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An Optimal Scheme for WSN Based on Compressed Sensing

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Computers, Materials & Continua 2022年第7期72卷 1053-1069页

作者： Firas Ibrahim AlZobi ahmad Ali AlZubi Kulakov Yurii Abdullah Alharbi Jazem Mutared alanazi Sami Smadi Department of Information Systems and Networks Faculty of Information TechnologyThe World Islamic Sciences&Education UniversityJordan Computer Science Department Community CollegeKing Saud UniversitySaudi Arabia Department of Computer Engineering National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic InstituteUkraine

Wireless sensor networks(WSNs)is one of the renowned ad hoc network technology that has vast varieties of applications such as in computer networks,bio-medical engineering,agriculture,industry and many *** has been used in the internet-of-things(IoTs)applications.A method for data collecting utilizing hybrid compressive sensing(CS)is developed in order to reduce the quantity of data transmission in the clustered sensor network and balance the network *** cluster head nodes are chosen first from each temporary cluster that is closest to the cluster centroid of the nodes,and then the cluster heads are selected in order based on the distance between the determined cluster head node and the undetermined candidate cluster head ***,each ordinary node joins the cluster that is nearest to *** greedy CS is used to compress data transmission for nodes whose data transmission volume is greater than the threshold in a data transmission tree with the Sink node as the root node and linking all cluster head *** simulation results demonstrate that when the compression ratio is set to ten,the data transfer volume is reduced by a factor of *** compared to clustering and SPT without CS,it is reduced by 75%and 65%,*** compared to SPT with Hybrid CS and Clustering with hybrid CS,it is reduced by 35%and 20%,*** and SPT without CS are compared in terms of node data transfer volume standard *** with Hybrid CS and clustering with Hybrid CS were both reduced by 62%and 80%,*** compared to SPT with hybrid CS and clustering with hybrid CS,the latter two were reduced by 41%and 19%,respectively.

关键词： Compressed sensing computer networks sensor networks ad hoc networks

Melanoma Identification Through X-ray Modality Using Inception-v3 Based Convolutional Neural Network

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Computers, Materials & Continua 2022年第7期72卷 37-55页

作者： Saad Awadh alanazi College of Computer and Information Sciences Jouf UniversitySakakaAljouf72341Saudi Arabia

Melanoma,also called malignant melanoma,is a form of skin cancer triggered by an abnormal proliferation of the pigment-producing cells,which give the skin its *** is one of the skin diseases,which is exceptionally and globally dangerous,Skin lesions are considered to be a serious ***-based early recognition and detection procedure is fundamental for melanoma *** detection of melanoma using dermoscopy images improves survival rates *** the same time,well-experienced dermatologists dominate the precision of ***,precise melanoma recognition is incredibly hard due to several factors:low contrast between lesions and surrounding skin,visual similarity between melanoma and non-melanoma lesions,and so ***,reliable automatic detection of skin tumors is critical for pathologists’effectiveness and *** take care of this issue,numerous research centers around the world are creating autonomous image processing-oriented *** suggested deep learning methods in this article to address significant tasks that have emerged in the field of skin lesion image processing:we provided a Convolutional Neural Network(CNN)based framework using an Inception-v3(INCP-v3)melanoma detection scheme and accomplished very high precision(98.96%)against melanoma *** classification framework of CNN is created utilizing TensorFlow and Keras in the backend(in Python).It likewise utilizes Transfer-Learning(TL)*** is prepared on the data gathered from the“International Skin Imaging Collaboration(ISIC)”*** experiments show that the suggested technique outperforms state-of-the-art methods in terms of predictive performance.

关键词： Deep learning chronic obstructive pulmonary disease chronic bronchitis convolutional neural network X-ray images

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

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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

Anomaly Detection for Internet of Things Cyberattacks

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Computers, Materials & Continua 2022年第7期72卷 261-279页

作者： Manal alanazi Ahamed Aljuhani College of Computing and Information Technology University of TabukTabuk71491Saudi Arabia

The Internet of Things(IoT)has been deployed in diverse critical sectors with the aim of improving quality of service and facilitating human *** IoT revolution has redefined digital services in different domains by improving efficiency,productivity,and *** service providers have adapted IoT systems or plan to integrate them as integral parts of their systems’operation;however,IoT security issues remain a significant *** minimize the risk of cyberattacks on IoT networks,anomaly detection based on machine learning can be an effective security solution to overcome a wide range of IoT *** various detection techniques have been proposed in the literature,existing detection methods address limited cyberattacks and utilize outdated datasets for *** this paper,we propose an intelligent,effective,and lightweight detection approach to detect several IoT *** proposed model includes a collaborative feature selection method that selects the best distinctive features and eliminates unnecessary features to build an effective and efficient detection *** the detection phase,we also proposed an ensemble of learning techniques to improve classification for predicting several different types of IoT *** experimental results show that our proposed method can effectively and efficiently predict several IoT attacks with a higher accuracy rate of 99.984%,a precision rate of 99.982%,a recall rate of 99.984%,and an F1-score of 99.983%.

关键词： Anomaly detection anomaly-based IDS cybersecurity feature selection Internet of Things(IoT) intrusion detection

Generic sensor platform based on electro- responsive molecularly imprinted polymer nanoparticles (e-NanoMIPs)

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Microsystems & Nanoengineering 2020年第1期6卷 266-274页

作者： A.Garcia-Cruz O.S.ahmad K.alanazi E.Piletska S.A.Piletsky School of Chemistry University of LeicesterLeicesterUK

The present research describes the design of robust electrochemical sensors based on electro-responsive molecularly imprinted polymer nanoparticles(e-MIPs).The e-MIPs,tagged with a redox probe,combine both recognition and reporting *** system replaces enzyme-mediator pairs used in traditional *** analyte recognition process relies on the generic actuation phenomenon when the polymer conformation of e-MIPs is changing in response to the presence of the template *** analyte concentration is measured using voltammetric *** an exemplification of this technology,electrochemical sensors were developed for the determination of concentrations of trypsin,glucose,paracetamol,C4-homoserine lactone,and *** present technology allows for the possibility of producing generic,inexpensive,and robust disposable sensors for clinical,environmental,and forensic applications.

关键词： functions. methods. polymer

Secure and Anonymous Three-Factor Authentication Scheme for Remote Healthcare Systems

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Computer Systems Science & Engineering 2022年第8期42卷 703-725页

作者： Munayfah alanazi Shadi Nashwan Department of Computer Science College of Computer and Information SciencesJouf UniversitySakaka42421Saudi Arabia

Wireless medical sensor networks(WMSNs)play a significant role in increasing the availability of remote healthcare *** vital and physiological data of the patient can be collected using the WMSN via sensor nodes that are placed on his/her body and then transmitted remotely to a healthcare professional for proper *** protection of the patient’s privacy and their data from unauthorized access is a major concern in such ***,an authentication scheme with a high level of security is one of the most effective mechanisms by which to address these security *** authentication schemes for remote patient monitoring have been proposed ***,the majority of these schemes are extremely vulnerable to attacks and are unsuitable for practical *** paper proposes a secure three-factor authentication scheme for a patient-monitoring healthcare system that operates remotely using a *** proposed authentication scheme is formally verified using the Burrows,Abadi and Needham’s(BAN)logic model and an automatic cryptographic protocol verifier(ProVerif)*** show that our authentication scheme can prevent relevant types of security breaches in a practical context according to the discussed possible attack *** of the security and performance are carried out with recently proposed authentication *** results of the analysis show that the proposed authentication scheme is secure and practical for use,with reasonable storage space,computation,and communication efficiency.

关键词： Mutual authentication biometric feature perfect forward secrecy user anonymity proVerif tool BAN logic model

Personality Detection Using Context Based Emotions in Cognitive Agents

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Computers, Materials & Continua 2022年第3期70卷 4947-4964页

作者： Nouh Sabri Elmitwally Asma Kanwal Sagheer Abbas Muhammad A.Khan Muhammad Adnan Khan Munir ahmad Saad alanazi College of Computer and Information Sciences Jouf UniversitySakaka72341Saudi Arabia Department of Computer Science Faculty of Computers and Artificial IntelligenceCairo University12613Egyp t 3School of Computer Science National College of Business administration and EconomicsLahore54000Pakistan Department of Computer Science GC UniversityLahorePakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13120Korea

Detection of personality using emotions is a research domain in artificial *** present,some agents can keep the human’s profile for interaction and adapts themselves according to their ***,the effective method for interaction is to detect the person’s personality by understanding the emotions and context of the *** idea behind adding personality in cognitive agents begins an attempt to maximize adaptability on the basis of *** our daily life,humans socially interact with each other by analyzing the emotions and context of interaction from audio or visual *** paper presents a conceptual personality model in cognitive agents that can determine personality and behavior based on some text input,using the context subjectivity of the given data and emotions obtained from a particular situation/*** proposed work consists of Jumbo Chatbot,which can chat with *** this social interaction,the chatbot predicts human personality by understanding the emotions and context of interactive ***,the Jumbo chatbot is using the BFI technique to interact with a *** accuracy of proposed work varies and improve through getting more experiences of interaction.

关键词： Emotions fuzzy personality detection contextual analysis semantic analysis

Embedded System Based Raspberry Pi 4 for Text Detection and Recognition

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Intelligent Automation & Soft Computing 2023年第6期36卷 3343-3354页

作者： Turki M.alanazi Department of Electrical Engineering College of EngineeringJouf UniversitySakakaSaudi Arabia

Detecting and recognizing text from natural scene images presents a challenge because the image quality depends on the conditions in which the image is captured,such as viewing angles,blurring,sensor noise,***,in this paper,a prototype for text detection and recognition from natural scene images is *** prototype is based on the Raspberry Pi 4 and the Universal Serial Bus(USB)camera and embedded our text detection and recognition model,which was developed using the Python *** model is based on the deep learning text detector model through the Efficient and Accurate Scene Text Detec-tor(EAST)model for text localization and detection and the Tesseract-OCR,which is used as an Optical Character Recognition(OCR)engine for text *** prototype is controlled by the Virtual Network Computing(VNC)tool through a computer via a wireless *** experiment results show that the recognition rate for the captured image through the camera by our prototype can reach 99.75%with low computational ***,our proto-type is more performant than the Tesseract software in terms of the recognition ***,it provides the same performance in terms of the recognition rate with a huge decrease in the execution time by an average of 89%compared to the EasyOCR software on the Raspberry Pi 4 board.

关键词： Text detection text recognition OCR engine natural scene images Raspberry Pi USB camera

Anomaly Detection for Industrial Internet of Things Cyberattacks

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Computer Systems Science & Engineering 2023年第3期44卷 2361-2378页

作者： Rehab alanazi Ahamed Aljuhani College of Computing and Information Technology University of TabukTabuk71491Saudi Arabia

The evolution of the Internet of Things(IoT)has empowered modern industries with the capability to implement large-scale IoT ecosystems,such as the Industrial Internet of Things(IIoT).The IIoT is vulnerable to a diverse range of cyberattacks that can be exploited by intruders and cause substantial reputational andfinancial harm to *** preserve the confidentiality,integrity,and availability of IIoT networks,an anomaly-based intrusion detection system(IDS)can be used to provide secure,reliable,and efficient IIoT *** this paper,we propose an anomaly-based IDS for IIoT networks as an effective security solution to efficiently and effectively overcome several IIoT *** proposed anomaly-based IDS is divided into three phases:pre-processing,feature selection,and *** the pre-processing phase,data cleaning and nor-malization are *** the feature selection phase,the candidates’feature vectors are computed using two feature reduction techniques,minimum redun-dancy maximum relevance and neighborhood components *** thefinal step,the modeling phase,the following classifiers are used to perform the classi-fication:support vector machine,decision tree,k-nearest neighbors,and linear discriminant *** proposed work uses a new data-driven IIoT data set called *** experimental evaluation demonstrates our proposed model achieved a high accuracy rate of 99.58%,a sensitivity rate of 99.59%,a specificity rate of 99.58%,and a low false positive rate of 0.4%.

关键词： Anomaly detection anomaly-based IDS Industrial Internet of Things(IIoT) IoT industrial control systems(ICSs) X-IIoTID