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Networking and Communications (ICNWC), International Conference on

作者： Sujay Das Akshat Chotia K. M. Uma Maheswari Sadiq A K Department Of Computer Science & Engineering SRM Institute Of Science & Technology Chennai India College of Computing and Information Science University of Technology and Applied Sciences Oman

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

Autism is a complex neuro - developmental condition characterized by challenges in social interaction and communication. Early diagnosis and intervention are crucial for effective support. This paper presents a novel approach for autism detection leveraging Convolutional Neural Networks (CNNs) to analyze eye gaze patterns. The pro0posed method aims to provide an objective and efficient tool for the early screening of Autism. We begin by collecting eye gaze data from a diverse sample of individuals, including both Autism -diagnosed and non Autism individuals. The dataset is preprocessed to extract relevant features from the eye gaze sequences, capturing subtle but significant differences in gaze patterns between the two groups. A CNN architecture is designed and trained on this preprocessed dataset. The network is optimized to automatically learn discriminative features that distinguish between Autism and non Autism gaze patterns. Transfer learning techniques mobilenetv2 are employed to further enhance model performance. The trained CNN demonstrates promising results in accurately classifying individuals into Autism and neurotypical categories. Comprehensive evaluation metrics, including accuracy and loss, attest to the model's effectiveness.

关键词： Measurement Autism Adaptation models Network topology Transfer learning Neural networks Feature extraction

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Message Verification Protocol Based on Bilinear Pairings and Elliptic Curves for Enhanced Security in Vehicular Ad Hoc Networks

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computers, Materials & Continua 2024年第10期81卷 1029-1057页

作者： Vincent Omollo Nyangaresi Arkan A.Ghaib Hend Muslim Jasim Zaid Ameen Abduljabbar Junchao Ma Mustafa A.Al Sibahee Abdulla J.Y.Aldarwish Ali Hasan Ali Husam A.Neamah Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo40601Kenya Department of Applied Electronics Saveetha School of EngineeringSIMATSChennaiTamilnadu602105India Information Technology Department Management Technical CollegeSouthern Technical UniversityBasrah61004Iraq Department of Computer Science College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq College of Big Data and Internet Shenzhen Technology UniversityShenzhen518118China Huazhong University of Science and Technology Shenzhen InstituteShenzhen518000China National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen518060China Computer Technology Engineering Department Iraq University CollegeBasrah61004Iraq Department of Mathematics College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq Institute of Mathematics University of DebrecenDebrecenH-4002Hungary Department of Electrical Engineering and Mechatronics Faculty of EngineeringUniversity of DebrecenDebrecen4028Hungary

Vehicular ad hoc networks(VANETs)provide intelligent navigation and efficient route management,resulting in time savings and cost reductions in the transportation ***,the exchange of beacons and messages over public channels among vehicles and roadside units renders these networks vulnerable to numerous attacks and privacy *** address these challenges,several privacy and security preservation protocols based on blockchain and public key cryptography have been proposed ***,most of these schemes are limited by a long execution time and massive communication costs,which make them inefficient for on-board units(OBUs).Additionally,some of them are still susceptible to many *** such,this study presents a novel protocol based on the fusion of elliptic curve cryptography(ECC)and bilinear pairing(BP)*** formal security analysis is accomplished using the Burrows–Abadi–Needham(BAN)logic,demonstrating that our scheme is verifiably *** proposed scheme’s informal security assessment also shows that it provides salient security features,such as non-repudiation,anonymity,and ***,the scheme is shown to be resilient against attacks,such as packet replays,forgeries,message falsifications,and *** the performance perspective,this protocol yields a 37.88%reduction in communication overheads and a 44.44%improvement in the supported security ***,the proposed scheme can be deployed in VANETs to provide robust security at low overheads.

关键词： Attacks bilinear elliptic curve cryptography(ECC) privacy security vehicular ad hoc network(VANET)

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Deep Learning Enabled Microarray Gene Expression Classification for Data science Applications

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computers, Materials & Continua 2022年第11期73卷 4277-4290页

作者： Areej A.Malibari Reem M.Alshehri Fahd N.Al-Wesabi Noha Negm Mesfer Al Duhayyim Anwer Mustafa Hilal Ishfaq Yaseen Abdelwahed Motwakel Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Science&Art at 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 bioinformatics applications,examination of microarray data has received significant interest to diagnose *** gene expression data can be defined by a massive searching space that poses a primary challenge in the appropriate selection of *** data classification incorporates multiple disciplines such as bioinformatics,machine learning(ML),data science,and pattern *** paper designs an optimal deep neural network based microarray gene expression classification(ODNN-MGEC)model for bioinformatics *** proposed ODNN-MGEC technique performs data normalization process to normalize the data into a uniform ***,improved fruit fly optimization(IFFO)based feature selection technique is used to reduce the high dimensionality in the biomedical ***,deep neural network(DNN)model is applied for the classification of microarray gene expression data and the hyperparameter tuning of the DNN model is carried out using the Symbiotic Organisms Search(SOS)*** utilization of IFFO and SOS algorithms pave the way for accomplishing maximum gene expression classification *** examining the improved outcomes of the ODNN-MGEC technique,a wide ranging experimental analysis is made against benchmark *** extensive comparison study with recent approaches demonstrates the enhanced outcomes of the ODNN-MGEC technique in terms of different measures.

关键词： Bioinformatics data science microarray gene expression data classification deep learning metaheuristics

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Automated Spam Review Detection Using Hybrid Deep Learning on Arabic Opinions

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computer Systems science & Engineering 2023年第9期46卷 2947-2961页

作者： IbrahimM.Alwayle Badriyya B.Al-onazi Mohamed K.Nour Khaled M.Alalayah Khadija M.Alaidarous Ibrahim Abdulrab Ahmed Amal S.Mehanna Abdelwahed Motwakel Department of Computer Science College of Science and ArtsSharurahNajran UniversitySaudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Online reviews regarding purchasing services or products offered are the main source of users’*** gain fame or profit,generally,spam reviews are written to demote or promote certain targeted products or *** practice is called review *** the last few years,various techniques have been recommended to solve the problem of spam *** spam detection study focuses on English reviews,with a lesser interest in other *** review detection in Arabic online sources is an innovative topic despite the vast amount of data ***,this study develops an Automated Spam Review Detection using optimal Stacked Gated Recurrent Unit(SRD-OSGRU)on Arabic Opinion *** presented SRD-OSGRU model mainly intends to classify Arabic reviews into two classes:spam and ***,the presented SRD-OSGRU model follows different levels of data preprocessing to convert the actual review data into a compatible ***,unigram and bigram feature extractors are *** SGRU model is employed in this study to identify and classify Arabic spam *** the trial-and-error adjustment of hyperparameters is a tedious process,a white shark optimizer(WSO)is utilized,boosting the detection efficiency of the SGRU *** experimental validation of the SRD-OSGRU model is assessed under two datasets,namely DOSC *** extensive comparison study pointed out the enhanced performance of the SRD-OSGRU model over other recent approaches.

关键词： Arabic text spam reviews machine learning deep learning white shark optimizer

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Discovering High Utility Episodes in Sequences

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第3期4卷 473-486页

作者： Gan, Wensheng Lin, Jerry Chun-Wei Chao, Han-Chieh Yu, Philip S. Jinan University College of Cyber Security Guangzhou510632 China Pazhou Lab Guangzhou510330 China Western Norway University of Applied Sciences Department of Computing Mathematics and Physics Bergen5063 Norway National Dong Hwa University Department of Electrical Engineering Hualien974 Taiwan University of Illinois at Chicago Department of Computer Science ChicagoIL60607 United States

Sequence data are more commonly seen than other types of data (e.g., transaction data) in real-world applications. For the mining task from sequence data, several problems have been formulated, such as sequential pattern mining, episode mining, and sequential rule mining. As one of the fundamental problems, episode mining has often been studied. The common wisdom is that discovering frequent episodes is not useful enough. In this article, we propose an efficient utility mining approach, namely, UMEpi: utility mining of high-utility episodes from complex event sequences. We propose the concept of remaining utility of episodes and achieve a tighter upper bound, namely, episode-weighted utilization (EWU), which will provide better pruning. Thus, the optimized EWU-based pruning strategies can achieve better improvements in mining efficiency. The search space of UMEpi w.r.t. a prefix-based lexicographic sequence tree is spanned and determined recursively for mining high-utility episodes, by prefix-spanning in a depth-first way. Finally, extensive experiments on four real-life datasets demonstrate that UMEpi can discover the complete high-utility episodes from complex event sequences. Furthermore, the improved variants of UMEpi significantly outperform the baseline in terms of execution time, memory consumption, and scalability. © 2020 IEEE.

关键词： Artificial intelligence

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Model for Recognition of Sign Language and Hand Gesture by Hard Hearing People

Model for Recognition of Sign Language and Hand Gesture by H...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Manjula, R. Jagadeesan, S. Ramalingam, Venkadeshan Vetriselvi, T. Johnpeter, T. Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technologies Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Tamilnadu Vellore India Information Technology Department University of Technology and Applied Sciences-Shinas Branch Sultanate of Oman Oman Vellore Institute of Technology School of Computer Science and Engineering Department of Iot Tamilnadu Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798350313987

Hard hearing and deaf people are communicating with other people using the sign language. This is the only way of communication of such people to convey Since this is the only means by which these individuals can communicate, it is imperative that others be able to understand their language. Sign language recognition by computer begins with the acquisition of sign gestures and ends with recognition and conversion to text or voice. Static and dynamic are the two ways of sign gestures used. This study aims at focusing on the dynamic gesture recognition and the steps for recognizing sign language are discussed. The collected data are preprocessed and feature are extracted for classification. Finally the results are all investigated and developed for single user. © 2023 IEEE.

关键词： Gesture analysis Hand gesture recognition Hand tracking Sign language

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Analyzing darknet traffic through machine learning and neucube spiking neural networks

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Intelligent and Converged Networks 2024年第4期5卷 265-283页

作者： Iman Akour Mohammad Alauthman Khalid M.O.Nahar Ammar Almomani Brij B.Gupta Information Systems Department College of Computing&InformaticsUniversity of SharjahSharjahUnited Arab Emirates Department of Information Security Faculty of Information TechnologyUniversity of PetraAmman 11196Jordan the Faculty of Computer Studies Arab Open UniversityRiyadh 11681Saudi Arabia Department of Information Technology Al-Huson University CollegeAl-Balqa Applied UniversityIrbid 19117Jordan the Department of Computer Information Science Higher Colleges of TechnologySharjahUnited Arab Emirates the Department of Computer Science and Information Engineering Asia UniversityTaichung 413ChinaSymbiosis Centre for Information TechnologySymbiosis International(Deemed University)Pune 412115India Centre of Inter-disciplinary Research&Innovation University of Petroleum and Energy StudiesDehradun 248007India

The rapidly evolving darknet enables a wide range of cybercrimes through anonymous and untraceable communication *** detection of clandestine darknet traffic is therefore critical yet immensely *** research demonstrates how advanced machine learning and specialized deep learning techniques can significantly enhance darknet traffic analysis to strengthen *** diverse classifiers such as random forest and naïve Bayes with a novel spiking neural network architecture provides a robust foundation for identifying concealed *** on the CIC-Darknet2020 dataset establishes state-of-the-art results with 98%accuracy from the random forest model and 84.31%accuracy from the spiking neural *** pioneering application of artificial intelligence advances the frontiers in analyzing the complex characteristics and behaviours of darknet *** proposed techniques lay the groundwork for improved threat intelligence,real-time monitoring,and resilient cyber defense systems against the evolving landscape of cyber threats.

关键词： darknet traffic machine learning deep learning spiking neural network PCA

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Convergence of Blockchain, AI and IoT for Effective Monitoring and Securing Smart City Infrastructure 2

Convergence of Blockchain, AI and IoT for Effective Monitori...

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2nd International Conference on Multidisciplinary Engineering and applied science, ICMEAS 2023

作者： Agajo, James Imoru, Odunayo Aigbavboa, Clinton Mishra, Amit Onah, Fidelis Ijala, Abdullahi Danjuma Federal University of Technology Department of Computer Engineering Niger State Minna Nigeria University of Johannesburg Department of Electrical and Computer Engineering South Africa University of Johannesburg Faculty of Engineering and the Built Environment South Africa Department of Computer Science School of Computing and Applied Science Baze University Abuja Nigeria Veritas University Faculty of Engineering Abuja Nigeria Nigeria Communication Commission Abuja Nigeria

ISBN: (纸本)9798350358834

The convergence of block chain, artificial intelligence, and the internet of things (IoT) for effective monitoring and securing smart city infrastructure combines these three hot trends in technology and promises to advance the development of smart cities. Blockchain, AI, and IoT represent one of the most notable technological revolutions since the combination of computing and transaction processing systems, which is now having a positive impact. Due to the actions of cyber assault, the applications of these technologies will soon have such a significant impact on our society that it will be difficult to picture life without them. The Internet of Things Infrastructure is vulnerable to assaults since it is hosted on the cloud. Modern methods are thoroughly examined to talk about the advantages and difficulties in each domain. The combination of these three will change technology and advance a number of fields that will benefit academics and experts in industry, fostering improved service delivery and enhancing corporate structures. The convergence will change the game since it will address security and data privacy concerns. © 2023 IEEE.

关键词： Internet of things

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Toxic Comment Classification Using S-BERT Vectorization and Random Forest Algorithm 1

Toxic Comment Classification Using S-BERT Vectorization and ...

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1st IEEE International Conference on Contemporary computing and Communications, InC4 2023

作者： Kumar, Aparna Ashok Pati, Peeta Basa Deepa, K. Sangeetha, S.Tresa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Karnataka India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electrical and Electronics Engineering Karnataka India University of Technology and Applied Sciences Department of Engineering AlMusannah Oman

ISBN: (纸本)9798350335774

The growing popularity of social media platforms and microblogging websites has led to an increase in the expression of views and opinions. However, conversations and debates on these platforms often lead to the use of toxic comments, which consists of insulting and hateful remarks. To address this issue, it is important for social media systems to be able to recognize harmful comments. With the rising incidence of cyberbullying, it is crucial to study the classification of toxic comments using various algorithms. This study compares the effectiveness of different word and sentence embedding methods, including TF-IDF, InferSent, Bert, and T5 for toxic comments classification. A comparative study is also conducted on the impact of using SMOTE to balance the highly imbalanced dataset. The results of these models are compared and analysed. It is observed that T5 embedding with Random Forest Classifier works best at 0.91 F1-Score. © 2023 IEEE.

关键词： Social networking (online)

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ON NON-REFLECTIVE BOUNDARIES FOR OUTDOOR SOUND PROPAGATION USING THE DISCONTINUOUS GALERKIN METHOD 10

ON NON-REFLECTIVE BOUNDARIES FOR OUTDOOR SOUND PROPAGATION U...

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10th Convention of the European Acoustics Association, EAA 2023

作者： Feriani, Sophia Julia Engsig-Karup, Allan P. Pind, Finnur Cosnefroy, Matthias Jeong, Cheol-Ho Acoustic Technology Department of Electrical and Photonics Engineering Technical University of Denmark Denmark Scientific Computing Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Treble Technologies Hafnartorg Kalkofnsvegur 2 Reykjavík101 Iceland

ISBN: (纸本)9788888942674

In environmental acoustics, sound propagates in open air and can be mathematically modelled in terms of a semi-infinite domain. The calculations for accurate numerical solutions for outdoor acoustics are computationally heavy when the chosen computation domain is large enough to fulfil the Sommerfeld radiation condition. To lower the computational cost, the domain must therefore be truncated to not significantly impact the numerical solution inside the domain and allow outgoing acoustic waves to leave the domain undisturbed. Various radiation conditions (sometimes considering the wind) have been developed to achieve this goal such as the Perfectly Matched Layer method. The aim of this study is to compare the accuracy of different non-reflecting boundaries using the high-order nodal discontinuous Galerkin finite element method (DG-FEM) for the case of an acoustic pulse propagating in a static and moving medium in an unstructured mesh. For each boundary treatment, the tunable parameters of the methods are defined to balance accuracy and computational cost. © 2023 S.J. Feriani et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

关键词： Galerkin methods

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