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Kink movement on a periodic background

Physical Review E 2025年第2期111卷 024203-024203页

作者： Tomasz Dobrowolski Jacek Gatlik Panayotis G. Kevrekidis Department of Computer Physics and Quantum Computing University of the National Education Commission in Krakow Podchorążych 2 30-084 Cracow Poland AGH University of Krakow Faculty of Physics and Applied Computer Science 30-059 Krakow Poland Department of Mathematics and Statistics University of Massachusetts Amherst Massachusetts 01003-4515 USA

The behavior of the kink in the sine-Gordon (sG) model in the presence of periodic inhomogeneity is studied. An ansatz is proposed that allows for the construction of a reliable effective model with two degrees of freedom. Effective models featuring very good agreement with the original field-theoretic partial differential equation are constructed, including in the nonperturbative region and for relativistic velocities. The numerical solutions of the sG model describing the evolution of the kink in the presence of a barrier as well as in the case of a periodic heterogeneity under the potential additional influence of a switched bias current and/or dissipation were obtained. The results of the field equation and the effective models were favorably compared. The effect of the choice of initial conditions in the field model on the agreement of the results with the effective model is also discussed.

关键词： sine-Gordon equation

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HYP-PSO-XGB:Efficient Hyperparameter Tuning of XGBoost for EEG-Based Hand Gesture Classification using Particle Swarm Optimization

HYP-PSO-XGB:Efficient Hyperparameter Tuning of XGBoost for E...

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IEEE International Conference on Electronics, computing and Communication Technologies (CONECCT)

作者： Priyadharshini Jayadurga N M. Chandralekha Kashif Saleem Mehmet A Orgun Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Sciences and Engineering College of Applied Studies and Community Service King Saud University Riyadh Saudi Arabia Department of Computing Macquarie University North Ryde NSW Australia

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

A very crucial pursuit in Brain-computer Interface technology is to achieve highly reliable classification of hand gestures with the use of Electroencephalogram (EEG) data. It is momentous to enrich the communication of audio-vocally challenged individuals. This paper proposes a novel method for the calibration of the hyperparameters of the Extreme Gradient Boosting (XGBoost) Classifier, dependent on the data complexity, through Particle Swarm Optimization (PSO), aiming to increase the overall sensibility of the model for the hand movements of different kinds, based on the EEG. In this approach, several crucial hyperparameters were adjusted multiple times based on the best trade-offs of the other hyperparameters. The datasets involving EEG data for monitoring distinct hand movements were preprocessed prior to being divided into training and testing datasets. The use of Particle Swarm Optimization (PSO) enabled a more flexible and several iterations quality-adjustment of the hyperparameters, with the eventual goal of boosting the classification accuracy. The new PSO-optimized XGBoost model reached an accuracy of 93.57%. This value represents a substantial increase over existing models, i.e. Improved Particle Swarm Optimization model with 76.67%, PSO SVM model for Motor Imagery with 92.92%, PSO SVM model with 80.63%, and GAPSO-SVM model with 88.89%. It proves the potential of PSO to significantly improve the efficiency of XGBoost, making it possible to create even more sensitive and nuanced BCIs aiding hand gesture classification.

关键词： Support vector machines Accuracy Brain modeling Motors Boosting Electroencephalography Brain-computer interfaces

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Machine-Learning-Based Profanity Detection and Removal in Cartoons Videos

Machine-Learning-Based Profanity Detection and Removal in Ca...

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International Conference on Control and Robots (ICCR)

作者： Porawat Visutsak Apiwut Wijitemee Akaphon Mahaphon Orawan Chaowalit Department of Computer and Information Science Faculty of Applied Science KMUTNB Bangkok Thailand Department of Computing Faculty of Science Silpakorn University Muang Nakhon Pathom Nakhon Pathom Thailand

Swear words and offensive languages in cartoons are prohibited and should be avoided by our children. This paper aims to detect swear words and remove them from cartoons videos for the kids. The system was implemented via a web-based application. The user is allowed to upload cartoons videos to the cloud and footage of video is converted to text using Google Speech-to-Text. We used the linear SVM to detect all swear words which appeared in a text file. The system is capable of muting these swear words to the audio file. After the removal of swear words, the result of cartoons videos can be downloaded and playback offline. The precision, recall, F1-score, and accuracy of our system are 0.957, 0.958, 0.931, and 0.958 respectively.

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Analytic weak-stable manifolds in unfoldings of saddle-nodes

arXiv

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arXiv 2024年

作者： Kristiansen, Kristian Uldall Szmolyan, Peter Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs Lyngby2800 Denmark Institute of Analysis and Scientific Computing Technical University of Vienna Austria

Any attracting, hyperbolic and proper node of a two-dimensional analytic vector-field has a unique strong-stable manifold. This manifold is analytic. The corresponding weak-stable manifolds are, on the other hand, not unique, but in the nonresonant case there is a unique weak-stable manifold that is analytic. As the system approaches a saddle-node (under parameter variation), a sequence of resonances (of increasing order) occur. In this paper, we give a detailed description of the analytic weak-stable manifolds during this process. In particular, we relate a "flapping-mechanism", corresponding to a dramatic change of the position of the analytic weak-stable manifold as the parameter passes through the infinitely many resonances, to the lack of analyticity of the center manifold at the saddle-node. Our work is motivated and inspired by the work of Merle, Raphaël, Rodnianski, and Szeftel, where this flapping mechanism is the crucial ingredient in the construction of C∞-smooth self-similar solutions of the compressible Euler *** Codes 34C23, 34C45, 37G05, 37G10 Copyright © 2024, The Authors. All rights reserved.

关键词： Euler equations

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Computational Linguistics with Optimal Deep Belief Network Based Irony Detection in Social Media

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computers, Materials & Continua 2023年第5期75卷 4137-4154页

作者： Manar Ahmed Hamza Hala J.Alshahrani Abdulkhaleq Q.A.Hassan Abdulbaset Gaddah Nasser Allheeib Suleiman Ali Alsaif Badriyya B.Al-onazi Heba Mohsen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of English College of Science and Arts at MahayilKing Khalid UniversityAbha62217Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia Department of Computer Deanship of Preparatory Year and Supporting StudiesImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Computational linguistics refers to an interdisciplinary field associated with the computational modelling of natural language and studying appropriate computational methods for linguistic *** number of social media users has been increasing over the last few years,which have allured researchers’interest in scrutinizing the new kind of creative language utilized on the Internet to explore communication and human opinions in a *** and sarcasm detection is a complex task inNatural Language Processing(NLP).Irony detection has inferences in advertising,sentiment analysis(SA),and opinion *** the last few years,irony-aware SA has gained significant computational treatment owing to the prevalence of irony in web ***,this study develops Computational Linguistics with Optimal Deep Belief Network based Irony Detection and Classification(CLODBN-IRC)model on social *** presented CLODBN-IRC model mainly focuses on the identification and classification of irony that exists in social *** attain this,the presented CLODBN-IRC model performs different stages of pre-processing and TF-IDF feature *** irony detection and classification,the DBN model is exploited in this *** last,the hyperparameters of the DBN model are optimally modified by improved artificial bee colony optimization(IABC)*** experimental validation of the presentedCLODBN-IRCmethod can be tested by making use of benchmark *** simulation outcomes highlight the superior outcomes of the presented CLODBN-IRC model over other approaches.

关键词： Computational linguistics natural language processing deep learning irony detection social media

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Improved Fruitfly Optimization with Stacked Residual Deep Learning Based Email Classification

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Intelligent Automation & Soft computing 2023年第6期36卷 3139-3155页

作者： Hala J.Alshahrani Khaled Tarmissi Ayman Yafoz Abdullah Mohamed Abdelwahed Motwakel Ishfaq Yaseen Amgad Atta Abdelmageed Mohammad Mahzari Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah22230Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16242Saudi Arabia Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharj16242Saudi Arabia

applied linguistics means a wide range of actions which include addressing a few language-based problems or solving some language-based *** stay in the leading positions for business as well as personal *** popularity grabs the interest of individuals with malevolent inten-tions—phishing and spam email *** filtering mechanisms were developed incessantly to follow unwanted,malicious content advancement to protect the *** prevailing solutions were focused on phishing email filtering and spam and whereas email labelling and analysis were not fully ***,this study provides a solution related to email message body text automatic classification into phishing and email *** paper presents an Improved Fruitfly Optimization with Stacked Residual Recurrent Neural Network(IFFO-SRRNN)based on applied Linguistics for Email *** presented IFFO-SRRNN technique examines the intrinsic features of email for the identification of spam *** the preliminary level,the IFFO-SRRNN model follows the email pre-processing stage to make it compatible with further ***,the SRRNN method can be useful in recognizing and classifying spam *** hyperparameters of the SRRNN model need to be effectually tuned,the IFFO algorithm can be utilized as a hyperparameter *** investigate the effectual email classification results of the IFFO-SRDL technique,a series of simulations were taken placed on public datasets,and the comparison outcomes highlight the enhancements of the IFFO-SRDL method over other recent approaches with an accuracy of 98.86%.

关键词： Email classification applied linguistics improved fruitfly optimization deep learning recurrent neural network

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Learning Latent Structures in Network Games via Data-Dependent Gated-Prior Graph Variational Autoencoders 41

Learning Latent Structures in Network Games via Data-Depende...

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41st International Conference on Machine Learning, ICML 2024

作者： Yu, Xue Li, Muchen Leng, Yan Liao, Renjie Center for Applied Statistics School of Statistics Renmin University of China Beijing China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing China Department of Computer Science University of British Columbia VancouverBC Canada McCombs School of Business The University of Texas at Austin AustinTX United States Vector Institute for AI Canada Department of Electrical and Computer Engineering University of British Columbia VancouverBC Canada

In network games, individuals interact strategically within network environments to maximize their utilities. However, obtaining network structures is challenging. In this work, we propose an unsupervised learning model, called data-dependent gated-prior graph variational autoencoder (GPGVAE), that infers the underlying latent interaction type (strategic complement vs. substitute) among individuals and the latent network structure based on their observed actions. Specially, we propose a spectral graph neural network (GNN) based encoder to predict the interaction type and a data-dependent gated prior that models network structures conditioned on the interaction type. We further propose a Transformer based mixture of Bernoulli encoder of network structures and a GNN based decoder of game actions. We systematically study the Monte Carlo gradient estimation methods and effectively train our model in a stage-wise fashion. Extensive experiments across various synthetic and real-world network games demonstrate that our model achieves state-of-the-art performances in inferring network structures and well captures interaction types. Copyright 2024 by the author(s)

关键词： Monte Carlo methods

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A Novel Federated Learning Framework for Healthcare Applications Using Wearable Devices

A Novel Federated Learning Framework for Healthcare Applicat...

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AI in Cybersecurity (ICAIC), International Conference on

作者： Rajesh Natarajan Sujatha Krishna Christodoss Prasanna Ranjith Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences Al-Aqr 324 Shinas Oman Computer Science Department Messiah University Mechanicsburg PA USA

ISBN: (数字)9798331518882

ISBN: (纸本)9798331518899

A key necessity in managing signals for processing and generating useful information in the healthcare domain is the use of many algorithms. Traditional computing methods have been successful but may not be effective in the future as secure privacy matters. This has led to the increasing popularity of machine learning technologies. However, we present a novel device for devices and sensors for federated learning (FL), which is aimed at establishing a new, safer framework. This pattern uses machine learning with a lot of emphasis on the availability of the devices. In such a case, models for the detection, monitoring, and management of diseases can be created. The FL approach is also believed to be applicable to several healthcare contexts.

关键词： Data privacy Accuracy Federated learning Biological system modeling Transfer learning Medical services Organizations Data models Sensors Biosensors

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Optimizing Handwritten Numeral Recognition for English and Devanagari Using MNIST and CPAR Data 7

Optimizing Handwritten Numeral Recognition for English and D...

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7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Choudhury, Tanupriya Kotecha, Ketan Graphic Era Hill University School of Computing Dehradun India GLA University Department of Computer Engineering & Applications UP Mathura India Graphic Era Deemed to Be University Dehradun Department of Computer Science and Engineering India Symbiosis Institute of Technology CSE Dept. Lavale Campus Pune India Symbiosis Centre for Applied Artificial Intelligence Pune India

ISBN: (纸本)9798350383065

In this paper, a technique for the categorization of offline handwritten digits in the Devanagari script as well as the Roman script (English numbers) is presented. Finding out how to get the best possible recognition results while concurrently working with several scripts is the primary purpose of this study. The approach that has been described makes use of an easy profiling method for the extraction of features, together with an innovative implementation of Linear Discriminant Analysis (LDA), and a neural network architecture that has been adapted specifically for numeral classification. An exhaustive series of trials including 36,000 examples of handwritten numerals were carried out as part of the evaluation of the effectiveness of this approach. These samples were chosen at random from the CPAR datasets, with 22,000 being put to use in the construction of the training set and the remaining 14,000 being assigned to the role of the test set. In addition, the well-known MNIST database was used, which provided 60,000 samples for training purposes and 10,000 samples specifically for testing. It was determined via thorough testing that the LDA classifier produced variable results. These varying results are essential in emphasising the success of the method as well as possible areas for improvement for the method. According to the results, this strategy, which combines profile-based feature extraction with advanced classification algorithms, has the potential to lead to major breakthroughs in the area of handwritten number identification. © 2023 IEEE.

关键词： Extraction

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An Integration Of Wireless Communications And Artificial Intelligence For Autonomous Vehicles 3

An Integration Of Wireless Communications And Artificial Int...

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3rd International Conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Raghuram, Kadambari Altarawni, Ibrahim Vijayalakshmi, K. Kumar, Ajay Jadeja, Abhijeetsinh Chandra, Pankaj Jntuk Department of Computer Science and Engineering Andhra Pradesh Kakinada India Aqaba University of Technology Applied Computing and Artificial Intelligent Department Aqaba Jordan Ramapuram Campus Srmist India INurture Education Solution Pvt. Ltd It Academics Delhi India Sankalchand Patel University Faculty of Computer Science Shri C.J. Patel College of Computer Studies India Guru Ghasi das Viswavidyalaya Dept of Information Technology Chattisgarh Bilaspur India

ISBN: (纸本)9798350399264

The pinnacle of transportation is the development of autonomous driving, which, with the help of CAVs and related traffic management systems, can eventually lead to congestion- and accident-free driving. This vision has as of late prodded extraordinary examination interest in fields including IoV, LTE-V2X, and 5G. In any case, the huge volume of traffic information that CAVs produce makes issues for both the current organizations and the approaching 5G correspondence organizations. For outside network innovations, the VMBS fills in as both a client hub and an edge processing hub. For CAVs, it fills in as a base station and a data caching hub, melding correspondence and calculation. People offer both the VMBS-empowered handset and figuring for CAVs as well as the VMBS-helped wireless innovation for other wireless gadgets to achieve this. It is underlined and addressed that there are a number of research obstacles and open questions. Last but not least, the results of the simulation show that the planned VMBS-CCNA may significantly enhance throughput, latency, and the average amount of links. © 2023 IEEE.

关键词： Autonomous vehicles

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