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Smart-City-based Data Fusion Algorithm for Internet of Things

Computers, Materials & Continua 2022年第11期73卷 2407-2421页

作者： Jawad Khan Muhammad Amir Khan n.z.jhanjhi Mamoona Humayun Abdullah Alourani Department of Computer Science IIC University of TechnologyPhnom PenhCambodia Department of Computer Science COMSATS University IslamabadAbbottabad Campus22060Pakistan School of Computer Science and Engineering SCETaylor’s UniversitySubang JayaSelangorMalaysia Department of Information Systems College of Computer and Information SciencesJouf Universityal-JoufKSA Department of Computer Science and Information College of Science in ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia

Increasingly,Wireless Sensor networks(WSns)are contributing enormous amounts of *** the recent deployments of wireless sensor networks in Smart City infrastructures,significant volumes of data have been produced every day in several domains ranging from the environment to the healthcare system to *** wireless sensor nodes,a Smart City environment may now be shown for the benefit of *** Smart City delivers intelligent infrastructure and a stimulating environment to citizens of the Smart Society,including the elderly and ***,Quality of Service(QoS)and poor data performance are common problems in WSns,caused by the data fusion method,where a small amount of bad data can significantly impact the total fusion *** our proposed research,a WSn multisensor data fusion technique employing fuzzy logic for event *** the new proposed Algorithm,sensor nodes will collect less repeated data,and redundant data will be used to increase the data’s overall *** network’s fusion delay problem is investigated,and a minimum fusion delay approach is provided based on the nodes’fusion waiting *** proposed algorithm performs well in fusion,according to the results of the *** a result of these discoveries,It is concluded that the algorithm describe here is effective and dependable instrument with a wide range of applications.

关键词： Internet of things data fusion wireless sensor network fuzzy inference system

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SVM and Knn Based Cnn Architectures for Plant Classification

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Computers, Materials & Continua 2022年第6期71卷 4257-4274页

作者： Sukanta Ghosh Amar Singh Kavita n.z.jhanjhi Mehedi Masud Sultan Aljahdali Lovely Professional University Jalandhar144005India Department of Computer Science and Engineering Chandigarh UniversityMohaliIndia School of Computer Science and Engineering Tailor’s UniversitySubang Jaya47500Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Automatic plant classification through plant leaf is a classical problem in Computer *** classification is challenging due to the introduction of new species with a similar pattern and *** efforts are made to automate plant classification using plant leaf,plant flower,bark,or *** much effort,it has been proven that leaf is the most reliable source for plant *** it is challenging to identify a plant with the help of leaf structure because plant leaf shows similarity in morphological variations,like sizes,textures,shapes,and ***,it is required to normalize all plant leaves into the same size to get better *** neural networks(Cnn)provides a fair amount of accuracy when leaves are classified using this *** the performance can be improved by classifying using the traditional approach after applying *** this paper,two approaches,namely Cnn+Support Vector Machine(SVM)and Cnn+K-nearest neighbors(knn)used on 3 datasets,namely LeafSnap dataset,Flavia Dataset,and MalayaKew *** datasets are augmented to take care all the *** assessments and correlations of the predetermined feature extractor models are ***+knn managed to reach maximum accuracy of 99.5%,97.4%,and 80.04%,respectively,in the three datasets.

关键词： Plant leaf classification artificial intelligence SVM knn deep learning deep Cnn training epoch

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Curvelet Transform Based on Edge Preserving Filter for Retinal Blood Vessel Segmentation

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Computers, Materials & Continua 2022年第5期71卷 2459-2476页

作者： Sonali Dash Sahil Verma Kavita n.z.jhanjhi Mehedi Masud Mohammed Baz Department of Electronics and Communication Engineering Raghu Institute of Technology(A)Visakhapatnam531162India Department of Computer Science and Engineering Chandigarh UniversityMohali140413India School of Computer Science and Engineering SCETaylor’s UniversitySubang Jaya Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering College of Computer and Information TechnologyTaif UniversityTaif21994Saudi Arabia

Segmentation of vessel in retinal fundus images is a primary step for the clinical identification for specific eye *** diagnosis of vascular pathologies from angiographic images is thus a vital aspect and generally depends on segmentation of vascular *** various approaches for retinal vessel segmentation are extensively utilized,however,the responses are lower at vessel’s *** curvelet transform signifies edges better than wavelets,and hence convenient for multiscale edge *** bilateral filter is a nonlinear filter that is capable of providing effective smoothing while preserving strong *** bilateral filter is an advanced version of bilateral filter that regulates the contrast while preserving the ***,in this paper a fusion algorithm is recommended by fusing fast bilateral filter that can effectively preserve the edge details and curvelet transform that has better capability to detect the edge direction feature and better investigation and tracking of significant characteristics of the *** C mean thresholding is used for the extraction of *** recommended fusion approach is assessed on DRIVE *** results illustrate that the fusion algorithm preserved the advantages of the both and provides better *** results demonstrate that the recommended method outperforms the traditional approaches.

关键词： Blood vessel extraction curvelet transform fast bilateral filter C mean thresholding

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Hybrid GrabCut Hidden Markov Model for Segmentation

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Computers, Materials & Continua 2022年第7期72卷 851-869页

作者： Soobia Saeed Afnizanfaizal Abdullah n.z.jhanjhi Mehmood naqvi Mehedi Masud Mohammed A.Alzain Department of Software Engineering UniversitiTeknologi Malaysia-UTM81310Malaysia School of Computer Science and Engineering SCE Taylor’s UniversitySubang Jaya47500Malaysia Department of Engineering and Technology Mohak CollegeAlbertaCanada Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Diagnosing data or object detection in medical images is one of the important parts of image segmentation especially those data which is less effective to identify inMRI such as low-grade tumors or cerebral spinal fluid(CSF)leaks in the *** aim of the study is to address the problems associated with detecting the low-grade tumor and CSF in brain is difficult in magnetic resonance imaging(MRI)images and another problem also relates to efficiency and less execution time for segmentation of medical *** tumor and CSF segmentation using trained light field database(LFD)datasets of MRI *** research proposed the new framework of the hybrid k-nearest neighbors(k-nn)model that is a combination of hybridization of Graph Cut and Support Vector Machine(GCSVM)and Hidden Markov Model of k-Mean Clustering Algorithm(HMMkC).There are four different methods are used in this research namely(1)SVM,(2)GrabCut segmentation,(3)HMM,and(4)k-mean clustering *** this framework,on the one hand,phase one is to perform the classification of SVM and Graph Cut algorithm to create the maximum margin *** research use GrabCut segmentation method which is the application of the graph cut algorithm and extract the data with the help of scaleinvariant features *** the other hand,in phase two,segment the low-grade tumors and CSF using a method adapted for HMkC and extract the information of tumor or CSF fluid by GCHMkC including iterative conditional maximizing mode(ICMM)with identifying the range of *** evaluation is also performing by the comparison of existing techniques in this *** conclusion,our proposed model gives better results than *** proposed model helps to common man and doctor that can identify their condition of brain *** future,this will model will use for other brain related diseases.

关键词： SVM tumor CSF k-nn MRI grabcut segmentation HMM

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Packet Optimization of Software Defined network Using Lion Optimization

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Computers, Materials & Continua 2021年第11期69卷 2617-2633页

作者： Jagmeet Kaur Shakeel Ahmed Yogesh Kumar A.Alaboudi n.z.jhanjhi Muhammad Fazal Ijaz Department of Computer Science&Engineering Chandigarh UniversityMohaliPunjab140413India Department of Computer Science College of Computer Sciences and Information TechnologyKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Science&Engineering Chandigarh Group of CollegesMohaliPunjab140307India College of Computer Science Shaqra UniversitySaudi Arabia School of Computer Science and Engineering SCETaylor’s UniversitySubang Jaya47500 Malaysia Department of Intelligent Mechatronics Engineering Sejong UniversitySeoul05006Korea

There has been an explosion of cloud services as organizations take advantage of their continuity,predictability,as well as quality of service and it raises the concern about latency,energy-efficiency,and *** increase in demand requires new configurations of networks,products,and service *** this purpose,the software-defined network is an efficient technology that enables to support the future network functions along with the intelligent applications and packet *** work analyzes the offline cloud scenario in which machines are efficiently deployed and scheduled for user processing *** is evaluated in terms of reducing bandwidth,task execution times and latencies,and increasing throughput.A minimum execution time algorithm is used to compute the completion time of all the available resources which are allocated to the virtual machine and lion optimization algorithm is applied to packets in a cloud *** proposed work is shown to improve the throughput and latency rate.

关键词： Software-defined network cloud computing packet optimization energy efficiency lion optimization minimum execution time

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Plant Disease Diagnosis and Image Classification Using Deep Learning

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Computers, Materials & Continua 2022年第5期71卷 2125-2140页

作者： Rahul Sharma Amar Singh Kavita n.z.jhanjhi Mehedi Masud Emad Sami Jaha Sahil Verma School of Computer Applications Lovely Professional University Jalandhar144411India Department of Computer Science and Engineering Chandigarh UniversityMohaliIndia Department of Computer Science and Engineering Tailor’s UniversityMalaysia Department of Computer Science College of Computer and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science Faculty of Computer and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

Indian agriculture is striving to achieve sustainable intensification,the system aiming to increase agricultural yield per unit area without harming natural resources and the *** farming employs technology to improve *** and accurate analysis and diagnosis of plant disease is very helpful in reducing plant diseases and improving plant health and food crop *** disease experts are not available in remote areas thus there is a requirement of automatic low-cost,approachable and reliable solutions to identify the plant diseases without the laboratory inspection and expert’s *** learning-based computer vision techniques like Convolutional neural network(Cnn)and traditional machine learning-based image classification approaches are being applied to identify plant *** this paper,the Cnn model is proposed for the classification of rice and potato plant leaf *** leaves are diagnosed with bacterial blight,blast,brown spot and tungro *** leaf images are classified into three classes:healthy leaves,early blight and late blight *** leaf dataset with 5932 images and 1500 potato leaf images are used in the *** proposed Cnn model was able to learn hidden patterns from the raw images and classify rice images with 99.58%accuracy and potato leaves with 97.66%*** results demonstrate that the proposed Cnn model performed better when compared with other machine learning image classifiers such as Support Vector Machine(SVM),K-nearest neighbors(Knn),Decision Tree and Random Forest.

关键词： Plant diseases detection Cnn image classification deep learning in agriculture

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A Secure Communication Protocol for Unmanned Aerial Vehicles

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Computers, Materials & Continua 2022年第1期70卷 601-618页

作者： navid Ali Khan n.z.jhanjhi Sarfraz nawaz Brohi Abdulwahab Ali Almazroi Abdulaleem Ali Almazroi School of Computer Science and Engineering(SCE) Taylor’s University Lake-side CampusSubang Jaya47500Malaysia School of Information and Technology Monash UniversityMalaysia University of Jeddah College of Computing and Information Technology at KhulaisDepartment of Information TechnologyJeddahSaudi Arabia Department of Computer Science Rafha Community CollegeNorthern Border UniversityArar91431Saudi Arabia

Mavlink is a lightweight and most widely used open-source communication protocol used for Unmanned Aerial *** UAVs and autopilot systems support it,and it provides bi-directional communication between the UAV and Ground Control *** communications contain critical information about the UAV status and basic control commands sent from GCS to UAV and UAV to *** order to increase the transfer speed and efficiency,the Mavlink does not encrypt the *** a result,the protocol is vulnerable to various security attacks such as Eavesdropping,GPS Spoofing,and *** this study,we tackle the problem and secure the Mavlink communication *** leveraging the Mavlink packet’s vulnerabilities,this research work introduces an experiment in which,first,the Mavlink packets are compromised in terms of security requirements based on our threat *** results show that the protocol is insecure and the attacks carried out are *** overcomeMavlink security,an additional security layer is added to encrypt and secure the *** encryption technique is proposed that makes the communication between the UAV and GCS *** results show that the Mavlink packets are encrypted using our technique without affecting the performance and *** results are validated in terms of transfer speed,performance,and efficiency compared to the literature solutions such as MAVSec and benchmarked with the original Mavlink *** achieved results have significant improvement over the literature and Mavlink in terms of security.

关键词： Unmanned aerial vehicles mavlink protocol drones security UAVs communication

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Information-Centric IoT-Based Smart Farming with Dynamic Data Optimization

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Computers, Materials & Continua 2023年第2期74卷 3865-3880页

作者： Souvik Pal Hannah VijayKumar D.Akila n.z.jhanjhi Omar A.Darwish Fathi Amsaad Department of Computer Science and Engineering Sister Nivedita UniversityKolkataIndia Post Doctoral Researcher Sambalpur UniversitySambalpurIndia Department of Computer Science Anna Adarsh College for WomenChennaiIndia Department of Information Technology Vels Institute of ScienceTechnology and Advanced StudiesChennaiIndia School of Computer Science and Engineering SCETaylor’s UniversitySubang Jaya47500SelangorMalaysia FITTaylor’s UniversitySubang Jaya47500SelangorMalaysia Department of Information Security and Applied Computing Eastern Michigan UniversityUSA School of Information Security and Applied Computing Eastern Michigan UniversityUSA

Smart farming has become a strategic approach of sustainable agriculture management and monitoring with the infrastructure to exploit modern technologies,including big data,the cloud,and the Internet of Things(IoT).Many researchers try to integrate IoT-based smart farming on cloud platforms *** define various frameworks on smart farming and monitoring system and still lacks to define effective data management *** IoT-cloud systems involve massive structured and unstructured data,data optimization comes into the ***,this research designs an Information-Centric IoT-based Smart Farming with Dynamic Data Optimization(ICISF-DDO),which enhances the performance of the smart farming infrastructure with minimal energy consumption and improved ***,a conceptual framework of the proposed scheme and statistical design model has beenwell *** information storage and management with DDO has been expanded individually to show the effective use of membership parameters in data *** simulation outcomes state that the proposed ICISF-DDO can surpass existing smart farming systems with a data optimization ratio of 97.71%,reliability ratio of 98.63%,a coverage ratio of 99.67%,least sensor error rate of 8.96%,and efficient energy consumption ratio of 4.84%.

关键词： Smart farming data optimization information management internet of things cloud

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Robust Cluster-Based Routing Protocol for IoT-Assisted Smart Devices in WSn

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Computers, Materials & Continua 2021年第6期67卷 3505-3521页

作者： Maryam Shaq Humaira Ashraf Ata Ullah Mehedi Masud Muhammad Azeem n.z.jhanjhi Mamoona Humayun Department of Computer Science and Software Engineering International Islamic University IslamabadIslamabadPakistan Department of Computer Science National University of Modern LanguagesIslamabadPakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia School of Computer Science and Engineering(SCE) Taylor’s UniversitySelangorMalaysia Department of Information Systems College of Computer and Information ScienceJouf UniversityAl-JoufSaudi Arabia

The Internet of Things(IoT)is gaining attention because of its broad applicability,especially by integrating smart devices for massive communication during sensing ***-assisted Wireless Sensor networks(WSn)are suitable for various applications like industrial monitoring,agriculture,and *** this regard,routing is challenging to nd an efcient path using smart devices for transmitting the packets towards big data repositories while ensuring efcient energy *** paper presents the Robust Cluster Based Routing Protocol(RCBRP)to identify the routing paths where less energy is consumed to enhances the network *** scheme is presented in six phases to explore ow and *** propose the two algorithms:(i)energy-efcient clustering and routing algorithm and (ii)distance and energy consumption calculation *** scheme consumes less energy and balances the load by clustering the smart *** work is validated through extensive simulation using *** elucidate the dominance of the proposed scheme is compared to counterparts in terms of energy consumption,the number of packets received at BS and the number of active and dead *** the future,we shall consider edge computing to analyze the performance of robust clustering.

关键词： Energy efciency routing load balancing cluster selection

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Performance Comparison of Deep Cnn Models for Detecting Driver’s Distraction

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Computers, Materials & Continua 2021年第9期68卷 4109-4124页

作者： Kathiravan Srinivasan Lalit Garg Debajit Datta Abdulellah A.Alaboudi n.z.jhanjhi Rishav Agarwal Anmol George Thomas School of Information Technology and Engineering Vellore Institute of TechnologyVellore632014India Faculty of Information and Communication Technology University of MaltaMsida2080MSDMalta School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India College of Computer Science Shaqra UniversityKingdom of Saudi Arabia School of Computer Science and Engineering SCETaylor’s UniversitySubang Jaya47500Malaysia

According to various worldwide statistics,most car accidents occur solely due to human *** person driving a car needs to be alert,especially when travelling through high traffic volumes that permit high-speed transit since a slight distraction can cause a fatal *** though semiautomated checks,such as speed detecting cameras and speed barriers,are deployed,controlling human errors is an arduous *** key causes of driver’s distraction include drunken driving,conversing with co-passengers,fatigue,and operating gadgets while *** these distractions are accurately predicted,the drivers can be alerted through an alarm ***,this research develops a deep convolutional neural network(deep Cnn)models for predicting the reason behind the driver’s *** deep Cnn models are trained using numerous images of distracted *** performance of deep Cnn models,namely the VGG16,Resnet,and Xception network,is assessed based on the evaluation metrics,such as the precision score,the recall/sensitivity score,the F1 score,and the specificity *** Resnet model outperformed all other models as the best detection model for predicting and accurately determining the drivers’activities.

关键词： Deep-Cnn Resnet Xception VGG16 data classification

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