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共 20691 条记录，以下是4511-4520 订阅

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Optimization Strategy for Task Offloading and Service Caching in Mobile Edge Computing Based on Deep Reinforcement learning 3

Optimization Strategy for Task Offloading and Service Cachin...

引用

3rd international conference on Digital Society and intelligent Systems, DSInS 2023

作者： Zhu, Chonglai Ou, Guojian Qin, Changchun Chongqing College of Electronic Engineering Shapingba Chongqing401331 China School of Infornation Technology Xichang University Sichuan Xichang615013 China

ISBN: (纸本)9798350331387

para>This paper proposes a joint optimization solution for task offloading and service caching in a multi-edge server environment to address the issue of limited computational and storage resources in edge computing. A resource-constrained optimization model is formulated and transformed into a mixed-integer nonlinear programming problem. To tackle this problem, an improved Deep Deterministic Policy Gradient (DDPG) algorithm is introduced. This algorithm utilizes deep neural networks to approximate the policy and value functions and incorporates experience replay and target networks to enhance training stability. The agent interacts with the environment, observes the current state, selects an action accordingly, and receives a reward. The policy network is updated based on the observed reward using experience replay. Experimental results demonstrate that the proposed DDPG-based joint optimization method achieves excellent performance in terms of reducing task execution latency and alleviating network load in the edge computing environment. In the context of edge computing, the limited computational and storage resources of edge servers can lead to increased task processing latency when too many tasks are offloaded to them. © 2023 IEEE.

关键词： Constrained optimization

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Machine learning Techniques for Detecting DDOS Attacks 3

Machine Learning Techniques for Detecting DDOS Attacks

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3rd international conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Saeed, Mamoon M. Rashad Mohammed, Husam Nedhal Hazaea Gazem, Othman Abdulkafi Saeed, Rashid A. Ahmed Morei, Hossam Mohammed Thabet Eidah, Aimen Ebdulkreem Gaid, AbdulGuddoos S.A. Ameen Saif Al-Uosfi, Ameen Sami Qasem Al-Madhagi, Mohamed Galal Faculty of Engineering Department of Communications and Electronics Engineering Yemen College of Computers and Information Technology Taif University Department of Computer Engineering Taif21944 Saudi Arabia Taiz University Faculty of Engineering & Information Technology Dept. of Communication & Computer Engineering Taiz Yemen

ISBN: (纸本)9798350305333

The development witnessed by the world of science and technology and the emergence of the Internet, where cybersecurity has become one of the most important areas that are growing rapidly every day, and one of its goals is to maintain access to users at any time. Where it is noted that the biggest enemy for the availability is Distributed Denial of Service (DDoS) attack, which works to prevent access to the service and slow down the service. Through this paper, machine learning will be used to discover the DDoS attack and know its type to be aware of it and take the necessary measures for that. Where the CICDDoS2019 dataset will be used. After it is studied, analyzed, cleaned and the best algorithm was selected. Five classification algorithms (Random Forest, Decision Tree, SVM, Naive Bayes, and xgboost) were used to train and test the data from the datasets, and it can be said that of the five, Random Forest algorithms had the highest level of accuracy (99.95426%). © 2023 IEEE.

关键词： Denial-of-service attack

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Enhancing Disease Prediction in Healthcare: A Comparative Analysis of PSO and Extreme learning Approach 3

Enhancing Disease Prediction in Healthcare: A Comparative An...

引用

3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Prabakaran, Manjula Bhole, Manisha Kishor Kalpana, V. Dixit, Shriniket Divya, K. Chauhan, Dr Amit Andhra Pradesh India Bharati Vidyapeeth College of Engineering Department of Instrumentation Engineering Navi Mumbai India RMK College of Engineering and Technology Puduvoyal Department of Science and Humanities Tamilnadu Thiruvallur India Vellore Institute of Technology School of Computer Science Engineering Tamilnadu Vellore India Prince Shri Venkateshwara Padmavathy Engineering College Department of ECE Chennai India School of Sciences Department of Life Sciences Karnataka Bengaluru India

ISBN: (纸本)9798350343632

The healthcare business generates a tremendous quantity of data, and the goal is to collect it and use it effectively for analysis, prediction, and treatment. The best approach to disease management is disease prevention through early intervention. There are a number of methods that can advise you on how to treat a specific sickness, but much fewer that can tell you with any degree of certainty if you will actually get sick in the first place. Preprocessing, feature selection, feature extraction, and model training are all parts of the proposed method. The suggested layout includes a preprocessing stage that takes care of things like moving average, missing values, and normalization. Feature selection describes the process of selecting the most relevant features from a dataset. After gathering features, the models are trained using PSO-ELM. The proposed strategy is superior to the widely used PSO and ELM. © 2023 IEEE.

关键词： Disease Prediction System Extreme learning Machine (ELM) Particle Swarm Optimization (PSO)

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Adaptive Industrial Edge Intelligence: A Reinforcement learning Approach 3

Adaptive Industrial Edge Intelligence: A Reinforcement Learn...

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3rd international conference on Computing, Communication, Perception and Quantum Technology, CCPQT 2024

作者： Ren, Yuzheng Fan, Liuqi Li, Xuehan Wang, Pengfei Yuan, Shuai Wen, Yao University of Science and Technology Beijing Beijing China Beijing Jiaotong University Beijing China Beijing Institute of Tracking and Communication Technology Beijing China North China University of Science and Technology Tangshan China Academy of Broadcasting Science Beijing China

ISBN: (纸本)9798331528386

As intelligent factories evolve, the need to process large volumes of real-time data and manage complex computational tasks places increasing demands on resource-limited devices, while traditional cloud computing struggles to meet low-latency and real-time processing requirements. Multi-access Edge Computing (MEC) helps reduce latency and energy consumption by intelligently offloading tasks to edge nodes. However, in industrial scenarios where task requests and network conditions frequently fluctuate, traditional static methods fail to meet real-time demands. Static resource allocation in task offloading often leads to mismatches between allocated resources and dynamic service requirements. To address this issue, we propose an adaptive industrial edge intelligence algorithm to optimize resource management for task offloading in dynamic environments. By performing real-time task analysis and making adjustments during execution, this approach enhances the efficiency of resource utilization. Simulations demonstrate that the proposed strategy significantly improves resource allocation and system performance in dynamic environments. © 2024 IEEE.

关键词： Computation offloading

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Detection of Schizophrenia using Machine learning 3

Detection of Schizophrenia using Machine Learning

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3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Sharma, Ruchi Tripathi, Sweta Sekhon, Karamjit Kaur Amity University Department of Electronics and Communication Engineering Haryana India

ISBN: (纸本)9798350399264

Schizophrenia is a mental illness that impacts approximately 1% of the global population. The condition can make it challenging for individuals with schizophrenia to differentiate between internally generated and externally generated stimuli due to abnormalities in the corollary discharge process of the nervous system. This phenomenon provides an explanation for certain symptoms associated with schizophrenia. Exploring this process and its connection to the disease's symptoms can contribute to our understanding of the abnormal brain processes observed in affected individuals. Advancements in the study of electrophysiological brain activity, along with improved access to EEG data, offer new possibilities for investigating and diagnosing neuronal disorders in real-world settings. In this study, a larger dataset of EEG data was utilized, including data from previous research published in Schizophrenia Bulletin Advance Access on July 10, 2013. The dataset combined EEG records from 22 healthy individuals and 36 individuals with schizophrenia, as well as additional data from 10 healthy individuals and 13 schizophrenia patients from a prior study. The classification phase involved considering two different methods for diagnosing schizophrenia using EEG signals. Traditional machine learning techniques, such as xgboost, decision trees, naïve Bayes, random forest, Long short-term memories (LSTMs), support vector machines, two-dimensional convolutional networks (2D-CNNs), and two-dimensional convolutional networks-LSTMs, were employed to classify the EEG signals. Deep learning models were also implemented, and various activation functions were compared. Among the proposed deep learning models, the SVM architecture demonstrated the highest performance level. The SVM model utilized the RBF Kernel with cross-validation values (CV) of 3, 6, and 8. Remarkably, the SVM model achieved a precision rate of 100 percent. © 2023 IEEE.

关键词： Convolutional neural networks

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SFPGRN: Spectral detection method of plant diseases based on deep learning 3

SFPGRN: Spectral detection method of plant diseases based on...

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3rd international conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Zhou, Yi Pan, Zeng Liu, Zuhao He, Tingwei Yan, Jingwen Department of Information Science and Technology Southwest Jiaotong University China Department of Biomedical Engineering and Instrument Science Zhejiang University China Department of Electronic Information Engineering Shantou University China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

At present, most of the spectral detection methods with plant diseases are based on machine learning, we present a novel approach to spectral analysis using a spectral feature pseudo-graph-based residual network, referred to as SFPGRN, for data in the near-infrared band. The proposed method involves constructing a residual network model using a characteristic surface obtained via natural neighborhood interpolation based on preprocessed near-infrared spectral reflection signal and first-order differential spectral index. The input layer is a pseudo spectral map that is generated through surface projection of the characteristic surface. The generated spectral pseudo image contains a diverse set of spectral features that augment the representation capability of the pseudo-image while retaining the spectral band reflectance information. We evaluated the proposed method on the 2015 spectral dataset of apple leaf diseases and insect pests in China and found that it outperforms traditional machine learning algorithms, achieving a classification accuracy of 93.21%. © 2023 SPIE.

关键词： Image recognition

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Non intrusive load identification method based on deep learning 3

Non intrusive load identification method based on deep learn...

引用

3rd international conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Xia, Xuwei Zhang, Shuang Yan, Zhenhua Liu, Jia Cai, Jianhui Ma, Rui Electric Power Research Institute State Grid Ningxia Electric Power Co. Ltd. Ningxia Yinchuan750002 China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

The current non-invasive load identification methods have limited recognition range and are prone to increasing time consumption. Therefore, a non-invasive load identification method based on deep learning is proposed. Based on actual recognition requirements and standards, extract recognition features, adopt multi-level processing forms, break through the limitations of recognition range, and set them as multi-level adaptive recognition nodes. We constructed a deep learning non-invasive load identification model and utilized GAGOA multi-objective recognition optimization to achieve non-invasive processing. The test results show that the time consumption of the non-invasive load identification unit obtained by applying the proposed method is controlled below 0.5 seconds, which is shorter. This indicates that the practical application effect of this method is better and has certain practical application value. © 2023 SPIE.

关键词： Deep learning

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Experimental Study on data Update Rate of the Display Interface of Coal Mine intelligent Production System 3rd

Experimental Study on Data Update Rate of the Display Interf...

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3rd international conference on Smart Manufacturing, Industrial and Logistics engineering, SMILE 2023 and the 7th Asian conference of Management Science and Applications, ACMSA 2023

作者： Wu, Haokun Sun, Linhui Yuan, Xiaofang Gao, Yukun School of Management Xi’an University of Science and Technology Shaanxi Xi’an710054 China Human Factors and Management Ergonomics Research Center Xi’an University of Science and Technology Shaanxi Xi’an710054 China

ISBN: (纸本)9789819701933

Objective: The development of coal mine production construction to modernization has led to a dramatic increase in the amount of information on the display interface of the system, and the contradiction of "huge amount of information, limited display" makes the operator need to pay attention to more information display areas at the same time when facing the complex operation tasks, which reduces the reliability of the work of the human factors. This study investigates the influence of data update rate among the three factors of the human–computer interaction interface (information display rate, information provision rate, and data update rate) in the intelligent coal mine production system. Methods: In this paper, we take the data update rate of the display interface of a coal mine system as the research object, use the E-prime software to simulate the state of the display interface of the coal mine system in the real working environment, design multi-group controlled experiments, and analyze the performance of the subjects to complete the visual searching task under different data update rates (1 s, 2 s, and 3 s). Results: The experimental results showed that in a display interface with 24 changing regions, the operators’ task performance (average reaction time, number of correct mouse clicks) was best when the data update rate was 2 s. Conclusion: Changes in the data update rate affect the operator's task performance. This study provides a relevant basis for subsequent research on the design of the display interface of coal mine intelligent production systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Coal mines

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Research on Self-driving Vehicle Driving Status Monitoring and Trajectory Tracking Technology 3

Research on Self-driving Vehicle Driving Status Monitoring a...

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2023 IEEE 3rd international conference on data Science and Computer Application, ICDSCA 2023

作者： Zhai, Yuxin Gao, Yuchao Wang, Xin Shandong University of Science and Technology College of Transportation Qingdao China Shandong University of Science and Technology College of Electrical Engineering and Automation Qingdao China

ISBN: (纸本)9798350341546

Considering the research of driving state monitoring and trajectory tracking technology of autonomous vehicles in roads under low permeability conditions is an important technical means to alleviate traffic problems and ensure traffic safety, which is of great significance to improve the safety and robustness of autonomous driving. However, the traditional trajectory tracking algorithm has the problem of insufficient data accuracy and cannot effectively obtain the holographic traffic trajectory state. Accordingly, this paper proposes a forward vehicle state prediction algorithm based on the combination of the Auto-Regressice Moving Average (ARMA) model and Kalman filter to achieve accurate prediction of vehicle trajectory. At the same time, this paper also proposes a spatiotemporal fusion strategy based on the topological statistical distance of vehicle network data and roadside wide-area radar data and constructs a spatiotemporal correlation mechanism of the absolute position of vehicle GNSS and relative position information of millimeter wave radar detection to realize continuous and accurate location tracking service of vehicles and provide reliable data support for intelligent traffic control. © 2023 IEEE.

关键词： Autopilot Autoregressive sliding average model Kalman filtering Topological statistics distance Track tracking algorithm

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A Trust Based Security in Mobile Ad Hoc Network 3

A Trust Based Security in Mobile Ad Hoc Network

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3rd international conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2023

作者： Khandekar, Varsha Ashok Gupta, Praveen Chatrapati Shivaji Maharaj University Computer Engineering Panvel Navi Mumbai India Chatrapati Shivaji Maharaj University Department of Computer Engineering Panvel Navi Mumbai India

ISBN: (纸本)9798350306989

The main goal of the routing protocol in a mobile ad hoc network is to determine appropriate paths for communication between two nodes. WSN currently faces many security-related difficulties while sending packets among the networks. The objective of this research was to develop an efficient data mining classification approach. An initial set of input nodes is grouped in order to increase the network's durability and scalability. Work is accomplished through network simulation, and as a result, data is produced during simulation. To evaluate the dataset and look into the attack, we use the dataset, which has 41 attributes classed as attack or normal. Four categories i.e. similar service features, content features, intrinsic features and same host features are articulated using continuous and symbolic forms for these features. Designing and implementing effective routing in a secure environment to fend off various MANET assaults is the main goal of this research. Other sensor nodes are also provided with an intrusion detection system and high level security mechanism to enable secure packet transmission between them. With this concept objective in mind, block-chain architecture must be applied in a MANET setting to tackle faith appraisal and maintenance. In particular, a public block-chain implementation was adopted in a complex and latency-sensitive setting to reduce the excessive usage of resources and lengthy confirmation times of existing block-chain technology. © 2023 IEEE.

关键词： Mobile ad hoc networks

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