检索结果-内蒙古大学图书馆

QoS-Aware Cloud Service Optimization Algorithm in Cloud Manufacturing Environment

Intelligent Automation & Soft computing 2023年第8期37卷 1499-1512页

作者： Wenlong Ma Youhong Xu Jianwei Zheng Sadaqat ur Rehman School of Information Engineering Quzhou College of TechnologyQuzhou324000China School of Computer Science and Technology Zhejiang University of TechnologyHangzhou310023China Department of Natural and Computing Science University of AberdeenScotlandAberdeenAB243FXUK

In a cloud manufacturing environment with abundant functionally equivalent cloud services,users naturally desire the highest-quality service(s).Thus,a comprehensive measurement of quality of service(QoS)is ***-mizing the plethora of cloud services has thus become a top *** ser-vice optimization is negatively affected by untrusted QoS data,which are inevitably provided by some *** resolve these problems,this paper proposes a QoS-aware cloud service optimization model and establishes QoS-information awareness and quantiﬁcation *** data are assessed by an information correction *** weights discovered by the variable precision Rough Set,which mined the evaluation indicators from historical data,providing a comprehensive performance ranking of service *** manufacturing cloud service optimization algorithm thus provides a quantitative reference for service *** experimental simulations,this method recommended the optimal services that met users’needs,and effectively reduced the impact of dis-honest users on the selection results.

关键词： Cloud manufacturing quality of service optimization algorithm rough set

来源：评论

学校读者我要写书评

暂无评论

Pattern-Based Trading by Continual Learning of Price and Volume Patterns 37th

Pattern-Based Trading by Continual Learning of Price and ...

引用

37th Australasian Joint Conference on Artificial Intelligence, AJCAI 2024

作者： Liston, Patrick Gretton, Charles Lensky, Artem The Australian National University College of Engineering Computing and Cybernetics Canberra Australia School of Engineering and Technology The University of New South Wales CanberraACT Australia School of Biomedical Engineering Faculty of Engineering The University of Sydney SydneyNSW Australia

ISBN: (纸本)9789819603473

Automating trading decisions has been a pursuit of researchers and practitioners alike for decades. We contribute to the literature focusing on "pattern based" strategies. Dynamic time warping is used to group similar patterns into a representative category, while the method of continual learning augmentation is used to maintain the set of patterns used for decision-making. Thus, we implement a novel approach to pattern-based trading, utilising adaptive memory structures to enable adaptability of agent decision making and overall agent performance. Two new online pattern-based trading agents are introduced and tested on two-sets of historical cryptocurrency data, for the BTCUSDT pair over the periods of 2017–2023 and 2023–2024. We compare our newly formulated agents against an established baseline of rule-based agents, thereby comparing the relative profit generating abilities of a wide range of agents. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Marketplaces

来源：评论

学校读者我要写书评

暂无评论

Improving Quality of Service Using a Innovative Energy Effective Reinforcement Based Path Finding in Mobile Ad Hoc Network 2

Improving Quality of Service Using a Innovative Energy Effec...

引用

2nd International Conference on computing and Data Analytics, ICCDA 2024

作者： Singaravelan, M. Saleem Raja, A. Thangam, S. School of Computing Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Avadi Chennai India College of Computing and information Sciences University of Technology and Applied Sciences Shinas Oman Amrita school of computing Department of computer science and engineering Bengaluru India

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542573

RL stands for reinforcement learning, is a component of the broader field of machine learning. It provides a framework its features help to select an efficient action using previously learned information of interaction and its environment. Mobile nodes are grouped to form a network for communicating themselves termed autonomous network with mobile nodes (MANET). This is a dynamic and independent network requiring no administrative control. The mobile nodes in the network are to be part or apart at any time that causes the topology changes. The frequent topology changes make it hard to determine the destination route. The route discovery in the dynamic topology that consumes more node energy and, minimize the network lifetime. The energy usage and network lifetime are a vital part of the MANET. This paper proposes a new global routing protocol using a reinforcement learning algorithm called modified energy and signal strength Q-learning ad hoc on-demand distance vector algorithm (ESQAODV). This process conducts a result comparison to a local routing algorithm known as AODVSARSA. The proposed algorithm proved a better result with reference to average distribution ratio compared to the other, and complete delay metrics © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

A Novel Predictive Analysis and Classification of Land Subsidence Vulnerability Mapping based on GIS using Hybrid Optimized Machine Learning Techniques and Computer Vision 5

A Novel Predictive Analysis and Classification of Land Subsi...

引用

5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Senthil, G.A. Prabha, R. Arun, M. Sahaya Anselin Nisha, A. Prabu, S. Department of Information Technology Agni College of Technology Chennai India Department of Electronics and Communication Engineering Sri Sairam Institute of Technology Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

Land subsidence is a natural disaster caused by the extraction of the earth's material strength on the surface that causes the earth's surface to settle and sink. Land subsidence can also result in significant financial losses since it causes structural damage and expensive maintenance costs for roads, trains, barriers, pipelines, ground water, oil, natural gas, mines, and buildings. The Geographic Information System (GIS) and Google Earth Platform (GEP) can be used to map this land subsidence. This provides a comprehensive view of land pattern subsidence and its interactions with other geographical elements. It applies to both progressive subsidence caused by fluid extraction (groundwater, oil, or natural gas) and rapid surface subsidence caused by the collapse of an underground mine. Risk mitigation is the most effective way to deal with the experimental model. This research study focuses on the use of Predictive Data Analysis (PDA), a powerful environment in data analytics that provides a set of tools that make it easier to evaluate optimal models. The proposed model makes use of a dataset that includes specific land subsidence circumstances as input. The prediction result is achieved using a hybrid machine learning technique that combines Stochastic Gradient Descent (SGD) with Long Short-Term Memory (LSTM) and is compared to K-Nearest Neighbor (KNN), Generative Adversarial Network (GAN), and Artificial Neural Network (ANN). The proposed system employs LSTM and SGD as optimizers, resulting in a hybrid model that improves accuracy. Among the four algorithms investigated the LSTM-SGD network has the highest accuracy (97%), followed by KNN (96%), GAN (90%), and ANN (89%). Due to their ability to capture complicated nonlinear correlations in data and manage temporal dependencies, optimized hybrid ML models LSTM-SGD and KNN models appear to be particularly well-suited for land subsidence prediction. © 2024 The Authors. Published by Elsevier B.V.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

A Survey on Evolutionary Computation Based Drug Discovery

引用

IEEE Transactions on Evolutionary Computation 2024年第3期29卷 1-1页

作者： Yu, Qiyuan Lin, Qiuzhen Ji, Junkai Zhou, Wei He, Shan Zhu, Zexuan Tan, Kay Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science University of Birmingham Bir-mingham UK Department of Computing The Hong Kong Poly-technic University Hong Kong Hong Kong

Drug discovery is an expensive and risky process. To combat the challenges in drug discovery, an increasing number of researchers and pharmaceutical companies recognize the benefits of utilizing computational techniques. Evolutionary computation (EC) offers promise as most drug discovery problems are essentially complex optimization problems beyond conventional optimization algorithms. EC methods have been widely applied to solve these complex optimization problems especially in lead com-pound generation and molecular virtual evaluation, substantially speeding up the process of drug discovery and development. This article presents a comprehensive survey of EC-based drug discovery methods. Particularly, a new taxonomy of the methods is provided and the advantages and limitations of the methods are reviewed. In addition, the potential future directions of EC-based drug discovery are discussed and the publicly available resources including databases and computational tools are compiled for the convenience of researchers seeking to pursue this field. IEEE

关键词： Lead compounds

来源：评论

学校读者我要写书评

暂无评论

Integrated Approach to Detect Cyberbullying Text:Mobile Device Forensics Data

引用

Computer Systems Science & engineering 2022年第3期40卷 963-978页

作者： G.Maria Jones S.Godfrey Winster P.Valarmathie Department of Computer Science and Engineering Saveetha Engineering CollegeChennai602105India Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyChengalpattu603203India Department of Information Technology Saveetha Engineering CollegeChennai602105India

Mobile devices and social networks provide communication opportunities among the young generation,which increases vulnerability and cybercrimes activities.A recent survey reports that cyberbullying and cyberstalking constitute a developing issue among *** paper focuses on cyberbullying detection in mobile phone text by retrieving with the help of an oxygen forensics *** describe the data collection using forensics technique and a corpus of suspicious activities like cyberbullying annotation from mobile phones and carry out a sequence of binary classification experiments to determine cyberbullying *** use forensics techniques,Machine Learning(ML),and Deep Learning(DL)algorithms to exploit suspicious patterns to help the forensics investigation where every evidence contributes to the *** on a real-time dataset reveal better results for the detection of cyberbullying *** Random Forest in ML approach produces 87%of accuracy without SMOTE technique,whereas the value of F1Score produces a good result with SMOTE *** LSTM has 92%of validation accuracy in the DL algorithm compared with Dense and BiLSTM algorithms.

关键词： Mobile forensics cyberbullying machine learning investigation model suspicious pattern

来源：评论

学校读者我要写书评

暂无评论

Weather Data Analysis and Visualization Using PySpark and PowerBI

Weather Data Analysis and Visualization Using PySpark and Po...

引用

2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Dash, Bivanshu Khatri, Garv Sinha, Abhijat Gouthaman, P. School of Computing College of Engineering & Technology Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India

ISBN: (纸本)9798331521394

Weather analysis data comprises a lot of information to understand atmospheric conditions. The following document provides a 10-year summary of local metric weather data analysis. The main activities focused on discovering different patterns of weather data related to temperature, rainfall, wind velocity, and humidity apart from extreme heat, air quality and effects on health issues due to diseases transmitted by vectors for protection. Several data analysis techniques were put into application on the part of the research team, broadly categorized into summary statistics, time series analyses, and classification algorithms. Results are as follows: A clear trend of increasing average temperature has emerged over the course of the study. Seasonal fluctuations have therefore caused varying levels of precipitation, resulting in rainfall occurring more frequently during some periods. Lastly, unique weather clusters have been identified in the isolated corridors around the regional weather. This information impacts other areas such as agriculture, road construction, and adaptation to climate. Results of microclimate assessment can also be used in public awareness programs and inform at-risk youth in early warning systems for weather-related health risks. The thunderstorm that produces the wind is driven by changed precipitation, air stability, and radiant energy, though winds are important. We then discussed the location and number of wind energy plants to be placed. © 2025 IEEE.

关键词： Rain

来源：评论

学校读者我要写书评

暂无评论

Multi-layer network embedding on scc-based network with motif

引用

Digital Communications and Networks 2024年第3期10卷 546-556页

作者： Lu Sun Xiaona Li Mingyue Zhang Liangtian Wan Yun Lin Xianpeng Wang Gang Xu Department of Communication Engineering Institute of Information Science TechnologyDalian Maritime UniversityDalian116026China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province DUT School of Software Technology&DUT-RU International School of Information Science and EngineeringDalian University of TechnologyDalian 116620China College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China School of Information and Communication Engineering Hainan UniversityHaikou 570228China State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and computing(SCC)will become new *** is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based *** previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node ***,the content of semantic information is quite *** graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of ***,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology *** Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node *** verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

关键词： Semantic communication and computing Multi-layer network Graph neural network Motif

来源：评论

学校读者我要写书评

暂无评论

An Explorative Study on Extractive Text Summarization through k-means, LSA, and TextRank

An Explorative Study on Extractive Text Summarization throug...

引用

2023 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2023

作者： Ramani, Kasarapu Bhavana, K. Akshaya, A. Harshita, K. Sai Thoran Kumar, C.R. Srikanth, M. School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Tirupati India School of Computing Mohan Babu University Tirupati India Sree Vidyanikethan Engineering College Department of Information Technology Tirupati India

ISBN: (纸本)9798350300451

Notably the difficult and exciting issue in the field of Natural Language Processing (NLP) is summarizing the text. Understanding the main objective of any type of document is crucial. Some of the applications of text summarization are media monitoring, social media, marketing, health care, literature, and books. Text summarization techniques are implemented using extractive summarization techniques in the health care domain in which it considers patient health history. To visualize a lengthy patient health history document quickly we use machine learning techniques like k-means, Text Rank, and Latent Semantic Analysis to comprehend and identify the sections that communicate important information to produce the summarized texts. These methods are evaluated using ROUGE-1, ROUGE-2, and ROUGE-N metrics to obtain the highest similarity of extracted text. k-means outperformed the considered approaches compared to Text Rank and Latent Semantic Analysis in summarizing the documents. k-Means was more efficient, where it achieved an average of 94.52% precision, 90.98% recall, and 91.25% F1-score. © 2023 IEEE.

关键词： Health care

来源：评论

学校读者我要写书评

暂无评论

An Improved Convolutional Networks Model for Carbon Emission Prediction in Power Systems

An Improved Convolutional Networks Model for Carbon Emission...

引用

2024 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2024

作者： Cai, Dongsheng Acen, Caroline Ukwuoma, Chiagoziem C. Zhang, Nan Liu, Wei Zhang, Zhenyuan Huang, Qi College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China School of Computing and Cyber Security Chengdu University of Technology Chengdu China School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China School of Information Engineering Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9798350352290

In response to the imperative need for carbon reduction, tracking the carbon footprint of power systems has emerged as a critical concern. Traditional methods have struggled to accurately capture the nuances of carbon emissions, necessitating a fresh approach. To address this gap, the study introduces a pioneering deep learning-based model. We contend that the rapid advancement of deep learning offers a solution by harnessing neural network models, particularly the temporal convolutional networks (TCN) and gated recurrent units (GRU). These models are adept at handling the intricate patterns and dependencies inherent in carbon emission data, which exhibit both short-term fluctuations and long-term trends. Sequel to that, we propose a novel TCN-GRU-ATT neural network model designed explicitly for carbon footprint data. This innovative model combines TCN, GRU, and attention mechanisms to comprehensively capture both short-term variations and long-term dependencies within carbon emission patterns. Using power generation data from a gas-fired unit within the power grid as a foundation, the model calculates carbon emissions across various time intervals. Results demonstrate the model's exceptional performance, with a low Mean Absolute Percentage Error (MAPE) of 1.7123%, a Root Mean Square Error (RMSE) of 8.8436 kg/CO2, and a high Coefficient of Determination R2 of 0.9802. © 2024 IEEE.

关键词： Low emission

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：