检索结果-内蒙古大学图书馆

Intelligent Slime Mould Optimization with Deep Learning Enabled Traffic Prediction in Smart Cities

computers, Materials & Continua 2022年第12期73卷 6563-6577页

作者： Manar Ahmed Hamza Hadeel Alsolai Jaber S.Alzahrani Mohammad Alamgeer Mohamed Mahmoud Sayed Abu Sarwar Zamani Ishfaq Yaseen Abdelwahed Motwakel Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science and Bioinformatics Singhania UniversityPacheri BariDistrict JhnujhunuRajasthanIndia Department of Architectural Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt

Intelligent Transportation System(ITS)is one of the revolutionary technologies in smart cities that helps in reducing traffic congestion and enhancing traffic *** the help of big data and communication technologies,ITS offers real-time investigation and highly-effective traffic *** Flow Prediction(TFP)is a vital element in smart city management and is used to forecast the upcoming traffic conditions on transportation network based on past *** Network(NN)and Machine Learning(ML)models are widely utilized in resolving real-time issues since these methods are capable of dealing with adaptive data over a period of *** Learning(DL)is a kind of ML technique which yields effective performance on data classification and prediction *** this motivation,the current study introduces a novel Slime Mould Optimization(SMO)model with Bidirectional Gated Recurrent Unit(BiGRU)model for Traffic Prediction(SMOBGRU-TP)in smart ***,data preprocessing is performed to normalize the input data in the range of[0,1]using minmax normalization ***,BiGRUmodel is employed for effective forecasting of traffic in smart ***,the novelty of the work lies in using SMO algorithm to effectively adjust the hyperparameters of BiGRU *** proposed SMOBGRU-TP model was experimentally validated and the simulation results established the model’s superior performance in terms of prediction compared to existing techniques.

关键词： Smart cities traffic flow prediction slime mould optimization algorithm deep learning intelligent models

来源：评论

学校读者我要写书评

暂无评论

Comparative Evaluation of Machine Learning-Based Controller Area Network Intrusion Detection systems 14

Comparative Evaluation of Machine Learning-Based Controller ...

引用

14th International Conference on information and Communication systems, ICICS 2023

作者： Al-Jarrah, Omar Y. Ramadan, Omar A. Alsaify, Baha Alkhushayni, Suboh Jordan University of Science and Technology Faculty of Computer and Information Technology Dept. Network Engineering and Security P.O. Box 3030 Irbid22110 Jordan Jordan University of Science and Technology Faculty of Engineering Dept. Mechanical Engineering P.O. Box 3030 Irbid22110 Jordan Yarmouk University Dept. Information Systems Irbid Jordan

ISBN: (纸本)9798350307870

The controller area network (CAN) protocol is widely used in vehicle networks. However, it lacks essential security features like confidentiality and authentication. To enhance vehicle security, researchers have proposed and developed various methods, including intrusion detection systems (IDSs). These IDSs play a key role in early detection of potential cyberattacks, safeguarding vehicles, passengers, and road users. Machine learning (ML) algorithms are increasingly employed in CAN IDSs but they face challenges due to diverse experimental settings, datasets, and metrics used for evaluation, impeding progress and comparison of different approaches in this field. This study aims to benchmark well-recognized ML algorithms, including gaussianNB, k-nearest neighbors (KNN), decision trees (DT), random forest (RF), long short-term memory (LSTM), and convolutional neural networks (CNN), for intrusion detection in CAN. We evaluate the performance of these algorithms using seven evaluation metrics on the real ORNL automotive dynamometer (ROAD) dataset, a state-of-the-art benchmark in this field. Experimental results demonstrate that KNN, DT, RF, LSTM, and CNN can detect various types of attacks (e.g., Fuzzing, targeted ID, and masquerade attacks) with high accuracy. However, the computational and time efficiency of tree-based classifiers, namely DT and RF, makes them a potentially appealing choice for real-time intrusion detection tasks in CAN. © 2023 IEEE.

关键词： Intrusion detection

来源：评论

学校读者我要写书评

暂无评论

Accurate Prediction of Workloads and Resources with Multi-Head Attention and Hybrid LSTM for Cloud Data Centers

Accurate Prediction of Workloads and Resources with Multi-He...

引用

作者： Bi, Jing Ma, Haisen Yuan, Haitao Zhang, Jia Beijing University of Technology School of Software Engineering Faculty of Information Technology Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Southern Methodist University Department of Computer Science Lyle School of Engineering DallasTX75205 United States

Currently, cloud computing service providers face big challenges in predicting large-scale workload and resource usage time series. Due to the difficulty in capturing nonlinear features, traditional forecasting methods usually fail to achieve high prediction performance for resource usage and workload sequences. Besides, there is much noise in original time series of resources and workloads. If these time series are not de-noised by smoothing algorithms, the prediction results can fail to meet the providers' requirements. To do so, this work proposes a hybrid prediction model named VAMBiG that integrates Variational mode decomposition, an Adaptive Savitzky-Golay (SG) filter, a Multi-head attention mechanism, Bidirectional and Grid versions of Long and Short Term Memory (LSTM) networks. VAMBiG adopts a signal decomposition method named variational mode decomposition to decompose complex and non-linear original time series into low-frequency intrinsic mode functions. Then, it adopts an adaptive SG filter as a data pre-processing tool to eliminate noise and extreme points in such functions. Afterwards, it adopts bidirectional and grid LSTM networks to capture bidirectional features and dimension ones, respectively. Finally, it adopts a multi-head attention mechanism to explore importance of different data dimensions. VAMBiG aims to predict resource usage and workloads in highly variable traces in clouds. Extensive experimental results demonstrate that it achieves higher-Accuracy prediction than several advanced prediction approaches with datasets from Google and Alibaba cluster traces. © 2016 IEEE.

关键词： Time series analysis

来源：评论

学校读者我要写书评

暂无评论

Search and Rescue Optimization with Machine Learning Enabled Cybersecurity Model

引用

computer systems Science & engineering 2023年第5期45卷 1393-1407页

作者： Hanan Abdullah Mengash Jaber S.Alzahrani Majdy M.Eltahir Fahd N.Al-Wesabi Abdullah Mohamed Manar Ahmed Hamza Radwa Marzouk Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Research Centre Future University in EgyptNew Cairo11745Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt

Presently,smart cities play a vital role to enhance the quality of living among human beings in several ways such as online shopping,e-learning,ehealthcare,*** the benefits of advanced technologies,issues are also existed from the transformation of the physical word into digital word,particularly in online social networks(OSN).Cyberbullying(CB)is a major problem in OSN which needs to be addressed by the use of automated natural language processing(NLP)and machine learning(ML)*** article devises a novel search and rescue optimization with machine learning enabled cybersecurity model for online social networks,named SRO-MLCOSN *** presented SRO-MLCOSN model focuses on the identification of CB that occurred in social networking *** SRO-MLCOSN model initially employs Glove technique for word embedding ***,a multiclass-weighted kernel extreme learning machine(M-WKELM)model is utilized for effectual identification and categorization of ***,Search and Rescue Optimization(SRO)algorithm is exploited to fine tune the parameters involved in the M-WKELM *** experimental validation of the SRO-MLCOSN model on the benchmark dataset reported significant outcomes over the other approaches with precision,recall,and F1-score of 96.24%,98.71%,and 97.46%respectively.

关键词： Cybersecurity cyberbullying social networking machine learning search and rescue optimization

来源：评论

学校读者我要写书评

暂无评论

OPTIMAL SCHEDULE OF A SPORT SHOOTING COMPETITION 17

OPTIMAL SCHEDULE OF A SPORT SHOOTING COMPETITION

引用

17th International Symposium on Operational Research in Slovenia, SOR 2023

作者： Ercsey, Zsolt Kovács, Zoltán Storcz, Tamás Department of Systems and Software Technologies Faculty of Engineering and Information Technology University of Pécs Boszorkány Street 2 H-7624 Hungary Optin Ltd. Oroszlán u. 4 SzegedH-6720 Hungary

ISBN: (纸本)9789616165617

The paper explains the problem of amateur sport shooting competition scheduling with a manual and an automated solution. The automated solution provides an optimal schedule to minimize the infrastructure and human resource occupation. The proposed programming model also minimizes the attendance of participating sport clubs, because considers that the amateur competitors want to spend minimum time necessary (with required overhead) for the competition and the team of a sport club usually travel together. © Proceedings of the 17th International Symposium on Operational Research in Slovenia, SOR 2023. All rights reserved.

关键词： Sports

来源：评论

学校读者我要写书评

暂无评论

A Robust Methodology for Fruit Quality Prediction and Estimation Using Intelligent Learning Based Image Processing Logic

A Robust Methodology for Fruit Quality Prediction and Estima...

引用

2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Malathi, K. Babu, Vunnava Dinesh Ramkumar, M. Ayyadurai, M. Vivek, K. Aljawawdeh, Hamzeh J. Saveetha University Department of Cse Chennai India Rv Institute of Technology Department of Cse Guntur India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Department of software engineering Faculty of information technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Fruit quality prediction plays a vital role in agricultural and food industries, ensuring that only high-quality produce reaches the consumer. Traditional methods of assessing fruit quality rely on manual inspection, which is both time-consuming and prone to human error. This paper presents a robust methodology for fruit quality prediction and estimation, utilizing a hybrid deep learning model that integrates DenseN et for feature extraction with LightGBM for efficient classification. The proposed model is designed to automate the process of fruit quality assessment by analyzing key attributes such as color, texture, and shape from high-resolution fruit images. The Fruit360 dataset was used for training and testing, with images classified into categories such as fresh, ripe, overripe, and damaged. A comprehensive comparison with nine existing models, including SVM, Random Forest, and XGBoost, was conducted using performance metrics such as accuracy, precision, recall, and Fl-score. The proposed model achieved an accuracy of 96.78%, significantly outperforming all other models. Furthermore, the model's precision and recall reached 95.33% and 95.67%, respectively, demonstrating its reliability in distinguishing high-quality fruits from low-quality ones. © 2024 IEEE.

关键词： Fruits

来源：评论

学校读者我要写书评

暂无评论

Patterns of Remote Work Environments 27

Patterns of Remote Work Environments

引用

2022 27th European Conference on Pattern Languages of Programs, EuroPLoP 2022

作者： Toldy, Kristian Sulaiman Khail, Waheedullah Vranic, Valentino Institute of Informatics Faculty of Informatics and Information Technologies Information Systems and Software Engineering Slovak University of Technology in Bratislava Bratislava Slovakia Helmholtz-Zentrum Berlin fur Materialien und Energie Gmbh Berlin Germany

ISBN: (纸本)9781450395946

People are more and more in a position to work remotely. They face the same problems of establishing and running remote work environments over and over again. In order to help them a bit, six organizational patterns of remote work environments are presented in this paper: Unofficial Meetings, Remote Mentor, Unified Communication Channels, Standard Operating Procedures, information Hub, and Cut Meetings. The patterns come from a firsthand remote work experience obtained in a company focusing on quality assurance through a crowd testing methodology where software companies get real users to test their newest products. © 2022 ACM.

关键词： Quality assurance

来源：评论

学校读者我要写书评

暂无评论

Estimating the severity of knee osteoarthritis using Deep Convolutional Neural Network based on Contrast Limited Adaptive Histogram Equalization technique

Estimating the severity of knee osteoarthritis using Deep Co...

引用

2022 International Conference on engineering and MIS, ICEMIS 2022

作者： Abdo, Amina A. El-Tarhouni, Wafa Abdulsalam, Asma Fathi Altajori, Abdelgader Bubaker College of Computer Technology-Benghazi Department of Artificial Intelligence Benghazi Libya University of Benghazi Faculty of Information Technology Benghazi Libya College of Computer Technology-Benghazi Department of Software Engineering Benghazi Libya

ISBN: (数字)9781665454360

ISBN: (纸本)9781665454360

Osteoarthritis is a degenerative joint disease that affects larger joints, including the knee, foot, hip, and spine by infecting the cartilage, which causes bones to rub against each other in extreme pain. Knee osteoarthritis (KOA) manual inspections demand a skilled physician to examine the x-ray image. In this paper, a method for detecting the severity of knee osteoarthritis using a Deep Convolutional Neural Network (3D CNN) is proposed in order to benefit physicians by examining the X-ray images. Here, the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique is used to enhance contrast in images, and this strengthens the current approach. This study presents two models: one for identifying and categorizing normal (KL 0,1) and osteoarthritic results (KL 2, 3, 4). The second model is to assist in distinguishing between severe grades (KL 3, 4) and non-severe grades (KL 2) or normal findings (KL 0,1). For examination, X-ray images from the Knee Osteoarthritis severity grading dataset and the Osteoarthritis Initiative (OAI) dataset are used. Experimental results have demonstrated that the proposed technique achieves accuracy rate of 85.50%, outperforming a number of existing approaches. © 2022 IEEE.

关键词： Grading

来源：评论

学校读者我要写书评

暂无评论

Gymnasium Library Interface for Multi Access Edge Computing

Gymnasium Library Interface for Multi Access Edge Computing

引用

International Conference on Control systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Ivan Filianin Alexander Kapitonov Artem Timoshchuk-Bondar Faculty of Software Engineering and Computer Systems ITMO University St. Petersburg Russia Faculty of Infocommunication Technologies ITMO University St. Petersburg Russia

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

This paper addresses the problem of developing reinforcement learning algorithms for capsule placement decision making tasks in a geo-distributed Multi Access Edge Computing environment. A Python library called gym-Iwmecps has been developed to speed up and simplify the process of developing machine learning algorithms with reinforcement learning for decision-making system tasks in MEC networks. At the same time, the Gymnasium Capability API has not been used previously in the tasks of organizing and testing MEC platforms. We also tested basic algorithms Q-network and DQN in conjunction with gym-lwmecps and proved the performance of the developed Python library.

关键词： Training Machine learning algorithms Multi-access edge computing Computational modeling Decision making Process control Reinforcement learning Libraries Mathematical models Testing

来源：评论

学校读者我要写书评

暂无评论

Deep Reinforcement Learning for Addressing Disruptions in Traffic Light Control

引用

computers, Materials & Continua 2022年第5期71卷 2225-2247页

作者： Faizan Rasheed Kok-Lim Alvin Yau Rafidah Md Noor Yung-Wey Chong School of Engineering and Computer Science University of HertfordshireHatfieldAL109ABUK Department of Computing and Information Systems Sunway UniversitySubang Jaya47500Malaysia Faculty of Computer Science and Information Technology University of MalayaKuala Lumpur50603Malaysia National Advanced IPv6 Centre Universiti Sains MalaysiaUSMPenang11800Malaysia

This paper investigates the use of multi-agent deep Q-network(MADQN)to address the curse of dimensionality issue occurred in the traditional multi-agent reinforcement learning(MARL)*** proposed MADQN is applied to traffic light controllers at multiple intersections with busy traffic and traffic disruptions,particularly *** is based on deep Q-network(DQN),which is an integration of the traditional reinforcement learning(RL)and the newly emerging deep learning(DL)*** enables traffic light controllers to learn,exchange knowledge with neighboring agents,and select optimal joint actions in a collaborative manner.A case study based on a real traffic network is conducted as part of a sustainable urban city project in the Sunway City of Kuala Lumpur in *** is also performed using a grid traffic network(GTN)to understand that the proposed scheme is effective in a traditional traffic *** proposed scheme is evaluated using two simulation tools,namely Matlab and Simulation of Urban Mobility(SUMO).Our proposed scheme has shown that the cumulative delay of vehicles can be reduced by up to 30%in the simulations.

关键词： Artificial intelligence traffic light control traffic disruptions multi-agent deep Q-network deep reinforcement learning

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：