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An Improved Evolutionary Algorithm for Data Mining and Knowledge Discovery

Computers, Materials & Continua 2022年第4期71卷 1233-1247页

作者： Mesfer Al Duhayyim Radwa Marzouk Fahd N.Al-Wesabi Maram Alrajhi manar ahmed hamza Abu Sarwar Zamani Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza 12613Egypt Department of Computer Science Faculty of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems Faculty of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Recent advancements in computer technologies for data processing,collection,and storage have offered several chances to improve the abilities in production,services,communication,and *** mining(DM)is an interdisciplinary field commonly used to extract useful patterns from the *** the same time,educational data mining(EDM)is a kind of DM concept,which finds use in educational ***,artificial intelligence(AI)techniques can be used for mining a large amount of *** the same time,in DM,the feature selection process becomes necessary to generate subset of features and can be solved by the use of metaheuristic optimization *** this motivation,this paper presents an improved evolutionary algorithm based feature subsets election with neuro-fuzzy classification(IEAFSS-NFC)for data mining in the education *** presented IEAFSS-NFC model involves data pre-processing,feature selection,and ***,the Chaotic Whale Optimization Algorithm(CWOA)is used for the selection of the highly related feature subsets to accomplish improved classification ***,Neuro-Fuzzy Classification(NFC)technique is employed for the classification of education *** IEAFSS-NFC model is tested against a benchmark Student Performance DataSet from the UCI *** simulation outcome has shown that the IEAFSS-NFC model is superior to other methods.

关键词： Educational data mining feature selection whale optimization classification

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Securing Arabic Contents Algorithm for Smart Detecting of Illegal Tampering Attacks

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Computers, Materials & Continua 2022年第2期70卷 2879-2894页

作者： Mesfer Al Duhayyim Manal Abdullah Alohali Fahd N.Al-Wesabi Anwer Mustafa Hilal Mohammad Medani manar ahmed hamza Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Themost common digital media exchanged via the Internet is in text *** Arabic language is considered one of themost sensitive languages of content modification due to the presence of diacritics that can cause a change in the *** this paper,an intelligent scheme is proposed for improving the reliability and security of the text exchanged via the *** core mechanism of the proposed scheme depends on integrating the hidden Markov model and zero text watermarking *** watermark key will be generated by utilizing the extracted features of the text analysis process using the third order and word level of the Markov *** Embedding and detection processes of the proposed scheme will be performed logically without the effect of the original *** proposed scheme is implemented using PHP with VS code *** simulation results,using varying sizes of standard datasets,show that the proposed scheme can obtain high reliability and provide better accuracy of the common illegal tampering *** results with other baseline techniques show the added value of the proposed scheme.

关键词： Tampering detection zero-watermarking soft computing text analysis hidden Markov model

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Metaheuristic Resource Allocation Strategy for Cluster Based 6G Industrial Applications

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Computers, Materials & Continua 2022年第4期71卷 667-681页

作者： Anwer Mustafa Hilal Lamia Osman Widaa Fahd N.Al-Wesabi Mohammad Medani manar ahmed hamza Mesfer Al Duhayyim Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

The emergence of Beyond 5G(B5G)and 6G networks translated personal and industrial operations highly effective,reliable,and gainful by speeding up the growth of next generation Internet of Things(IoT).Industrial equipment in 6G encompasses a huge number of wireless sensors,responsible for collecting massive quantities of *** the same time,6G network can take real-world intelligent decisions and implement automated equipment *** the inclusion of different technologies into the system increased its energy consumption for which appropriate measures need to be *** has become mandatory for optimal resource allocation in 6G-enabled industrial *** this scenario,the current research paper introduces a new metaheuristic resource allocation strategy for clusterbased 6G industrial applications,named MRAS-CBIA *** technique aims at accomplishing energy efficiency and optimal resource allocation in 6G-enabled industrial *** proposed MRAS-CBIR technique involves three major ***,Weighted Clustering Technique(WCT)is employed to elect the optimal Cluster Heads(CHs)or coordinating agents with the help of three parameters namely,residual energy,distance,and node ***,Decision Tree-based Location Prediction(DTLP)mechanism is applied to determine the exact location of Management Agent(MA).Finally,Fuzzy C-means with Tunicate Swarm Algorithm(FCMTSA)is used for optimal resource allocation in 6G industrial *** performance of the proposed MRAS-CBIA technique was validated and the results were examined under different *** resultant experimental values highlighted the superior performance of MRAS-CBIR technique over existing state-of-the-art methods.

关键词： 6G industrial internet of things energy efficiency resource allocation metaheuristics

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Deep Transfer Learning Driven Automated Fall Detection for Quality of Living of Disabled Persons

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Computers, Materials & Continua 2023年第3期74卷 6719-6736页

作者： Nabil Almalki Mrim M.Alnfiai Fahd N.Al-Wesabi Mesfer Alduhayyem Anwer Mustafa Hilal manar ahmed hamza King Salman Center for Disability Research RiyadhSaudi Arabia Department of Special Education College of EducationKing Saud UniversityRiyadh12372Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of Science&ArtsKing Khaled UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Mobile communication and the Internet of Things(IoT)technologies have recently been established to collect data from human beings and the *** data collected can be leveraged to provide intelligent services through different *** is an extreme challenge to monitor disabled people from remote *** is because day-to-day events like falls heavily result in *** a person with disabilities,a fall event is an important cause of mortality and post-traumatic ***,detecting the fall events of disabled persons in smart homes at early stages is essential to provide the necessary support and increase their survival *** current study introduces a Whale Optimization Algorithm Deep Transfer Learning-DrivenAutomated Fall Detection(WOADTL-AFD)technique to improve the Quality of Life for persons with *** primary aim of the presented WOADTL-AFD technique is to identify and classify the fall events to help disabled *** attain this,the proposed WOADTL-AFDmodel initially uses amodified SqueezeNet feature extractor which proficiently extracts the feature *** addition,the WOADTLAFD technique classifies the fall events using an extreme Gradient Boosting(XGBoost)*** the presented WOADTL-AFD technique,the WOA approach is used to fine-tune the hyperparameters involved in the modified SqueezeNet *** proposedWOADTL-AFD technique was experimentally validated using the benchmark datasets,and the results confirmed the superior performance of the proposedWOADTL-AFD method compared to other recent approaches.

关键词： Quality of living disabled persons intelligent models deep learning fall detection whale optimization algorithm

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Big Data Analytics with Artificial Intelligence Enabled Environmental Air Pollution Monitoring Framework

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Computers, Materials & Continua 2022年第11期73卷 3235-3250页

作者： manar ahmed hamza Hadil Shaiba Radwa Marzouk Ahmad Alhindi Mashael M.Asiri Ishfaq Yaseen Abdelwahed Motwakel Mohammed Rizwanullah Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia&Research and InnovationSalla Holding LimitedMakkahSaudi Arabia Department of Computer Science College of Science and ArtsKing Khalid UniversityMahayil Asir62529Saudi Arabia

Environmental sustainability is the rate of renewable resourceharvesting, pollution control, and non-renewable resource exhaustion. Airpollution is a significant issue confronted by the environment particularlyby highly populated countries like India. Due to increased population, thenumber of vehicles also continues to increase. Each vehicle has its individualemission rate;however, the issue arises when the emission rate crosses thestandard value and the quality of the air gets degraded. Owing to the technological advances in machine learning (ML), it is possible to develop predictionapproaches to monitor and control pollution using real time data. With thedevelopment of the Internet of Things (IoT) and Big Data Analytics (BDA),there is a huge paradigm shift in how environmental data are employed forsustainable cities and societies, especially by applying intelligent *** this view, this study develops an optimal AI based air quality prediction andclassification (OAI-AQPC) model in big data environment. For handling bigdata from environmental monitoring, Hadoop MapReduce tool is *** addition, a predictive model is built using the hybridization of ARIMAand neural network (NN) called ARIMA-NN to predict the pollution *** improving the performance of the ARIMA-NN algorithm, the parametertuning process takes place using oppositional swallow swarm optimization(OSSO) algorithm. Finally, Adaptive neuro-fuzzy inference system (ANFIS)classifier is used to classify the air quality into pollutant and non-pollutant.A detailed experimental analysis is performed for highlighting the betterprediction performance of the proposed ARIMA-NN method. The obtainedoutcomes pointed out the enhanced outcomes of the proposed OAI-AQPCtechnique over the recent state of art techniques.

关键词： Sustainability environmental air quality predictive model pollution monitoring statistical models artificial intelligence

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Artificial Fish Swarm Optimization with Deep Learning Enabled Opinion Mining Approach

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Computer Systems Science & Engineering 2023年第4期45卷 737-751页

作者： Saud S.Alotaibi Eatedal Alabdulkreem Sami Althahabi manar ahmed hamza Mohammed Rizwanullah Abu Sarwar Zamani Abdelwahed Motwakel Radwa Marzouk Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversityMecca24382Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt

Sentiment analysis or opinion mining(OM)concepts become familiar due to advances in networking technologies and social ***,massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process *** OM find useful in business sectors to improve the quality of the product as well as services,machine learning(ML)and deep learning(DL)models can be considered into ***,the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification ***,in this paper,a new Artificial Fish Swarm Optimization with Bidirectional Long Short Term Memory(AFSO-BLSTM)model has been developed for OM *** major intention of the AFSO-BLSTM model is to effectively mine the opinions present in the textual *** addition,the AFSO-BLSTM model undergoes pre-processing and TF-IFD based feature extraction ***,BLSTM model is employed for the effectual detection and classification of ***,the AFSO algorithm is utilized for effective hyperparameter adjustment process of the BLSTM model,shows the novelty of the work.A complete simulation study of the AFSO-BLSTM model is validated using benchmark dataset and the obtained experimental values revealed the high potential of the AFSO-BLSTM model on mining opinions.

关键词： Sentiment analysis opinion mining natural language processing artificial fish swarm algorithm deep learning

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Hybridization of Metaheuristics Based Energy Efficient Scheduling Algorithm for Multi-Core Systems

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Computer Systems Science & Engineering 2023年第1期44卷 205-219页

作者： J.Jean Justus U.Sakthi K.Priyadarshini B.Thiyaneswaran Masoud Alajmi Marwa Obayya manar ahmed hamza Department of Computer Science&Engineering St.Joseph’s College of EngineeringChennai600119India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai602105India Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Electronics and Communication Engineering Sona College of TechnologySalem636005India Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The developments of multi-core systems(MCS)have considerably improved the existing technologies in thefield of computer *** MCS comprises several processors that are heterogeneous for resource capacities,working environments,topologies,and so *** existing multi-core technology unlocks additional research opportunities for energy minimization by the use of effective task *** the same time,the task scheduling process is yet to be explored in the multi-core *** paper presents a new hybrid genetic algorithm(GA)with a krill herd(KH)based energy-efficient scheduling techni-que for multi-core systems(GAKH-SMCS).The goal of the GAKH-SMCS tech-nique is to derive scheduling tasks in such a way to achieve faster completion time and minimum energy *** GAKH-SMCS model involves a multi-objectivefitness function using four parameters such as makespan,processor utilization,speedup,and energy consumption to schedule tasks *** performance of the GAKH-SMCS model has been validated against two datasets namely random dataset and benchmark *** experimental outcome ensured the effectiveness of the GAKH-SMCS model interms of makespan,pro-cessor utilization,speedup,and energy *** overall simulation results depicted that the presented GAKH-SMCS model achieves energy effi-ciency by optimal task scheduling process in MCS.

关键词： Task scheduling energy efficiency multi-core systems fitness function makespan

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A Novel Peak-to-Average Power Ratio Reduction for 5G Advanced Waveforms

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Computers, Materials & Continua 2022年第10期73卷 1637-1648页

作者： Rajneesh Pareek Karthikeyan Rajagopal Himanshu Sharma Nidhi Gour Arun Kumar Sami Althahabi Haya Mesfer Alshahrani Mohamed Mousa manar ahmed hamza Department of Computer Science&Engineering JECRC UniversityJaipur303905India Centre for Nonlinear Systems Chennai Institute of TechnologyChennaiIndia Department of Electronics and Communication JECRC UniversityJaipur303905India Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Electrical Engineering Department Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Multi and single carrier waveforms are utilized in cellular systems for high-speed data *** The Fifth Generation(5G)system,several waveform techniques based on multi carrier waveforms are ***,the Peak to Average Power Ratio(PAPR)is seen as one of the significant concerns in advanced waveforms as it degrades the efficiency of the *** proposed article documents the study,progress,and implementation of PAPR reduction algorithms for the 5G radio *** compare the PAPR algorithm performance for advanced and conventional *** simulation results reveal that the advanced Partial Transmission Sequence(PTS)and Selective Mapping(SLM)methods enhanced the throughput and gain of the 5G ***,we have also analyzed the performance of Orthogonal Time Frequency Space Modulation(OTFSM)based on a single carrier system and found that PAPR is significantly low and is best suited to fading *** is seen that the conventional algorithms lower the PAPR but increase the *** proposed PTS and SLM have shown good performance with low computational complexity.

关键词： PAPR 5G OFDM NOMA OTFSM PTS SLM

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Novel Image Encryption and Compression Scheme for IoT Environment

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Computers, Materials & Continua 2022年第4期71卷 1443-1457页

作者： Mesfer Al Duhayyim Fahd N.Al-Wesabi Radwa Marzouk manar ahmed hamza Anwer Mustafa Hilal Majdy M.Eltahir Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Latest advancements made in the processing abilities of smartdevices have resulted in the designing of Intelligent Internet of Things (IoT)environment. This advanced environment enables the nodes to connect, collect, perceive, and examine useful data from its surroundings. Wireless Multimedia Surveillance Networks (WMSNs) form a vital part in IoT-assistedenvironment since it contains visual sensors that examine the surroundingsfrom a number of overlapping views by capturing the images *** IoT devices generate a massive quantity of digital media, it is thereforerequired to save the media, especially images, in a secure way. In order toachieve security, encryption techniques as well as compression techniques areemployed to reduce the amount of digital data, being communicated overthe network. Encryption Then Compression (ETC) techniques pave a wayfor secure and compact transmission of the available data to prevent unauthorized access. With this background, the current research paper presentsa new ETC technique to accomplish image security in IoT *** proposed model involves three major processes namely, IoT-based imageacquisition, encryption, and compression. The presented model involves optimal Signcryption Technique with Whale Optimization Algorithm (NMWOA)abbreviated as ST-NMWOA. The optimal key generation of signcryptiontechnique takes place with the help of NMWOA. Besides, the presented modelalso uses Discrete Fourier Transform (DFT) and Matrix Minimization (MM)algorithm-based compression technique. Extensive set of experimental analysis was conducted to validate the effective performance of the proposed *** obtained values infer that the presented model is superior in terms of bothcompression efficiency and data secrecy in resource-limited IoT environment.

关键词： Data compression image security encryption signcryption optimal key generation

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An Optimized Deep Learning Model for Emotion Classification in Tweets

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Computers, Materials & Continua 2022年第3期70卷 6365-6380页

作者： Chinu Singla Fahd NAl-Wesabi Yash Singh Pathania Badria Sulaiman Alfurhood Anwer Mustafa Hilal Mohammed Rizwanullah manar ahmed hamza Mohammad Mahzari Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatialaIndia Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer Science College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

The task of automatically analyzing sentiments from a tweet has more use now than ever due to the spectrum of emotions expressed from national leaders to the average *** this data can be critical for any *** are often expressed with different intensity and topics which can provide great insight into how something affects *** analysis in Twittermitigates the various issues of analyzing the tweets in terms of views expressed and several approaches have already been proposed for sentiment analysis in *** used for analyzing tweet emotions are also briefly presented in literature survey *** this paper,hybrid combination of different model’s LSTM-CNN have been proposed where LSTMis Long Short TermMemory andCNNrepresents ConvolutionalNeural ***,the main contribution of our work is to compare various deep learning and machine learning models and categorization based on the techniques *** main drawback of LSTM is that it’s a timeconsuming process whereas CNN do not express content information in an accurate way,thus our proposed hybrid technique improves the precision rate and helps in achieving better *** step of our mentioned technique is to preprocess the data in order to remove stop words and unnecessary data to improve the efficiency in terms of time and accuracy also it shows optimal results when it is compared with predefined approaches.

关键词： Meta level features lexical mistakes sentiment analysis count vector natural language processing deep learning machine learning naive bayes

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