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5th International Conference on Medical Imaging and computer Aided Diagnosis, MICAD 2024

作者： Dutta, Srinjan Roy, Jibesh Mandal, Diptarka Kaplun, Dmitrii Shpakovskaya, Irina Sarkar, Ram Department of Computer Science and Engineering Jalpaiguri Government Engineering College Jalpaiguri India School of Computer Engineering and Mathematical Sciences Defence Institute of Advanced Technology Pune India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Process Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India I.P. Pavlov Saint Petersburg First State Medical University St. Petersburg Russia

ISBN: (纸本)9789819638628

Oral cancer remains a critical global health challenge, characterized by high morbidity and mortality due to late-stage diagnosis. This paper addresses the need for improved diagnostic accuracy by introducing a novel hybrid-attention aided deep learning model for oral cancer classification. Our approach integrates the EfficientNetB0 model for feature extraction with a customized hybrid attention module, combining self, spatial, and soft attention mechanisms to emphasize crucial features. The model has been trained and evaluated on both microscopic and macroscopic oral cancer datasets, demonstrating its generalizability. Our proposed model achieves an accuracy of 96.97% on a macroscopic dataset and 92% on a microscopic dataset, with respective F1-scores of 0.97 and 0.89, outperforming baseline models. This hybrid attention-aided deep learning model shows significant potential in improving early detection and treatment planning of oral cancer. Source code and additional results of this work can be found at the GitHub repository. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Adaptive Particle Swarm Optimization of U-net Parameters for Marine Aquaculture Image Segmentation 13

Adaptive Particle Swarm Optimization of U-net Parameters for...

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

作者： Zhao, Jin Wang, Jun Han, Min Fan, Jianchao School of Computer Science and Engineering Chongqing Three Gorges University Chongqing China Department of Computer Science Department of Data Science City University of HongKong Hong Kong School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798331516147

Marine aquaculture image segmentation plays a crucial role in managing aquatic resources and environmental protection. Traditional deep learning models rely on manual parameter tuning for image segmentation, which limits their efficiency and accuracy. This paper proposes an adaptive Particle Swarm Optimization (PSO) algorithm to optimize the parameters of the U-net model automatically. The algorithm dynamically adjusts the PSO parameters based on the population’s entropy and clustering metrics and employs a hill-climbing algorithm to address the issue of the PSO easily falling into local optima, enhancing the algorithm’s adaptability. The proposed algorithm updates and iterates particles to automatically find suitable model parameters. The remote sensing data used in this experiment were from the Yellow Sea aquaculture area near Dalian, China, with a segmentation accuracy of 91.8%. This method improves segmentation accuracy, reduces the burden of manual parameter tuning, and provides an effective solution for optimizing deep learning models. © 2025 IEEE.

关键词： Aquaculture

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Sustainable Mining in the Era of Artificial Intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 1-4页

作者： Long Chen Yuting Xie Yutong Wang Shirong Ge Fei-Yue Wang IEEE the State Key Laboratory for Management and Control of Complex Systems at the Institute of Automation Chinese Academy of SciencesBeijing 100190China the National Laboratory of Pattern Recognition at the Institute of Automation and Waytous Ltd.China the School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 510275GuangdongChina the School of Mechanical Electronic and Information Engineering China University of Mining and Technology BeijingBejing 100083China the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190China

The mining sector historically drove the global economy but at the expense of severe environmental and health repercussions,posing sustainability challenges[1]-[3].Recent advancements on artificial intelligence(AI)are revolutionizing mining through robotic and data-driven innovations[4]-[7].While AI offers mining industry advantages,it is crucial to acknowledge the potential risks associated with its widespread ***-reliance on AI may lead to a loss of human control over mining operations in the future,resulting in unpredictable consequences.

关键词： Sustainable mining consequences

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Obstacle avoidance parking based on improved deep reinforcement learning SAC algorithm

Obstacle avoidance parking based on improved deep reinforcem...

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2024 China automation Congress, CAC 2024

作者： Liu, Ting Liu, Ping Liu, Mingjie Piao, Changhao Chang, Kyunghi College of Automation Chongqing University of Posts and Telecommunications Chongqing China Electrical and Computer Engineering Department Inha University Incheon Korea Republic of

ISBN: (纸本)9798350368604

Recent research uses neural-network-based approaches to generate time-optimized parking trajectories in linear time. However, the generalization of these neural networks in different parking lot scenarios is not fully considered and relies on high-quality data. To address these issues, this paper proposes an improved soft actor critic (SAC)based parking trajectory planning method to achieve fast convergence and high success rate. A trajectory data-based reward function and an experience replay sampling strategy are designed simultaneously improve the learning efficiency. Meanwhile, the Flatten-T Swish Plus (FTSPlus) activation function is firstly introduced into the SAC's neural network structure to enhance the convergence capabilities. The simulation results show that compared with traditional SAC methods, the success rate has been greatly improved and convergence rate has also been improved by more than 60%. Furthermore, it has the good model generalization ability while maintaining collision-free trajectories. © 2024 IEEE.

关键词： Trajectories

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Vision-based fingerspelling recognition for an assistive technology system

Vision-based fingerspelling recognition for an assistive tec...

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2023 International Conference on Industry Sciences and computer Science Innovation, iSCSi 2023

作者： Kapuscinski, Tomasz Majcher, Marcel Department of Computer and Control Engineering Rzeszow University of Technology W. Pola 2 Rzeszow35-959 Poland

In this paper, a vision-based method for fingerspelled acronyms recognition is proposed. First, the visual information carried by the image is reduced to the person's edges. The heterogeneous and changing background is removed by semantic segmentation of the person using a pre-trained R-CNN Mask. Then, the feature vector is generated by the encoder - part of the autoencoder, previously trained in an unsupervised manner. Finally, the sequence of feature vectors obtained in this way is fed into the input of the Bidirectional Long Short-Term Memory network used for classification. The method is tested on a demanding dataset, including the execution of acronyms fingerspelled by Deaf people who use sign language on a daily basis. Promising results suggest that the method can be incorporated into the Signed Communication System developed by the research team. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Deep neural networks

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PVWA: Privacy-preserving and Verifiable Weighted Aggregation for Federated Learning

PVWA: Privacy-preserving and Verifiable Weighted Aggregation...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Li, Zihan Wang, Xiaodong Yuan, Shuai Guan, Zhitao Du, Xiaojiang Guizani, Mohsen North China Electric Power University School of Control and Computer Engineering China Canada Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States Machine Learning Department United Arab Emirates

ISBN: (纸本)9798350351255

Federated learning provides clients with a means of collaboratively training a global model without sharing their local data, managed by a central server. However, this server cannot always be trusted, as it may act dishonestly and compromise the privacy of clients' local data. Consequently, mechanisms for privacy preservation and aggregation verification become crucial components of a secure federated learning system. In addition, support for weighted aggregation is also essential to address the challenges posed by non-IID training data. In this article, we present the Privacy-Preserving and Verifiable Weighted Aggregation (PVWA) scheme. Our approach introduces a new privacy-preserving solution by leveraging masking and homomorphic encryption techniques to protect local and global models, respectively. The masking protocol facilitates secure weighted aggregation, whereas a verification mechanism based upon homomorphic hashing and bilinear aggregated signatures ensures the correctness of aggregated results. Experimental evaluations of the performance, compared against alternative methods on two datasets, demonstrate its effectiveness and efficiency. © 2024 IEEE.

关键词： Federated learning

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Innovative Automatic Tool Changing Mechanism for Robotic Arms

Innovative Automatic Tool Changing Mechanism for Robotic Arm...

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2024 Intermountain engineering, Technology and Computing, IETC 2024

作者： Das, Shaibal Berry, Matthew Deemyad, Taher Idaho State University Department of Electrical and Computer Engineering PocatelloID United States Idaho State University Department of Mechanical and Measurement & Control Engineering PocatelloID United States

ISBN: (纸本)9798350373073

This paper presents the development and implementation of an automatic tool-changing mechanism for industrial and collaborative robotic arms. The primary objective is to enable integration with a variety of end effectors, both active and passive, allowing the robot to autonomously switch tools without manual intervention. The system comprises three key components: a universal end-effector connector, various tools, and a customized toolbox. The universal connector will be screwed to the end of the robotic arm. It features a mechanical lock mechanism and accommodates electrical and pneumatic inlets for diverse tools. A dedicated toolbox, situated within the robot's workspace range, houses all tools for easy access. The study focuses on the design, 3D printing, and testing of two tools - an active and a passive tool - along with the innovative mechanical lock mechanism, which has been successfully developed and tested. Additionally, two industrial tasks have been defined to demonstrate the mechanism's functionality in real-world scenarios, emphasizing non-singular path trajectories between tasks. Future work includes the integration of additional sensors for enhanced automatic robot control. Overall, this research will contribute to the advancement of fully autonomous robotic systems in industrial settings. © 2024 IEEE.

关键词： End effectors

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Gesture Recognition for Human-Robot Interaction Through Virtual Characters 15th

Gesture Recognition for Human-Robot Interaction Through Vir...

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15th International Conference on Social Robotics, ICSR 2023

作者： Sabbella, Sandeep Reddy Kaszuba, Sara Leotta, Francesco Nardi, Daniele Department of Computer Control and Management Engineering DIAG Sapienza Universitá di Roma Via Ariosto 25 Rome Italy

ISBN: (纸本)9789819987177

Human-Robot Interaction (HRI) has grown increasingly important with the integration of various types of robots into industrial and daily life aspects. Non-verbal communication plays a vital role in HRI and heavily relies on gesture recognition. The ability of robot’s interpretation of human gestures is essential for enhancing the overall user experience. To achieve precision and effectiveness of gesture recognition in HRI, the incorporation of Virtual Reality (VR) technology offers promising avenues. In this article, we describe the development of a gesture recognition pipeline tailored to HRI in Precision Agriculture scenarios. Its key feature is the use of Virtual characters and Machine learning techniques to allow agricultural robots to recognize gestures performed by both digital agents and humans. We address challenges by presenting a set of gesture definitions, data generation, and system evaluation using varies combinations of real and virtual simulated data. Our results demonstrate how the adoption of virtual simulations can significantly increase the system’s accuracy and efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Human robot interaction

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RLVNA: a Platform for Experimenting with Virtual Networks Adaptations over Public Testbeds 3

RLVNA: a Platform for Experimenting with Virtual Networks Ad...

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3rd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2023

作者： Angi, Antonino Sacco, Alessio Alberti, Enrico Marchetto, Guido Esposito, Flavio Politecnico di Torino Department of Control and Computer Engineering Italy Saint Louis University Computer Science Department United States

ISBN: (纸本)9798350333732

Network emulators and simulation environments traditionally support computer networking and distributed system research. The continued use of multiple approaches highlights both the value and inadequacy of each approach. To this end, several large-scale virtual networks testbeds, such as GENI and CloudLab, have emerged, allowing testing of a networked system in controlled yet realistic environments, focusing in particular on facilitating the test of network management schema in Software-Defined Network (SDN) scenarios. Nevertheless, setting up those experiments first and integrating machine learning models later in these deployments is challenging. In this paper, we propose designing and implementing a web-based platform that integrates Reinforcement Learning (RL)-based models with a virtual network experiment using resources acquired within a real-world testbed, e.g., GENI. Users are able to reserve the network resources (links, switches, and hosts) and configure them through our intuitive interface with little effort. The RL algorithm is then launched to learn how to steer traffic dynamically and according to diverse traffic network conditions. Such a model can be easily customized by the user, while our architecture enables fast reprogramming of the Open Virtual Switches via the SDN controller instantiated. We experimented with trace-based traffic to validate this user-friendly platform and evaluated how centralized and decentralized RL algorithms can effectively lead to self-driving networks. While in this paper, the system focuses on the deployment of experiments for virtual network adaptation, the platform can be easily extended to other network management mechanisms and machine learning algorithms. © 2023 IEEE.

关键词： Network management

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Estimating the State of Health for Lithium-ion Batteries:A Particle Swarm Optimization-Assisted Deep Domain Adaptation Approach

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IEEE/CAA Journal of Automatica Sinica 2023年第7期10卷 1530-1543页

作者： Guijun Ma Zidong Wang Weibo Liu Jingzhong Fang Yong Zhang Han Ding Ye Yuan The School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China the College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China the Department of Computer Science Brunel University LondonUxbridgeMiddlesexUB83PHUnited Kingdom the School of Information Science and Engineering Wuhan University of Science and TechnologyWuhan 430081China the School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China

The state of health(SOH)is a critical factor in evaluating the performance of the lithium-ion batteries(LIBs).Due to various end-user behaviors,the LIBs exhibit different degradation modes,which makes it challenging to estimate the SOHs in a personalized *** this article,we present a novel particle swarm optimization-assisted deep domain adaptation(PSO-DDA)method to estimate the SOH of LIBs in a personalized manner,where a new domain adaptation strategy is put forward to reduce cross-domain distribution *** standard PSO algorithm is exploited to automatically adjust the chosen hyperparameters of developed DDA-based *** proposed PSODDA method is validated by extensive experiments on two LIB datasets with different battery chemistry materials,ambient temperatures and charge-discharge *** results indicate that the proposed PSO-DDA method surpasses the convolutional neural network-based method and the standard DDA-based *** Py Torch implementation of the proposed PSO-DDA method is available at https://***/mxt0607/PSO-DDA.

关键词： Deep transfer learning domain adaptation hyperparameter selection lithium-ion batteries(LIBs) particle swarm optimization state of health estimation(SOH)

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