Journal of Intelligent Systems最新文献_第10页

Research on intelligent interactive music information based on visualization technology 基于可视化技术的智能交互式音乐信息研究

IF 3

Journal of Intelligent Systems Pub Date : 2022-01-01 DOI: 10.1515/jisys-2022-0016

Ningjie Liao

引用次数: 2

Data mining applications in university information management system development 数据挖掘在高校信息管理系统开发中的应用

IF 3

Journal of Intelligent Systems Pub Date : 2022-01-01 DOI: 10.1515/jisys-2022-0006

Minshun Zhang, Jun-Chen Fan, A. Sharma, Ashima Kukkar

{"title":"Data mining applications in university information management system development","authors":"Minshun Zhang, Jun-Chen Fan, A. Sharma, Ashima Kukkar","doi":"10.1515/jisys-2022-0006","DOIUrl":"https://doi.org/10.1515/jisys-2022-0006","url":null,"abstract":"Abstract Nowadays, the modern management is promoted to resolve the issue of unreliable information transmission and to provide work efficiency. The basic aim of the modern management is to be more effective in the role of the school to train talents and serve the society. This article focuses on the application of data mining (DM) in the development of information management system (IMS) in universities and colleges. DM provides powerful approaches for a variety of educational areas. Due to the large amount of student information that can be used to design valuable patterns relevant to student learning behavior, research in the field of education is continuously expanding. Educational data mining can be used by educational institutions to assess student performance, assisting the institution in recognizing the student’s accomplishments. In DM, classification is a well-known technique that has been regularly used to determine student achievement. In this study, the process of DM and the application research of association rules is introduced in the development of IMS in universities and colleges. The results show that the curriculum covers the whole field and the minimum transaction support count be 2, minconf = 70%. The results also suggested that students who choose one course also tend to choose the other course. The application of DM theory in university information will greatly upsurge the data analysis capability of administrators and improve the management level.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"9 1","pages":"207 - 220"},"PeriodicalIF":3.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89481337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

A novel method to find the best path in SDN using firefly algorithm 一种利用萤火虫算法寻找SDN中最佳路径的新方法

IF 3

Journal of Intelligent Systems Pub Date : 2022-01-01 DOI: 10.1515/jisys-2022-0063

Tameem Hameed Obaida, Hanan Abbas Salman

引用次数: 2

Automatic recognition method of installation errors of metallurgical machinery parts based on neural network 基于神经网络的冶金机械零件安装误差自动识别方法

IF 3

Journal of Intelligent Systems Pub Date : 2022-01-01 DOI: 10.1515/jisys-2022-0021

Hailong Cui, Bo Zhan

{"title":"Automatic recognition method of installation errors of metallurgical machinery parts based on neural network","authors":"Hailong Cui, Bo Zhan","doi":"10.1515/jisys-2022-0021","DOIUrl":"https://doi.org/10.1515/jisys-2022-0021","url":null,"abstract":"Abstract The installation error of metallurgical machinery parts is one of the common sources of errors in mechanical equipment. Because the installation error of different parts has different influences on different mechanical equipment, a simple linear formula cannot be used to identify the installation error. In the past, the manual recognition method and the touch recognition method lack of error information analysis, which leads to inaccurate recognition results. To improve the problem, an automatic recognition method based on the neural network for metallurgical machinery parts installation error is proposed. According to the principle of automatic recognition of installation error based on the neural network, the nonlinear relation matrix between layers of the neural network is established. The operating state parameters of mechanical equipment are calculated, and the time series of the parameters are divided into several segments averagely. Based on the recognition algorithm, the inspection steps of depth, perpendicularity and center position of reserved hole, base board construction, short-circuit motor line and terminal installation, center mark board, and reference point installation are designed. The experimental results show that the recall rate of the proposed method is 97.66%, and the maximum absolute deviation is 0.09. The experimental data verify the feasibility of the proposed method.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"8 1","pages":"321 - 331"},"PeriodicalIF":3.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84178353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Image denoising algorithm of social network based on multifeature fusion 基于多特征融合的社交网络图像去噪算法

IF 3

Journal of Intelligent Systems Pub Date : 2022-01-01 DOI: 10.1515/jisys-2022-0019

Lanfei Zhao, Qidan Zhu

引用次数: 1

Behavior feature extraction method of college students’ social network in sports field based on clustering algorithm 基于聚类算法的体育领域大学生社交网络行为特征提取方法

IF 3

Journal of Intelligent Systems Pub Date : 2022-01-01 DOI: 10.1515/jisys-2022-0030

Yonggan Wang, Haiou Sun

引用次数: 0

Topology optimization of computer communication network based on improved genetic algorithm 基于改进遗传算法的计算机通信网络拓扑优化

IF 3

Journal of Intelligent Systems Pub Date : 2022-01-01 DOI: 10.1515/jisys-2022-0050

Hua Ai, Yuhong Fan, Jilei Zhang, K. Ghafoor

引用次数: 1

Modeling and PID control of quadrotor UAV based on machine learning 基于机器学习的四旋翼无人机建模与PID控制

IF 3

Journal of Intelligent Systems Pub Date : 2022-01-01 DOI: 10.1515/jisys-2021-0213

Lirong Zhou, A. Pljonkin, Pradeep Kumar Singh

{"title":"Modeling and PID control of quadrotor UAV based on machine learning","authors":"Lirong Zhou, A. Pljonkin, Pradeep Kumar Singh","doi":"10.1515/jisys-2021-0213","DOIUrl":"https://doi.org/10.1515/jisys-2021-0213","url":null,"abstract":"Abstract The aim of this article was to discuss the modeling and control method of quadrotor unmanned aerial vehicle (UAV). In the process of modeling, mechanism modeling and experimental testing are combined, especially the motor and propeller are modeled in detail. Through the understanding of the body structure and flight principle of the quadrotor UAV, the Newton–Euler method is used to analyze the dynamics of the quadrotor UAV, and the mathematical model of the UAV is established under the small angle rotation. Process identifier (PID) is used to control it. First, the attitude angle of the model is controlled by PID, and based on this, the speed in each direction is controlled by PID. Then, the PID control of the four rotor aircraft with the center of gravity offset is simulated by MATLAB. The results show that the pitch angle and roll angle can be controlled by 5 degrees together without center of gravity deviation, and the PID can effectively control the control quantity and achieve the desired effect in a short time. Classical BP algorithm, classical GA-BP algorithm, and improved GA-BP algorithm were trained, respectively, with a total of 150 sets of training data, training function uses Levenberg-Marquardt (trainlm), and performance function uses mean squared error (MSE). In the background of the same noise, the improved GA-BP algorithm has the highest detection rate, classical GA-BP algorithm is the second, and classical BP algorithm is the worst. The simulation results show that the PID control law can effectively control the attitude angle and speed of the rotor UAV in the case of center of gravity deviation.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"17 1","pages":"1112 - 1122"},"PeriodicalIF":3.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84584314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Conception and realization of an IoT-enabled deep CNN decision support system for automated arrhythmia classification 基于物联网的心律失常自动分类深度CNN决策支持系统的构想与实现

IF 3

Journal of Intelligent Systems Pub Date : 2022-01-01 DOI: 10.1515/jisys-2022-0015

Ann Varghese, Midhun Muraleedharan Sylaja, J. Kurian

{"title":"Conception and realization of an IoT-enabled deep CNN decision support system for automated arrhythmia classification","authors":"Ann Varghese, Midhun Muraleedharan Sylaja, J. Kurian","doi":"10.1515/jisys-2022-0015","DOIUrl":"https://doi.org/10.1515/jisys-2022-0015","url":null,"abstract":"Abstract Arrhythmias are irregular heartbeats that may be life-threatening. Proper monitoring and the right care at the right time are necessary to keep the heart healthy. Monitoring electrocardiogram (ECG) patterns on continuous monitoring devices is time-consuming. An intense manual inspection by caregivers is not an option. In addition, such an inspection could result in errors and inter-variability. This article proposes an automated ECG beat classification method based on deep neural networks (DNN) to aid in the detection of cardiac arrhythmias. The data collected by an Internet of Things enabled ECG monitoring device are transferred to a server. They are analysed by a deep learning model, and the results are shared with the primary caregiver. The proposed model is trained using the MIT-BIH ECG arrhythmia database to classify into four classes: normal beat (N), left bundle branch block beat (L), right bundle branch block beat (R), and premature ventricular contraction (V). The received data are sampled with an overlapping sliding window and divided into an 80:20 ratio for training and testing, with tenfold cross-validation. The proposed method achieves higher accuracy with a simple model without any preprocessing when compared with previous works. For the train and test sets, we achieved accuracy rates of 99.09 and 99.03%, respectively. A precision, recall, and F1 scores of 0.99 is obtained. The proposed model achieves its goal of developing a simple and accurate ECG monitoring system with improved performance. This simple and efficient deep learning approach for heartbeat classification could be applied in real-time telehealth monitoring systems.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"120 1","pages":"407 - 419"},"PeriodicalIF":3.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80772093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Supervision method of indoor construction engineering quality acceptance based on cloud computing 基于云计算的室内建筑工程质量验收监督方法

IF 3

Journal of Intelligent Systems Pub Date : 2022-01-01 DOI: 10.1515/jisys-2022-0056

Jian Zhang

{"title":"Supervision method of indoor construction engineering quality acceptance based on cloud computing","authors":"Jian Zhang","doi":"10.1515/jisys-2022-0056","DOIUrl":"https://doi.org/10.1515/jisys-2022-0056","url":null,"abstract":"Abstract As an important part of Chinese economy, the construction industry has a great contribution to the economy, and plays an important role in the Chinese economic development. Therefore, it has certain research significance for the quality acceptance supervision method of construction engineering. This article takes Shenzhen S project as an example, combined with cloud computing, discusses the quality acceptance and supervision methods of indoor construction projects. In the introduction to the technical part, this article first briefly introduces the definition of cloud computing and then introduces the particle swarm algorithm and traditional genetic algorithm in the cloud computing task scheduling method. The algorithm is introduced into the quality acceptance of indoor construction projects to obtain the quality, most efficient method for acceptance supervision. The experimental part of this article takes S project as the research object and the residents’ satisfaction with the project as the experimental purpose. Finally, through statistical analysis, it is concluded that the residents’ satisfaction with S project reaches more than 70%.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"8 1","pages":"795 - 805"},"PeriodicalIF":3.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85840103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0