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15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Shi, Hao-Jie Pan, Jeng-Shyang Chu, Shu-Chuan Kong, Lingping Snášel, Václav College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Innovative Information Industry Research Center Harbin Institute of Technology Shenzhen Graduate School Shenzhen China Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Ostrava Czech Republic Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

ISBN: (纸本)9789819700677

The Bamboo Forest Growth Optimization(BFGO) algorithm is an algorithm proposed by the growth law of bamboo forest, which has the advantages of fast convergence and not easily falling into local optimum. In this paper we have experimented the BFGO with several other population intelligence optimization algorithms on 23 benchmark test functions and proved its superiority. Also we apply the bamboo forest optimization algorithm to nighttime image enhancement in this paper, and the algorithm achieves better results on this application. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image enhancement

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Advancements and Challenges in Networking Technologies: A Comprehensive Survey 7

Advancements and Challenges in Networking Technologies: A Co...

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7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023

作者： Shihab, Mustafa A. Aswad, Salma Abdullah Othman, Rawshan Nuree Ahmed, Saadaldeen Rashid Sciences College of Sharia Tikrit University Department of Banking and Finance Salahalden Iraq College of Science University of Duhok Department of Computer Science Duhok42001 Iraq College of Engineering and Computer Science Lebanese French University Department of Information Technology Erbil Iraq College of Engineering and Science Bayan University Dept Computer Science Erbil Iraq

ISBN: (纸本)9798350342154

This survey paper provides a comprehensive overview of emerging technologies in networking, focusing on caching in Information-Centric Networking (ICN), context-aware radio access technology (RAT) selection in 5G ultra-dense networks, cryptocurrency adoption, and mobility support for routing in Low Power and Lossy Networks (LLNs). Adaptive RAT selection mechanisms are stressed in 5G context-aware RAT selection. RAT selection processes address context acquisition, decision-making algorithms, and resource allocation. 5G context-aware RAT selection study is suggested. The report then discusses bitcoin adoption, including its current state and benefits. Scalability, security, and regulation are difficulties. Future studies should address these issues and increase cryptocurrency adoption. Finally, LLN mobility support investigates routing techniques for mobility needs. LLN reactive and proactive routing protocols are analyzed. LLN routing mobility support research is suggested. This survey article discusses techniques, difficulties, and future research by summarizing each technology. To help researchers and practitioners make educated judgments and enhance networking technologies. © 2023 IEEE.

关键词： Decision making

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Multi-Criteria Decision-Making for Energy Management in Smart Homes Using Hybridized Neuro-Fuzzy Approach

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Distributed Generation and Alternative Energy Journal 2024年第1期39卷 83-109页

作者： Anbazhagu, U.V. Koti, Manjula Sanjay Muthukumaran, V. Geetha, V. Munrathnam, Meram Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur TN Chennai India Department of MCA Dayananda Sagar Academy of Technology and Management Bangalore560082 India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur Campus 603 203 India Department of Computer Science School of Applied Sciences REVA University Bangalore India Department of Mathematics Rajiv Gandhi University of Knowledge Technologies R.k.Valley Idupulapaya Andhra Pradesh Kadapa516330 India

The necessity for smart energy oversight solutions has arisen in response to the rising popularity of energy-efficient home automation and other energy-saving technologies. Optimizing smart home energy use using multi-criteria decision-making (MCDM) is a proven methodology. However, the procedure for making decisions and MCDM’s capacity to handle various criteria are typically limiting factors. Hybrid methods, which integrate multiple decision-making approaches like Fuzzy Logic (FL) and Modular Neural Networks (MNN), could potentially be able to circumvent these restrictions and boost energy management systems’ efficacy and precision. This investigation presents a hybrid Neuro-Fuzzy (H-NF) method for MCDM in regulating energy for smart homes by combining FL with an MNN. The suggested approach would optimize energy use in smart homes by considering several parameters, notably cost, ease of use, and environmental effects. In addition, this study aims to examine how the H-NF model fares in comparison to other methods of making important decisions in terms of several performance metrics. The suggested hybridized approach has the potential to deliver more precise and effective decision-making processes for energy management in smart homes, allowing users to optimize their energy consumption while preserving comfort and lowering environmental impact. © 2024 Taylor and Francis Inc.. All rights reserved.

关键词： Decision making

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A Dynamic Multipath Load Balancing Algorithm Based on Particle Swarm Optimization in DCN 14

A Dynamic Multipath Load Balancing Algorithm Based on Partic...

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14th IEEE International Conference on Software Engineering and Service science, ICSESS 2023

作者： Shu, Yongan Gao, Dongsong College of Artificial Intelligence Anhui University Hefei China College of Computer Science and Technology Anhui University Hefei China

ISBN: (纸本)9798350336269

Aiming at effectively coping with the elephant flow conflicts in the data center network(DCN), this paper proposes a dynamic multipath load balancing algorithm based on particle swarm optimization(DMLBP) in DCN. Experimental results show that compared with the traditional ECMP and SA algorithms, the DMLBP algorithm improves bandwidth utilization and network throughput, while reducing the network propagation delay. © 2023 IEEE.

关键词： Particle swarm optimization (PSO)

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PiCNet: Physics-infused Convolution Network for Radar-Based Precipitation Nowcasting

PiCNet: Physics-infused Convolution Network for Radar-Based ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zheng Wang Hanyi Zhang Cong Bai College of Computer Science and Technology Zhejiang University of Technology

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Meteorological disasters, especially extreme precipitation, cause significant socioeconomic damage, highlighting the need for effective quantitative precipitation nowcasting. Existing methods, often data-driven and resource-intensive, struggle to capture the underlying physical laws of meteorology. This paper introduces a simple yet effective model using an advection simulator to learn precipitation’s physical dynamics, making the predictions more interpretable. Our model also incorporates a physics-guided module to enhance sensitivity to high-intensity rainfall, improving rainfall prediction accuracy. Experiments on the KNMI radar echo dataset demonstrate that our model outperforms state-of-the-art methods, offering better insights into physics-infused precipitation nowcasting.

关键词： Meteorological radar Rain Sensitivity Convolution Disasters Predictive models Mathematical models Convolutional neural networks Speech processing Socioeconomics

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Review of Deep Learning Models for Remote Healthcare 20th

Review of Deep Learning Models for Remote Healthcare

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20th International Conference on Mobile Web and Intelligent Information Systems, MobiWIS 2024

作者： Jat, Avnish Singh Grønli, Tor-Morten Ghinea, George School of Economics Innovation and Technology Kristiania University College Oslo Norway Department of Computer Science Brunel University Uxbridge United Kingdom

ISBN: (纸本)9783031680045

This review article delves into the transformative role of deep learning in remote healthcare, highlighting its potential to enhance diagnostic accuracy, patient monitoring, and personalized treatment in a landscape increasingly reliant on digital health solutions. Deep learning models, including convolutional and recurrent neural networks, are examined for their proficiency in analyzing a diverse array of healthcare data types, such as electronic health records (EHRs), wearable device data, genomic information, and speech and language patterns. The integration of these advanced algorithms with remote healthcare practices offers a promising avenue for making healthcare more accessible, particularly in underserved regions, and for advancing towards a predictive and preventive healthcare model. The article navigates through the challenges inherent in the application of deep learning in healthcare, including issues related to data privacy and security, the heterogeneity and integration of healthcare data, the interpretability of AI models, and the substantial computational resources required. Solutions and strategies to overcome these challenges are discussed, emphasizing the importance of ethical considerations, regulatory compliance, and cross-disciplinary collaboration in realizing the full potential of deep learning in healthcare. The article underscores the significant promise of deep learning in reshaping remote healthcare, making it more personalized, efficient, and patient-centered. It calls for a balanced approach to harnessing this potential, addressing the associated challenges through innovation and collaboration, to fully realize the benefits of deep learning in improving healthcare outcomes and accessibility. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Electronic health record

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Multi-Robot Multi-Objective Path Planning Based on Improved DWA Algorithm 3

Multi-Robot Multi-Objective Path Planning Based on Improved ...

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3rd International Conference on computer science, Electronic Information Engineering and Intelligent Control technology, CEI 2023

作者： Qi, Yuhan Zhang, Yuxin Pu, Xingcheng School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China College of Mathematics and Computer Science Tongling University Tongling244061 China

ISBN: (纸本)9798350372052

In order to solve problems of DWA (Dynamic-Window Approach) in path planning, such as easily trapping into local optimal solutions, fixing evaluation function coefficients and using a single obstacle avoidance strategy for multi-robots under multi-objective environments, a novel FA-DWA (Fast Adaptive-Dynamic Window Approach) algorithm is proposed. In this new FA-DWA algorithm, three mainly aspects are considered. Firstly, based on the distance between the mobile robot and the obstacle, the evaluation function of DWA can be dynamically adjusted to improve the adaptability. Secondly, based on a deflection angle assigned to the low-priority robot, an encounter coordination strategy is used to avoid collision problems for multi-robots. Thirdly, the F-RRT∗ algorithm is used to obtain the global path to avoid falling into the local optimal. Finally, some simulation experiments are given to prove the validity and effectiveness of the improved algorithms. © 2023 IEEE.

关键词： Motion planning

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Research on the Application of Network Coding technology in Cloud Storage and Digital Signature 2

Research on the Application of Network Coding Technology in ...

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2nd International Conference on Electronic Information Engineering and computer Communication, EIECC 2022

作者： Zhang, Yuxiang Kong, Congwang Xian, Ming Zhang, Hongjiang College of Electronic Science and Technology National University of Defense Technology Hunan Changsha410000 China College of Electronics and Information Engineering Ankang University Shaanxi Ankang725000 China

ISBN: (纸本)9781510663121

Since network coding technology was proposed, researchers have been enriching the theory of network coding and its related research has gradually become a field of interest for scholars, but the application and research of network coding technology in cloud storage and digital signature are still relatively missing. This paper mainly studies the application and development of network coding technology in cloud storage and digital signature, firstly introduces the technical knowledge about network coding and reviews its development history, introduces the main ideas and technical points of network coding in detail, then discusses the application of network coding in the field of cloud storage and digital signature, researches and analyzes its development respectively, and finally puts forward its views and open issues, and gives an outlook on the application prospect of network coding technology. © 2023 SPIE.

关键词： Network coding

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Fusing UAV and Ultra-Wideband technology for Target Visual Intelligent Perception Without GNSS 11th

Fusing UAV and Ultra-Wideband Technology for Target Visual I...

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11th International Conference on Robot Intelligence technology and Applications, RiTA 2023

作者： Zhao, Zongqing Wei, Junyu Zuo, Zhen Su, Shaojing Guo, Xiaojun Hu, Liushun Tong, Xiaozhong Chen, Wei College of Intelligence Science and Technology National University of Defense Technology Changsha China School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Jiangsu China School of Energy Science and Power Engineering Central South University Hunan Changsha China

ISBN: (纸本)9783031706837

This paper introduces an innovative framework for target perception by merging Unmanned Aerial Vehicle (UAV) capabilities with Ultra-Wideband (UWB) technology. UAVs are proficient at capturing and analyzing visual data employing intelligent algorithms, while UWB technology excels in providing high-precision positioning information, particularly when Global Navigation Satellite System (GNSS) signals are inaccessible. This study advocates the synergistic integration of UWB technology and UAVs, harnessing their inherent strengths to establish a robust and versatile framework for efficient target perception, which encompasses target detection and localization across diverse scenarios. The presented experimental results showcased in this paper substantiate the effectiveness of our proposed approach in accurately detecting and localizing targets, even in challenging conditions marked by GNSS signal disruptions. This framework not only capitalizes on the amalgamation of UWB technology and UAVs but also offers a resilient and adaptable solution for target detection and localization in a variety of real-world scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Unmanned aerial vehicles (UAV)

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Cross-Modal Adaptive Prototype Learning for Continuous Sign Language Recognition

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Wei, Dong Yang, Xu-Hua Weng, Yiyang Lin, Xuanyu Hu, Hongxiang Liu, Sheng Zhejiang University of Technology College of Computer Science and Technology Hangzhou310023 China

Continuous sign language recognition technology enables effective communication for hearing-impaired individuals by recognizing and interpreting sign language. However, existing research has not fully addressed the large amount of temporal and spatial redundancy in sign language videos, which limits recognition accuracy. Additionally, the scarcity of frame-level annotated data hinders the widerspread utilization of gloss-level features from sign language videos by existing weakly supervised learning methods, thereby impedes the model from sufficient training. To address the above challenges, we propose a novel Cross-Modal Adaptive Prototype Learning model for Continuous Sign Language Recognition (CAP-SLR), which leverages prototype learning to fuse features across different modalities and improves recognition accuracy. Initially, we propose a lightweight Keyframe Extractor and a Multi-Scale Dilated Convolutional Attention to alleviate data redundancy and bolster the visual representation of sign language based on spatial-temporal information. Subsequently, we employ the Contextual Position Encoding-assisted (CoPE) transformer to learn the semantic of sign language, ameliorating the issue of cross-modal semantic prior bias inherent in pre-trained models. Finally, we design a Cross-Modal Adaptive Prototype Updating mechanism (CAP), which adaptively fuse visual features, gloss prototypes, and textual features, and subsequently update the gloss prototypes through the classifier-aware feature states, effectively mitigating the problem of easily introducing erroneous features in traditional momentum update methods. Extensive experiments on the PHOENIX14, PHOENIX14-T, and CSL-Daily datasets demonstrate that the proposed CAP-SLR method can effectively align cross-modal features at the gloss level, and achieves competitive recognition performance. © 2025 IEEE.

关键词： Semantics

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