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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Shanthi, S. Nirmaladevi, K. Mathiarasi, N. Tharani, As. Krithika Monisha, A. Kuppuchamy, R. Krishnamoorthy, N. Kongu Engineering College Department of Computer Science & Engineering Perundurai India PSNA College of Engineering and Technology Department of Computer Applications Tamilnadu Dindigul India Vellore Institute of Technology Department of Software Systems and Engineering Vellore Campus India

ISBN: (纸本)9798350348217

Today's marketing strategies place a high priority on comprehending customer sentiments. It will not only give businesses a better understanding of how their clients view their goods and/or services, but it will also give them suggestions on how to enhance their offerings. We make an effort to comprehend the relationship between various factors in customer reviews on an online store for women's clothing. The proposed system also classifies each review, according to, whether it recommends the product in question or not, and whether it has a positive, negative, or neutral sentiment. With the exception of review titles and review texts, we used univariate and multivariate analyses on datasets features to accomplish these goals. Additionally, we have implemented a bidirectional recurrent neural network (RNN) with a long-short term memory unit (LSTM) for sentiment and recommendation. © 2023 IEEE.

关键词： Long short-term memory

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Adapt PointFormer: 3D Point Cloud Analysis via Adapting 2D Visual Transformers 27

Adapt PointFormer: 3D Point Cloud Analysis via Adapting 2D V...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Li, Mengke Li, Da Yang, Guoqing Cheung, Yiu-Ming Huang, Hui Shenzhen China VCC College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science Hong Kong Baptist University Hong Kong

ISBN: (纸本)9781643685489

Pre-trained large-scale models have exhibited remarkable efficacy in computer vision, particularly for 2D image analysis. However, when it comes to 3D point clouds, the constrained accessibility of data, in contrast to the vast repositories of images, poses a challenge for the development of 3D pre-trained models. This paper therefore attempts to directly leverage pre-trained models with 2D prior knowledge to accomplish the tasks for 3D point cloud analysis. Accordingly, we propose the Adaptive PointFormer (APF), which fine-tunes pre-trained 2D models with only a modest number of parameters to directly process point clouds, obviating the need for mapping to images. Specifically, we convert raw point clouds into point embeddings for aligning dimensions with image tokens. Given the inherent disorder in point clouds, in contrast to the structured nature of images, we then sequence the point embeddings to optimize the utilization of 2D attention priors. To calibrate attention across 3D and 2D domains and reduce computational overhead, a trainable PointFormer with a limited number of parameters is subsequently concatenated to a frozen pre-trained image model. Extensive experiments on various benchmarks demonstrate the effectiveness of the proposed APF. The source code and more details are available at https://***/research/2024/PointFormer. © 2024 The Authors.

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Analysis of the spatiotemporal trends and influencing factors of Hyphantria cunea in China

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Journal of Forestry Research 2024年第6期35卷 294-305页

作者： Hongwei Zhou Chaoqun Gong Xiaodong Li Yue Wang Yunbo Yan College of Computer and Control Engineering Northeast Forestry UniversityHarbin 150040People’s Republic of China Key Laboratory of National Forestry and Grassland Administration On Forest and Grassland Pest Monitoring and Warning Center for Biological Disaster Prevention and ControlNational Forestry and Grassland AdministrationShenyang 110034People’s Republic of China

In recent years,the situation of the Hyphantria cunea(Drury)(Lepidoptera:Erebidae),infestation in China has been serious and has a tendency to continue to spread.A comprehensive analysis was carried out to examine the spa-tial distribution trends and influencing factors of *** analysis involved integrating administrative division and boundary data,distribution data of ***,and envi-ronmental variables for *** and gravity analysis techniques were employed for data processing and *** results show that *** exhibited high aggregation patterns in 2021 and 2022 concentrated mainly in eastern *** these years,the focal point of the infestation was in Shandong Province with a spread towards the *** such as high vegetation density in eastern China provided favorable situations for growth and development of *** China,the spatial distribution of the moth is primarily influenced by two critical factors:precipitation during the driest month and *** play a pivotal role in determining the spread of the *** on these results,suggestions are provided for a mul-tifaceted approach to prevention and control of *** infestation.

关键词： Hyphantria cunea Temporal trends GeoDetector spatial analysis Spatial heterogeneity

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The Internal Drive Force Analysis of Learning for engineering Students 5th

The Internal Drive Force Analysis of Learning for Engineer...

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5th International Conference on computer Science and Educational Informatization, CSEI 2023

作者： Du, Xiaoyu Zhou, Guanying Han, Zhijie Du, Ying Qiao, Baojun College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Engineering Laboratory of Spatial Information Processing Henan University Kaifeng475004 China College of Software Henan University Kaifeng475004 China

ISBN: (纸本)9789819994984

Emerging engineering Education is oriented to cultivate the ability to solve complex engineering problems, which required engineering students to have higher learning initiative and self-discipline. This paper explores the formation mechanism of college students’ learning drive from teachers, counselors, learning environment and learning motivation, then we propose methods and suggestions for cultivating learning drive for these aspects. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： engineering education

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Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systems

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IEEE Communications Surveys and Tutorials 2024年第3期27卷 2085-2128页

作者： Wang, Xiaofei Zhao, Yunfeng Qiu, Chao Hu, Qinghua Leung, Victor C. M. Tianjin University College of Intelligence and Computing Tianjin300072 China Shenzhen518000 China Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverV6T 1Z4 Canada

Amidst the robust impetus from artificial intelligence (AI) and big data, edge intelligence (EI) has emerged as a nascent computing paradigm, synthesizing AI with edge computing (EC) to become an exemplary solution for unleashing the full potential of AI services. Nonetheless, challenges in communication costs, resource allocation, privacy, and security continue to constrain its proficiency in supporting services with diverse requirements. In response to these issues, this paper introduces socialized learning (SL) as a promising solution, further propelling the advancement of EI. SL is a learning paradigm predicated on social principles and behaviors, aimed at amplifying the collaborative capacity and collective intelligence of agents within the EI system. SL not only enhances the system's adaptability but also optimizes communication, and networking processes, essential for distributed intelligence across diverse devices and platforms. Therefore, a combination of SL and EI may greatly facilitate the development of collaborative intelligence in the future network. This paper presents the findings of a literature review on the integration of EI and SL, summarizing the latest achievements in existing research on EI and SL. Subsequently, we delve comprehensively into the limitations of EI and how it could benefit from SL. Special emphasis is placed on the communication challenges and networking strategies and other aspects within these systems, underlining the role of optimized network solutions in improving system efficiency. Based on these discussions, we elaborate in detail on three integrated components: socialized architecture, socialized training, and socialized inference, analyzing their strengths and weaknesses. Finally, we identify some possible future applications of combining SL and EI, discuss open problems and suggest some future research, with the hope of arousing the research community's interest in this emerging and exciting interdisciplinary field. © 1

关键词： Resource allocation

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Cooperative-competitive Game-based Method for Local Path Planning with Obstacle Avoidance in Racing Scenarios 41

Cooperative-competitive Game-based Method for Local Path Pla...

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第41届中国控制会议

作者： Lina Li Quan Yuan Shuai Li Chen Wang Guangming Xie College of Computer Science and Technology Changchun University College of Engineering Peking University National Engineering Research Center of Software Engineering Peking University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,a cooperative-competitive game based planning method(CCGP) is proposed to address the local path planning with obstacle avoidance in racing *** the racing,the robots in the same group cooperate with each other to achieve the goal,while competing with another group of *** goal of each group of robots is to reach the same target point ahead of the other group of robots,and there is no collision with other robots and static *** cooperation competition relationship is described as a cooperation competition game in *** objective function of each robot is formulated as an optimization problem,and the collision avoidance in robot-robot and robot-obstacle is regarded as *** rationality of the proposed objective function in the local path planning problem is theoretically *** MPC is used iteratively to compute the approximate optimal local trajectory of each robot under Nash equilibrium by considering the best response of other robots,without collision with other robots and *** compare our method with the MPC method by conducting racing simulations in a scene with static *** results show that our method has better performance,and the robots in our group can obstruct and overtake robots of the MPC group,while they avoiding collision with teammates and static obstacles.

关键词： local path planning cooperative-competitive game collision avoidance Nash equilibrium obstacle avoidance

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A Performance Investigation of Genetic Algorithm with Different Evolutionary Operators 11

A Performance Investigation of Genetic Algorithm with Differ...

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11th International Conference on Machine Intelligence Theory and Applications, MiTA 2024

作者： Li, Jian-Yu Shao, Zhao-Kun Wang, Zi-Jia Zhang, Yuhui Zhu, Ruoyu Chen, Zong-Gan Li, Tao Nankai University College of Artificial Intelligence Tianjin300350 China Hanyang University College of Electrical and Electronic Engineering Ansan15588 Korea Republic of Guangzhou University College of Software Engineering Guanzhou510006 China Dongguan University of Technology College of Computer Science and Technology Dongguan523808 China South China Normal University School of Computer Science Guangzhou510631 China Henan Normal University College of Computer and Information Engineering Xinxiang453007 China

ISBN: (纸本)9798350385670

Genetic Algorithm (GA) as a bio-inspired optimization technique, has shown significant potential for applications in many fields. This paper aims to provide a comprehensive overview of the main types of evolutionary operators in GA - selection, crossover, and mutation, which have a direct and significant influence on optimization performance. First, this study systematically summarizes the various evolutionary operators currently used in the field of GA. Subsequently, these different operators are embedded into the standard GA framework to assess their influence on algorithm performance. Last, this paper also explores the effect of setting crossover and mutation rates on GA performance, determining the optimal values of these parameters through a series of experiments. This research not only offers a comprehensive overview of the evolutionary operators in GA but also demonstrates the specific performance of different operator combinations through empirical evidence, providing valuable reference for further research and application of GA. © 2024 IEEE.

关键词： Genetic algorithms

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Empirical Study on Mispronunciation Detection for Tajweed Rules during Quran Recitation 6

Empirical Study on Mispronunciation Detection for Tajweed Ru...

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Alsahafí, Yousef S. Asad, Muhammad Department of Information technology Computing and Information technology Khulis College University of Jeddah Jeddah Saudi Arabia Department of Computer and Software Engineering College of Electrical and Mechanical Engineering National University of Science and Technology Islamabad Pakistan

ISBN: (纸本)9798350373875

This paper presents an extensive empirical study aiming to identify the optimal combination of feature extraction techniques and machine learning algorithms, including deep learning, for automated mispronunciation detection during Quran recitation. Three feature extraction methods-Mel-Frequency Cepstral Coefficients (MFCC), Spectrogram, and Kaldi pitch - are systematically evaluated in conjunction with a diverse set of classifiers, including Gradient Boosting (GB), Support Vector Machines (SVM), Logistic Regression (LR), Random Forest (RF), Artificial Neural Network (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks. The study leverages the QDAT dataset, comprising 1500 audio samples from 150 readers, with metadata specifying age, gender, and three distinct rules for mispronunciation detection. Experimental results demonstrate that the integration of MFCC with deep learning classifiers consistently achieves the highest performance across multiple evaluation metrics. Model robustness is validated using receiver operator characteristic curves (ROC) and accuracy metrics. © 2024 IEEE.

关键词： Speech recognition

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Accurate threat hunting in industrial internet of things edge devices

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Digital Communications and Networks 2023年第5期9卷 1123-1130页

作者： Abbas Yazdinejad Behrouz Zolfaghari Ali Dehghantanha Hadis Karimipour Gautam Srivastava Reza M.Parizi Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical and Software Engineering University of CalgaryAlbertaCanada Department of Mathematics and Computer Science Brandon UniversityBrandonCanada College of Computing and Software Engineering Kennesaw State UniversityGAUSA Research Center for Interneural Computing China Medical UniversityTaichungTaiwanChina Department of Computer Science and Mathematics Lebanese American UniversityBeirut1102Lebanon

Industrial Internet of Things(IIoT)systems depend on a growing number of edge devices such as sensors,controllers,and robots for data collection,transmission,storage,and *** kind of malicious or abnormal function by each of these devices can jeopardize the security of the entire ***,they can allow malicious software installed on end nodes to penetrate the *** paper presents a parallel ensemble model for threat hunting based on anomalies in the behavior of IIoT edge *** proposed model is flexible enough to use several state-of-the-art classifiers as the basic learner and efficiently classifies multi-class anomalies using the Multi-class AdaBoost and majority *** evaluations using a dataset consisting of multi-source normal records and multi-class anomalies demonstrate that our model outperforms existing approaches in terms of accuracy,F1 score,recall,and precision.

关键词： IIoT Threat hunting Edge devices Multi-class anomalies Ensemble methods

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A Novel Method for Pneumothorax Diagnosis and Segmentation Using Deep Convolution Neural Network

A Novel Method for Pneumothorax Diagnosis and Segmentation U...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

ISBN: (纸本)9798350348217

A pneumothorax is the accumulation of air in the pleural space between the chest and lungs. Air that is in the lungs during breathing can leak out of the lungs and become trapped between the chest and lungs in the thoracic cavity, causing chest pain, difficulty breathing, and ultimately the complete collapse of the taut lungs. pneumothorax. Therefore, early diagnosis and treatment should be started. Neural networks have proven effective in image classification tasks, and many researchers believe that training neural network models can detect radiologist-diagnosed chest abnormalities such as pneumonia and pneumothorax. is proving. Moreover, we utilize various techniques, including Stochastic Weight Averaging (SWA), aggressive data augmentation, and learning rate schedule. We use the chest x-ray dataset provided by the 2019 SIIM-ACR In this study, a ResNet-34 neural network is trained on a data set of chest x-rays containing over 10,675 images to create a model that predicts the presence of pneumothorax from chest *** suggested system receives a Dice Similarity Coefficient (DSC) of 0.8410 © 2023 IEEE.

关键词： Image classification

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