检索结果-内蒙古大学图书馆

3rd IEEE KhPI Week on Advanced Technology, KhPI Week 2022

作者： Gavrylenko, Svitlana Hornostal, Oleksii Chelak, Viktor National Technical University 'Kharkiv Polytechnic Institute' Department of Computer Engineering and Programming Kharkiv Ukraine

ISBN: (数字)9798350399202

ISBN: (纸本)9798350399202

Peculiarities of constructing ensemble bagging classifiers for identifying the state of a computer system under conditions of noisy data are studied. Decision trees and multilayer perceptron were used as basic classifiers. It was found that the accuracy of the bagging algorithm with decision trees as basic classifiers with standard settings ranges from 84.4% to 88.7%. The use of Bootstrap algorithms for the formation of data samples: Pasting, Bootstrapping, Random Subspace, Random Patches Ensemble and the selection of the number of basic classifiers in the ensemble made it possible to increase the classification accuracy to 90.2%. The following parameters were added to improve the accuracy of bagging classifiers based on the multilayer perceptron: the algorithm for forming data samples, the number of basic classifiers in the ensemble, the function of optimizing the neural network, the function of activating hidden layer, size of hidden layers. The recommendation was made to choose the value of the analyzed parameters for the creation of bagging ensembles with multilayer perceptrons, which made it possible to increase the accuracy of computer system identification up to 92.2%. The obtained results have further practical significance and can be used in improving the methods of identifying the state of computer systems. © 2022 IEEE.

关键词： Multilayer neural networks

来源：评论

学校读者我要写书评

暂无评论

METHOD OF DETECTION OF INTRUSIONS BASED ON STACKING META-ALGORITHM 5

METHOD OF DETECTION OF INTRUSIONS BASED ON STACKING META-ALG...

引用

5th IEEE KhPI Week on Advanced Technology, KhPIWeek 2024

作者： Gavrylenko, Svitlana Zozulia, Vladislav Khatsko, Nataliia Department of Computer Engineering and Programming National Technical University "Kharkiv Polytechnic Institute" Kharkiv Ukraine Department Information Systems and Technologies National Technical University «Kharkiv Polytechnic Institute» Kharkiv Ukraine

ISBN: (纸本)9798350384499

An analysis of modern computer network intrusion detection systems was carried out. The application of machine and deep learning methods for classification problems has been investigated. The UNSW-NB15 dataset, developed at the Australian Cyber Security Center's (ACCS) Cyber Range Laboratory, contains data on normal network operations and synthetic intrusions. Data pre-processing was performed, including class balancing using the SMOTEENN method and selection of informative features using the Recursive Feature Elimination method. The possibility of using the stacking meta-algorithm to detect intrusions into computer networks has been investigated. A new algorithm for generating packets of raw data is proposed, which generates two sets of training data: one for training basic models, the other for a meta-model. A study of the effectiveness of using Random Forest, ANN, K Nearest Neighbor methods and Support Vector Machine and Random Forest as a decision-making meta-model was conducted. The use of the stacking meta-algorithm with the proposed algorithm for forming packets of output data, as well as basic models and a meta-model, led to a significant improvement in the quality of the model. It was found that, on average, recall and f1 score increased by 55.6% and 37.4%, respectively, compared to raw data and other models. ©2024 IEEE.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

New approach to predict warranty costs using a new bivariate reliability prediction model 32nd

New approach to predict warranty costs using a new bivariate...

引用

32nd European Safety and Reliability Conference, ESREL 2022

作者： Krini, Abderrahim Börcsök, Josef Engineering of Reliability and Quality Robert Bosch GmbH Germany Computer and System Programming University of Kassel Germany

ISBN: (纸本)9789811851834

An important marketing strategy of automotive manufacturers is to promote high-quality products by extending warranty periods, which inevitably generates additional costs. In general, different loads on a control unit cause a failure. Therefore, a failure can be traced back to the loads experienced. In reliability theory, the operating time t is commonly used as a reference value for describing the load acting on a system, since the electronic control units (ECUs) are only under current and voltage for the duration t and are therefore only loaded during this time. However, obtaining a suitable database is difficult. The failure data of ECUs collected during the warranty period from the supplier in the event of complaints can be regarded as complete and therefore represent a suitable database for determining reliability parameters. The warranty data are time-censored and reflect the field behaviour of the ECUs during the warranty period. As a rule, when collecting the data, the duration (production and complaint date) of the ECUs in the field is recorded in addition to the cause of failure. With a new multivariate prediction model that uses this data to forecast the expected ECU failures and knowledge of the costs of a warranty case, the expected additional costs of a warranty extension can be calculated. © 2022 ESREL2022 Organizers. Published by Research Publishing, Singapore.

关键词： Automotive industry

来源：评论

学校读者我要写书评

暂无评论

Design of a Novel Safe Approach to Monitor Drivers While Operating Their Cars 7

Design of a Novel Safe Approach to Monitor Drivers While Ope...

引用

7th International Conference on Advances in Biomedical engineering, ICABME 2023

作者： Elhawawsha, Aseel Zeine, Carla Zeid Daou, Roy Abi Kalinowski, Dominik Khattar, Joseph Telawi, Samer Boercsoek, Josef University of Kassel Department of Computer Architecture and System Programming Kassel Germany Université la Sagesse Saculty of Engineering Mechatronics Eng. Department Beirut Lebanon

ISBN: (纸本)9798350325850

Road accidents have been known to be one of the leading death causes around the world for a long time. Thus, cars and all kinds of road vehicles form a huge source of danger, and they relate to multiple high risks. There are many factors that can contribute to road traffic accidents such as the health condition of the driver if a medical emergency event happened while driving, drowsiness, or mind-wandering. The proposed system can monitor the behavior of the driver while driving as well as his/her health status. Moreover, it analyzes the monitored data, alerts the driver in a timely manner, and assists him/her in adjusting the driving behavior. Different dangerous and risky situations which can be faced during driving can be captured and avoided by using such a system which leads at the end to increase road traffic safety and reduce vehicle collisions. Notifications are developed in cases of drowsiness, loss of focus, or other concerns that may emerge while driving. However, when the driver's ability to operate the vehicle safely is hampered by a health concern, the implemented system is only able at this stage to alarm the driver itself and the surrounding cars as a first level of alarms. © 2023 IEEE.

关键词： Roads and streets

来源：评论

学校读者我要写书评

暂无评论

Analysis of methods of building and researching of fuzzy cognitive models 5

Analysis of methods of building and researching of fuzzy cog...

引用

5th IEEE KhPI Week on Advanced Technology, KhPIWeek 2024

作者： Adamenko, Anatolii Zakovorotnyi, Oleksandr Kuchuk, Nina Scientific center of the Air Force Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine Department of Computer Engineering and Programming National Technical University "Kharkiv Polytechnic Institute" Kharkiv Ukraine

ISBN: (纸本)9798350384499

the report presents the results of a comparative analysis of methods for parameterization of cognitive models based on the use of fuzzy set theory and antonym logic. Reasoned expediency of using methods of the logic of antonyms in the construction and research of cognitive models of weakly structured situations. An approach to calculating the magnitudes of the indirect and total influence of factors on each other in certain types of cognitive models built based on the logic of antonyms is proposed. ©2024 IEEE.

关键词： Fuzzy models

来源：评论

学校读者我要写书评

暂无评论

Al-Biruni Earth Radius Optimization for COVID-19 Forecasting

引用

computer Systems Science & engineering 2023年第7期46卷 883-896页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Tatiana Makarovskikh Amal H.Alharbi Doaa Sami Khafaga Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of System Programming South Ural State University454080ChelyabinskRussia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Several instances of pneumonia with no clear etiology were recorded in Wuhan,China,on December 31,*** world health organization(WHO)called it COVID-19 that stands for“Coronavirus Disease 2019,”which is the second version of the previously known severe acute respiratory syndrome(SARS)Coronavirus and identified in short as(SARSCoV-2).There have been regular restrictions to avoid the infection spread in all countries,including Saudi *** prediction of new cases of infections is crucial for authorities to get ready for early handling of the virus ***:Analysis and forecasting of epidemic patterns in new SARSCoV-2 positive patients are presented in this research using metaheuristic optimization and long short-term memory(LSTM).The optimization method employed for optimizing the parameters of LSTM is Al-Biruni Earth Radius(BER)***:To evaluate the effectiveness of the proposed methodology,a dataset is collected based on the recorded cases in Saudi Arabia between March 7^(th),2020 and July 13^(th),*** addition,six regression models were included in the conducted experiments to show the effectiveness and superiority of the proposed *** achieved results show that the proposed approach could reduce the mean square error(MSE),mean absolute error(MAE),and R^(2)by 5.92%,3.66%,and 39.44%,respectively,when compared with the six base *** the other hand,a statistical analysis is performed to measure the significance of the proposed ***:The achieved results confirm the effectiveness,superiority,and significance of the proposed approach in predicting the infection cases of COVID-19.

关键词： COVID-19 prediction meta-heuristic optimization LSTM Al-Biruni earth radius algorithm

来源：评论

学校读者我要写书评

暂无评论

AbotalebNet: A Novel Deep Learning Architecture for Time Series Forecasting with Convolutional and LSTM Layers Enhanced by Multi-Head Attention 2

AbotalebNet: A Novel Deep Learning Architecture for Time Ser...

引用

2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Abotaleb, Mostafa Makarovskikh, Tatiana Mijwil, Maad M. Bala, Indu Hiran, Kamal Kant Doshi, Ruchi South Ural State University Dep. of System Programming Chelyabinsk454080 Russia Baghdad College of Economic Sciences University Computer Techniques Engineering Department Baghdad Iraq Lovely Professional University Electrical and Electronics Engineering Punjab India Sir Padampat Singhania University Dep. of Computer Science Udaipur India Universidad Azteca Dep. of Computer Science and Engineering Chalco Mexico

ISBN: (纸本)9798350384277

This paper introduces AbotalebNet, a novel deep learning architecture optimized for time series forecasting, with a particular focus on the complexities of COVID-19 data. AbotalebNet's architecture is mathematically represented as:\begin{equation*}\hat Y = {\text{Attention}}(\operatorname{LSTM} ({\text{CNN}}(X)))\tag{1}\end{equation*}Here, X represents the input time series data, and Ŷ is the forecasted output. AbotalebNet integrates the feature extraction capabilities of Convolutional Neural Networks (CNNs) with the sequential data processing power of Long Short-Term Memory (LSTM) networks, further enhanced by a Multi-Head Attention *** address overfitting, a key challenge in deep learning, the model incorporates regularization strategies, dropout mechanisms, and batch normalization:\begin{equation*}\mathop {\min }\limits_\Theta ( Loss (X,\hat Y;\Theta ) + \lambda R(\Theta ))\tag{2}\end{equation*}In this equation, Θ denotes the model parameters, R(Θ) represents regularization terms, and λ is the regularization coefficient. These additions aid in preventing the model from overfitting to the training data, ensuring robust performance on unseen *** evaluation on COVID-19 time series data demonstrates AbotalebNet's enhanced predictive accuracy over traditional models, solidifying its potential for advanced non-linear time series analysis. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection Systems

引用

computers, Materials & Continua 2023年第2期74卷 2695-2709页

作者： Reem Alkanhel Doaa Sami Khafaga El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Rashid Amin Mostafa Abotaleb B.M.El-den Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science University of Engineering and TechnologyTaxilaPakistan Department of System Programming South Ural State UniversityChelyabinsk454080Russia

The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices *** to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing ***’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT *** imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network ***,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization *** prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE *** results showed that the proposed approach outperforms the other approaches and could boost the detection *** addition,a statistical analysis is performed to study the significance and stability of the proposed *** conducted experiments include seven different types of attack cases in the RPL-NIDS17 *** on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).

关键词： Metaheuristics grey wolf optimization dipper throated optimization dataset balancing locality sensitive hashing SMOTE

来源：评论

学校读者我要写书评

暂无评论

Multi-Agent Technology for Assessing the Availability of Information at the Software Development Initial Stages 16

Multi-Agent Technology for Assessing the Availability of Inf...

引用

16th IEEE International Conference on computer Science and Information Technologies, CSIT 2021

作者： Hovorushchenko, Tetiana Pavlova, Olga Computer Engineering and System Programming Department Khmelnytskyi Ukraine

ISBN: (纸本)9781665442572

The development of multi-agent technology for assessing the availability of information at the software development initial stages is an actual task, the solution of which is the purpose of this study. The paper presents the structure and functioning of multi-agent technology for assessing the availability of information at the software development initial stages, which is suitable for working with requirements specifications in modern conditions, when software requirements often change, - due to the speed and ease of specifications' analysis. Both the developer and the customer can always check the availability of information in the updated requirements specification, without spending a lot of time and effort. © 2021 IEEE.

关键词： Requirements engineering

来源：评论

学校读者我要写书评

暂无评论

Mathematical Model of Crane Scheduling for a Defined Slab Storage Locations Assignment in a Continuous Cast Billet Warehouse

Mathematical Model of Crane Scheduling for a Defined Slab St...

引用

International Russian Automation Conference (RusAutoCon)

作者： Ivan S. Shchegolikhin Sergey M. Andreev Department of Computer Engineering and Programming Nosov Magnitogorsk State Technical University Magnitogorsk Russia

ISBN: (数字)9798350349818

ISBN: (纸本)9798350349825

An increasing number of steel factories around the world are integrating two processes: continuous casting and hot rolling. The slab warehouse is the link between these two processes. Slabs of certain sizes are cast on the continuous casting machine streams and then transported to the slab warehouse. In the warehouse, overhead cranes move the slabs to storage locations, into stacks. The sequence of slabs arriving at the slab warehouse plays an important role: slabs that arrive earlier should, as a rule, be stacked earlier. If several cranes may be operating in a warehouse in an adjacent space, the scheduling of cranes becomes much more difficult: a safe distance must be maintained between the cranes. This paper proposes a mathematical model of crane scheduling for a defined solution to the storage location assignment problem in a slab warehouse. This mathematical model considers a large number of constraints specific to the mentioned process, such as: orthogonality of crane movement; order of slab processing; minimum distance between cranes; maximum and minimum height of stacks; and many others. The developed mathematical model takes into account the space-time characteristics of the warehouse and crane trajectories, which allows its application to create a digital twin of the slab warehouse, which will allow to check the acceptability of the crane schedule for some solution of the problem of storage location assignment._

关键词： Schedules Casting Cranes Mathematical models Trajectory Digital twins Time factors Steel Slabs Streams

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：