Hybrid journal (It can contain Open Access articles) ISSN (Print) 1755-0556 - ISSN (Online) 1755-0564 Published by Inderscience Publishers[447 journals]
Authors:Jinxing Niu, Hengcan Li Pages: 4 - 10 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. Keywords: multimedia big data; foggy image; effective foggy image acquisition algorithm; image restoration Citation: International Journal of Reasoning-based Intelligent Systems, Vol. 10, No. 1 (2018) pp. 4 - 10 PubDate: 2018-04-11T23:20:50-05:00 DOI: 10.1504/IJRIS.2018.091123 Issue No:Vol. 10, No. 1 (2018)
Authors:Jinxing Niu, Hengcan Li Pages: 11 - 19 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. Keywords: big data; railway logistics centre; layout of functional domains; Manhattan distance; circuitous path Citation: International Journal of Reasoning-based Intelligent Systems, Vol. 10, No. 1 (2018) pp. 11 - 19 PubDate: 2018-04-11T23:20:50-05:00 DOI: 10.1504/IJRIS.2018.091121 Issue No:Vol. 10, No. 1 (2018)
Authors:Han Kaiyan, Wang Qin Pages: 20 - 25 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. Keywords: intelligent manufacturing; college sports venues; national fitness; improved parallel heuristic map reduce algorithm; stability; concurrency Citation: International Journal of Reasoning-based Intelligent Systems, Vol. 10, No. 1 (2018) pp. 20 - 25 PubDate: 2018-04-11T23:20:50-05:00 DOI: 10.1504/IJRIS.2018.091126 Issue No:Vol. 10, No. 1 (2018)
Authors:Han Kaiyan, Wang Qin Pages: 26 - 31 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. Keywords: bi-level optimisation; greener transportation; intelligent transport system Citation: International Journal of Reasoning-based Intelligent Systems, Vol. 10, No. 1 (2018) pp. 26 - 31 PubDate: 2018-04-11T23:20:50-05:00 DOI: 10.1504/IJRIS.2018.091124 Issue No:Vol. 10, No. 1 (2018)
Authors:Wei Song, Shiwei Zhang, Lizhen Liu, Hanshi Wang Pages: 32 - 42 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. Keywords: attribute word; user preference rule; hill climbing algorithm; ranking Citation: International Journal of Reasoning-based Intelligent Systems, Vol. 10, No. 1 (2018) pp. 32 - 42 PubDate: 2018-04-11T23:20:50-05:00 DOI: 10.1504/IJRIS.2018.091122 Issue No:Vol. 10, No. 1 (2018)
Authors:Xiaokan Wang, Hairong Dong, Xiuming Yao, Xubin Sun Pages: 43 - 50 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. Keywords: annealing temperature; big data analysis; fuzzy control; self learning; improved genetic algorithm Citation: International Journal of Reasoning-based Intelligent Systems, Vol. 10, No. 1 (2018) pp. 43 - 50 PubDate: 2018-04-11T23:20:50-05:00 DOI: 10.1504/IJRIS.2018.091129 Issue No:Vol. 10, No. 1 (2018)
Authors:Yegang Chen, Jun Luo, XiaoRong Diao, Xingyan Yao Pages: 51 - 58 Abstract: In the latest progress in healthcare, the continuous creation of digital medical information is an important basis for the analysis of large medical data. This paper proposes a medical big data analysis algorithm based on access control system that provides reliable security protection to big medical data (BMD) by considering various quantitative parameters. The proposed algorithm calculates the reputation values of different users, which correspond to each parameter. Based on the calculated reputation values, the proposed algorithm grants access authority to each user. Simulation results are performed to verify the effectiveness of security system solving protection of the sensitive personal information of patients. Keywords: medical big data analysis algorithm; big medical data; BMD; access control system; sensitive personal information; SPI Citation: International Journal of Reasoning-based Intelligent Systems, Vol. 10, No. 1 (2018) pp. 51 - 58 PubDate: 2018-04-11T23:20:50-05:00 DOI: 10.1504/IJRIS.2018.091127 Issue No:Vol. 10, No. 1 (2018)
Authors:Lionel J. Khalil, Marie G. Khair Pages: 59 - 67 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. Keywords: social network analysis; SNA; friendship; student performance; GPA; drop off; commuting time Citation: International Journal of Reasoning-based Intelligent Systems, Vol. 10, No. 1 (2018) pp. 59 - 67 PubDate: 2018-04-11T23:20:50-05:00 DOI: 10.1504/IJRIS.2018.091119 Issue No:Vol. 10, No. 1 (2018)
Authors:Xia Wendong, Liu Yuanfeng, Chen Deli Pages: 68 - 76 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. Keywords: public resource; scheduling; mining algorithm; model Citation: International Journal of Reasoning-based Intelligent Systems, Vol. 10, No. 1 (2018) pp. 68 - 76 PubDate: 2018-04-11T23:20:50-05:00 DOI: 10.1504/IJRIS.2018.091120 Issue No:Vol. 10, No. 1 (2018)
Hybrid journal (It can contain Open Access articles)
[447 journals]
