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Drift DetectionMethod Using DistanceMeasures and Windowing Schemes for Sentiment Classification

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Computers, Materials & Continua 2023年第3期74卷 6001-6017页

作者： Idris Rabiu Naomie Salim Maged Nasser Aminu Da’u taiseer abdalla elfadil eisa Mhassen Elnour Elneel Dalam School of Computing Univerti Teknologi MalaysiaJohor81310Malaysia UTM Big Data Centre Ibnu Sina Institute for Scientific and Industrial ResearchUniversiti Teknologi MalaysiaJohor81310Malaysia Ibrahim Badamasi Babangida University LapaiPMB 11Niger SteateNigeria UNITAR Graduate School UNITAR International UniversityTierra CrestJln SS6//3Petaling Jaya47301SelangorMalaysia Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia Department of Mathematics-Girls Section King Khalid UniversityMahayil62529Saudi Arabia

Textual data streams have been extensively used in practical applications where consumers of online products have expressed their views regarding online *** to changes in data distribution,commonly referred to as concept drift,mining this data stream is a challenging problem for *** majority of the existing drift detection techniques are based on classification errors,which have higher probabilities of false-positive or missed *** improve classification accuracy,there is a need to develop more intuitive detection techniques that can identify a great number of drifts in the data *** paper presents an adaptive unsupervised learning technique,an ensemble classifier based on drift detection for opinion mining and sentiment *** improve classification performance,this approach uses four different dissimilarity measures to determine the degree of concept drifts in the data *** a drift is detected,the proposed method builds and adds a new classifier to the *** add a new classifier,the total number of classifiers in the ensemble is first checked if the limit is exceeded before the classifier with the least weight is removed from the *** this end,a weighting mechanism is used to calculate the weight of each classifier,which decides the contribution of each classifier in the final classification *** experiments were conducted on real-world datasets and the resultswere evaluated on the false positive rate,miss detection rate,and accuracy *** proposed method is also compared with the state-of-the-art methods,which include DDM,EDDM,and PageHinkley with support vector machine(SVM)and Naive Bayes classifiers that are frequently used in concept drift detection *** all cases,the results show the efficiency of our proposed method.

关键词： Data streams sentiment analysis concept drift ensemble classification adaptive window

Optimal Load Forecasting Model for Peer-to-Peer Energy Trading in Smart Grids

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Computers, Materials & Continua 2022年第1期70卷 1053-1067页

作者： Lijo Jacob Varghese K.Dhayalini Suma Sira Jacob Ihsan Ali Abdelzahir Abdelmaboud taiseer abdalla elfadil eisa Department of Electrical and Electronics Engineering Christian College of Engineering and TechnologyOddanchatram624619India Department of Electrical and Electronics Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering Christian College of Engineering and TechnologyOddanchatram624619India Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala LumpurMalaysia Department of Information Systems King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia

Peer-to-Peer(P2P)electricity trading is a significant research area that offers maximum fulfilment for both prosumer and *** also decreases the quantity of line loss incurred in Smart Grid(SG).But,uncertainities in demand and supply of the electricity might lead to instability in P2P market for both prosumer and *** recent times,numerous Machine Learning(ML)-enabled load predictive techniques have been developed,while most of the existing studies did not consider its implicit features,optimal parameter selection,and prediction *** order to overcome fulfill this research gap,the current research paper presents a new Multi-Objective Grasshopper Optimisation Algorithm(MOGOA)with Deep Extreme Learning Machine(DELM)-based short-term load predictive technique i.e.,MOGOA-DELM model for P2P Energy Trading(ET)in *** proposed MOGOA-DELM model involves four distinct stages of operations namely,data cleaning,Feature Selection(FS),prediction,and parameter *** addition,MOGOA-based FS technique is utilized in the selection of optimum subset of ***,DELM-based predictive model is also applied in forecasting the load *** proposed MOGOA model is also applied in FS and the selection of optimalDELM parameters to improve the predictive *** inspect the effectual outcome of the proposed MOGOA-DELM model,a series of simulations was performed using UK Smart Meter *** the experimentation procedure,the proposed model achieved the highest accuracy of 85.80%and the results established the superiority of the proposed model in predicting the testing data.

关键词： Peer to Peer energy trade smart grid load forecasting machine learning feature selection

Deep Reinforcement Learning Enabled Smart City Recycling Waste Object Classification

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Computers, Materials & Continua 2022年第6期71卷 5699-5715页

作者： Mesfer Al Duhayyim taiseer abdalla elfadil eisa Fahd NAl-Wesabi Abdelzahir Abdelmaboud Manar Ahmed Hamza Abu Sarwar Zamani Mohammed Rizwanullah Radwa Marzouk Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Information Systems King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt Corresponding Author:Manar Ahmed Hamza.Email:ma.hamza@psau.edu.sa

The Smart City concept revolves around gathering real time data from citizen,personal vehicle,public transports,building,and other urban infrastructures like power grid and waste disposal *** understandings obtained from the data can assist municipal authorities handle assets and services *** the same time,the massive increase in environmental pollution and degradation leads to ecological imbalance is a hot research ***,the progressive development of smart cities over the globe requires the design of intelligent waste management systems to properly categorize the waste depending upon the nature of *** of the commonly available wastes are paper,paper boxes,food,glass,*** order to classify the waste objects,computer vision based solutions are cost effective to separate out the waste from the huge dump of garbage and *** to the recent developments of deep learning(DL)and deep reinforcement learning(DRL),waste object classification becomes possible by the identification and detection of *** this aspect,this paper designs an intelligence DRL based recycling waste object detection and classification(IDRL-RWODC)model for smart *** goal of the IDRLRWODC technique is to detect and classify waste objects using the DL and DRL *** IDRL-RWODC technique encompasses a twostage process namely Mask Regional Convolutional Neural Network(Mask RCNN)based object detection and DRL based object *** addition,DenseNet model is applied as a baseline model for the Mask RCNN model,and a deep Q-learning network(DQLN)is employed as a ***,a dragonfly algorithm(DFA)based hyperparameter optimizer is derived for improving the efficiency of the DenseNet *** order to ensure the enhanced waste classification performance of the IDRL-RWODC technique,a series of simulations take place on benchmark dataset and the experimental results pointed out the better performance over the recent techniques

关键词： Smart cities deep reinforcement learning computer vision image classification object detection waste management

Elevating Localization Accuracy in Wireless Sensor Networks:A Refined DV-Hop Approach

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Computers, Materials & Continua 2024年第10期81卷 1511-1528页

作者： Muhammad Aamer Ejaz Kamalrulnizam Abu Bakar Ismail Fauzi Bin Isnin Babangida Isyaku taiseer abdalla elfadil eisa Abdelzahir Abdelmaboud Asma Abbas Hassan Elnour Department of Computer Science Faculty of ComputingUniversiti Technologi MalaysiaJohor Bahru81310Malaysia Department of Computer Science Faculty of Computing and Information TechnologySule Lamido UniversityP.M.B.048Kafin Hausa741103Nigeria Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia Humanities Research Center Sultan Qaboos UniversityMuscat123Oman

Localization is crucial in wireless sensor networks for various applications,such as tracking objects in outdoor environments where GPS(Global Positioning System)or prior installed infrastructure is ***,traditional techniques involve many anchor nodes,increasing costs and reducing *** solutions do not address the selection of appropriate anchor nodes and selecting localized nodes as assistant anchor nodes for the localization process,which is a critical element in the localization ***,an inaccurate average hop distance significantly affects localization *** propose an improved DV-Hop algorithm based on anchor sets(AS-IDV-Hop)to improve the localization *** simulation analysis,we validated that the ASIDV-Hop proposed algorithm is more efficient in minimizing localization errors than existing *** ASIDV-Hop algorithm provides an efficient and cost-effective solution for localization in Wireless Sensor *** strategically selecting anchor and assistant anchor nodes and rectifying the average hop distance,AS-IDV-Hop demonstrated superior performance,achieving a mean accuracy of approximately 1.59,which represents about 25.44%,38.28%,and 73.00%improvement over other algorithms,*** estimated localization error is approximately 0.345,highlighting AS-IDV-Hop’s *** substantial reduction in localization error underscores the advantages of implementing AS-IDV-Hop,particularly in complex scenarios requiring precise node localization.

关键词： Localization anchor nodes collinearity connectivity hop based

QoS Aware Multicast Routing Protocol for Video Transmission in Smart Cities

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Computers, Materials & Continua 2022年第8期72卷 2483-2499页

作者： Khaled Mohamad Almustafa taiseer abdalla elfadil eisa Amani Abdulrahman Albraikan Mesfer Al Duhayyim Manar Ahmed Hamza Abdelwahed Motwakel Ishfaq Yaseen Muhammad Imran Babar Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz University16278Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia Department of Computer Science FAST-National University of Computer and Emerging SciencesChiniotFaisalabad44090Pakistan

In recent years,Software Defined Networking(SDN)has become an important candidate for communication infrastructure in smart *** produces a drastic increase in the need for delivery of video services that are of high resolution,multiview,and large-scale in ***,this entity gets easily influenced by heterogeneous behaviour of the user’s wireless link features that might reduce the quality of video stream for few or all *** development of SDN allows the emergence of new possibilities for complicated controlling of video ***,multicast routing protocol with multiple constraints in terms of Quality of Service(QoS)is a Nondeterministic Polynomial time(NP)hard problem which can be solved only with the help of metaheuristic optimization *** this motivation,the current research paper presents a new Improved BlackWidow Optimization with Levy Distribution model(IBWO-LD)-based multicast routing protocol for smart *** presented IBWO-LD model aims at minimizing the energy consumption and bandwidth utilization while at the same time accomplish improved quality of video streams that the clients ***,a priority-based scheduling and classifier model is designed to allocate multicast request based on the type of applications and deadline constraints.A detailed experimental analysis was carried out to ensure the outcomes improved under different *** results from comprehensive comparative analysis highlighted the superiority of the proposed IBWO-LD model over other compared methods.

关键词： Smart cities software defined networking quality of service video data transmission multicast routing

Coyote Optimization Using Fuzzy System for Energy Efficiency in WSN

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Computers, Materials & Continua 2022年第8期72卷 3269-3281页

作者： Ahmed S.Almasoud taiseer abdalla elfadil eisa Marwa Obayya Abdelzahir Abdelmaboud Mesfer Al Duhayyim Ishfaq Yaseen Manar Ahmed Hamza Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Science and Artsat 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 recent days,internet of things is widely implemented in Wireless Sensor Network(WSN).It comprises of sensor hubs associated together through the *** WSNis generally affected by the power in battery due to the linked sensor *** order to extend the lifespan of WSN,clustering techniques are used for the improvement of energy *** methods divide the nodes in WSN and form a ***,it consists of unique Cluster Head(CH)in each *** the existing system,Soft-K means clustering techniques are used in energy consumption in *** soft-k means algorithm does not work with the large-scale wireless sensor networks,therefore it causes reliability and energy consumption *** overcome this,the proposed Load-Balanced Clustering conjunction with Coyote Optimization with Fuzzy Logic(LBC-COFL)algorithm is *** main objective is to perform the lifespan by balancing the gateways with the load of less *** proposed algorithm is evaluated using the metrics such as energy consumption,throughput,central tendency,network lifespan,and total energy utilization.

关键词： Clustering wireless sensor networks fuzzy logic energy efficient optimization

Machine Learning Enabled e-Learner Non-Verbal Behavior Detection in IoT Environment

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Computers, Materials & Continua 2022年第7期72卷 679-693页

作者： Abdelzahir Abdelmaboud Fahd N.Al-Wesabi Mesfer Al Duhayyim taiseer abdalla elfadil eisa Manar Ahmed Hamza Mohammed Rizwanullah Abu Serwar Zamani Radwa Marzouk Department of Information Systems College of Science and ArtsKing Khalid UniversityMahayil AsirSaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Science and Arts-Girls SectionKing Khalid UniversityMahayil AsirSaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia&Department of MathematicsFaculty of ScienceCairo UniversityGiza12613Egypt

Internet of Things(IoT)with e-learning is widely employed to collect data from various smart devices and share it with other ones for efficient e-learning *** the same time,machine learning(ML)and data mining approaches are presented for accomplishing prediction and classification *** this motivation,this study focuses on the design of intelligent machine learning enabled e-learner non-verbal behaviour detection(IML-ELNVBD)in IoT *** proposed IML-ELNVBD technique allows the IoT devices such as audio sensors,cameras,*** are then connected to the cloud server for further *** addition,the modelling and extraction of behaviour take ***,extreme learning machine sparse autoencoder(ELM-SAE)model is employed for the detection and classification of non-verbal ***,the Ant Colony Optimization(ACO)algorithm is utilized to properly tune the weight and bias parameters involved in the ELM-SAE *** order to ensure the improved performance of the IML-ELNVBD model,a comprehensive simulation analysis is carried out and the results highlighted the betterment compared to the recent models.

关键词： Internet of things e-learning machine learning non-verbal behavior extreme learning machine classification parameter tuning

Efficiency Effect of Obstacle Margin on Line-of-Sight in Wireless Networks

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Computers, Materials & Continua 2022年第7期72卷 227-242页

作者： Murad A.A.Almekhlafi taiseer abdalla elfadil eisa Fahd N.Al-Wesabi Abdelzahir Abdelmaboud Manar Ahmed Hamza Abdelwahed Motwakel Ishfaq Yaseen Mohammed Rizwanullah Department of Electrical Engineering–Communication Engineering Sana’a University61101Yemen Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia Department of Computer Science King Khalid UniversityMuhayel Aseer62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a61101Yemen Department of Information Systems King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia

Line-of-sight clarity and assurance are essential because they are considered the golden rule in wireless network planning,allowing the direct propagation path to connect the transmitter and receiver and retain the strength of the signal to be *** the increasing literature on the line of sight with different scenarios,no comprehensive study focuses on the multiplicity of parameters and basic concepts that must be taken into account when studying such a topic as it affects the results and their ***,this research aims to find limited values that ensure that the signal reaches the future efficiently and enhances the accuracy of these values’*** have designed MATLAB simulation and programming programs by Visual *** for a semi-realistic communication *** includes all the basic parameters of this system,taking into account the environment’s diversity and the characteristics of the obstacle between the transmitting station and the receiving *** we verified the correctness of the system’s ***,we begin by analyzing and studying multiple and branching cases to achieve the *** get several values from the results,which are finite values,which are a useful reference for engineers and designers of wireless networks.

关键词： Line of sight obstacle margin wireless networks communication link radio wave propagation

Artificial Intelligence-Based Fusion Model for Paddy Leaf Disease Detection and Classification

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Computers, Materials & Continua 2022年第7期72卷 1391-1407页

作者： Ahmed SAlmasoud Abdelzahir Abdelmaboud taiseer abdalla elfadil eisa Mesfer Al Duhayyim Asma Abbas Hassan Elnour Manar Ahmed Hamza Abdelwahed Motwakel Abu Sarwar Zamani Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Science and ArtsKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversityAflaj16828Saudi Arabia Computer Science Department Community College-Girls SectionKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

In agriculture,rice plant disease diagnosis has become a challenging issue,and early identification of this disease can avoid huge loss incurred from less crop *** of the recently-developed computer vision and Deep Learning(DL)approaches can be commonly employed in designing effective models for rice plant disease detection and classification *** this motivation,the current research work devises an Efficient Deep Learning based FusionModel for Rice Plant Disease(EDLFM-RPD)detection and *** aim of the proposed EDLFM-RPD technique is to detect and classify different kinds of rice plant diseases in a proficient *** addition,EDLFM-RPD technique involves median filtering-based preprocessing and K-means segmentation to determine the infected *** study also used a fusion of handcrafted Gray Level Co-occurrence Matrix(GLCM)and Inception-based deep features to derive the ***,Salp Swarm Optimization with Fuzzy Support Vector Machine(FSVM)model is utilized for *** order to validate the enhanced outcomes of EDLFM-RPD technique,a series of simulations was *** results were assessed under different *** obtained values infer the improved performance of EDLFM-RPD technique over recent approaches and achieved a maximum accuracy of 96.170%.

关键词： Rice plant disease classification model artificial intelligence deep learning fusion model parameter optimization