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Enhancing sustainable 5G powered agriculture 4.0 : Summary of low power connectivity, internet of UAV things, AI solutions and research trends

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Multimedia Tools and Applications 2025年第17期84卷 17389-17433页

作者： Majumdar, Parijata Mitra, Sanjoy Bhattacharya, Diptendu Bhushan, Bharat Department of Computer Science and Engineering Techno College of Engineering Agartala Maheshkhola Madhuban799004 India Department of Computer Science and Engineering Tripura Institute of Technology Agartala Narsingarh Agartala799009 India Department of Computer Science and Engineering National Institute of Technology Agartala Jirania Agartala799046 India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida201310 India

Emerging technologies of Agriculture 4.0 such as the Internet of Things (IoT), Cloud Computing, Artificial Intelligence (AI), and 5G network services are being rapidly deployed to address smart farming implementation-related concerns and to fulfill the increasing food demand of the expanding demographics. Adoption of 5G offers numerous benefits including Ultra-Reliable Low Latency Communication (URLLC) required to reduce communication delays in time-sensitive applications, non-public networks to allocate part of the frequency spectrum on demand, and network slicing to fulfill the diverse requirements requested by a specific application etc. for realizing the full potential of Agriculture 4.0 operations. In this paper, a comprehensive review of 5G radio enhancements, enabling technologies, and multi-tier heterogeneous 5G network architecture are presented. The main focus is to concisely cover AI integration with Unmanned Aerial Vehicles (UAVs) leveraging 5G technologies and Low Power Wide Area Networks (LPWANs) for supporting intelligent, long-range, cost-effective and energy-saving data transmission across the network and seamless Agriculture 4.0 operations. Cloud computing architectures that permit centralized management of Agriculture 4.0 operations through sharing of computations, configurable networks, and servers are also studied in detail to reveal the need for edge computation as increasing number of IoT devices allows only to perform complex computations on-site. Extensive survey reveals the recent advances of 5G-enabled Agriculture 4.0 operations, future research opportunities of 5G networks including Energy Harvesting techniques for the deployment of massive to critical IoT applications, and the socio-demographic impacts of 5G networks in Agriculture 4.0 to support ubiquitous connectivity of exponentially increasing IoT devices. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Energy efficiency

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Citrus objects recognition in orchard scene using HSV methods

Citrus objects recognition in orchard scene using HSV method...

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2024 International Conference on Image Processing and Artificial Intelligence, ICIPAl 2024

作者： Hua, Yanzhi Wu, Shuqin Liang, Liling Jiang, Minghui Fan, Yong Xie, Wu School of Mechanical and Electrical Engineering Guilin University of Electronic Technology Guilin541004 China School of Architecture and Transportation Engineering Guilin University of Electronic Technology Guilin541004 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9781510681514

There is a problem that it is always difficult for picking and spraying robots to identify the objects for such factors such as leaf occlusion, similar color and others. In this paper, based on the citrus image data set collected from the actual orchard scene, image features are extracted and a mathematical model is constructed to identify and count the number of citruses in each image, and the distribution of apples in the whole data set is displayed. A model is constructed to be used to identify and count citrus via these images with diversity of citrus colors and the complexity of the background. Firstly, the yellow mature citruses are focused on using the HSV (Hue, Saturation, Value) method for the data sets. Secondly, the empirical HSV range for two citrus fruits, cyan and yellow, was defined, and contour processing is introduced to optimize the recognition model to process more citrus image datasets. Finally, such three types of objects as immature citrus, leaves with similar colors, and citrus covered by leaves are distinguish. The research results indicate that we adopt more detailed color threshold settings and morphological operations with the eccentricity of the contour is less than 0.85, and can distinguish citrus and leaves through image segmentation,and the recognition effect on citrus objects is improved. © 2024 SPIE.

关键词： Morphology

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Solving Multi-Objective Vehicle Routing Problems with Time Windows: A Decomposition-Based Multiform Optimization Approach

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Tsinghua science and technology 2024年第2期29卷 305-324页

作者： Yiqiao Cai Zifan Lin Meiqin Cheng Peizhong Liu Ying Zhou Xiamen Key Laboratory of Data Security and Blockchain Technology College of Computer Science and TechnologyHuaqiao UniversityXiamen361021China College of Engineering Huaqiao UniversityQuanzhou362000China School of Computer Sciences Shenzhen Institute of Information TechnologyShenzhen518000China

In solving multi-objective vehicle routing problems with time windows (MOVRPTW),most existing algorithms focus on the optimization of a single problem formulation. However,little effort has been devoted to exploiting valuable knowledge from the alternate formulations of MOVRPTW for better optimization performance. Aiming at this insufficiency,this study proposes a decomposition-based multi-objective multiform evolutionary algorithm (MMFEA/D),which performs the evolutionary search on multiple alternate formulations of MOVRPTW simultaneously to complement each other. In particular,the main characteristics of MMFEA/D are three folds. First,a multiform construction (MFC) strategy is adopted to construct multiple alternate formulations,each of which is formulated by grouping several adjacent subproblems based on the decomposition of MOVRPTW. Second,a transfer reproduction (TFR) mechanism is designed to generate offspring for each formulation via transferring promising solutions from other formulations,making that the useful traits captured from different formulations can be shared and leveraged to guide the evolutionary search. Third,an adaptive local search (ALS) strategy is developed to invest search effort on different alternate formulations as per their usefulness for MOVRPTW,thus facilitating the efficient allocation of computational resources. Experimental studies have demonstrated the superior performance of MMFEA/D on the classical Solomon instances and the real-world instances.

关键词： MOVRPTW multiform optimization multiform construction knowledge transfer adaptive local search

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Correction to: Adaptive learning-enhanced lightweight network for real-time vehicle density estimation (The Visual computer, (2024), 10.1007/s00371-024-03572-3)

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Visual computer 2024年第6期41卷 4393-4394页

作者： Qin, Ling-Xiao Sun, Hong-Mei Duan, Xiao-Meng Che, Cheng-Yue Jia, Rui-Sheng College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

Correction: The Visual computer https://***/10.1007/s00371-024-03572-3 The publication of this article unfortunately contained mistakes. Figure 3 was not correct. The corrected figure is given below. Wrong figure 3: (Figure presented.) Correct figurure 3: (Figure presented.) The original article has been corrected. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

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Multimodal Sentiment Analysis based on Supervised Contrastive Learning and Cross-modal Translation under Modalities Missing∗ 14

Multimodal Sentiment Analysis based on Supervised Contrastiv...

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14th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2023

作者： Miao, Yuqing Zhu, Gaowei Liu, Tonglai Zhang, Wanzhen Wen, Yimin Zhou, Ming Guilin University of Electronic Technology School of Computer Science and Information Securityy Guilin China Zhongkai University of Agriculture and Engineering College of Information Science and Technology Guangzhou China Guilin Hivision Technology Company Guilin China

ISBN: (纸本)9798350371024

Multimodal sentiment analysis tasks have been extensively researched in recent years. However, real-world multimodal data collection is often problematic due to missing modalities, making it suboptimal. To address these issues, this paper presents a supervised contrast learning and cross-modal translation-based multimodal sentiment analysis model that can operate even when modalities are missing. First, supervised contrast learning is employed to carry out cluster analysis on the same sample batch and establish the connection between different samples. Incomplete features of the target samples are then supplemented with the other samples. Following that, a cross-modal translation network, based on Transformer, is utilised to learn the collective representation of different modalities. A feature reconstruction network is used, employing center distance difference loss, to reduce the difference between features obtained by the model under modality-missing and modality-complete conditions. This aims to enhance the robustness of feature extraction. Experimental findings from two publicly available datasets demonstrate the model's improved performance for varying levels of modalities missing. © 2023 IEEE.

关键词： Sentiment analysis

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DACSC: Secure Authentication Protocol Based on Dynamic Authentication Credentials and IntelSGX in Cloud Computing Environments

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第3期8卷 948-964页

作者： Ni, Lina Ni, Boguang Tang, Yuncan Zhang, Jinquan College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

Cloud computing is a new super computing paradigm, which has greatly changed the way users store and process data. Meanwhile, with the prosperity of cloud computing, there are many hidden dangers in data privacy and security. Many secure authentication protocols in cloud computing environment have been proposed. However, most of the authentication schemes are vulnerable to various attacks. Therefore, it is vital to plan a secure and effective authentication protocol in the cloud computing sur-roundings. In this paper, we propose a secure authentication protocol DACSC based on dynamic authentication credential (DAC) and Intel software guard extensions (SGX) for the cloud computing environment. In order to prevent internal attacks and information leakage stored in memory, we store key data in the SGX. Furthermore, we adopt dynamic identity authentication credentials to timely update the server’s pseudo identity information. We conduct a formal safety analysis of our DACSC protocol adopting ProVerif which is a formal security evidence tool under the stochastic prediction model. Moreover, We also conduct other analysis to prove that DACSC can resist internal attacks, simulation attacks, and achieve user anonymity. By comparing security and performance, we concluded that DACSC is relatively secure and protects the privacy of data. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Authentication

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Redundant Transmission Control Algorithm for Information-Centric Vehicular IoT Networks

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computers, Materials & Continua 2023年第8期76卷 2217-2234页

作者： Abdur Rashid Sangi Satish Anamalamudi Mohammed SAlkatheiri Murali Krishna Enduri Anil Carie Mohammed AAlqarni Department of Computer Science College of Science and TechnologyWenzhou-Kean UniversityWenzhou325060China Department of Computer Science Engineering SRM University APAmaravati522502India Department of Cybersecurity College of Computer Science&EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Software Engineering College of Computer Science&EngineeringUniversity of JeddahJeddahSaudi Arabia

Vehicular Adhoc Networks(VANETs)enable vehicles to act as mobile nodes that can fetch,share,and disseminate information about vehicle safety,emergency events,warning messages,and passenger ***,the continuous dissemination of information fromvehicles and their one-hop neighbor nodes,Road Side Units(RSUs),and VANET infrastructures can lead to performance degradation of VANETs in the existing hostcentric IP-based ***,Information Centric Networks(ICN)are being explored as an alternative architecture for vehicular communication to achieve robust content distribution in highly mobile,dynamic,and errorprone *** ICN-based Vehicular-IoT networks,consumer mobility is implicitly supported,but producer mobility may result in redundant data transmission and caching inefficiency at intermediate vehicular *** paper proposes an efficient redundant transmission control algorithm based on network coding to reduce data redundancy and accelerate the efficiency of information *** proposed protocol,called Network Cording Multiple Solutions Scheduling(NCMSS),is receiver-driven collaborative scheduling between requesters and information sources that uses a global parameter expectation deadline to effectively manage the transmission of encoded data packets and control the selection of information *** results for the proposed NCMSS protocol is demonstrated to analyze the performance of ICN-vehicular-IoT networks in terms of caching,data retrieval delay,and end-to-end application *** end-to-end throughput in proposed NCMSS is 22%higher(for 1024 byte data)than existing solutions whereas delay in NCMSS is reduced by 5%in comparison with existing solutions.

关键词： Caching data dissemination redundancy control ICN-vehicular IoT networks

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Empathetic Dialogue Generation with Contrastive Learning and Multi-Source Information Fusion 14

Empathetic Dialogue Generation with Contrastive Learning and...

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14th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2023

作者： Miao, Yuqing Lu, Chenggang Liu, Tonglai Zhang, Wanzhen Wen, Yimin Zhou, Ming Guilin University of Electronic Technology School of Computer Science and Information Security Guilin China Zhongkai University of Agriculture and Engineering College of Information Science and Technology Guangzhou China Guilin Hivision Technology Company Guilin China

ISBN: (纸本)9798350371024

An empathic dialogue generation model based on comparative learning and multi-source data fusion is proposed to address problems of the under-extraction of contextual emotional expressions in discourse and limited empathic response diversity in current empathic dialogue generation tasks. The model introduces cognitive, affective, and dialogue intention information to guide dialogue generation. In the emotion classification stage, contrastive learning is used to capture the emotion information of the contextual discourse and to improve the contextual discourse representation. In the empathic response generation phase, the multi-attention mechanism fuses multiple sources of cognitive, emotional, and dialogue intention information, capturing more in-depth semantic and emotional information in the dialogue. Experimental results on the public dialogue dataset ED show that the proposed model can fully exploit cognitive, affective, and dialogue intent information to improve classification accuracy and generate more empathetic dialogue responses. © 2023 IEEE.

关键词： Data fusion

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Autonomous Pharmaceutical Care with Large Language Models

Autonomous Pharmaceutical Care with Large Language Models

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Dou, Yutao Deng, Zike Xing, Tao Xiao, Jian Peng, Shaoliang Hunan University College of Computer Science and Electronic Engineering Changsha410082 China University of Hong Kong Faculty of Engineering Department of Computer Science Hong Kong999077 Hong Kong Guilin University of Technology College of Computer Science and Engineering Guilin541006 China Central South University Xiangya Hospital Department of Pharmacy Changsha410008 China

ISBN: (纸本)9798350386226

In modern healthcare systems, physicians often diagnose and prescribe within their specific fields of expertise. However, this compartmentalized approach may not fully assess the overall health condition of patients, leading to potential risks associated with polypharmacy, such as drug-drug interactions. Consequently, pharmaceutical care becomes a critical systemic task, typically managed by clinical pharmacists. Despite their crucial role, clinical pharmacists face challenges in providing personalized medication guidance due to the vast and rapidly evolving body of medical knowledge. With the advancement of artificial intelligence and large language models (LLMs), these technologies have demonstrated significant potential in pharmaceutical care by effectively reducing medication errors and adverse drug events, and alleviating the workload of healthcare professionals. However, existing LLMs still struggle with the accurate interpretation and automated execution of tasks in complex clinical scenarios like pharmaceutical care. To address these challenges, we developed the Shennong-Agent system, a multi-agent framework for LLMs with integrated multimodal inputs. Shennong-Agent is enabled to analyze and segment tasks using chained reasoning and autonomously perform complex pharmacy care tasks using tools such as knowledge retrieval and web search. Rigorously evaluated by medical experts, this system not only surpasses existing LLMs in performance but also enhances its capabilities through reinforcement learning with human feedback. © 2024 IEEE.

关键词： Multi agent systems

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SmartEagleEye:A Cloud-Oriented Webshell Detection System Based on Dynamic Gray-Box and Deep Learning

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Tsinghua science and technology 2024年第3期29卷 766-783页

作者： Xin Liu Yingli Zhang Qingchen Yu Jiajun Min Jun Shen Rui Zhou Qingguo Zhou School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China College of Computer Science and Technology Zhejiang UniversityHangzhou 310058China School of Computing and Information Technology University of WollongongWollongong 2500Australia

Compared with traditional environments,the cloud environment exposes online services to additional vulnerabilities and threats of cyber attacks,and the cyber security of cloud platforms is becoming increasingly prominent.A piece of code,known as a Webshell,is usually uploaded to the target servers to achieve multiple *** Webshell attacks has become a hot spot in current ***,the traditional Webshell detectors are not built for the cloud,making it highly difficult to play a defensive role in the cloud ***,a Webshell detection system based on deep learning that is successfully applied in various scenarios,is proposed in this *** system contains two important components:gray-box and neural network *** gray-box analyzer defines a series of rules and algorithms for extracting static and dynamic behaviors from the code to make the decision *** neural network analyzer transforms suspicious code into Operation Code(OPCODE)sequences,turning the detection task into a classification *** experiment results show that SmartEagleEye achieves an encouraging high detection rate and an acceptable false-positive rate,which indicate its capability to provide good protection for the cloud environment.

关键词： Webshell detection cloud web security deep learning

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