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Int. J. Reason. based Intell. Syst.最新文献

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Social network analysis: friendship inferred by chosen courses, commuting time and student performance at university 社会网络分析:通过选课、通勤时间和学生在校表现推断出的友谊
Int. J. Reason. based Intell. Syst. Pub Date : 2018-04-16 DOI: 10.1504/IJRIS.2018.10012204
Lionel J. Khalil, M. Khair
{"title":"Social network analysis: friendship inferred by chosen courses, commuting time and student performance at university","authors":"Lionel J. Khalil, M. Khair","doi":"10.1504/IJRIS.2018.10012204","DOIUrl":"https://doi.org/10.1504/IJRIS.2018.10012204","url":null,"abstract":"Our social network analysis (SNA) evaluates the performance of students taking courses with a group of friends versus students used to take courses alone. We evaluate the probability to befriend by comparing the number of courses shared by students with the probability to be assigned in the same classroom randomly based on curriculum constraints. A minimum of courses taken in common is used as a criterion to identify students belonging to a tribe of friends. The main findings are that students in tribes over perform other students by about half point of GPA, and are dropping and repeating fewer courses. Considering student without friends, we measured the impact of the commuting distance on GPA and drop off rate: students with very low GPA and high drop off are mostly students with significantly higher commuting time.","PeriodicalId":360794,"journal":{"name":"Int. J. Reason. based Intell. Syst.","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123074700","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: 2
Design of public bicycle scheduling model based on data mining algorithm 基于数据挖掘算法的公共自行车调度模型设计
Int. J. Reason. based Intell. Syst. Pub Date : 2018-04-16 DOI: 10.1504/IJRIS.2018.10012209
Xia Wendong, Yuanfeng Liu, Deli Chen
{"title":"Design of public bicycle scheduling model based on data mining algorithm","authors":"Xia Wendong, Yuanfeng Liu, Deli Chen","doi":"10.1504/IJRIS.2018.10012209","DOIUrl":"https://doi.org/10.1504/IJRIS.2018.10012209","url":null,"abstract":"Guangzhou Public Bicycle System has been the largest bike-sharing program in the world. The software of the system was developed by our research team. To meet the fluctuating demand for bicycles and for vacant lockers at each station, employees need to actively shift bicycles between stations by a fleet of vehicles. Hybrid GRA-SP metaheuristic which incorporate a path-relinking procedure have been successfully applied for different combinatorial problems. Data mining is a process that uses a variety of data analysis tools to discover patterns and relationships in data. In this paper, a new hybrid data mining metaheuristic combines GRA-SP which incorporate path-relinking procedure with data mining process is proposed and some improvement are made.","PeriodicalId":360794,"journal":{"name":"Int. J. Reason. based Intell. Syst.","volume":"92 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128016760","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
Bi-level optimisation model for greener transportation with intelligent transport system 基于智能交通系统的绿色交通双层优化模型
Int. J. Reason. based Intell. Syst. Pub Date : 2018-04-16 DOI: 10.1504/IJRIS.2018.10012213
Kun Liu
{"title":"Bi-level optimisation model for greener transportation with intelligent transport system","authors":"Kun Liu","doi":"10.1504/IJRIS.2018.10012213","DOIUrl":"https://doi.org/10.1504/IJRIS.2018.10012213","url":null,"abstract":"In this paper, we propose a bi-level optimisation model (BLOM) with three algorithms. BLOM is intended for fuel saving and carbon dioxide emission reduction in both upper-level and lower-level model with intelligent transport system. Traffic signal schemes are optimised for minimising total fuel consumption passing through a road intersection in unit time in the upper-level model. At the same time, traffic signal information data are sent to the lowerlevel model in which vehicle motion states are optimised for greener transportation. Three algorithms include hybrid genetic algorithm and particle swarm optimisation in upper-level model with hybrid genetic algorithm and particle swarm optimisation in lower-level model (GA-PSO/GA-PSO), GA in upper-level model with PSO in lower-level model (GA/PSO) and GA in both level model (GA/GA) are realised to compare and improve the performance of the model. The simulation results derive GA-PSO/GA-PSO hybrid algorithm converges faster with the best resolution and least calculation time than other GA/PSO and GA/GA algorithms.","PeriodicalId":360794,"journal":{"name":"Int. J. Reason. based Intell. Syst.","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130837046","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
Fuzzy self-learning control of glass tempering and annealing temperature based on the optimised genetic big data analysis algorithm 基于优化遗传大数据分析算法的玻璃钢化退火温度模糊自学习控制
Int. J. Reason. based Intell. Syst. Pub Date : 2018-04-16 DOI: 10.1504/IJRIS.2018.10012220
Xiaokan Wang, Hai-rong Dong, Xiu-ming Yao, Xubin Sun
{"title":"Fuzzy self-learning control of glass tempering and annealing temperature based on the optimised genetic big data analysis algorithm","authors":"Xiaokan Wang, Hai-rong Dong, Xiu-ming Yao, Xubin Sun","doi":"10.1504/IJRIS.2018.10012220","DOIUrl":"https://doi.org/10.1504/IJRIS.2018.10012220","url":null,"abstract":"The temperature control of glass tempering and annealing process has the problems of the time varying parameters and time lag characteristic. In order to solve this problem, this paper proposes a self-learning fuzzy controller based on improved genetic algorithm and big data analysis. The proposed algorithm can quickly search the global optimal factor by using the big data temperature. Thus the fuzzy control rules are perfected and corrected. The simulation results demonstrate that the proposed control algorithm is suitable for systems with time varying parameters and time lag characteristic.","PeriodicalId":360794,"journal":{"name":"Int. J. Reason. based Intell. Syst.","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133063634","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
An effective foggy image acquisition algorithm in multimedia big data era 多媒体大数据时代一种有效的雾图像采集算法
Int. J. Reason. based Intell. Syst. Pub Date : 2018-04-16 DOI: 10.1504/IJRIS.2018.10012212
Jinxing Niu, Hengcan Li
{"title":"An effective foggy image acquisition algorithm in multimedia big data era","authors":"Jinxing Niu, Hengcan Li","doi":"10.1504/IJRIS.2018.10012212","DOIUrl":"https://doi.org/10.1504/IJRIS.2018.10012212","url":null,"abstract":"Outdoor images are often degraded by fog weather conditions in the era of multimedia big data, which affect computer vision applications severely. In this paper, an effective fog image acquisition algorithm based on big data analysis is proposed in the big data environment, and single image defogging algorithm based on histogram equalisation and dark channel prior methods is proposed. The transmission and air light of the fog image need to be estimated by the dark channel prior theory methods, and then clear images can be received after defogging and keep the original colour. The experimental results show that the image by fog removal dark channel prior method can get clear images and keep the original colour, the treatment effect is better than that of the histogram equalisation method.","PeriodicalId":360794,"journal":{"name":"Int. J. Reason. based Intell. Syst.","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114867768","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
Personalised ranking online reviews based on user individual preferences 根据用户个人偏好对在线评论进行个性化排名
Int. J. Reason. based Intell. Syst. Pub Date : 2018-04-16 DOI: 10.1504/IJRIS.2018.10012211
Wei Song, Shiwei Zhang, Lizhen Liu, Hanshi Wang
{"title":"Personalised ranking online reviews based on user individual preferences","authors":"Wei Song, Shiwei Zhang, Lizhen Liu, Hanshi Wang","doi":"10.1504/IJRIS.2018.10012211","DOIUrl":"https://doi.org/10.1504/IJRIS.2018.10012211","url":null,"abstract":"With the development of e-commerce sites, online reviews have become important data resources for e-customers. Nowadays, there have been many literatures on the category of reviews category or ranking for public. However, they only satisfy common preferences, and ignore personalised preferences of individual users. In view of this phenomenon, this paper is trying to put forward a ranking method for individual preferences. It begins with collecting the rules of user preferences by showing reviews to them to let them mark the reviews they like. Then it combines the common rules with user personalised rules to get the range of features. Finally, after calculating the optimal solution of features, the paper strives to structure a ranking model to rank reviews with the set of optimal solution.","PeriodicalId":360794,"journal":{"name":"Int. J. Reason. based Intell. Syst.","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126378245","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
The development and popularisation of network platform of college sports venues in intelligent manufacturing 智能制造中高校体育场馆网络平台的开发与推广
Int. J. Reason. based Intell. Syst. Pub Date : 2018-04-16 DOI: 10.1504/IJRIS.2018.10012214
Kaiyan Han, Wang Qin
{"title":"The development and popularisation of network platform of college sports venues in intelligent manufacturing","authors":"Kaiyan Han, Wang Qin","doi":"10.1504/IJRIS.2018.10012214","DOIUrl":"https://doi.org/10.1504/IJRIS.2018.10012214","url":null,"abstract":"With the promotion of the national fitness campaign, the number of physical exercise shows explosive growth in China. The question that block the development of sports exposed which is a lack of public sports venues. This paper focuses on building a network platform of all college sports venues resources which can reach the goal to serve national fitness and proposes an improved parallel heuristic map reduce algorithm. The experimental results show the stability, concurrency and feasibility of the network platform of college sports venues in big data era.","PeriodicalId":360794,"journal":{"name":"Int. J. Reason. based Intell. Syst.","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125001144","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: 2
An effective system layout planning method for railway logistics centre in the background of big data 大数据背景下铁路物流中心有效的系统布局规划方法
Int. J. Reason. based Intell. Syst. Pub Date : 2018-04-16 DOI: 10.1504/IJRIS.2018.10012203
Jie Li
{"title":"An effective system layout planning method for railway logistics centre in the background of big data","authors":"Jie Li","doi":"10.1504/IJRIS.2018.10012203","DOIUrl":"https://doi.org/10.1504/IJRIS.2018.10012203","url":null,"abstract":"In the background of big data, railway logistics has become the inevitable trend of freight transportation. This paper puts forward a new method of logistics centre function area layout. Systematic layout planning (SLP) method is firstly used to analyse functional domains to construct a comprehensive correlation chart of the functional domains according to certain weights. Manhattan distance and circuitous path are used to express the distances among the functional domains and to construct a double-object function with minimised total trucking expense and maximised total integrated relations. In the practical application of R. Muther line chart method, the proposed method can get the feasible scheme of layout of functional areas, and it has good application value.","PeriodicalId":360794,"journal":{"name":"Int. J. Reason. based Intell. Syst.","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132831948","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
Solving medical classification problems with RBF neural network and filter methods 用RBF神经网络和滤波方法解决医学分类问题
Int. J. Reason. based Intell. Syst. Pub Date : 2017-12-14 DOI: 10.1504/IJRIS.2017.10009600
J. Novakovic, A. Veljovic
{"title":"Solving medical classification problems with RBF neural network and filter methods","authors":"J. Novakovic, A. Veljovic","doi":"10.1504/IJRIS.2017.10009600","DOIUrl":"https://doi.org/10.1504/IJRIS.2017.10009600","url":null,"abstract":"This paper evaluates classification accuracy of radial basis function (RBF) neural network and filter methods for feature selection in medical datasets. To improve the diagnostic procedure in the daily routine and to avoid wrong diagnosis, machine learning methods can be used. Diagnosis of tumours, heart disease, hepatitis, liver and Parkinson's diseases are a few of the medical problems which we have used in artificial neural networks. The main objective of this paper is to show that it is possible to improve the performance of the system for inductive learning rules with RBF neural network for medical classification problems, using the filter methods for feature selections. The aim of this research is also to present and compare different algorithm approach for the construction system that learns from experience and makes decisions and predictions and reduce the expected number or percentage of errors.","PeriodicalId":360794,"journal":{"name":"Int. J. Reason. based Intell. Syst.","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129389871","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: 2
A performance analysis of computing the LU and the QR matrix decompositions on the CPU and the GPU 在CPU和GPU上计算LU和QR矩阵分解的性能分析
Int. J. Reason. based Intell. Syst. Pub Date : 2017-12-14 DOI: 10.1504/IJRIS.2017.10009618
Dušan B. Gajić, R. Stankovic, Milos Radmanovic
{"title":"A performance analysis of computing the LU and the QR matrix decompositions on the CPU and the GPU","authors":"Dušan B. Gajić, R. Stankovic, Milos Radmanovic","doi":"10.1504/IJRIS.2017.10009618","DOIUrl":"https://doi.org/10.1504/IJRIS.2017.10009618","url":null,"abstract":"We present an analysis of time efficiency of five different implementations of the LU and the QR decomposition of matrices performed on central processing unit (CPUs) and graphics processing units (GPUs). Three of the considered implementations, developed using the Eigen C++ library, Intel MKL, and MATLAB are executed on a multi-core CPU. The remaining two implementations are processed on a GPU and employ MATLAB's Parallel Computing Toolbox and Nvidia CUDA augmented with the cuSolver library. Computation times are compared using randomly generated single- and double-precision floating-point matrices. The experiments for the LU decomposition show that the two GPU implementations offer best performance for matrices that can fit into the GPU global memory. For larger LU decomposition problem instances, Intel MKL on the CPU is found to be the fastest approach. Furthermore, Intel MKL also proves to be the fastest method for computing QR decomposition for all considered sizes of matrices.","PeriodicalId":360794,"journal":{"name":"Int. J. Reason. based Intell. Syst.","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122553368","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
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