检索结果-南通市图书馆

Security Monitoring and Management for the Network Services in the Orchestration of SDN-NFV Environment Using Machine Learning Techniques

引用

Computer Systems Science & Engineering 2024年第2期48卷 363-394页

作者： Nasser Alshammari Shumaila Shahzadi Saad Awadh Alanazi Shahid Naseem Muhammad Anwar madallah alruwaili Muhammad Rizwan Abid Omar alruwaili Ahmed Alsayat Fahad Ahmad Department of Computer Science College of Computer and Information SciencesJouf UniversitySakakaAl Jouf72341Saudi Arabia Department of Computer Sciences Kinnaird College forWomenLahorePunjab54700Pakistan Department of Information Sciences Division of Sciences and TechnologyUniversity of EducationLahore54770Pakistan Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72341Saudi Arabia Computer Science Florida Polytechnic UniversityLakelandFlorida33805USA Delta3T LahorePunjab54700Pakistan Department of Basic Sciences Common First YearJouf UniversitySakaka72341Saudi Arabia

Software Defined Network(SDN)and Network Function Virtualization(NFV)technology promote several benefits to network operators,including reduced maintenance costs,increased network operational performance,simplified network lifecycle,and policies *** vulnerabilities try to modify services provided by Network Function Virtualization MANagement and Orchestration(NFV MANO),and malicious attacks in different scenarios disrupt the NFV Orchestrator(NFVO)and Virtualized Infrastructure Manager(VIM)lifecycle management related to network services or individual Virtualized Network Function(VNF).This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and handle security functions in order to enhance the quality of experience for end *** anomaly detector investigates these identified risks and provides secure network *** enables virtual network security functions and identifies anomalies in Kubernetes(a cloud-based platform).For training and testing purpose of the proposed approach,an intrusion-containing dataset is used that hold multiple malicious activities like a Smurf,Neptune,Teardrop,Pod,Land,IPsweep,etc.,categorized as Probing(Prob),Denial of Service(DoS),User to Root(U2R),and Remote to User(R2L)*** anomaly detector is anticipated with the capabilities of a Machine Learning(ML)technique,making use of supervised learning techniques like Logistic Regression(LR),Support Vector Machine(SVM),Random Forest(RF),Naïve Bayes(NB),and Extreme Gradient Boosting(XGBoost).The proposed framework has been evaluated by deploying the identified ML algorithm on a Jupyter notebook in Kubeflow to simulate Kubernetes for validation *** classifier has shown better outcomes(99.90%accuracy)than other classifiers in detecting anomalies/intrusions in the containerized environment.

关键词： Software defined network network function virtualization network function virtualization management and orchestration virtual infrastructure manager virtual network function Kubernetes Kubectl artificial intelligence machine learning

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

Optimization of Deep Learning Model for Plant Disease Detection Using Particle Swarm Optimizer

引用

Computers, Materials & Continua 2022年第5期71卷 4019-4031页

作者： Ahmed Elaraby Walid Hamdy madallah alruwaili Department of Computer Science Faculty of Computers and InformationSouth Valley UniversityQenaEgypt Department of Math. &Computer Science Faculty of SciencePort Said UniversityEgypt College of Computer and Information Sciences Jouf UniversitySakakaAljoufKingdom of Saudi Arabia

Plant diseases are a major impendence to food security,and due to a lack of key infrastructure in many regions of the world,quick identification is still *** losses owing to illnesses are a severe problem for both large farming structures and rural communities,motivating our *** of the large range of diseases,identifying and classifying diseases with human eyes is not only time-consuming and labor intensive,but also prone to being mistaken with a high error *** learning-enabled breakthroughs in computer vision have cleared the road for smartphone-assisted plant disease and *** proposed work describes a deep learning approach for detection plant ***,we proposed a deep learning model strategy for detecting plant disease and classification of plant leaf *** our research,we focused on detecting plant diseases in five crops divided into 25 different types of classes(wheat,cotton,grape,corn,and cucumbers).In this task,we used a public image database of healthy and diseased plant leaves acquired under realistic *** our work,a deep convolutional neural model AlexNet and Particle Swarm optimization was trained for this task we found that the metrics(accuracy,specificity,Sensitivity,precision,and Fscore)of the tested deep learning networks achieves an accuracy of 98.83%,specificity of 98.56%,Sensitivity of 98.78%,precision of 98.67%,and F-score of 98.47%,demonstrating the feasibility of this approach.

关键词： Deep neural networks plant diseases detection classification AlexNet PSO

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

A Template Matching Based Feature Extraction for Activity Recognition

引用

Computers, Materials & Continua 2022年第7期72卷 611-634页

作者： Muhammad Hameed Siddiqi Helal Alshammari Amjad Ali madallah alruwaili Yousef Alhwaiti Saad Alanazi M.M.Kamruzzaman College of Computer and Information Sciences Jouf UniversitySakakaAljouf2014Kingdom of Saudi Arabia Department of Computer Science COMSATS University IslamabadLahore CampusPakistan

Human activity recognition(HAR)can play a vital role in the monitoring of human activities,particularly for healthcare conscious *** accuracy of HAR systems is completely reliant on the extraction of prominent *** methods find it very challenging to extract optimal features due to the dynamic nature of activities,thereby reducing recognition *** this paper,we propose a robust feature extraction method for HAR systems based on template ***,in this method,we want to associate a template of an activity frame or sub-frame comprising the corresponding *** this regard,the template is placed on the frame pixels to calculate the equivalent number of pixels in the template correspondent those in the *** process is replicated for the whole frame,and the pixel is directed to the optimum *** best count is estimated to be the pixel where the silhouette(provided via the template)presented inside the *** this way,the feature vector is *** feature vector generation,the hiddenMarkovmodel(HMM)has been utilized to label the incoming *** utilized different publicly available standard datasets for *** proposed method achieved the best accuracy against existing state-of-the-art systems.

关键词： Activity recognition feature extraction template matching video surveillance

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

Performance Analysis of Intelligent Neural-Based Deep Learning System on Rank Images Classification

引用

Computer Systems Science & Engineering 2023年第11期47卷 2219-2239页

作者： Muhammad Hameed Siddiqi Asfandyar Khan Muhammad Bilal Khan Abdullah Khan madallah alruwaili Saad Alanazi College of Computer and Information Sciences Jouf UniversitySakakaAljouf73211Kingdom of Saudi Arabia Institute of Computer Science and Information Technology ICS/IT FMCSUniversity of AgriculturePeshawar25000Pakistan

The use of the internet is increasing all over the world on a daily basis in the last two *** increase in the internet causes many sexual crimes,such as sexual misuse,domestic violence,and child *** research has been done for pornographic image detection and *** of the used models used machine learning techniques and deep learning models which show less accuracy,while the deep learning model ware used for classification and detection performed better as compared to machine ***,this research evaluates the performance analysis of intelligent neural-based deep learning models which are based on Convolution neural network(CNN),Visual geometry group(VGG-16),VGG-14,and Residual Network(ResNet-50)with the expanded dataset,trained using transfer learning approaches applied in the fully connected layer for datasets to classify rank(Pornographic ***)classification in *** simulation result shows that VGG-16 performed better than the used model in this study without augmented *** VGG-16 model with augmented data reached a training and validation accuracy of 0.97,0.94 with a loss of 0.070,*** precision,recall,and f-measure values for explicit and non-explicit images are(0.94,0.94,0.94)and(0.94,0.94,0.94).Similarly,The VGG-14 model with augmented data reached a training and validation accuracy of 0.98,0.96 with a loss of 0.059,*** f-measure,recall,and precision values for explicit and non-explicit images are(0.98,0.98,0.98)and(0.98,0.98,0.98).The CNN model with augmented data reached a training and validation accuracy of 0.776&0.78 with losses of 0.48&*** f-measure,recall,and precision values for explicit and non-explicit images are(0.80,0.80,0.80)and(0.78,0.79,0.78).The ResNet-50 model with expanded data reached with training accuracy of 0.89 with a loss of 0.389 and 0.86 of validation accuracy and a loss of *** f-measure,recall,and precision values for explicit and non-explicit

关键词： VGG-16 VGG-14 pornography detection expansion ResNet-50 convolution neural network(CNN) machine learning

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

A Health Monitoring System Using IoT-Based Android Mobile Application

引用

Computer Systems Science & Engineering 2023年第11期47卷 2293-2311页

作者： madallah alruwaili Muhammad Hameed Siddiqi Kamran Farid Muhammad Azad Saad Alanazi Asfandyar Khan Abdullah Khan College of Computer and Information Sciences Jouf UniversitySakakaAl-Jouf73211Kingdom of Saudi Arabia Institute of Computer Science and Information Technology The University of Agriculture PeshawarPeshawar25000Pakistan

Numerous types of research on healthcare monitoring systems have been conducted for calculating heart rate,ECG,nasal/oral airflow,temperature,light sensor,and fall detection *** researchers have done different work in the field of health monitoring with sensor *** researchers used built-in apps,such as some used a small number of parameters,while some other studies used more than one microcontroller and used senders and receivers among the microcontrollers to communicate,and outdated tools for study *** no efficient,cheap,and updated work is proposed in the field of sensor-based health monitoring ***,this study developed an android-based mobile system that can remotely monitor electrocardiograms(ECGs),pulse oximetry,heart rate,and body *** microcontroller’s Wi-Fi device is used to manage wireless data *** findings of the patient are saved on the Firebase server for further usage in the mobile *** performance of the proposed device is tested on ten numbers of different patients age-wise in terms of beats per minute(BPM),ECG,Temperature,and *** system uses temperature,pulse,ECG,blood pressure,and eye blink *** device makes the usage of a tiny pulse sensor that has been designed to provide an accurate and optimal readout of the pulse rate and a temperature sensor is also *** the help of an MCU,our system measures the pulse rate in beats per minute(bpm),blood oxygen level temperature measurements,and ECG readings and communicates this information to the Firebase *** check the performance of the proposed system first,the BPM parameter was checked on the cardiac ***,the proposed model is tested on different patients *** simulation result shows that the BPM reading is not much different than the BPM of the cardiac *** to the simulation findings,the proposed model achieved the best performance as compared to commercially available

关键词： ICUs DS18B20 sensors SpO2 AD8232 sensors ECGs Internet of Things

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

Diagnosis of Various Skin Cancer Lesions Based on Fine-Tuned ResNet50 Deep Network

引用

Computers, Materials & Continua 2021年第7期68卷 117-135页

作者： Sameh Abd ElGhany Mai Ramadan Ibraheem madallah alruwaili Mohammed Elmogy Department of Information Systems College of Computers and Information SciencesJouf UniversitySakakaSaudi Arabia Department of Information Systems Faculty of Computers and InformationMansoura UniversityEgypt Department of Information Technology Faculty of Computers and InformationKafrelsheikh UniversityEgypt Department of Computer Engineering and Networks College of Computers and Information SciencesJouf UniversitySaudi Arabia Department of Information Technology Faculty of Computers and InformationMansoura UniversityMansoura35516Egypt

With the massive success of deep networks,there have been signi-cant efforts to analyze cancer diseases,especially skin *** this purpose,this work investigates the capability of deep networks in diagnosing a variety of dermoscopic lesion *** paper aims to develop and ne-tune a deep learning architecture to diagnose different skin cancer grades based on dermatoscopic ***-tuning is a powerful method to obtain enhanced classication results by the customized pre-trained ***,batch normalization,and hyperparameter optimization are performed for ne-tuning the proposed deep *** proposed ne-tuned ResNet50 model successfully classied 7-respective classes of dermoscopic lesions using the publicly available HAM10000 *** developed deep model was compared against two powerful models,i.e.,InceptionV3 and VGG16,using the Dice similarity coefcient(DSC)and the area under the curve(AUC).The evaluation results show that the proposed model achieved higher results than some recent and robust models.

关键词： Deep learning model multiclass diagnosis dermatoscopic images analysis ResNet50 network

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 Ammunition Detection and Classification System Using Convolutional Neural Network

引用

Computers, Materials & Continua 2021年第5期67卷 2585-2600页

作者： Gulzar Ahmad Saad Alanazi madallah alruwaili Fahad Ahmad Muhammad Adnan Khan Sagheer Abbas Nadia Tabassum School of Computer Science NCBA&ELahore54000Pakistan College of Computer and Information Sciences Jouf UniversitySakakaAljouf72341Saudi Arabia Department of Basic Sciences Jouf UniversitySakakaAljouf72341Saudi Arabia Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Computer Sciences Kinnaird College for WomenLahore54000Pakistan

Security is a significant issue for everyone due to new and creative ways to commit *** Closed-Circuit Television(CCTV)systems are being installed in offices,houses,shopping malls,and on streets to protect *** monitor CCTV;however,it is difficult for a single person to monitor the actions of multiple people at one ***,there is a dire need for an automated monitoring system that detects a person with ammunition or any other harmful material Based on our research and findings of this study,we have designed a new Intelligent Ammunition Detection and Classification(IADC)system using Convolutional Neural Network(CNN).The proposed system is designed to identify persons carrying weapons and ammunition using CCTV *** weapons are identified,the cameras sound an *** the proposed IADC system,CNN was used to detect firearms and *** CNN model which is a Deep Learning technique consists of neural networks,most commonly applied to analyzing visual imagery has gained popularity for unstructured(images,videos)data ***,this system generates an early warning through detection of ammunition before conditions become *** the faster and earlier the prediction,the lower the response time,loses and potential *** proposed IADC system provides better results than earlier published models like VGGNet,OverFeat-1,OverFeat-2,and OverFeat-3.

关键词： CCTV CNN IADC deep learning intelligent ammunition detection DnCNN

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

Machine Learning Empowered Security Management and Quality of Service Provision in SDN-NFV Environment

引用

Computers, Materials & Continua 2021年第3期66卷 2723-2749页

作者： Shumaila Shahzadi Fahad Ahmad Asma Basharat madallah alruwaili Saad Alanazi Mamoona Humayun Muhammad Rizwan Shahid Naseem Department of Computer Sciences Kinnaird College for WomenLahore54000Pakistan College of Computer and Information Sciences Jouf UniversitySakaka72341Saudi Arabia Division of Computer Science&Information Technology University of EducationLahore54000Pakistan

With the rising demand for data access,network service providers face the challenge of growing their capital and operating costs while at the same time enhancing network capacity and meeting the increased demand for *** increase efficacy of Software Defined Network(SDN)and Network Function Virtualization(NFV)framework,we need to eradicate network security configuration errors that may create vulnerabilities to affect overall efficiency,reduce network performance,and increase maintenance *** existing frameworks lack in security,and computer systems face few abnormalities,which prompts the need for different recognition and mitigation methods to keep the system in the operational state *** fundamental concept behind SDN-NFV is the encroachment from specific resource execution to the programming-based *** research is around the combination of SDN and NFV for rational decision making to control and monitor traffic in the virtualized *** combination is often seen as an extra burden in terms of resources usage in a heterogeneous network environment,but as well as it provides the solution for critical problems specially regarding massive network traffic *** attacks have been expanding step by step;therefore,it is hard to recognize and protect by conventional *** overcome these issues,there must be an autonomous system to recognize and characterize the network traffic’s abnormal conduct if there is *** four types of assaults,including HTTP Flood,UDP Flood,Smurf Flood,and SiDDoS Flood,are considered in the identified dataset,to optimize the stability of the SDN-NFVenvironment and security management,through several machine learning based characterization techniques like Support Vector Machine(SVM),K-Nearest Neighbors(KNN),Logistic Regression(LR)and Isolation Forest(IF).Python is used for simulation purposes,including several valuable utilities like the mine package,the open-source Python ML libraries Scikit-lea

关键词： Software defined network network function virtualization machine learning support vector machine K-nearest neighbors logistic regression isolation forest anomaly detection attacks

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

Machine Learning Enabled Early Detection of Breast Cancer by Structural Analysis of Mammograms

引用

Computers, Materials & Continua 2021年第4期67卷 641-657页

作者： Mavra Mehmood Ember Ayub Fahad Ahmad madallah alruwaili Ziyad AAlrowaili Saad Alanazi Mamoona Humayun Muhammad Rizwan Shahid Naseem Tahir Alyas Department of Computer Sciences Kinnaird College for WomenLahore54000Pakistan College of Computer and Information Sciences Jouf UniversitySakakaAljouf72341Saudi Arabia Physics Department College of ScienceJouf UniversitySakakaAljouf72341Saudi Arabia Division of Computer Science&Information Technology University of EducationLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Basic Sciences Deanship of Common First YearJouf UniversitySakakaAljouf72341Saudi Arabia

Clinical image processing plays a signicant role in healthcare systems and is currently a widely used *** carcinogenic diseases,time is crucial;thus,an image’s accurate analysis can help treat disease at an early *** carcinoma in situ(DCIS)and lobular carcinoma in situ(LCIS)are common types of malignancies that affect both women and *** number of cases of DCIS and LCIS has increased every year since 2002,while it still takes a considerable amount of time to recommend a controlling *** processing is a powerful technique to analyze preprocessed images to retrieve useful information by using some remarkable processing *** this paper,we used a dataset from the Mammographic Image Analysis Society and MATLAB 2019b software from MathWorks to simulate and extract our *** this proposed study,mammograms are primarily used to diagnose,more precisely,the breast’s tumor *** detection of DCIS and LCIS on breast mammograms is done by preprocessing the images using contrast-limited adaptive histogram *** resulting images’tumor portions are then isolated by a segmentation process,such as threshold ***,morphological operations,such as erosion and dilation,are applied to the images,then a gray-level co-occurrence matrix texture features,Harlick texture features,and shape features are extracted from the regions of *** classication purposes,a support vector machine(SVM)classier is used to categorize normal and abnormal ***,the adaptive neuro-fuzzy inference system is deployed for the amputation of fuzziness due to overlapping features of patterns within the images,and the exact categorization of prior patterns is gained through the *** detection of DCIS and LCIS can save lives and help physicians and surgeons todiagnose and treat these *** results are obtained through cubic support vector machine(CSVM),respectively,showing 98.95%and 98.01%accuracies

关键词： Image processing tumor segmentation dilation erosion machine learning classication support vector machine adaptive neuro-fuzzy inference system

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

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

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：