Int. J. Comput. Commun. Control最新文献_第9页

M-generalised q-neutrosophic extension of CoCoSo method CoCoSo方法的m -广义q-嗜中性扩展

Int. J. Comput. Commun. Control Pub Date : 2022-01-09 DOI: 10.15837/ijccc.2022.1.4646

Zenonas Turskis, R. Baušys, F. Smarandache, Giruta Kazakevicute-Januskeviciene, E. Zavadskas

引用次数: 6

Romanian Language Technology - a view from an academic perspective 罗马尼亚语言技术-从学术角度的观点

Int. J. Comput. Commun. Control Pub Date : 2022-01-05 DOI: 10.15837/ijccc.2022.1.4641

D. Tufis

引用次数: 1

Multi-Objective Model to Improve Network Reliability Level under Limited Budget by Considering Selection of Facilities and Total Service Distance in Rescue Operations 考虑救援设施选择和总服务距离的有限预算下提高网络可靠性的多目标模型

Int. J. Comput. Commun. Control Pub Date : 2022-01-05 DOI: 10.15837/ijccc.2022.1.4573

Yiying Wang, Zeshui Xu, F. Filip

引用次数: 3

Performance Improvement of Low-Cost Iterative Learning-Based Fuzzy Control Systems for Tower Crane Systems 基于低成本迭代学习的塔机模糊控制系统性能改进

Int. J. Comput. Commun. Control Pub Date : 2022-01-05 DOI: 10.15837/ijccc.2022.1.4623

R. Precup, Raul-Cristian Roman, Elena-Lorena Hedrea, C. Dragos, Miruna-Maria Damian, Monica-Lavinia Nedelcea

{"title":"Performance Improvement of Low-Cost Iterative Learning-Based Fuzzy Control Systems for Tower Crane Systems","authors":"R. Precup, Raul-Cristian Roman, Elena-Lorena Hedrea, C. Dragos, Miruna-Maria Damian, Monica-Lavinia Nedelcea","doi":"10.15837/ijccc.2022.1.4623","DOIUrl":"https://doi.org/10.15837/ijccc.2022.1.4623","url":null,"abstract":"This paper is dedicated to the memory of Prof. Ioan Dzitac, one of the fathers of this journal and its founding Editor-in-Chief till 2021. The paper addresses the performance improvement of three Single Input-Single Output (SISO) fuzzy control systems that control separately the positions of interest of tower crane systems, namely the cart position, the arm angular position and the payload position. Three separate low-cost SISO fuzzy controllers are employed in terms of first order discrete-time intelligent Proportional-Integral (PI) controllers with Takagi-Sugeno-Kang Proportional-Derivative (PD) fuzzy terms. Iterative Learning Control (ILC) system structures with PD learning functions are involved in the current iteration SISO ILC structures. Optimization problems are defined in order to tune the parameters of the learning functions. The objective functions are defined as the sums of squared control errors, and they are solved in the iteration domain using the recent metaheuristic Slime Mould Algorithm (SMA). The experimental results prove the performance improvement of the SISO control systems after ten iterations of SMA.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129725291","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: 8

Effect of Sample Sizes in Fingerprinting Database for Wi-Fi System Wi-Fi系统指纹数据库样本大小的影响

Int. J. Comput. Commun. Control Pub Date : 2021-12-05 DOI: 10.15837/ijccc.2021.6.4394

A. H. A. Sa'ahiry, A. Ismail, L. M. Kamaruddin, Mohd Sani Mohamad Hashim, Muhamad Safwan Muhamad Azmi, Muhammad Juhairi Aziz Satar, M. Toyoura

{"title":"Effect of Sample Sizes in Fingerprinting Database for Wi-Fi System","authors":"A. H. A. Sa'ahiry, A. Ismail, L. M. Kamaruddin, Mohd Sani Mohamad Hashim, Muhamad Safwan Muhamad Azmi, Muhammad Juhairi Aziz Satar, M. Toyoura","doi":"10.15837/ijccc.2021.6.4394","DOIUrl":"https://doi.org/10.15837/ijccc.2021.6.4394","url":null,"abstract":"Indoor positioning system has been an essential work to substitute the Global Positioning System (GPS). GPS utilizing Global Navigation Satellite Systems (GNSS) cannot provide an accurate positioning in the indoor due to the multipath effect and shadow fading. Fingerprinting method with Wi-Fi technology is a promising system to solve this issue. However, there are several problems with the fingerprinting method. The fingerprinting database collected has different sample sizes where the previous researcher does not indicate any standard for the sample size to be used. In this paper, the effect of the sample sizes in fingerprinting database for Wi-Fi technology has been discussed deeply. The statistical analyzation for different sample sizes has been analyzed. Furthermore, two methods which are K- Nearest Neighbor (KNN) and Deep Neural Network (DNN) are being used to examine the effect of the sample sizes in term of accuracy and distance error. The discussion in this paper will contribute to the better sample size selection depending on the method taken by the user. The result shows that sample sizes are an important metrics in developing the indoor positioning system as it effects the result of the location estimation.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133856733","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

Failure in Stock Price Prediction: A Comparison bettwen the Curve-Shape-Feature and Non-Curve-Shape-Feature Modes of Existing Machine Learning Algorithms 股票价格预测的失败:现有机器学习算法中曲线-形状-特征与非曲线-形状-特征模式的比较

Int. J. Comput. Commun. Control Pub Date : 2021-12-03 DOI: 10.15837/ijccc.2021.6.4549

Ping Zhang, Jia-Yao Yang, Hao Zhu, Yue-Jie Hou, Yi Liu, Chi-Chun Zhou

{"title":"Failure in Stock Price Prediction: A Comparison bettwen the Curve-Shape-Feature and Non-Curve-Shape-Feature Modes of Existing Machine Learning Algorithms","authors":"Ping Zhang, Jia-Yao Yang, Hao Zhu, Yue-Jie Hou, Yi Liu, Chi-Chun Zhou","doi":"10.15837/ijccc.2021.6.4549","DOIUrl":"https://doi.org/10.15837/ijccc.2021.6.4549","url":null,"abstract":"In the era of artificial intelligence, machine learning methods are successfully used in various fields. Machine learning has attracted extensive attention from investors in the financial market, especially in stock price prediction. However, one argument for the machine learning methods used in stock price prediction is that they are black-box models which are difficult to interpret. In this paper, we focus on the future stock price prediction with the historical stock price by machine learning and deep learning methods, such as support vector machine (SVM), random forest (RF), Bayesian classifier (BC), decision tree (DT), multilayer perceptron (MLP), convolutional neural network (CNN), bi-directional long-short term memory (BiLSTM), the embedded CNN, and the embedded BiLSTM. Firstly, we manually design several financial time series where the future price correlates with the historical stock prices in pre-designed modes, namely the curve-shape-feature (CSF) and the non-curve-shape-feature (NCSF) modes. In the CSF mode, the future prices can be extracted from the curve shapes of the historical stock prices. Conversely, in the NCSF mode, they can’t. Secondly, we apply various algorithms to those pre-designed and real financial time series. We find that the existing machine learning and deep learning algorithms fail in stock price prediction because in the real financial time series, less information of future prices is contained in the CSF mode, and perhaps more information is contained in the NCSF. Various machine learning and deep learning algorithms are good at handling the CSF in historical data, which are successfully applied in image recognition and natural language processing. However, they are inappropriate for stock price prediction on account of the NCSF. Therefore, accurate stock price prediction is the key to successful investment, and new machine learning algorithms handling the NCSF series are needed. \u0000","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121908028","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

Exploring the Antecedents of Online Learning Satisfaction: Role of Flow and Comparison Between use Contexts 在线学习满意度的前因:心流的作用与使用情境的比较

Int. J. Comput. Commun. Control Pub Date : 2021-12-03 DOI: 10.15837/ijccc.2021.6.4398

Quan Xiao, Xia Li

{"title":"Exploring the Antecedents of Online Learning Satisfaction: Role of Flow and Comparison Between use Contexts","authors":"Quan Xiao, Xia Li","doi":"10.15837/ijccc.2021.6.4398","DOIUrl":"https://doi.org/10.15837/ijccc.2021.6.4398","url":null,"abstract":"Learners’ satisfaction plays a critical role in the success of online learning platform. Many factors that affect online learning satisfaction have been addressed by previous studies. However, the mechanisms by which these factors are associated with online learning satisfaction are not sufficiently clear. Moreover, the difference in the antecedents of online learning satisfaction between two use contexts- Mobile context and PC context, was rarely examined. Based on the Stimulus-Organism-Response (S-O-R) framework, we investigate the key factors (self-efficacy, social interaction, platform quality, teacher’s expertise) affecting flow and highlights its role in online learning satisfaction, which is empirically tested through an online survey of 333 online learners. Results show that self-efficacy, teacher’s expertise, platform quality, and social interaction positively affect online learning satisfaction through the mediation of flow. Use contexts not only moderate the relationship between flow and online learning satisfaction, but also between social interaction, platform quality, teacher’s expertise, and flow. These new findings expand educators with ways to increase flow, add to knowledge about the relationship between flow and online learning satisfaction and provide references for online learning platforms to enhance learners’ online learning satisfaction under multiple-version affordances.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128604460","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

Fixed Point Theory in Fuzzy Normed Linear Spaces: A General View 模糊赋范线性空间中的不动点理论

Int. J. Comput. Commun. Control Pub Date : 2021-11-19 DOI: 10.15837/ijccc.2021.6.4587

S. Dzitac, H. Oros, D. Deac, Sorin Nădăban

引用次数: 3

Cloud Computing Cloud Computing in Remote Sensing : High Performance Remote Sensing Data Processing in a Big data Environment 遥感中的云计算:大数据环境下的高性能遥感数据处理

Int. J. Comput. Commun. Control Pub Date : 2021-11-16 DOI: 10.15837/ijccc.2021.6.4236

Yassine Sabri, S. Aouad

{"title":"Cloud Computing Cloud Computing in Remote Sensing : High Performance Remote Sensing Data Processing in a Big data Environment","authors":"Yassine Sabri, S. Aouad","doi":"10.15837/ijccc.2021.6.4236","DOIUrl":"https://doi.org/10.15837/ijccc.2021.6.4236","url":null,"abstract":"Multi-area and multi-faceted remote sensing (SAR) datasets are widely used due to the increasing demand for accurate and up-to-date information on resources and the environment for regional and global monitoring. In general, the processing of RS data involves a complex multi-step processing sequence that includes several independent processing steps depending on the type of RS application. The processing of RS data for regional disaster and environmental monitoring is recognized as computationally and data demanding.Recently, by combining cloud computing and HPC technology, we propose a method to efficiently solve these problems by searching for a large-scale RS data processing system suitable for various applications. Real-time on-demand service. The ubiquitous, elastic, and high-level transparency of the cloud computing model makes it possible to run massive RS data management and data processing monitoring dynamic environments in any cloud. via the web interface. Hilbert-based data indexing methods are used to optimally query and access RS images, RS data products, and intermediate data. The core of the cloud service provides a parallel file system of large RS data and an interface for accessing RS data from time to time to improve localization of the data. It collects data and optimizes I/O performance. Our experimental analysis demonstrated the effectiveness of our method platform.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127988059","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: 1

Improving the Performance of Heterogeneous Network Systems in Machine Learning-based 5G Mobile Communication System 基于机器学习的5G移动通信系统中异构网络系统性能的改进

Int. J. Comput. Commun. Control Pub Date : 2021-11-16 DOI: 10.15837/ijccc.2021.6.4583

Y. Kim, Dae-Young Lee, Sanghyun Bae, T. Kim

{"title":"Improving the Performance of Heterogeneous Network Systems in Machine Learning-based 5G Mobile Communication System","authors":"Y. Kim, Dae-Young Lee, Sanghyun Bae, T. Kim","doi":"10.15837/ijccc.2021.6.4583","DOIUrl":"https://doi.org/10.15837/ijccc.2021.6.4583","url":null,"abstract":"Mobile traffic, which has increased significantly with the emergence of Fourth generation longterm evolution (4G-LTE) communications and advances in video streaming services, is still currently increasing at an incredible pace. Fifth-generation (5G) mobile communication systems, which were developed to deal with such a drastic increase in mobile traffic, aim to achieve ultra-high-speed data transmission, low latency, and the accommodation of many more connected devices compared to 4G-LTE systems. 5G communication uses high-frequency bandwidth to implement these features, which leads to an inevitable drawback of a high path loss. In order to overcome this disadvantage, small cell technology was developed, and is defined as small, low-power base stations that can extend the network coverage and solve the shadow area problem. Although small cell technology has these advantages, different problems, such as the effects of interference due to the deployment of a large number of small cells and the differences in devices accessing the network, need to be solved. To do so, it is necessary to develop an algorithm for a service method. However, general algorithms have difficulties in responding to the diverse environment of mobile communication systems, such as sudden increase in traffic in certain areas or sudden changes in the mobile population, and machine learning technology has been applied to solve this problem. This study employs a machine learning algorithm to determine small cell connections. In addition, a 5G macro system, the application of small cells, and the application of machine learning algorithms are compared to determine the performance improvement in the machine learning algorithm. Moreover, Support Vector Machine (SVM), Logistic Regression and Decision Tree algorithm are employed to show a training method that uses basic training data and a small cell on-off method, and the performance enhancement is verified based on this method.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133140171","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: 1