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Cancellable Multi-Biometric Feature Veins Template Generation Based on sHA-3 Hashing

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Computers, Materials & Continua 2023年第1期74卷 733-749页

作者： salwa M.serag Eldin Ahmed sedik sultan s.alshamrani Ahmed M.Ayoup Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Robotics and Intelligent Machine Faculty of Artificial IntelligentKafrelsheikh UniversityEgypt Department of Information Technology College of Computer and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Electrical Communications Engineering Faculty of EngineeringMinia UniversityMinia61111Egypt

In this paper,a novel cancellable biometrics technique calledMulti-Biometric-Feature-Hashing(MBFH)is *** MBFH strategy is utilized to actualize a single direction(non-invertibility)biometric *** is a typical model security conspire that is distinguished in the utilization of this protection insurance framework in numerous sorts of biometric feature strategies(retina,palm print,Hand Dorsum,fingerprint).A more robust and accurate multilingual biological structure in expressing human loneliness requires a different format to record clients with inseparable comparisons from individual biographical *** may raise worries about their utilization and security when these spread out designs are subverted as everybody is acknowledged for another biometric *** proposed structure comprises of four sections:input multi-biometric acquisition,feature extraction,Multi-Exposure Fusion(MEF)and secure hashing calculation(sHA-3).Multimodal biometrics systems that are more powerful and precise in human-unmistakable evidence require various configurations to store a comparative customer that can be contrasted with biometric wellsprings of *** top words,biometrics graphs can’t be denied and change to another request for positive Identifications(IDs)while *** biometrics is may be the special procedure used to recognize this issue.

关键词： Feature extraction multi-biometrics sHA-3 MEF

Effective Diagnosis of Lung Cancer via Various Data-Mining Techniques

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Intelligent Automation & soft Computing 2023年第4期36卷 415-428页

作者： subramanian Kanageswari D.Gladis Irshad Hussain sultan s.alshamrani Abdullah Alshehri Bharathiar University Coimbatore641046India Bharathi Women’s College Chennai600108India Faculty of Electrical and Computer Engineering University of Engineering and TechnologyPeshawar25000PeshawarPakistan Department of Information Technology College of Computer and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Technology Al Baha UniversityP.O.Box 1988Al Baha65431Saudi Arabia

One of the leading cancers for both genders worldwide is lung *** occurrence of lung cancer has fully augmented since the early 19th *** this manuscript,we have discussed various data mining techniques that have been employed for cancer *** to air pollution has been related to various adverse health *** work is subject to analysis of various air pollutants and associated health hazards and intends to evaluate the impact of air pollution caused by lung *** have introduced data mining in lung cancer to air pollution,and our approach includes preprocessing,data mining,testing and evaluation,and knowledge ***,we will eradicate the noise and irrelevant data,and following that,we will join the multiple informed sources into a common *** that source,we will designate the information relevant to our investigation to be regained from that *** that,we will convert the designated data into a suitable mining *** patterns are abstracted by utilizing a relational suggestion rule mining *** patterns have revealed information,and this information is categorized with the help of an Auto Associative Neural Network classiﬁcation method(AANN).The proposed method is compared with the existing method in various *** conclusion,the projected Auto associative neural network and relational suggestion rule mining methods accomplish a high accuracy status.

关键词： Relational association rule mining auto associative neural network preprocessing data mining biological neural network

Deep Optimal VGG16 Based COVID-19 Diagnosis Model

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Computers, Materials & Continua 2022年第1期70卷 43-58页

作者： M.Buvana K.Muthumayil s.senthil kumar Jamel Nebhen sultan s.alshamrani Ihsan Ali Department of Computer science and Engineering PSNA College of Engineering and TechnologyDindigul624622India Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and TechnologyChennai600073India Prince sattam Bin Abdulaziz University College of Computer Engineering and SciencesAlkharj11942Saudi Arabia Department of Information Technology College of Computer and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer system and Technology Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala Lumpur50603Malaysia

Coronavirus(COVID-19)outbreak was first identified in Wuhan,China in December *** was tagged as a pandemic soon by the WHO being a serious public medical *** spite of the fact that the virus can be diagnosed by qRT-PCR,COVID-19 patients who are affected with pneumonia and other severe complications can only be diagnosed with the help of Chest X-Ray(CXR)and Computed Tomography(CT)*** this paper,the researchers propose to detect the presence of COVID-19 through images using Best deep learning model with various *** features like speeded-Up Robust Features(sURF),Features from Accelerated segment Test(FAsT)and scale-Invariant Feature Transform(sIFT)are used in the test images to detect the presence of *** optimal features are extracted from the images utilizing DeVGGCovNet(Deep optimal VGG16)model through optimal learning *** task is accomplished by exceptional mating conduct of Black Widow *** this strategy,cannibalism is *** this phase,fitness outcomes are rejected and are not satisfied by the proposed *** results acquired from real case analysis demonstrate the viability of DeVGGCovNet technique in settling true issues using obscure and testing ***16 model identifies the imagewhich has a place with which it is dependent on the distinctions in *** impact of the distinctions on labels during training stage is studied and predicted for test *** proposed model was compared with existing state-of-the-art models and the results from the proposed model for disarray grid estimates like sen,spec,Accuracy and F1 score were promising.

关键词： COVID 19 multi-feature extraction vgg16 optimal learning rate

Detection Collision Flows in sDN Based 5G Using Machine Learning Algorithms

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Computers, Materials & Continua 2023年第1期74卷 1413-1435页

作者： Aqsa Aqdus Rashid Amin sadia Ramzan sultan s.alshamrani Abdullah Alshehri El-sayed M.El-kenawy Department of Computer science University of Engineering and Technology Taxila47050Pakistan Department of Computer science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Technology Al Baha UniversityAl BahaSaudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt

The rapid advancement of wireless communication is forming a hyper-connected 5G network in which billions of linked devices generate massive amounts of *** traffic control and data forwarding functions are decoupled in software-defined networking(sDN)and allow the network to be *** switch in sDN keeps track of forwarding information in a flow *** sDN switches must search the flow table for the flow rules that match the packets to handle the incoming *** to the obvious vast quantity of data in data centres,the capacity of the flow table restricts the data plane’s forwarding ***,the sDN must handle traffic from across the whole *** flow table depends on Ternary Content Addressable Memorable Memory(TCAM)for storing and a quick search of regulations;it is restricted in capacity owing to its elevated cost and energy *** the flow table is abused and overflowing,the usual regulations cannot be executed *** this case,we consider lowrate flow table overflowing that causes collision flow rules to be installed and consumes excessive existing flow table capacity by delivering packets that don’t fit the flow table at a low *** study introduces machine learning techniques for detecting and categorizing low-rate collision flows table in sDN,using Feed ForwardNeuralNetwork(FFNN),K-Means,and Decision Tree(DT).We generate two network topologies,Fat Tree and simple Tree Topologies,with the Mininet simulator and coupled to the OpenDayLight(ODL)*** efficiency and efficacy of the suggested algorithms are assessed using several assessment indicators such as success rate query,propagation delay,overall dropped packets,energy consumption,bandwidth usage,latency rate,and *** findings showed that the suggested technique to tackle the flow table congestion problem minimizes the number of flows while retaining the statistical consistency of the 5G *** putting the proposed flow method

关键词： 5G networks software-defined networking(sDN) OpenFlow load balancing machine learning(ML) feed forward neural network(FFNN) k-means and decision tree(DT)

Deep Learning Approach for Analysis and Characterization of COVID-19

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Computers, Materials & Continua 2022年第1期70卷 451-468页

作者： Indrajeet Kumar sultan s.alshamrani Abhishek Kumar Jyoti Rawat Kamred Udham singh Mamoon Rashid Ahmed saeed AlGhamdi school of Computing Graphic Era Hill UniversityDehradun(UK)India Department of Information Technology College of Computer and Information TechnologyTaif UniversityTaif21944Saudi Arabia school of Computer science&IT JAIN(to be Deemed University)BangaloreIndia school of Computing Dehradun Institute of TechnologyUttarakhandIndia Department of Computer Engineering Faculty of Science and TechnologyVishwakarma UniversityPuneIndia Department of Computer Engineering College of Computer and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Early diagnosis of a pandemic disease like COVID-19 can help deal with a dire situation and help radiologists and other experts manage human resources more *** a recent pandemic,laboratories perform diagnostics manually,which requires a lot of time and expertise of the laboratorial technicians to yield accurate ***,the cost of kits is high,and well-equipped labs are needed to perform this ***,other means of diagnosis is highly *** is one of the existing methods that finds its use in the diagnosis of *** radiography observes change in Computed Tomography(CT)chest images of patients,developing a deep learning-based method to extract graphical features which are used for automated diagnosis of the disease ahead of laboratory-based *** proposed work suggests an Artificial Intelligence(AI)based technique for rapid diagnosis of COVID-19 from given volumetric chest CT images of patients by extracting its visual features and then using these features in the deep learning *** proposed convolutional neural network aims to classify the infectious and non-infectious sARs-COV2 *** proposed network utilizes 746 chests scanned CT images of 349 images belonging to COVID-19 positive cases,while 397 belong to negative cases of *** experiment resulted in an accuracy of 98.4%,sensitivity of 98.5%,specificity of 98.3%,precision of 97.1%,and F1-score of 97.8%.The additional parameters of classification error,mean absolute error(MAE),root-mean-square error(RMsE),and Matthew’s correlation coefficient(MCC)are used to evaluate our proposed *** obtained result shows the outstanding performance for the classification of infectious and non-infectious for COVID-19 cases.

关键词： Coronavirus covid-19 respiratory infection computed tomography deep neural network

Adaptive Power Control Aware Depth Routing in Underwater sensor Networks

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Computers, Materials & Continua 2021年第10期69卷 1301-1322页

作者： Ghufran Ahmed saiful Islam Ihsan Ali Isra Adil Hayder Abdelmuttlib Ibrahim Abdalla Ahmed Muhammad Talha sultan s.alshamrani Ag Asri Ag Ibrahim Department of Computer science FAST National University of Computer and Emerging Sciences(NUCES)KarachiPakistan Dr.A.Q.Khan Institute of Computer sciences and Information Technology KahutaPakistan Faculty of Computer science and Information Technology University of MalayaKuala Lumpur50603Malaysia Department of Computer science COMSATS University IslamabadIslamabadPakistan Deanship of scientific Research King Saud UniversityRiyadhSaudi Arabia Department of Information Technology College of Computer and Information TechnologyTaif UniversityTaif21944Saudi Arabia Faculty of Computing and Informatics Universiti Malaysia SabahKota KinabaluSabahMalaysia

Underwater acoustic sensor network(UAsN)refers to a procedure that promotes a broad spectrum of aquatic *** can be practically applied in seismic checking,ocean mine identification,resource exploration,pollution checking,and disaster *** confronts many difficulties and issues,such as low bandwidth,node movements,propagation delay,3D arrangement,energy limitation,and high-cost production and arrangement costs caused by antagonistic underwater *** wireless sensor networks(UWsNs)are considered a major issue being encountered in energy management because of the limited battery power of their ***,the harsh underwater environment requires vendors to design and deploy energy-hungry devices to fulfil the communication requirements and maintain an acceptable quality of ***,increased transmission power levels result in higher channel interference,thereby increasing packet *** the facts mentioned above,this research presents a controlled transmission power-based sparsity-aware energy-efficient clustering in *** contributions of this technique is ***,it uses the adaptive power control mechanism to utilize the sensor nodes’battery and reduce channel interference ***,thresholds are defined to ensure successful ***,clustering can be implemented in dense areas to decrease the repetitive transmission that ultimately affects the energy consumption of nodes and interference ***,mobile sinks are deployed to gather information locally to achieve the previously mentioned *** suggested protocol is meticulously examined through extensive simulations and is validated through comparison with other advanced UWsN *** show that the suggested protocol outperforms other procedures in terms of network lifetime and packet delivery ratio.

关键词： UWsNs UAsNs Qos

security Attacks on the IoT Network with 5G Wireless Communication

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Journal on Internet of Things 2021年第3期3卷 119-130页

作者： Ghada sultan Aljumaie Ghada Hisham Alzeer sultan s.alshamrani Department of Cyber security College of Computers and Information TechnologyTaif21944Saudi Arabia

The term Internet of Things has increased in popularity in recent years and has spread to be used in many applications around us,such as healthcare applications,smart homes and smart cities,IoT is a group of smart devices equipped with sensors that have the ability to calculate data,and carry out actions in the environment in which they are located,they are connected to each other through the Internet and recently it has become supported by 5G technology due to many advantages such as its ability to provide a fast connection,despite the efficiency of the IoT supported by the five G technology,it is subject to many security *** this paper,we conducted a comprehensive review of previous research related to the security requirements of the IoT and security attacks.

关键词： Internet of Things IoT 5G security

Extended Forgery Detection Framework for COVID-19 Medical Data Using Convolutional Neural Network

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Computers, Materials & Continua 2021年第9期68卷 3773-3787页

作者： sajid Habib Gill Noor Ahmed sheikh samina Rajpar Zain ul Abidin N.Z.Jhanjhi Muneer Ahmad Mirza Abdur Razzaq sultan s.alshamrani Yasir Malik Fehmi Jaafar Department of Computer science Shah Abdul Latif UniversityKhairpur66020SindhPakistan National College of Business Administration&Economics Lahore54000Pakistan school of Computer science and Engineering SCETaylor’s UniversityMalaysia Department of Information systems Faculty of Computer Science&Information TechnologyUniversiti MalayaMalaysia Department of Information Technology College of Computer and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer science College of Engineering&Computing SciencesNew York Institute of TechnologyVancouverCanada The Computer Research Institute of Montreal QuebecCanada

Medical data tampering has become one of the main challenges in the field of secure-aware medical data *** of normal patients’medical data to present them as COVID-19 patients is an illegitimate action that has been carried out in different ways ***,the integrity of these data can be *** detection is a method of detecting an anomaly in manipulated forged *** appropriate number of features are needed to identify an anomaly as either forged or non-forged data in order to find distortion or tampering in the original *** neural networks(CNNs)have contributed a major breakthrough in this type of *** has been much interest from both the clinicians and the AI community in the possibility of widespread usage of artificial neural networks for quick diagnosis using medical data for early COVID-19 patient *** purpose of this paper is to detect forgery in COVID-19 medical data by using CNN in the error level analysis(ELA)by verifying the noise pattern in the *** proposed improved ELA method is evaluated using a type of data splicing forgery and sigmoid and ReLU phenomenon *** proposed method is verified by manipulating COVID-19 data using different types of forgeries and then applying the proposed CNN model to the data to detect the data *** results show that the accuracy of the proposed CNN model on the test COVID-19 data is approximately 92%.

关键词： Data security data privacy medical-data forgery COVID-19 convolutional neural network machine learning deep learning

Novel Framework for an Intrusion Detection system Using Multiple Feature selection Methods Based on Deep Learning

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Tsinghua science and Technology 2024年第4期29卷 948-958页

作者： A.E.M.Eljialy Mohammed Yousuf Uddin sultan Ahmad Department of Information systems College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Computer science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabiaand also with University Center for Research and Development(UCRD)Department of Computer Science and EngineeringChandigarh UniversityPunjabIndia

Intrusion detection systems (IDss) are deployed to detect anomalies in real time. They classify a network’s incoming traffic as benign or anomalous (attack). An efficient and robust IDs in software-defined networks is an inevitable component of network security. The main challenges of such an IDs are achieving zero or extremely low false positive rates and high detection rates. Internet of Things (IoT) networks run by using devices with minimal resources. This situation makes deploying traditional IDss in IoT networks unfeasible. Machine learning (ML) techniques are extensively applied to build robust IDss. Many researchers have utilized different ML methods and techniques to address the above challenges. The development of an efficient IDs starts with a good feature selection process to avoid overfitting the ML model. This work proposes a multiple feature selection process followed by classification. In this study, the software-defined networking (sDN) dataset is used to train and test the proposed model. This model applies multiple feature selection techniques to select high-scoring features from a set of features. Highly relevant features for anomaly detection are selected on the basis of their scores to generate the candidate dataset. Multiple classification algorithms are applied to the candidate dataset to build models. The proposed model exhibits considerable improvement in the detection of attacks with high accuracy and low false positive rates, even with a few features selected.

关键词： random forest feature selection decision tree software-defined network logistic regression intrusion detection system XGB classifier AdaBoost