检索结果-内蒙古大学图书馆

Reinforcement Learning Based Vertical Scaling for Hybrid Deployment in Cloud Computing 1

17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Cao, Jianqi Li, Guiying Yang, Peng Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

To maximize the CPU utilization of the server, offline tasks are usually deployed to the same server where the online service is running. Considering the necessity to ensure the service quality of online services, it is common practice to isolate the resources of online services. How to set the resource quota for online services not only affects the service quality of online services, but also affects the number and the stability of offline tasks that can be run on the server. Traditional rule-based methods or prediction-based methods will cause over-provision and fail to consider the stability of offline tasks, which often cannot achieve stability and efficiency. In this paper, reinforcement learning is proposed for the first time to solve the hybrid deployment of online services and offline tasks and dynamically adjust the resource quota of online services more effectively. Compared with the original state of the server, our proposed method reduces CPU idleness rate by 35.32% and increases CPU resource utilization rate by 3.84%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Energy Efficiency Prediction Model of Suction Hopper Dredger Based on Correlation Analysis and Convolutional Neural Network 20

Energy Efficiency Prediction Model of Suction Hopper Dredger...

引用

20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Zhang, Jinyue Chen, Liheng Qin, Daqing Harbin Engineering University College of Intelligent Systems Science and Engineering Harbin150001 China University of Macau Faculty of Science and Technology Department of Electromechanical Engineering Macau China State Key Laboratory of Hydro-power Equipment Harbin150040 China Harbin Electric Machinery Company Limited Harbin150040 China

ISBN: (纸本)9798350320831

Suction hopper dredgers are the main type of ship used in dredging projects. Due to the complex dynamic characteristics of the dredging process and the fact that the power consumption characteristics of dredgers are affected by many factors such as soil, meteorological environment, and hydrology, it is difficult to obtain accurate prediction results using traditional methods. This paper proposes a data extraction method based on correlation analysis to filter out the most critical influencing features from a large amount of data that affects the working efficiency of dredgers. An energy efficiency prediction model is established using convolutional neural network method to estimate the actual production efficiency of bucket-wheel dredgers and applied to actual production cases. The results show that the energy efficiency prediction model established using this method can effectively predict the power consumption of suction hopper dredgers. © 2023 IEEE.

关键词： Energy efficiency

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Models for Early Prediction of COVID-19 Infections Based on Clinical Signs

引用

SN Computer science 2024年第1期5卷 1-10页

作者： Ben Ammar, Boulbaba Salem, Ali Ben Said, Mouna Ben Aouicha, Mohamed Data Engineering and Semantics Research Unit Faculty of Science University of Sfax Sfax Tunisia Digital Research Center of Sfax Ministry of Higher Education and Scientific Research Sfax Tunisia Signals systeMs aRtificial intelligence and neTworkS Laboratory (SM@RTS) Digital Research Center of Sfax Sfax Tunisia

Nowadays, the appearance of common symptoms, such as cough, fever, and loss of smell and taste, is the starting point of a battle against the coronavirus. The first standard method of COVID-19 infection assertion has become the RT-PCR test, which is however an uncomfortable solution for both patients and medical staff due to its high cost, timeliness, and false-negative result issue. This has raised the need for reliable automatic detection systems that aid in the early prediction of the COVID-19 infections with a lower cost. In this work, we aim at profiting from the Machine Learning (ML) advances to provide a reliable and low-cost COVID-19 prediction system. This system is based on the disease starting point, which is the patients’ clinical symptoms, that are still under-explored. We developed seven predictive models using traditional ML classification algorithms using a public dataset of obvious high-risk factors from patients’ clinical signs. The dataset has first undergone a pre-processing phase consisting of feature engineering and dataset resampling to deal with imbalanced dataset issue. Our best classification model is able to detect true positives and true negatives and weed out false positive and false negatives with an accuracy of 93%. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Clinical signs COVID-19 Early diagnosis Machine learning

来源：评论

学校读者我要写书评

暂无评论

Empowering Diagnosis: Cutting-Edge Segmentation and Classification in Lung Cancer Analysis

引用

Computers, Materials & Continua 2024年第6期79卷 4963-4977页

作者： Iftikhar Naseer Tehreem Masood Sheeraz Akram Zulfiqar Ali Awais Ahmad Shafiq Ur Rehman Arfan Jaffar Faculty of Computer Sciences&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Information System Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Lung cancer is a leading cause of global mortality *** detection of pulmonary tumors can significantly enhance the survival rate of ***,various Computer-Aided Diagnostic(CAD)methods have been developed to enhance the detection of pulmonary nodules with high ***,the existing method-ologies cannot obtain a high level of specificity and *** present study introduces a novel model for Lung Cancer Segmentation and Classification(LCSC),which incorporates two improved architectures,namely the improved U-Net architecture and the improved AlexNet *** LCSC model comprises two distinct *** first stage involves the utilization of an improved U-Net architecture to segment candidate nodules extracted from the lung ***,an improved AlexNet architecture is employed to classify lung *** the first stage,the proposed model demonstrates a dice accuracy of 0.855,a precision of 0.933,and a recall of 0.789 for the segmentation of candidate *** suggested improved AlexNet architecture attains 97.06%accuracy,a true positive rate of 96.36%,a true negative rate of 97.77%,a positive predictive value of 97.74%,and a negative predictive value of 96.41%for classifying pulmonary cancer as either benign or *** proposed LCSC model is tested and evaluated employing the publically available dataset furnished by the Lung Image database Consortium and Image database Resource Initiative(LIDC-IDRI).This proposed technique exhibits remarkable performance compared to the existing methods by using various evaluation parameters.

关键词： Lung cancer segmentation AlexNet U-Net classification

来源：评论

学校读者我要写书评

暂无评论

Design of Lowpass Filter and Wavelet Analysis for Electroretinogram in Dogs 38

Design of Lowpass Filter and Wavelet Analysis for Electroret...

引用

38th IEEE Region 10 Conference, TENCON 2023

作者： Tuntrik, Tunyawat Tuntivanich, Nalinee Banjerdpongchai, David Chulalongkorn University Center of Excellence in Intelligent Control Automation of Process Systems Faculty of Engineering Dept. of Electrical Engineering 254 Phayathai Road Pathumwan Bangkok10330 Thailand Faculty of Veterinary Science Dept. of Veterinary Surgery 254 Phayathai Road Pathumwan Bangkok10330 Thailand

ISBN: (纸本)9798350302196

In frequency domain, wavelet coefficient is one of the characteristics of ERG signal which is hidden in frequency-domain. This paper aims to develop a tool for analyzing the constituent frequencies and time of interest in the dog's ERG signal using wavelet analysis. We design a lowpass filter to reduce noise during the measurement of ERG response. ERG dataset was obtained for the light intensity series and their constituent frequencies are analyzed by using the continuous wavelet transform (CWT). The results show that, in time-domain, as light intensity increases, both amplitudes of a-wave and b-wave are higher. We can visualize the noise interference in the ERG signals. Then a lowpass filter is designed to attenuate the noise effect. The filtered signals have lower a-, and b-waves' amplitude which affect the important characteristics in time domain. Therefore, we choose to filter signal from 26ms onward, the main components are recovered, and the filtered signal becomes smoother. In frequency-domain, the peak magnitude of wavelet coefficients is increased as light intensity increases. © 2023 IEEE.

关键词： Wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

Lyapunov-guided Deep Reinforcement Learning for Semantic-aware AoI Minimization in UAV-assisted Wireless Networks

引用

IEEE Transactions on Wireless Communications 2025年

作者： Long, Yusi Gong, Shimin Sun, Sumei Lee, Gary C.F Li, Lanhua Niyato, Dusit Shenzhen Campus of Sun Yat-sen University School of Intelligent Systems Engineering Shenzhen518000 China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology China A*STAR Singapore Nanyang Technological University College of Computing and Data Science Singapore

This paper investigates an unmanned aerial vehicle (UAV)-assisted semantic network where the ground users (GUs) periodically capture and upload the sensing information to a base station (BS) via UAVs’ relaying. Both the GUs and the UAVs can extract semantic information from large-size raw data and transmit it to the BS for recovery. Smaller-size semantic information reduces latency and improves information freshness, while larger-size semantic information enables more accurate data reconstruction at the BS, preserving the value of original information. We introduce a novel semantic-aware age-of-information (SAoI) metric to capture both information freshness and semantic importance, and then formulate a time-averaged SAoI minimization problem by jointly optimizing the UAV-GU association, the semantic extraction, and the UAVs’ trajectories. We decouple the original problem into a series of subproblems via the Lyapunov framework and then use hierarchical deep reinforcement learning (DRL) to solve each subproblem. Specifically, the UAV-GU association is determined by DRL, followed by the optimization module updating the semantic extraction strategy and UAVs’ deployment. Simulation results show that the hierarchical structure improves learning efficiency. Moreover, it achieves low AoI through semantic extraction while ensuring minimal loss of original information, outperforming the existing baselines. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

EdgeAuth: An intelligent token-based collaborative authentication scheme

EdgeAuth: An intelligent token-based collaborative authentic...

引用

作者： Jiang, Xutong Dou, Ruihan He, Qiang Zhang, Xuyun Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Faculty of Mathematics University of Waterloo Waterloo Canada Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Department of Computing Macquarie University Sydney Australia College of Big Data and Intelligent Engineering Southwest Forestry University China

Edge computing is regarded as an extension of cloud computing that brings computing and storage resources to the network edge. For some Industrial Internet of Things (IIoT) applications such as supply-chain supervision and collaboration, Internet of Vehicles, real-time video analysis and so forth, users should be authenticated before visiting the geographically distributed edge servers. Limited by the considerable latency between the cloud and edge servers, and the limited capacity of edge servers, it is infeasible to copy the authentication method from cloud servers when users are authenticated in edge servers. In view of this challenge, this paper proposes a novel token-based authentication scheme, named EdgeAuth, that enables fast edge user authentication through collaboration among cloud servers and edge servers. Under the EdgeAuth scheme, edge servers can rapidly verify the credentials of users who have been authenticated by the cloud server. EdgeAuth can also protect users from a series of authentication attacks, for example, the replay attack, DoS attack and man-in-the-middle attack. The results of experiments conducted on a simulated edge computing environment validate the usefulness of EdgeAuth through a comparison in latency and throughput against two baseline schemes. © 2023 John Wiley & Sons Ltd.

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

Design decisions for integrating Prescriptive Analytics Use Cases into Smart Factories 34

Design decisions for integrating Prescriptive Analytics Use ...

引用

34th CIRP Design Conference, CIRP 2024

作者： Weller, Julian Migenda, Nico Enzberg, Sebastian Von Kohlhase, Martin Schenck, Wolfram Dumitrescu, Roman Fraunhofer Institute for Mechatronic Systems Design Zukunftsmeile 1 Paderborn33102 Germany Center for Applied Data Science Gütersloh Faculty of Engineering and Mathematics Bielefeld University of Applied Sciences and Arts Schulstraße 10 Gütersloh33330 Germany Chair for Advanced Systems Engineering Heinz Nixdorf Institute Fürstenallee 11 Paderborn33102 Germany

The emerging trend of Prescriptive Analytics tries to prescribe actionable decisions. The goal is to help production experts in automating their decisions in the factory. Decision making processes in factories mostly rely on experts inside the company. Current trends such as demographic shifts and shortages of skilled personnel are challenging organizations to find ways to become less dependent on the ad hoc availability of expert knowledge through digitalization and data analytics. Existing data analytics approaches for smart factories usually focus on description, diagnosis, and prediction. An approach to differentiate between different application areas of Prescriptive Analytics in a smart factory is presented. Focus areas for a useful application and integration of Prescriptive Analytics are derived. Based on these applications, design principles are constructed. They help decision makers to select future lighthouse use cases for development and help smart factory advocates to strategically decide which prescriptive analytics approaches to highlight. The resulting design principles are based on a literature review and validated in a workshop with experts from research. Approaches are differentiated and key characteristics for future Use Case design decisions are derived. © 2024 Elsevier B.V.. All rights reserved.

关键词： Smart manufacturing

来源：评论

学校读者我要写书评

暂无评论

Optimizing Feature Selection in Machine Learning with E-BPSO: A Dimensionality Reduction Approach 3rd

Optimizing Feature Selection in Machine Learning with E-BP...

引用

3rd International Semantic Intelligence Conference, ISIC 2023

作者： Shenbaga Moorthy, Rajalakshmi Arikumar, K.S. Prathiba, Sahaya Beni Pabitha, P. Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Chennai600 116 India Department of Data Science and Business Systems SRM University – Kattankulathur Chennai India Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India Department of Computer Technology Anna University Madras Institute of Technology Campus Chennai600044 India

ISBN: (纸本)9789819773558

In the era of informatics, the effectiveness of machine learning models is compromised due to the challenge of dimensionality in the data. The presence of redundant and irrelevant features significantly increases computational complexity, posing a central obstacle in the extraction of valuable insights from the extensive dataset. Any machine learning model’s performance suffers because of the issue of the plague of dimensionality. To improve the classifier’s performance, feature selection is applied beforehand on applying the machine learning model. Feature selection is accomplished using Enhanced Binary Particle Swarm Optimization (E-BPSO) with the aid of boosting the performance of the K-Nearest Neighbor (K-NN) classifier and is experimented on benchmarking real-world datasets. The conventional BPSO suffers from the problem of exploration which leads to premature convergence. In order to overcome the drawbacks of conventional BPSO, E-BPSO is proposed. The enhancement is made by integrating the self-adaptive velocity to drive the particle with the aid to balance exploration and exploitation. The performance of the proposed E-BPSO is evaluated against the traditional binary particle swarm optimization algorithm and genetic algorithm, considering metrics like accuracy, fitness, root mean square error, and dimensionality reduction ratio. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Dimensionality reduction

来源：评论

学校读者我要写书评

暂无评论

Evaluation of Chest CT-Scan Anomaly Detection Models Using Principal Component Analysis 25

Evaluation of Chest CT-Scan Anomaly Detection Models Using P...

引用

25th International Arab Conference on Information Technology, ACIT 2024

作者： Alyabrodi, Ala Al-Daja, Shrouq Injadat, MohammadNoor Moubayed, Abdallah Elrashidi, Ali Zarqa University Cyber Security Department Zarqa Jordan Zarqa University Data Science & Ai Department Zarqa Jordan King Fahd University of Petroleum & Minerals Research Center for Intelligent Secure Systems Computer Engineering Department and Interdisciplinary Dhahran Saudi Arabia University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Machine learning-based improvements in anomaly detection, visualization, and segmentation are made possible by the growing digitization of medical imaging, which reduces the workload for medical specialists. Nevertheless, trustworthy labeled data is necessary for supervised machine learning and is sometimes expensive, time-consuming, or impossible to get. Consequently, techniques that need either little labeling (unsupervised methods) or partial labeling (semi-supervised methods) have been used increasingly been used. One approach that can be used to tackle medical imaging tasks like segmentation and classification is anomaly detection, which makes use of both unsupervised and semi-supervised methods. This study investigates the anomaly detection capabilities of deep learning and machine learning models trained on chest CT scans. To adopt this approach, firstly, by evaluating several feature extraction techniques (Principal Component Analysis (PCA), Autoencoder, and variance threshold), PCA achieves the best results on the selected dataset. After that, different machine learning and deep learning models (Isolation Forest, K-means, feed-forward neural networks, and convolutional neural networks) are assessed to detect anomalies using PCA for dimensionality reduction. The experiments show that the NN-Feedforward model with PCA outperformed the other models, also attains remarkably low false alarm rate, demonstrating its effectiveness in reducing false positives and precisely detecting anomalies. © 2024 IEEE.

关键词： Computerized tomography

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：