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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Liu, Lin Yuan, Yuan School of Computer Science and Engineering Beihang University Beijing100191 China State Key Laboratory of Software Development Environment Zhongguancun Laboratory Beijing100191 China

ISBN: (纸本)9798350308365

Physics-Informed Neural Networks (PINNs) have recently received increasing attention, however, optimizing the loss function of PINNs is notoriously difficult, where the landscape of the loss function is often highly non-convex and rugged. Local optimization methods based on gradient information can converge quickly but are prone to being trapped in local minima for training PINNs. Evolutionary algorithms (EAs) are well known for the global search ability, which can help escape from local minima. It has been reported in the literature that EAs show some advantages over gradient-based methods in training PINNs. Inspired by the Memetic Algorithm, we combine global-search based EAs and local-search based batch gradient descent in order to make the best of both word. In addition, since the PINN loss function is composed of multiple terms, balancing these terms is also a challenging issue. Therefore, we also attempt to combine EAs with multiple-gradient descent algorithm for multi-objective optimization. Our experiments provide strong evidence for the superiority of the above algorithms. © 2024 IEEE.

关键词： Optimization algorithms

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Prediction of Advertising Click-Through Rate Based on Dual Attention Network 4

Prediction of Advertising Click-Through Rate Based on Dual A...

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4th International Conference on Machine Learning, Big Data and Business Intelligence, MLBDBI 2022

作者： Zhang, Chao Zhang, Chunna University of Science and Technology LiaoNing School of Computer and Software Engineering Anshan China

ISBN: (纸本)9798350333947

Click-through rate (CTR) prediction refers to whether users will click on the content distributed by manufacturers. In the field of computing advertising and recommendation systems, click-through rate prediction has always been a mainstream hot topic. The relationship between features is often hidden in the data, detailed enumeration will lead to a waste of resources and manpower, and meaningless features will also interfere with and influence the final prediction results. Therefore, the key challenge in CTR task is how to effectively model feature interaction. In this paper, we continue the framework of the traditional deep click rate prediction model, considering the sparsity of the effective feature interaction, we add a feature generation module in the feature input part, and a dual attention network is proposed to further interact and filter the input features. The experiments are conducted on Criteo and Avazu datasets, and the results are improved compared with other models. © 2022 IEEE.

关键词： Marketing

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Stability Analysis for Vehicular Platooning Subjects to Disturbances 7

Stability Analysis for Vehicular Platooning Subjects to Dist...

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2023 7th International Conference on Mechanics, Mathematics and Applied Physics, ICMMAP 2023

作者： Lei, Hongbo Zeng, Ye Zhang, Min School of Physics and Electronic Engineering Sichuan University of Science & Engineering 643000 China School of Artificial Intelligence Hezhou University 542800 China School of Computer and Software Engineering Xihua University 610039 China

In our work, a control problem of vehicular communications platooning, subject to disturbances, is researched. To eliminate the spacing errors of the platooning, a novel effective non-singular terminal sliding-mode control (TSMC) law is designed for the following vehicles, where the problem of singularity is addressed. Furthermore, a coupled sliding-mode surface is presented for vehicular platooning. Then, based on Lyapunov stability theory, the spacing errors for the platooning can be forced into the designed TSMC surface in finite time and finally reach zero. The string stability of the platooning is also proven via a series of Laplace transforms. Finally, the validity of the proposed law in dealing with disturbances is expressed in simulation results. © Published under licence by IOP Publishing Ltd.

关键词： Laplace transforms

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Surface Defects Detection of Cylindrical High-Precision Industrial Parts Based on Deep Learning Algorithms: A Review

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Operations Research Forum 2024年第3期5卷 58页

作者： Wei, Li Solihin, Mahmud Iwan Saruchi, Sarah ‘Atifah Astuti, Winda Hong, Lim Wei Kit, Ang Chun School of Computer Science and Technology (School of Software) Guangxi University of Science and Technology Guangxi 545006 China Faculty of Engineering Technology and Built Environment UCSI University Kuala Lumpur 56000 Malaysia Faculty of Manufacturing and Mechatronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Pekan 26600 Malaysia Computer Engineering Department Automotive and Robotics Engineering Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

High-precision cylindrical parts are critical components across various industries including aerospace, automotive, and manufacturing. Since these parts play a pivotal role in the performance and safety of the systems they are integrated into, they are often subject to stringent quality control measures. Defects on the interior and exterior wall surfaces of these cylindrical parts can severely undermine their function, leading to degraded performance, increased wear, and even catastrophic failures in extreme cases. This article aims to comprehensively summarize the task definition, challenges, mainstream methods, public datasets, evaluation metrics, and other aspects of surface defect detection for high-precision cylindrical parts, in order to help researchers quickly grasp this field. Specifically, the background and characteristics of industrial defect detection are first introduced. Owing to the unique geometric features of cylindrical part surfaces, algorithms and equipment for image data acquisition used in surface defect detection are elaborated in detail. This article presents an extensive overview of state-of-the-art surface defect detection techniques designed for high-precision cylindrical components, all rooted in deep learning. The methods are systematically classified into three main categories: fully supervised, unsupervised, and alternative approaches, based on their data labeling strategies. Additionally, the paper conducts a comprehensive analysis within each category, shedding light on their unique strengths, limitations, and practical use cases. Concluding the discussion, the paper provides insights into future development trends and potential research directions in this field that will lead to manufacturing innovation. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Anomaly detection computer vision Deep learning Defect detection High-precision cylindrical parts Image processing Optical illumination

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computer-Aided Potato Disease Detection by Using Deep Learning Techniques

Computer-Aided Potato Disease Detection by Using Deep Learni...

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International Conference on Green Energy, Computing, and Intelligent Technology, GEn-CITy 2023

作者： Razaq, Fareeha Bilal, Muhammad Ramzan, Muhammad Naveed, Muhammad Razzaq, Samreen Department of Computer Science University of Sargodha Sargodha Pakistan Department of Computing and Information Systems School of Engineering and Technology Sunway University Petaling Jaya Malaysia Department of Software Engineering University of Sargodha Sargodha Pakistan School of Electronics and Computer Science University of Southampton Malaysia Iskandar Puteri Malaysia

ISBN: (纸本)9789819998326

Potato is the most widely grown and consumed food throughout the world. There are a number of potato crop diseases that affect production, and these diseases differ in symptoms, circumstances, and controls. Early detection and recognition of disease information can aid in disease prevention and production. This paper presents deep learning models using proposed CNN and pre-trained models for potato disease detection and classification. The proposed model is more efficient and accurate at detection and classification. To perform classification, we used two datasets: PLD and PlantVillage. The Xception CNN model serves as the foundation for our proposed model. It achieved 1.00 accuracy, 0.99 precision, 1.00 recall, and 0.99 F1-score on the PLD dataset. On the PlantVillage dataset, it achieved 1.00 accuracy, 1.00 precision, 1.00 recall, and 1.00 F1-score. We also compared the results of Inception-ResNet-V2, MobileNet-V2, VGG-19, Inception-V3, and Xception models with the performance of our proposed model. For three classes of potato leaves, the proposed CNN model produced more accurate results than other pre-trained models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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A Case Study on Blockchain-based Anonymous Reviewer Incentive Token (BARIT)

A Case Study on Blockchain-based Anonymous Reviewer Incentiv...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Shrestha, Pratiksha Bhunia, Suman Carvalho, Arthur Anderson, Chad Lee, Gabe Miami University Department of Computer Science and Software Engineering OxfordOH United States Farmer School of Business Miami University OxfordOH United States

ISBN: (纸本)9781665410205

Peer review is an integral part of academic publication and is necessary to maintain high standards and novelty of published research. Despite its importance, peer reviewers are rarely provided incentives, leading to journals having difficulty finding reviewers inclined to accept invitations and submit reviews on time. This paper proposes a Blockchain-based Anonymous Reviewer Incentive Token (BARIT) to incentivize peer reviewers. BARIT introduces flexible incentive schemes to provide both recognition and tangible benefits for the reviewers' contribution while preserving the anonymity of reviewers. Using blockchain technology to record reward tokens ensures their permanence and acceptance across different publishers. Incentive models are designed to encourage the involvement of researchers as reviewers, reduce reviewer refusal rates, and prompt the timely submission of review reports. © 2024 IEEE.

关键词： Blockchain

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A noval approach for dual probabilistic linguistic multi-criteria decision making based on prioritized Maclaurin symmetric mean operators and VIKOR

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Journal of Intelligent and Fuzzy Systems 2025年第4期48卷 447-462页

作者： Guo, Jing Zhu, Xianjun Li, Wenfeng Li, Hui School of Software Engineering Jinling Institute of Technology Nanjing China School of Engineering Management Nanjing University Nanjing China Reproductive Medicine Center Jinling Hospital Nanjing China Research Center for New Technology in Intelligent Equipment Nanjing University Nanjing China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China

The evolving landscape of decision-making, especially in complex scenarios, poses a challenge in accurately capturing decision-makers’ cognitive information. This challenge becomes even more intricate in group decision-making situations due to the diverse cognitive perspectives of the participants. In response to these complexities, this study delves into the realm of dual probabilistic linguistic 2-tuple sets, an extension of dual-hesitant fuzzy sets known for their effectiveness in handling perplexing data. Dual probabilistic linguistic 2-tuple sets introduces a nuanced approach, considering multiple linguistic memberships, non-membership degrees, and their associated probabilistic and deviation information. We have devised diverse mechanisms aimed at augmenting the comprehension and utilization of dual probabilistic linguistic 2-tuple sets. These mechanisms encompass transformation functions, scoring methodologies and accuracy metrics. In this study, we introduce two aggregation operators for dual probabilistic linguistic 2-tuple sets based on prioritized averaging and Maclaurin symmetric mean principles. These operators serve as the cornerstone for a pioneering hybrid approach meticulously crafted for multiple criteria decision making within the framework of dual probabilistic linguistic 2-tuple sets. To demonstrate the practicality and efficacy of our approach, we present a real-world case study. The subsequent analytical comparison and discussion underscore the effectiveness of our methodology. The outcomes of this study not only showcase the excellent performance of our proposed method but also offer valuable practical implications for decision-makers navigating complex and diverse decision environments. Our work stands as a significant stride toward enhancing decision-making processes amid contemporary challenges. © 2025 – IOS Press. All rights reserved.

关键词： Fuzzy rules

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Time series classification based on Unet with 2D transformation 12

Time series classification based on Unet with 2D transformat...

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12th IEEE International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2024

作者： Du, Mingsen Wang, Yunxia Wei, Yanxuan Wu, Xiaona Ji, Cun Shandong Normal University School of Information Science and Engineering Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China

ISBN: (纸本)9798331510633

Time series classification has gained significant attention in the data mining field in recent years. Despite the proposal of numerous methods over the past decades, most of these approaches focus on feature extraction in one-dimensional space, which often results in lower classification accuracy. To address this issue, this paper proposes a Time Series Classification method based on U-Net with 2D transformation (TSC-Unet). This method first transforms univariate time series into 2D space and augments the data, effectively enhancing the diversity of the dataset. Subsequently, features are extracted from the 2D space using a fully convolutional network, fully exploiting multi-scale feature information. U-Netwith skip connections, effectively retains and transmits high-resolution features, significantly reducing information loss and improving segmentation performance. Additionally, the ability to learn multi-scale features enables the model to capture details at different levels, thereby enhancing its understanding of complex data. Experimental results demonstrate that TSC-Unet achieves high classification accuracy on the UCR datasets, validating the effectiveness and superiority of the proposed method. © 2024 IEEE.

关键词： Time series

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Staging of Different Tumor by Utilizing Laser Guidance (@405 nm) in CT Scan Images Along with Statistical Analysis

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Lasers in engineering 2025年第4-6期59卷 309-325页

作者： Amjad, M. Ullah, H. Siddique, M.A. Majid, M.A. Aleem, A. Akhtar, M. Gilanie, G. Abdullah-Al-Wadud, M. Farhan, M. Akram, A. Batool, Z. Nazir, A. Biophotonics Imaging Techniques Laboratory Institute of Physics The Islamia University of Bahawalpur 63100 Pakistan Department of Computer Science Faculty of Computing The Islamia University of Bahawalpur Bahawalpur Pakistan Dept. of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Earth Sciences and Engineering Hohai University Nanjing China

Early detection of lung cancer, liver cancer (HCC), and pancreatitis is crucial for effective treatment. This study assessed the use of laser guidance CT scans for diagnosis and staging. 26 patients of CT scan which were collected from BVH and civil hospital Bahawalpur, Pakistan. Visible light with a 405 nm wavelength is used. Results showed staging of lung cancer (2 at stage 1, 2 at stage 2, 3 at stage 4) and HCC (stages 1 to 4a). Pancreatic diseases detected included acute pancreatitis (44%), chronic pancreatitis (22%), gallstones (22%), and pancreatic tumors (11%). Alkaline phosphatase, SGOT, and blood urea levels were notably elevated in all three cancers compared to controls. CT scans show promise in identifying cancer stages and diagnosing pancreatitis, aiding in tailored treatment. Statistical analysis shows that alkaline phosphatase emerges as a potential biomarker for these conditions. ©2025 Old City Publishing, Inc.

关键词： Image analysis

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Comparison of Standard Ant Colony Optimization, Dijkstra and A∗ Algorithms for Path Planning of Robot in an Indoor Environment 9

Comparison of Standard Ant Colony Optimization, Dijkstra and...

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9th International Conference on Mechanical engineering and Robotics Research, ICMERR 2025

作者： Sharan, Sudeep Ngoc Minh, Anh Tong Dominguez-Jimenez, Juan Jose Nauth, Peter Frankfurt University of Applied Sciences Faculty of Computer Science and Engineering Frankfurt am Main Germany UCASE Software Engineering Group School of Engineering University of Cádiz Cadiz Spain

ISBN: (纸本)9798331509262

The research topic of Path planning is extremely challenging area of concentration within the field of mobile robots. However, path planning algorithms for mobile robot tasks are contingent upon the environment and its level of complexity. This paper analyzes four distinct path planning methods for simulated indoor environment. The proposed algorithms include conventional Ant Colony methods, Ant System (AS), Ant Colony System (ACS), as well as standard methods Dijkstra and AStar (A*). We analyzed and examined these algorithms by employing various metrics/maps with complexity. The results indicate that the traditional path planning algorithm Dijkstra and A∗ approaches surpass the other ant colony techniques in terms of both computation time and path distance. © 2025 IEEE.

关键词： Ant colony optimization

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