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Colossal pattern extraction using optimised length constraints based on differential evolutionary arithmetic optimisation algorithm

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International Journal of information and Communication technology 2024年第3期24卷 289-304页

作者： Reddy, T. Sreenivasula Sathya, R. Nuka, Mallikharjunarao Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Annamalai Nagar Tamil Nadu Chidambaram608002 India Department of Computer Science and Engineering Annamalai University Annamalai Nagar Tamil Nadu Chidambaram608002 India Department of Computer Applications Annamacharya Institute of Technology and Sciences YSR Kadapa Andhra Pradesh Rajampet516115 India

Extracting large amounts of information and knowledge from a large database is a trivial task. Existing bulk item mining algorithms for an extensive database are systematic and mathematically expensive and cannot be used for large-scale mining with interruptions. In this paper, the problem of mining the colossal patterns (CPs) is solved by using optimised length constraints (LCs). First, we describe the minimum LC and maximum LC problems and connect them to the optimal LC by identifying the optimal threshold values. Here, the differential evolutionary arithmetic optimisation algorithm (DAOA) is used to find the optimal threshold values of the constraints and extract the colossal patterns. The effectiveness of the proposed algorithm is proven by various experiments using different biological datasets. © 2024 Inderscience Enterprises Ltd.

关键词： Database systems

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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

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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

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Sparse Crowd Flow Analysis of Tawaaf of Kaaba During the COVID-19 Pandemic

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computers, Materials & Continua 2022年第6期71卷 5581-5601页

作者： Durr-e-Nayab Ali Mustafa Qamar Rehan Ullah Khan Waleed Albattah Khalil Khan Shabana Habib Muhammad Islam Department of Computer Systems Engineering University of Engineering and TechnologyPeshawarPakistan Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Information Technology and Computer Science Pak-Austria FachhochschuleInstitute of Applied Sciences and TechnologyHaripurPakistan Department of Electrical Engineering College of Engineering and Information TechnologyOnaizah CollegesAl-QassimSaudi Arabia

The advent of the COVID-19 pandemic has adversely affected the entire world and has put forth high demand for techniques that remotely manage crowd-related *** surveillance and crowd management using video analysis techniques have significantly impacted today’s research,and numerous applications have been developed in this *** research proposed an anomaly detection technique applied to Umrah videos in Kaaba during the COVID-19 pandemic through sparse crowd *** theKaaba rituals is crucial since the crowd gathers from around the world and requires proper analysis during these days of the *** Umrah videos are analyzed,and a system is devised that can track and monitor the crowd flow in *** crowd in these videos is sparse due to the pandemic,and we have developed a technique to track the maximum crowd flow and detect any object(person)moving in the direction unlikely of the major *** have detected abnormal movement by creating the histograms for the vertical and horizontal flows and applying thresholds to identify the non-majority *** algorithm aims to analyze the crowd through video surveillance and timely detect any abnormal activity tomaintain a smooth crowd flowinKaaba during the pandemic.

关键词： computer vision COVID sparse crowd crowd analysis flow analysis sparse crowd management tawaaf video analysis video processing

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A Trailblazing Framework of Security Assessment for Traffic Data Management

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Intelligent Automation & Soft Computing 2023年第8期37卷 1853-1875页

作者： Abdulaziz Attaallah Khalil al-Sulbi Areej Alasiry Mehrez Marzougui Neha Yadav Syed Anas Ansar Pawan Kumar Chaurasia Alka Agrawal Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Al-Qunfudah Computer CollegeUmm Al-Qura UniversityMeccaSaudi Arabia College of Computer Science King Khalid UniversityAbha61421Saudi Arabia Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow226025Uttar PradeshIndia Department of Computer Applications Babu Banarasi Das UniversityLucknow226028Uttar PradeshIndia

Connected and autonomous vehicles are seeing their dawn at this *** provide numerous benefits to vehicle owners,manufacturers,vehicle service providers,insurance companies,*** vehicles generate a large amount of data,which makes privacy and security a major challenge to their *** complicated machine-led mechanics of connected and autonomous vehicles increase the risks of privacy invasion and cyber security violations for their users by making them more susceptible to data exploitation and vulnerable to cyber-attacks than any of their *** could have a negative impact on how well-liked CAVs are with the general public,give them a poor name at this early stage of their development,put obstacles in the way of their adoption and expanded use,and complicate the economic models for their future *** the other hand,congestion is still a bottleneck for traffic management and *** research paper presents a blockchain-based framework that protects the privacy of vehicle owners and provides data security by storing vehicular data on the blockchain,which will be used further for congestion detection and *** devices placed along the road are used to communicate with passing cars and collect their *** collected data will be compiled periodically to find the average travel time of vehicles and traffic density on a particular road ***,this data will be stored in the memory pool,where other devices will also store their *** a predetermined amount of time,the memory pool will be mined,and data will be uploaded to the blockchain in the form of blocks that will be used to store traffic *** information is then used in two different ***,the blockchain’s final block will provide real-time traffic data,triggering an intelligent traffic signal system to reduce ***,the data stored on the blockchain will provide historical,statistical data that can facilitate the

关键词： Connected and autonomous vehicles(CAVs) traffic data management ethereum blockchain road side units smart cities

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Optimal Bottleneck-Driven Deep Belief Network Enabled Malware Classification on IoT-Cloud Environment

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computers, Materials & Continua 2023年第2期74卷 3101-3115页

作者： Mohammed Maray Hamed Alqahtani Saud S.Alotaibi Fatma S.Alrayes Nuha Alshuqayran Mrim M.Alnfiai Amal S.Mehanna Mesfer Al Duhayyim Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of information systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia

Cloud Computing(CC)is the most promising and advanced technology to store data and offer online services in an effective *** such fast evolving technologies are used in the protection of computerbased systems from cyberattacks,it brings several advantages compared to conventional data protection *** of the computer-based systems that effectively protect the data include Cyber-Physical systems(CPS),Internet of Things(IoT),mobile devices,desktop and laptop computer,and critical *** software(malware)is nothing but a type of software that targets the computer-based systems so as to launch cyberattacks and threaten the integrity,secrecy,and accessibility of the *** current study focuses on design of Optimal Bottleneck driven Deep Belief Network-enabled Cybersecurity Malware Classification(OBDDBNCMC)*** presentedOBDDBN-CMCmodel intends to recognize and classify the malware that exists in IoT-based cloud *** attain this,Zscore data normalization is utilized to scale the data into a uniform *** addition,BDDBN model is also exploited for recognition and categorization of *** effectually fine-tune the hyperparameters related to BDDBN model,GrasshopperOptimizationAlgorithm(GOA)is *** scenario enhances the classification results and also shows the novelty of current *** experimental analysis was conducted upon OBDDBN-CMC model for validation and the results confirmed the enhanced performance ofOBDDBNCMC model over recent approaches.

关键词： Malware detection security Internet of Things cloud computing machine learning parameter adjustment

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Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

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computers, Materials & Continua 2023年第1期74卷 607-621页

作者： Anwer Mustafa Hilal Aisha Hassan Abdalla Hashim Heba G.Mohamed Mohamed K.Nour Mashael M.Asiri Ali M.Al-Sharafi Mahmoud Othman Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University Malaysia 53100 Kuala LumpurMalaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital *** Uniform Resource Locators(URLs)can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their *** may result in compromised security of the systems,scams,and other such *** attacks hijack huge quantities of the available data,incurring heavy financial *** the same time,Machine Learning(ML)and Deep Learning(DL)models paved the way for designing models that can detect malicious URLs accurately and classify *** this motivation,the current article develops an Artificial Fish Swarm Algorithm(AFSA)with Deep Learning Enabled Malicious URL Detection and Classification(AFSADL-MURLC)*** presented AFSADL-MURLC model intends to differentiate the malicious URLs from genuine *** attain this,AFSADL-MURLC model initially carries out data preprocessing and makes use of glove-based word embedding *** addition,the created vector model is then passed onto Gated Recurrent Unit(GRU)classification to recognize the malicious ***,AFSA is applied to the proposed model to enhance the efficiency of GRU *** proposed AFSADL-MURLC technique was experimentally validated using benchmark dataset sourced from Kaggle *** simulation results confirmed the supremacy of the proposed AFSADL-MURLC model over recent approaches under distinct measures.

关键词： Malicious URL cybersecurity deep learning machine learning metaheuristics gated recurrent unit

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EarLock: Personal Authentication system for Hearables Using Sound Leakage Signals

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6183-6196页

作者： Amesaka, Takashi Watanabe, Hiroki Sugiura, Yuta Sugimoto, Masanori Shizuki, Buntarou Keio University Department of Information and Computer Science Yokohama108-0073 Japan Future University Hakodate Department of Media Architecture Hokkaido 041-0803 Japan Hokkaido University Graduate School of Information Science and Technology Sapporo060-0808 Japan University of Tsukuba Department of Computer Science Tsukuba305-8577 Japan

Earphone-type wearable devices, also known as "hearables," will have many functions in the future. Some of those functions will require authentication of the wearer for access to the user's privacy information or settlement of payments. In this study, we propose a new personal authentication system for hearables called EarLock. EarLock authenticates the wearer by acquiring and analyzing ear canal and auricle shape information using sound leakage from the device. The system can be implemented using a speaker and external microphone that are highly compatible with hearables. We implemented three prototype devices and investigated EarLock’s authentication performance under various practical scenarios, including walking conditions, noisy environments, and situations with object interference. Experimental results showed that the in-ear, open-ear, and bone-conduction devices achieved balanced accuracy (BAC) scores of 87.2–93.7%, 83.4– 94.7%, and 85.9–90.0%. © 2002-2012 IEEE.

关键词： information leakage

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Machine Learning Approaches for Early Detection of Heart Diseases 25

Machine Learning Approaches for Early Detection of Heart Dis...

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25th International Arab Conference on information technology, ACIT 2024

作者： Al-Sheyab, Asalla M. Kousa, Msallam Hamad, Hanadi Bani Almomani, Adai Elrashidi, Ali Philadelphia Private University College of Information Technology Department of Computer Science Irbid25113 Jordan Isfahan University Faculty of Computer Engineering Department of Artificial Intelligence Isfahan Iran Zarqa Private University College of Information Technology Department of Computer Science Irbid25113 Jordan Zarqa University Department of Computer Science Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

The main objective of the study is to predict the probability of developing heart disease using a Decision Tree Classifier, K-nearest neighbors, and Support Vector Machine, Logistic Regression. Age, sexual pressure, quantity, and levels are among the variables included in the Kaggle dataset. A combination of accuracy, sensitivity, and specificity was used to train and evaluate the algorithms. Based on the accuracy of participants' predictions, the results suggest that learning algorithms could help treat heart *** "Heart Disease Prediction"project contains all project information and results. Contributing to the use of multiple algorithms improves patient outcomes to enhance the detection and treatment of heart diseases. Logistic Regression has achieved an accuracy of 86.41%. A module capable of predicting heart disease in a patient has been implemented. A variety of techniques are employed to assess models, including F-measure, ROC, precision, recall, and accuracy. The bagging set learning method using logistic regression provides the best performance, according to the data. © 2024 IEEE.

关键词： Support vector regression

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Deep Learning with a Novel Concoction Loss Function for Identification of Ophthalmic Disease

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computers, Materials & Continua 2023年第9期76卷 3763-3781页

作者： Sayyid Kamran Hussain Ali Haider Khan Malek Alrashidi Sajid Iqbal Qazi Mudassar Ilyas Kamran Shah Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering Faculty of Computer ScienceLahore Garrison UniversityLahore54000Pakistan Department of Computer Science Applied CollegeUniversity of TabukTabukSaudi Arabia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityAl Hofuf31982Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl HofufSaudi Arabia

As ocular computer-aided diagnostic(CAD)tools become more widely accessible,many researchers are developing deep learning(DL)methods to aid in ocular disease(OHD)*** eye diseases like cataracts(CATR),glaucoma(GLU),and age-related macular degeneration(AMD)are the focus of this study,which uses DL to examine their *** imbalance and outliers are widespread in fundus images,which can make it difficult to apply manyDL algorithms to accomplish this analytical *** creation of efficient and reliable DL algorithms is seen to be the key to further enhancing detection *** the analysis of images of the color of the retinal fundus,this study offers a DL model that is combined with a one-of-a-kind concoction loss function(CLF)for the automated identification of *** study presents a combination of focal loss(FL)and correntropy-induced loss functions(CILF)in the proposed DL model to improve the recognition performance of classifiers for biomedical *** is done because of the good generalization and robustness of these two types of losses in addressing complex datasets with class imbalance and *** classification performance of the DL model with our proposed loss function is compared to that of the baseline models using accuracy(ACU),recall(REC),specificity(SPF),Kappa,and area under the receiver operating characteristic curve(AUC)as the evaluation *** testing shows that the method is reliable and efficient.

关键词： Deep learning multi-classification focal loss CNN eye disease

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