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13th International Conference on computer and Knowledge engineering, ICCKE 2023

作者： Asadi-Zeydabadi, Fatemeh Shabaninia, Elham Nezamabadi-Pour, Hossein Shojaee, Melika Shahid Bahonar University of Kerman Department of Electrical Engineering Kerman Iran Graduate University of Advanced Technology Faculty of Sciences and Modern Technologies Department of Applied Mathematics Kerman Iran Shahid Bahonar University of Kerman Department of Computer Engineering Kerman Iran

ISBN: (纸本)9798350330151

Optical Character Recognition (OCR) techniques have made significant advances in recent years using new approaches such as Transformers for Latin languages. However, research on low-resource languages like Farsi is still relatively scarce. One of the reasons for this is the complex nature of Farsi script, which poses unique challenges for OCR. Farsi OCR is essential for various applications, such as document management, digital archiving, and automated data entry. In the context of this investigation, we presents a deep neural network that utilizes transformer architecture for the recognition of Farsi words, yielding promising results. Additionally, we assess the effectiveness of our method through a comparison with state-of-the-art techniques on two datasets namely, Shotor and Sadri, achieving accuracy rates of 99.75% and 99.23%, respectively. Our results outperform previous methods and highlight the potential of transformer-based approaches for Farsi OCR. © 2023 IEEE.

关键词： Optical character recognition

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Renewable-Energy-System Applications of Ambiguous-Fuzzy-Hybrid-Averaging Operator 2

Renewable-Energy-System Applications of Ambiguous-Fuzzy-Hybr...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Singh, Nisha Joshi, Bhagawati Prasad Gupta, Sachin Singh, Akhilesh Rathee, Sachit Maharaja Surajmal Institute of Technology Department of Electrical & Electronics Engineering Janakpuri New Delhi India Graphic Era Hill University Department of MathematicsComputer Science & Engineering Bhimtal India Uttarakhand Technical University Veer Madho Singh Bhandari Department of Mechanical Engineering India Uttarakhand Technical University Veer Madho Singh Bhandari Department of Electrical Engineering India

ISBN: (纸本)9798350317558

The theory of Ambiguous-Intuitionistic-Fuzzy-Sets (IFS) termed as AIFS is a better way to deal with uncertain/vagueness information as compared to IFS. Considering the novelty of AIFS, this paper presents one of the applications of AIFS to the Renewable-Energy (RE) Systems (RESs). This is achieved by taking the foundation development of Ambiguous-Intuitionistic-Fuzzy-Hybrid-Averaging-Operator referred by the AFHA operator. The incorporation of the AFHA operator into RESs is a viable approach to tackle complex difficulties. This research examines the practical uses, theoretical basis, and operational consequences of implementing AFHA operator in renewable energy systems. It also demonstrates the effectiveness of AFHA operator in addressing various operational situations in RESs. A comparative study is also included in this study to show and observe the validity of the obtained results. © 2024 IEEE.

关键词： Ambiguous Intuitionistic fuzzy set (AIFS) Hybrid-Averaging operator multi-criteria decision-making (MCDM) Renewable energy (RE) systems (RESs)

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A Shallow Water Model: Analytical and Numerical Study for Simulating the Coastline Evolution 1

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4th International Conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

作者： Scarcella, Maria Antonietta Carini, Manuela Zampoli, Vittorio Department of Environmental Engineering DIAm University of Calabria via Pietro Bucci CS Rende87036 Italy Department of Information and Electrical Engineering and Applied Mathematics DIEM University of Salerno via Giovanni Paolo II SA Fisciano84084 Italy

ISBN: (数字)9783031812446

ISBN: (纸本)9783031812439

In a broader research context, we aim to explore and improve existing mathematical models able to predict and simulate the evolution of the coastline in time, taking into account the variation of seabed topography and morphology, as well as resistance conditions, time intervals between storm surges, seasonal variations and viscosity, all factors that greatly influence coastal dynamics. With this target, relying on a numerical approach, in the present contribution we implement our investigation considering waves analytically modeled through the Saint Venant’s equations. We exploit the COMSOL Multiphysics simulation platform, which allows to handle engineering problems governed by PDEs, highlighting if and how seabed topography, friction term and dispersion coefficient influence the elevation of the water column in space and time which, in turn, has an impact on the coastal erosion. Two particular topographies are taken into account, designed to simulate seabeds with common features actually existing. A number of graphical results are provided and commented, accompanying the conclusions with a brief note on potential future advancements and applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Coastal engineering

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Fluorescence Lifetime of NV Centers for Temperature-Insensitive All-Optical Magnetic Field Sensing 26

Fluorescence Lifetime of NV Centers for Temperature-Insensit...

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26th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2024

作者： Horsthemke, Ludwig Pogorzelski, Jens Stiegekotter, Dennis Hoffmann, Frederik Bulter, Ann-Sophie Langguth, Lutz Staacke, Robert Trinschek, Sarah Gregor, Markus Glosekotter, Peter FH Münster - University of Applied Sciences Department of Electrical Engineering and Computer Science Steinfurt48565 Germany Quantum Technologies GmbH Leipzig04103 Germany FH Münster - University of Applied Sciences Department of Engineering Physics Steinfurt48565 Germany

ISBN: (纸本)9798331532833

We investigate the combined temperature and magnetic field dependent behavior of high NV-density microdiamonds in an all-optical frequency-domain based setup, utilizing the change in fluorescence lifetime. We use these frequency-domain data to train neural networks for the prediction of temperatures and magnetic fields. At zero magnetic field, we show the ability to predict temperatures in the range of 0° C to 100°C, with a standard deviation of 1.24 °C. Furthermore, we show the ability to predict applied magnetic fields, while varying the ambient temperature. We achieve a higher accuracy, compared to the approach of observing of the fluorescence intensity at a single excitation frequency. © 2024 IEEE.

关键词： Magnetometry

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Novel Human-Posture Recognition System Based on Advanced Graph Convolutional Network Using Skeletal Data

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2024年 1卷 224-236页

作者： Liu, Guannan Xie, Rende Fang, Shih-Hau Wu, Hsiao-Chun Yan, Kun San Jose State University Department of Applied Data Science San JoseCA95112-3613 United States National Taiwan Normal University Department of Electrical Engineering Taipei106 Taiwan Yuan Ze University Department of Electrical Engineering Taoyuan320315 Taiwan Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Guilin University of Electronic Technology School of Information and Communication Guilin541004 China

Automatic human-posture or human-activity recognition is a very important research problem nowadays. In this work, we propose a novel human-posture recognition approach using the 3-D skeletal data acquired by the Kinect V2 sensor. The acquired skeletal data are first segmented using our recently proposed automatic-segmentation technique and each segment can be labeled with a particular kind of human-posture. We propose four different types of node feature matrices extracted from the segmented skeletal data, which can serve as the input features to the advanced graph convolutional network for multiclassification. The realworld experimental results demonstrate that our proposed novel human-posture recognition system can reach a very high average classification-accuracy of 91.56%. In addition, the ablation study of the effect of skeletal-graph variations on the recognition performance is also presented. The average classification-accuracy further reaches up to 92.33% when four confusing joint-nodes are removed from the skeletal graph. Our proposed novel human-posture recognition approach can be very useful for practical applications, such as human-computer interface, intelligent healthcare, robotics, etc. © 2024 IEEE.

关键词： Sensor nodes

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A SENSITIVITY ANALYSIS ON THE EFFECT OF HYPERPARAMETERS IN DEEP NEURAL OPERATORS applied TO SOUND PROPAGATION 10

A SENSITIVITY ANALYSIS ON THE EFFECT OF HYPERPARAMETERS IN D...

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10th Convention of the European Acoustics Association, EAA 2023

作者： Borrel-Jensen, Nikolas Engsig-Karup, Allan P. Jeong, Cheol-Ho Technical University of Denmark Dep. of Electrical and Photonics Engineering Denmark Technical University of Denmark Dep. of Applied Mathematics and Computer Science Denmark

ISBN: (纸本)9788888942674

Deep neural operators have seen much attention in the scientific machine learning community over the last couple of years due to their capability of efficiently learning the nonlinear operators mapping from input function spaces to output function spaces showing good generalization properties. This work will show how to set up a per-formant DeepONet architecture in acoustics for predicting 2-D sound fields with parameterized moving sources for real-time applications. A sensitivity analysis is carried out with a focus on the choice of network architectures, activation functions, Fourier feature expansions, and data fidelity to gain insight into how to tune these models. Specifically, a default feed-forward neural network (FNN), a modified FNN, and a convolutional neural network will be compared. This work will de-mystify the DeepONet and provide helpful knowledge from an acoustical point of view. © 2023 Nikolas Borrel-Jensen 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： Sensitivity analysis

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End-to-end dynamic residual focal transformer network for multimodal medical image fusion

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Neural Computing and Applications 2024年第19期36卷 11579-11601页

作者： Zhang, Weihao Yu, Lei Wang, Huiqi Pedrycz, Witold College of Computer and Information Science Chongqing Normal University Chongqing401331 China College of Mathematics and Statistics Chongqing University Chongqing401331 China Department of Electrical and Computer Engineering University of Alberta EdmontonT6G 2V4 Canada

Multimodal medical image fusion aims to improve the clinical practicability of medical images by integrating complementary information from multiple medical images. However, in traditional fusion methods, the fusion rules based on prior knowledge or logic usually cannot match the feature representation perfectly, which results in partial information loss. Furthermore, most deep learning-based fusion methods depend on convolutional operations, which only focus on local features and have limited retention of context information. To address the above issues, we propose an end-to-end dynamic residual focal transformer network for multimodal medical image fusion, termed DRFT. The DRFT framework is an end-to-end network with no need to manually design fusion rules. Firstly, the context-gated convolution is introduced to construct the context dynamic extraction module (CDEM) to extract the key semantic information more accurately from multimodal medical images. Then, a new residual transformer fusion module (RTFM) is designed by incorporating the focal transformer into the residual mechanism, which can not only extract the deep semantic features, but also adaptively learn the optimal fusion scheme. Finally, the nest architecture is employed to extract multiscale features. In addition, a new objective function consisting of global detail loss and fusion enhancement loss is designed to enrich the modal information in the fused image. Notably, the proposed network does not require the two-stage training strategy as opposed to the traditional encoder–decoder fusion structure. Extensive experimental results on mainstream datasets show that, compared with the state-of-the-art methods, the proposed DRFT delivers better performance in both qualitative and quantitative evaluation. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolution

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Performance Comparison of the Invasive Weed Optimization K-Means Algorithm With Particle Swarm Optimization K-Means for Banking Data 6

Performance Comparison of the Invasive Weed Optimization K-M...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Suwirmayanti, Ni Luh Gede Pivin Putra, I. Ketut Gede Darma Sudarma, Made Sukarsa, I. Made Setyaningsih, Emy Department of Computer Systems Faculty of Informatics and Computer Institut Teknologi dan Bisnis STIKOM Bali Bali Indonesia Department of Information Technology Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia Department of Electrical Engineering Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia Department of Computer Systems Engineering Faculty of Applied Science Universitas AKPRIND Indonesia Yogyakarta Indonesia

ISBN: (纸本)9798331539603

Banking produces extensive and diverse data, so a clustering process is needed to understand customer behavior patterns and transactions more effectively. This clustering has been widely utilized with the K-Means algorithm due to its simplicity and efficiency. However, this algorithm is limited in determining the optimal cluster center, affecting the accuracy of the clustering results. This study proposes applying two metaheuristic algorithms, Invasive Weed Optimization (IWO) and Particle Swarm Optimization (PSO), as additional solutions combined with K-Means. These two algorithms were chosen because of their ability to find global solutions and convergence speed to handle large and complex banking data. The results show the superiority of IWOKM compared to PSOKM in terms of SSE and DBI. From the SSE value, IWOKM produces a lower value (3029.77), which indicates that this method is better at minimizing clustering errors and producing more accurate clustering than PSOKM (80007.09). From the DBI value, IWOKM also produces a better value (1.6684) than PSOKM (1.6782), which shows that the clusters produced by IWOKM are more compact and more separated. However, in terms of computation time, PSOKM is more efficient, with a faster average computation time. Although IWOKM produces better clustering quality, PSOKM offers advantages in terms of processing speed. This finding confirms that the selection of the IWO algorithm is more appropriate for use on datasets with characteristics such as the German Credit Dataset. © 2024 IEEE.

关键词： Invasive weed optimization

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Adaptive Prescribed Time Path Following Controller for Underactuated Surface Vessels 13

Adaptive Prescribed Time Path Following Controller for Under...

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13th International Conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Ghommam, Jawhar Saad, Maarouf Zhu, Quanmin Rahman, Mohammed H. Dept. of Electrical and Computer Engineering Sultan Qaboos University Muscat Oman Dept. of Electrical Engineering Ecole de Technologie Superieure Montreal Canada Dept. of Engineering Design and Mathematics University of the West of England Bristol United Kingdom Mechanical Engineering Department University of Wisconsin-Milwaukee Wisconsin United States

ISBN: (纸本)9798331516147

Heading control of marine vehicles is an important issue in navigation and control engineering. This paper investigates the design of a prescribed-time robust adaptive path following control problem of an underactuated surface vessel with parametric uncertainties, unknown disturbances and control input constraints. The establishment of prescribed-time property consists of a prescribed-time regulation of the steering control systems and novel prescribed-time Line of Sight (LOS) guidance control, both of which satisfy the prescribed-time requirement. The feasibility of separate independent control design for the steering and the guidance control systems is guaranteed through the development of new results on nonlinear prescribed-time cascade systems with non-vanishing uncertainties. The Lyapunov method is employed to show that the equilibrium point of the whole system is prescribed-time input to state stable. Numerical simulations are carried out to validate the performance of the proposed control strategy. © 2025 IEEE.

关键词： Steering

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Analysis of Viral and Non-viral Categories of Video Through Social Media Using Machine Learning Techniques 15th

Analysis of Viral and Non-viral Categories of Video Through ...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Nagashree, B.A. Satheesh Kumar, S. Muthukumaran, V. Joseph, P. Rose Bindu Amrita Vishwa Vidyapeetham Mysuru Campus Mysuru570026 India Department of Computer Science School of Applied Sciences REVA University Bangalore India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Mathematics Dayananda Sagar College of Engineering Bangalore India

ISBN: (纸本)9783031889912

People frequently use technological means to collect information from many sources, share it with others on social media, and communicate with friends on these platforms. Social media is therefore a crucial new tool for communication. Collaborative wikis, blogs, and content communities are just a few of the tools and technologies that make up social media. YouTube is the most widely used of these. The influencer’s content gets visible to the users of such platforms. The expansion of social media use has enabled massive and rapid spread ability of content, often called virality. This paper aims to examine the virality of different categories of YouTube videos using machine learning technique, where it plays a major role in analysis of YouTube data such as likes, dislikes, comment count and views. YouTube can serve as a path to virality and fabricate online videos, hence finding the correlation analysis among the videos, which enables us to classify the viral and non-viral video categories, thereby analysing the linear relationship among the features (likes, dislike, views and comment count. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Tweets

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