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19th International Conference on Manufacturing Research, ICMR 2022

作者： Bandara, Shakya Jin, Yan Van, Mien Sun, Dan Fu, Rao Curley, Patrick Higgins, Colm Queen's University Belfast University Road BelfastBT7 1NN United Kingdom Department of Applied Computing University of Kelaniya Sri Lanka Dalian University of Technology Liaoning Province China

ISBN: (纸本)9781643683300

Parallel Kinematic Machine (PKM) is a new type of machine tool, which has the potential to fill in the gap between traditional CNC machines and industrial robots, due to its flexibility and superior motion dynamics. Stiffness is an essential property of a machine tool, as it will affect the machining capability. Although much research has been conducted on stiffness modelling and analysis, most of them employ simplified models and gravity effects have not been well considered or characterized for PKMs. To fill in the knowledge gap, this paper introduces a new experimental stiffness measurement method considering the effect of gravity on the machine tool. An experimental procedure was developed in order to separate the gravity effects on stiffness from the machine structure. © 2022 The authors and IOS Press.

关键词： Stiffness

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LGDB-Net: Dual-Branch Path for Building Extraction from Remote Sensing Image 30

LGDB-Net: Dual-Branch Path for Building Extraction from Remo...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Ronghuan Zhao, Jing Li, Ming Zou, Qingzhi Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong University of Traditional Chinese Medicine School of Intelligence and Information Engineering Jinan China

ISBN: (纸本)9798331515966

Extracting buildings from remote sensing images using deep learning techniques is a widely applied and crucial task. Convolutional Neural Networks (CNNs) adopt hierarchical feature representation, showcasing powerful capabilities in extracting local information but facing challenges in capturing global features. Transformers can address this limitation, but they perform poorly in extracting local features and significantly increase memory requirements and computational complexity. To overcome these challenges, we propose a method for building extraction from remote sensing images called LGDB-Net (Local-Global Dual-Branch Network), employing a dual-branch approach. Firstly, inspired by Swin Transformer, we designed GB-Former(Global Branch-Former) as the backbone network to model global information. We use a linear multi-head self-attention mechanism to reduce time and memory complexity while maintaining a large global receptive field. Additionally, we replace the traditional multi-layer perceptron with a convolution-enhanced multi-layer perceptron to improve channel feature representation, reduce model parameters, and enhance segmentation performance. Secondly, we use multiple Depth-wise Conv3×3 + LN (Layer Normalization) + GeLU (Gaussian Error Linear Unit) modules as the auxiliary branch for local detailed feature extraction. Finally, we adopt a multi-scale feature fusion strategy to integrate feature information from both branches. We conduct a series of experiments on three datasets: WHU, Massachusetts, and Inria. The experimental results demonstrate that the proposed method not only effectively improves segmentation accuracy with lower building omission and commission rates but also significantly reduces model parameters and computational complexity. © 2024 IEEE.

关键词： Convolutional neural networks

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Machine Learning and Deep Learning Methods for the Detection of Schizophrenia Using Magnetic Resonance Images and EEG Signals: An Overview of the Recent Advancements 7th

Machine Learning and Deep Learning Methods for the Detection...

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7th International Conference on Information and Communication technology for Competitive Strategies , ICTCS 2022

作者： Shaffi, Noushath Mahmud, Mufti Hajamohideen, Faizal Subramanian, Karthikeyan Shamim Kaiser, M. College of Computing and Information Science University of Technology and Applied Sciences Sohar Oman Department of Computer Science Nottingham Trent University NottinghamNG11 8NS United Kingdom CIRC and MTIF Nottingham Trent University NottinghamNG11 8NS United Kingdom Institute of Information Technology Jahangirnagar University Dhaka1342 Bangladesh

ISBN: (纸本)9789811996375

The availability of large-scale datasets, massively parallelizable GPUs and a wide spectrum of open-source tools have immensely capacitated Artificial Intelligence (AI) field in accurate analytics of medical data. Although it is a challenging task, the application of powerful AI-based computer-aided diagnosis has enabled health practitioners to predict Neurodegenerative diseases more accurately than ever before. This paper presents a brief review of methodological research papers dealing with the prediction of Schizophrenia disease based on machine learning and deep learning techniques using MRI and EEG signals data. The result shows that these techniques have permeated the Schizophrenia disease prediction/classification task with significantly higher accuracy. The paper concludes by providing future avenues based on challenges that are currently faced by this sector. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Magnetic resonance imaging

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A Scalable Approach to Counter Reliability and Load Balancing in Mobile Wireless Sensor Networks

A Scalable Approach to Counter Reliability and Load Balancin...

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Innovative Engineering Sciences and Technological Research (ICIESTR), International Conference on

作者： Siddharth Kumar Alankrita Aggarwal Rattan Deep Aneja Susama Bagchi Sanjoy Kumar Debnath Syed Mehr Ali Shah Computer Science and Engineering Apex Institute of Technology Chandigarh University Mohali Punjab India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India College of Computing & Information Sciences University of Technology & Applied Sciences Musandem Sultanate of Oman

ISBN: (数字)9798350348637

ISBN: (纸本)9798350348644

M-WSN, or Mobile Wireless Sensor Networks, are comprised of even smaller nodes and are run by even smaller batteries. Real-world factors like lint, corrosive substances, harsh weather, etc., can all cause a sensor node to fail. In this case, a trustworthy sensor node that can distribute the load among itself is necessary. Saving energy often wasted on crashes, resending information, and validating information is made possible by establishing reliable communication and load balancing between sensor nodes. This study aims to provide a novel framework that improves WSN capabilities by employing a method never previously utilized in this setting to tackle the issues of reliability and load balancing. For the purpose of load balancing, a novel hybridization of Genetic Algorithm-Particle Swarm Optimization (GA-PSO) is used along with the concept of Artificial Neural Network (ANN) as a deep learning. The proposed framework also uses some characteristics of Ad hoc network, but with fresh terminology to ensure that the packet delivered the sink in a timely fashion and achieves highest battery and network life together with minimized transmission delay. The proposed framework is assessed using delay, energy consumption and throughput as a Quality of Service (QoS) parameters. Here, combination of different bio-inspired techniques with routing approaches are used to achieve better performance of the simulator. The proposed scenario helps to provide better transmission lines together with the transmission node’s range capacity and decreases the rate of energy consumption.

关键词： Wireless sensor networks Energy consumption Terminology Quality of service Artificial neural networks Load management Throughput Delays Batteries Particle swarm optimization

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Fourth-order multiple-relaxation-time lattice Boltzmann model and equivalent finite-difference scheme for one-dimensional convection-diffusion equations

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Physical Review E 2023年第5期107卷 055305-055305页

作者： Ying Chen Zhenhua Chai Baochang Shi School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan 430074 China Institute of Interdisciplinary Research for Mathematics and Applied Science Huazhong University of Science and Technology Wuhan 430074 China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan 430074 China

In this paper, we first develop a fourth-order multiple-relaxation-time lattice Boltzmann (MRT-LB) model for the one-dimensional convection-diffusion equation (CDE) with the constant velocity and diffusion coefficient, where the D1Q3 (three discrete velocities in one-dimensional space) lattice structure is used. We also perform the Chapman-Enskog analysis to recover the CDE from the MRT-LB model. Then an explicit four-level finite-difference (FLFD) scheme is derived from the developed MRT-LB model for the CDE. Through the Taylor expansion, the truncation error of the FLFD scheme is obtained, and at the diffusive scaling, the FLFD scheme can achieve the fourth-order accuracy in space. After that, we present a stability analysis and derive the same stability condition for the MRT-LB model and FLFD scheme. Finally, we perform some numerical experiments to test the MRT-LB model and FLFD scheme, and the numerical results show that they have a fourth-order convergence rate in space, which is consistent with our theoretical analysis.

关键词： Lattice-Boltzmann methods

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A Comprehensive Review Analysis of Distinct Tools Image Plagiarism Detection

A Comprehensive Review Analysis of Distinct Tools Image Plag...

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Naudiyal, Anjali Singh, Vineeta Joshi, Kapil Roka, Sanjay Mamoria, Pushpa Shakya, Rajeev Kumar School of Applied and Life Sciences Uttaranchal University Premnagar Dehradun India School of Engineering and Technology Chhatrapati Shahu Ji Maharaj University Department of Computer Science and Engineering Uttar Pradesh Kanpur208024 India Uttaranchal University Department of Computer Science & Engineering Dehradun India Graphic Era deemed to be university dehradun India School of Electrical Engineering and Computing Adama Science and Technology University Department of Electronics and Communication Engineering Adama Ethiopia

ISBN: (纸本)9798350379716

In the era of artificial intelligence, academic activities tend to use Chat GPT more frequently. A Chabot called Chat GPT is used for brainstorming, writing, and learning. Although most academicians use Chat GPT to their advantage when taking notes, it is detrimental to research. A few authors wrote the papers swiftly by using AI techniques. These days, chat GPT is employed in a wide range of tasks. It prepares any type of content for the internet. The likelihood of plagiarism is rising as a result of its use. At present, plagiarism is the biggest challenge in the field of research. Plagiarism occurs when an author uses this technology to modify another person's work and transmit it off as their own without providing a reference or credit. Plagiarism is a term used to describe when someone copies something from another source without providing credit to the original author. Google is working quickly in the image field, but at that point, it will be used to detect plagiarism that is generated with the aid of artificial intelligence. The less papers that are utilized in favor of online resources, the more academics are working to make the globe more environmentally friendly. In the presented paper, a few software applications are discussed and compare a number of different techniques to detect the plagiarism in academics field like scientific equations or cross-lingual or multilingual texts. Various applications are developed in research to identify instances of plagiarism in research papers, assignments, and source code. After reviewing the papers pertaining to various tools for detecting plagiarism, not much more work is done in the area of detecting plagiarism in images or in image text. The article that is being presented comes to the conclusion that no software tool has been developed to identify plagiarism in images or in the text associated with images. © 2024 IEEE.

关键词： Application programs

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PHYLOGENETIC DIVERSITY INDICES FROM AN AFFINE AND PROJECTIVE VIEWPOINT

arXiv

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arXiv 2024年

作者： Moulton, Vincent Spillner, Andreas Wicke, Kristina School of Computing Sciences University of East Anglia UK. Merseburg University of Applied Sciences Germany. Department of Mathematical Sciences New Jersey Institute of Technology USA.

MSC Codes 05C05, 05C20, 05C90, 92D15Phylogenetic diversity indices are commonly used to rank the elements in a collection of species or populations for conservation purposes. The derivation of these indices is typically based on some quantitative description of the evolutionary history of the species in question, which is often given in terms of a phylogenetic tree. Both rooted and unrooted phylogenetic trees can be employed, and there are close connections between the indices that are derived in these two different ways. In this paper, we introduce more general phylogenetic diversity indices that can be derived from collections of subsets (clusters) and collections of bipartitions (splits) of the given set of species. Such indices could be useful, for example, in case there is some uncertainty in the topology of the tree being used to derive a phylogenetic diversity index. As well as characterizing some of the indices that we introduce in terms of their special properties, we provide a link between cluster-based and split-based phylogenetic diversity indices that uses a discrete analogue of the classical link between affine and projective geometry. This provides a unified framework for many of the various phylogenetic diversity indices used in the literature based on rooted and unrooted phylogenetic trees, generalizations and new proofs for previous results concerning tree-based indices, and a way to define some new phylogenetic diversity indices that naturally arise as affine or projective variants of each other. Copyright © 2024, The Authors. All rights reserved.

关键词： Conservation

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Sentiment Analysis in Social Media Marketing: Leveraging Natural Language Processing for Customer Insights 8th

Sentiment Analysis in Social Media Marketing: Leveraging Nat...

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8th International Conference on Information and Communication technology for Competitive Strategies, ICTCS 2023

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Choudhury, Tanupriya Singh, Teekam Kotecha, Ketan School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura India CSE Department Symbiosis Institute of Technology Symbiosis International University Lavale Campus Pune411045 India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune411045 India

ISBN: (纸本)9789819994885

In this day and age of online marketing, social media platforms have evolved into essential tools for companies to use in order to communicate with their consumers, as well as to advertise the goods and services they provide. Understanding client emotion, tastes, and views may be significantly aided by perusing the vast amounts of user-generated information that can be found on these many platforms. This study investigates the use of sentiment analysis, a branch of natural language processing (NLP), as a potent instrument for gleaning useful consumer insights from data collected from social media platforms like as Facebook and Twitter. The application of computer methods in the process of automatically determining and categorising the sentiment that is represented in textual data, such as tweets, Facebook posts, and online reviews, is what is known as sentiment analysis. In this article, the primary methodology and techniques used in sentiment analysis, such as lexicon-based, machine learning, and deep learning approaches, are dissected and discussed. In addition to this, it sheds light on the difficulties and factors to take into account that are unique to sentiment analysis when applied to the context of data from social media, such as the management of noisy and unstructured language, the management of sarcasm, and the management of context-dependent sentiment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sentiment analysis

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Improved hybrid Allen-Cahn phase-field-based lattice Boltzmann method for incompressible two-phase flows

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Physical Review E 2023年第3期107卷 035308-035308页

作者： Xi Liu Zhenhua Chai Baochang Shi School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan 430074 China Institute of Interdisciplinary Research for Mathematics and Applied Science Huazhong University of Science and Technology Wuhan 430074 China and Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan 430074 China

In this work we develop an improved phase-field based lattice Boltzmann (LB) method where a hybrid Allen-Cahn equation (ACE) with a flexible weight instead of a global weight is used to suppress the numerical dispersion and eliminate the coarsening phenomenon. Then two LB models are adopted to solve the hybrid ACE and the Navier-Stokes equations, respectively. Through the Chapman-Enskog analysis, the present LB model can correctly recover the hybrid ACE, and the macroscopic order parameter used to label different phases can be calculated explicitly. Finally, the present LB method is validated by five tests, including the diagonal translation of a circular interface, two stationary bubbles with different radii, a bubble rising under the gravity, the Rayleigh-Taylor instability in two-dimensional and three-dimensional cases, and the three-dimensional Plateau-Rayleigh instability. The numerical results show that the present LB method has a superior performance in reducing the numerical dispersion and the coarsening phenomenon.

关键词： Multiphase flows Lattice-Boltzmann methods Two-fluid & multi-fluid model

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Impact of User Innovativeness on Metaverse Adoption 25

Impact of User Innovativeness on Metaverse Adoption

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25th International Arab Conference on Information technology, ACIT 2024

作者： Salloum, Said Al-Maroof, Rana Saeed Tahat, Khalaf Alfaisal, Raghad Tahat, Dina University of Sharjah Health Economic and Financing Group Sharjah United Arab Emirates Al Buraimi University College English Language & Linguistics Department Al Buraimi Oman University of Jordan Digital Media Department Jordan Universiti Pendidikan Faculty of Computing and Meta-Technology Sultan Idris Malaysia Al Ain University Applied Sociology Department United Arab Emirates

ISBN: (纸本)9798331540012

the study focuses on the integration of the metaverse to enhance online learning platforms, particularly among Omani students. The research evaluates the acceptance of the metaverse using a conceptual model that emphasizes key adoption properties such as user mobility, users' accessibility and users' and ubiquity. The novelty of the paper lies in introducing the users' innovativeness as a moderator which positively influences other variables. The results indicate a favorable impact of the moderator on the mentioned variables. The practical implication of the study suggests that authorities in Oman should recognize the metaverse's significance as a transformative tool for online learning. Furthermore, managerial implications recommend the implementation of the metaverse in governmental sectors to enhance the learning environment in the Sultanate of Oman. © 2024 IEEE.

关键词： Adversarial machine learning

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