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共 1359 条记录，以下是561-570 订阅

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Exploring the Application of Computer intelligence Algorithms in Logistics and Supply Chain Management 7

Exploring the Application of Computer Intelligence Algorithm...

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7th international conference on Intelligent Information Processing, ICIIP 2022

作者： Xie, Mei-E Qiao, Lichen School of Business Administration Wuhan Business University Wuhan China College of Life Sciences Northeastern University Shenyang Liaoning China

ISBN: (纸本)9781450396714

Digital transformation and innovation are the current trend of the times, and computer intelligence algorithms will be an important supporting technology for a new generation of logistics industry. With the continuous development of artificial intelligence technology, the traditional system that originally relied on human beings to make empirical decisions is facing new challenges, and such a decision-making system is undergoing a transformation. The development of a series of technologies such as big data, cloud computing, machine learning and artificial intelligence has improved the ability of logistics data processing, and also helped logistics enterprises to analyze and predict customer behavior, and system equipment will gradually replace manual decision-making, ultimately achieve better cost reduction and efficiency. This paper firstly introduces the concept of artificial intelligence and supply chain management, related research results at home and abroad, the main impact of artificial intelligence on logistics industry, and analyzes the main types of computer intelligence algorithms, and studies the specific application of computer intelligence algorithms in logistics and supply chain management on the basis of the survey of enterprises. © 2022 ACM.

关键词： Supply chain management

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Spatial-Temporal Flow Holistic Interaction Graph Convolution Network for Bidirectional Traffic Flow Forecasting

Spatial-Temporal Flow Holistic Interaction Graph Convolution...

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2023 IEEE international conference on big data, bigdata 2023

作者： Zhang, Canyang Zheng, Qi Zhang, Yaying Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (纸本)9798350324457

Traffic flow forecasting is indispensable in modern urban life. Considering the complexity, variability and strong timeliness of traffic flow, traffic flow forecasting is a worth exploring but challenging research field. To achieve better traffic flow forecasting effect, we focus on two critical aspects that assume noteworthy importance: i) the features inside the traffic outflows and inflows. ii) the supplementary information regarding exterior region which is the area outside the grid division regions. To address these challenges, we propose a novel deep learning model Spatial-Temporal Flow Holistic Interaction Graph Convolution Network (STHGCN). In STHGCN, graph convolution based modules are applied through multi-step simulation. An exterior region feature estimation module is designed to estimate the influence of the special exterior region through the characteristics of complete trajectories, which enables a more comprehensive reasoning for traffic flow forecasting in grid division regions. Furthermore, a flow feature fusion integrator and stackable convolution modules are proposed to aggregate the intermediate features extracted from various perspectives, which simulate the constantly-updating and interlinked states of traffic flows through the process of multi-layer feature separation and fusion. We conduct extensive experiments on real-world traffic datasets and our proposed model outperforms all baselines. © 2023 IEEE.

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Design of Logistics Warehousing System Based on Artificial intelligence Technology 2

Design of Logistics Warehousing System Based on Artificial I...

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2nd IEEE international conference on Electronic Technology, Communication and Information, ICETCI 2022

作者： Zhong, Xiaolei Chen, Ze Yunnan University Dianchi College Kunming650228 China Engineering Practice Innovation Center Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9781728181158

ISBN: (纸本)9781728181158

In the traditional logistics warehousing system, the operations such as the entry, inventory, transfer, and delivery of goods are all done manually, which is time-consuming, laborious and inefficient. With the rapid development and application of new technologies such as AI[1], Internet of Things, big data, cloud computing, etc., there are more possibilities for the intelligent upgrade of the logistics industry. As an important part of economic development, logistics and warehousing require intelligence and automation to improve efficiency and reduce costs. Artificial intelligence technology has brought revolutionary changes to the logistics industry. Based on the development status of 'artificial intelligence +' logistics[2], this article discusses the entire logistics chain architecture and scenarios through packaging, transportation, warehousing, loading, unloading and handling, distribution, and customer service. application. © 2022 IEEE.

关键词： Artificial intelligence

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Distributed Consensus Problem in Federated Learning Paradigm 23

Distributed Consensus Problem in Federated Learning Paradigm

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23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Yan, Xin Qin, Yiming Hu, Xiaodong School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China The University of Hong Kong The Faculty of Education Hong Kong Hong Kong

ISBN: (纸本)9781665494571

Federated learning (FL) framework facilitates more and more applications of deep learning algorithms on the existing network architectures, where the model parameters are aggregated in a centralized manner. However, some of FL participants are often inaccessible, such as in a power shortage or device dormant state. That will force us to explore the probability that the parameter aggregation is operated in an ad hoc manner, which is based on consensus computing. In this paper, we first propose a novel FL paradigm, which supports an ad hoc operation mode for FL participants. Second, a discrete-time dynamic equation and its control law are formulated to satisfy the demands from FL framework, with a quantized caching scheme designed to mask the uncertainties from asynchronous updates and measurement noises. Then, the consensus conditions and the convergence of the consensus protocol are deduced analytically, and a strategy of quantized caching to optimize the convergence speed is provided. Last, the theoretical results are validated by numerical simulations. © 2021 IEEE.

关键词： Uncertainty Smart cities Delay effects Packet loss Collaborative work Particle measurements Numerical simulation

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A Novel Evaluation Model of data Security Protection Capability in Edge computing Environment 8th

A Novel Evaluation Model of Data Security Protection Capabil...

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8th international conference on Artificial intelligence and Security , ICAIS 2022

作者： Liu, Caiyun Sun, Yan Li, Jun Wang, Mo Wang, Tao China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China University of Leicester LeicesterLE1 7RH United Kingdom

ISBN: (纸本)9783031067600

With the rapid development and wide application of 5G networks, artificial intelligence, and big data, traditional cloud computing cannot handle the massive data generated by edge terminals. Edge computing has gradually been widely used as a computing method close to objects. However, due to the open features of edge computing, such as content perception, real-time computing, and parallel processing, the data security issues that already exist in the cloud computing environment have become more prominent. data security protection capability evaluation is an important part of the improvement of data security capabilities. However, current protection capability evaluations are mostly qualitative evaluations and lack quantitative evaluation models. Aiming at the edge computing network architecture, this paper proposes a data security protection capability evaluation model based on weight presets. By studying the edge architecture data security protection capability score and cost curve, the data security protection model is adaptively selected. Experiments show that the method can quantitatively calculate the data security protection capability in the edge computing environment, and can guide the construction of the protection model through the score-cost curve. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Edge computing

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MysuruCon 2022 - 2022 IEEE 2nd Mysore Sub Section international conference

MysuruCon 2022 - 2022 IEEE 2nd Mysore Sub Section Internatio...

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2nd IEEE Mysore Sub Section international conference, MysuruCon 2022

ISBN: (纸本)9781665497909

The proceedings contain 403 papers. The topics discussed include: automatic detection of temples in color images by using LBP;a literature review on application of artificial intelligence in human resource management and its practices in current organizational scenario;application of software simulation in guiding reform of engineering project with VR tools;a review paper on facial recognition techniques;affective music player for multiple emotion recognition using facial expressions with SVM;intelligent retrieval platform for university digital resources based on quantum cloud computing;English-Chinese corpus information collection and quantum translation based on big data;application of information technology in civil aviation safety management system under the background of Internet;and a game model of river eco-hydrological evolution under big data water resources allocation.

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TDH: An Efficient One-stop Enterprise-level big data Platform 7

TDH: An Efficient One-stop Enterprise-level Big Data Platfor...

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7th IEEE international conference on Smart Cloud, SmartCloud 2022

作者： Sun, Yuanhao Lv, Cheng Liu, Xi Lei, Tianyang Guo, Zhuoyi Li, Ning Yang, Hongshan Co.Ltd Shanghai China

ISBN: (数字)9781665451796

ISBN: (纸本)9781665451796

big data technology is rapidly changing IT industry. Among those technologies, Hadoop is the best-known one and keeps growing its popularity. Transwarp data Hub (shortened to TDH) is an enterprise-level big data platform developed by Transwarp Technology (Shanghai) Co., Ltd. The last five years have witnessed rapid development in its growth, and it has gained experience from the deployment and implementation in the fields such as postal service, transportation, and finance. Moreover, the company has been engaged in the exploration of the newborn big data technology. Transwarp data Hub provides five major products: Analytical database (Transwarp Inceptor and Transwarp ArgoDB), Real-time Streaming Engine (Transwarp Slipstream), Knowledge database (Transwarp Search and Transwarp StellarDB), Operational database (Transwarp Hyperbase), and data Science Platform (Transwarp Discover). Enterprises can leverage data to build core business systems more effectively and accelerate business innovation by deploying, installing, and using TDH. © 2022 IEEE.

关键词： big data

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A Comparative Study of Time Series data Forecasting Using Machine Learning Based on Improved Grey Model 23

A Comparative Study of Time Series Data Forecasting Using Ma...

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作者： Li, Yan Zhang, Jiaming Ma, Yanhong Li, Xiaomin Liu, Ziang Zhou, Xiaokang Zhou, Rui School of Information Science and Engineering Lanzhou University Lanzhou China State Grid Gansu Electric Power Company Lanzhou China Shiga University Faculty of Data Science Hikone Japan Riken Center for Advanced Intelligence Project Tokyo Japan

ISBN: (纸本)9781665494571

This paper proposes a time-series data prediction hybrid model suitable for groundwater level to further study its impact on new energy power generation through the close relationship between groundwater level and climate. The model is based on the improved grey model (IGM) and combined with other models such as backpropagation neural network (BP) and general vector machine (GVM), which are used to predict time series data. Based on the time series data of groundwater level, the prediction ability of IGM BP, IGM RBF, IGM support vector machine, and IGM-GVM hybrid model is studied, which proved the feasibility of the model. The statistical parameter values finally conclude that the IGM-GVM model outperforms other selected models in the dataset utilized in the paper. In addition, the mixed model has a better effect on the dataset than the corresponding single model. © 2021 IEEE.

关键词： Support vector machines Temperature distribution Smart cities Time series analysis Machine learning Predictive models data models

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A CVD Critical Level-aware Scheduling Model Based on Reinforcement Learning for ECG Service Request 12

A CVD Critical Level-aware Scheduling Model Based on Reinfor...

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12th international conference on Cyber-Enabled Distributed computing and Knowledge Discovery (CyberC)

作者： Gao, Jian Yu, Jie Wang, Ning Feng, Panpan Cheng, Huiqing Zhou, Bing Wang, Zongmin Zhengzhou Univ Cooperat Innovat Ctr Internet Healthcare Sch Comp & Artificial Intelligence Zhengzhou Peoples R China

ISBN: (纸本)9798350331547

In the cardiovascular disease (CVD) diagnosis scenario, the number of electrocardiogram (ECG) service request data is large and the severity of CVD is different. Efficient task scheduling is the key to large cluster computer-aided CVD diagnosis. Therefore, in task scheduling, the workload changes and the critical condition of CVD must be paid attention to. We propose a CVD critical level-aware scheduling model based on reinforcement learning (CLS-RL) to optimize ECG service request scheduling. To solve the problem that there is no publicly available ECG service request data, this paper proposes a method of composing it. Then, we utilize RL with Actor-Critic to improve the efficiency of scheduling. Finally, we define the new objective functions for ECG service request scheduling. The experimental results show that the proposed CLS-RL is the best in comprehensive performance.

关键词： electrocardiogram (ECG) service request task scheduling reinforcement learning cardiovascular disease (CVD) critical level

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SDCM: An Efficient Camellia Oleifera Fruits Detection Algorithm in Dense Occlusion Scenes 6

SDCM: An Efficient Camellia Oleifera Fruits Detection Algori...

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6th international conference on computing and big data, ICCBD 2023

作者： Zhang, Youyang Chen, Qiuxia Cui, Wenhua Yang, Jinfeng School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China Institute of Applied Artificial Intelligence The Guang-dong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic Shenzhen China

ISBN: (纸本)9798350317008

To realize intelligent management in the Camellia oleifera fruit industry and foster the efficient growth of its supply chain, the accurate identification and detection of Camellia oleifera fruits are essential. However, in natural environments, these fruits exhibit challenges such as clustering, mutual occlusion, and obstruction by leaves. This paper proposes a Camellia oleifera fruits detection algorithm based on an improved YOLOv7 deep convolutional neural network, which is referred to as the SDCM network model, and add the function of counting the number of fruits. We introduce an enhancement to the backbone layer of the YOLOv7 model by incorporating a module called SDCM (Shortcut and Dilation Convolution based Module). This module enriches multiscale edge information, enabling the network to extract features more effectively, thus improving detection accuracy. The experimental results show that the detection accuracy of the SDCM network model is significantly improved compared with the original YOLOv7 model, and the detection performance of the model in this paper is superior compared with other classical detection models. By enhancing the detection accuracy of the model and adding the function of fruit counting, the model in this paper can provide more reliable and accurate detection performance and the function of predicting fruit yield for the development of the Camellia oleifera fruit industry, improve the production efficiency, and reduce the artificial resources. © 2023 IEEE.

关键词： Supply chains

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