship between DSM and glycemic control(p>0.05)was *** was found to be associated with age,gender,level of education,and duration of *** the differences between two regions in Oman(Muscat&Aldhakiliyah)indicated that participants from Muscat differ only in practicing ***:Results indicated that many Omani patients with type-2 diabetes do not perform DSM *** implications:The findings of this study set the stage to develop teaching strategies that will improve DSM and subsequently improve diabetes management in patient with type-2 diabetes in Oman.

关键词： Diabetes Self-management Self-care Oman Patient education Glycemic control Type-2 diabetes

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Design of Intelligent Alzheimer Disease Diagnosis Model on CIoT Environment

引用

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

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：