Perez Joan¹, Fusco Giovanni¹, Sadahiro Yukio²

Revue Internationale de Géomatique, Vol.31, No.2, pp. 303-328, 2022, DOI:10.3166/rig31.303-328

Abstract This paper presents different methods implemented with the aim of studying urban dynamics at the building level. Building types are identified within a comprehensive vector-based building inventory, spanning over at least two time points. First, basic morphometric indicators are computed for each building: area, floor-area, number of neighbors, elongation, and convexity. Based on the availability of expert knowledge, different types of classification and clustering are performed: supervised tree-like classificatory model, expert-constrained k-means and combined SOM-HCA. A grid is superimposed on the test region of Osaka (Japan) and the number of building types per cell and More >

Jinglin Liang^1,2, Qian Sun^1,2,*, Xiaoyi Wang^3,2, Jiping Xu^1,2, Huiyan Zhang^1,2, Li Wang^1,2, Jiabin Yu^1,2, Jing Li⁴, Ruichao Wang⁵

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5459-5471, 2022, DOI:10.32604/cmc.2022.029362 - 28 July 2022

Abstract Underwater sensor networks have important application value in the fields of water environment data collection, marine environment monitoring and so on. It has some characteristics such as low available bandwidth, large propagation delays and limited energy, which bring new challenges to the current researches. The research on coverage control of underwater sensor networks is the basis of other related researches. A good sensor node coverage control method can effectively improve the quality of water environment monitoring. Aiming at the problem of high dynamics and uncertainty of monitoring targets, the random events level are divided into… More >

Ling-Jing Kao¹, Chih-Chou Chiu^1,*, Yu-Fan Lin², Heong Kam Weng¹

Computer Systems Science and Engineering, Vol.42, No.2, pp. 493-508, 2022, DOI:10.32604/csse.2022.022166 - 04 January 2022

Abstract Inter-purchase time is a critical factor for predicting customer churn. Improving the prediction accuracy can exploit consumer’s preference and allow businesses to learn about product or pricing plan weak points, operation issues, as well as customer expectations to proactively reduce reasons for churn. Although remarkable progress has been made, classic statistical models are difficult to capture behavioral characteristics in transaction data because transaction data are dependent and short-, medium-, and long-term data are likely to interfere with each other sequentially. Different from literature, this study proposed a hybrid inter-purchase time prediction model for customers of… More >

Maojian Chen^1,2,3, Xiong Luo^1,2,3,*, Yueqin Zhu⁴, Yan Li^1,2,3, Wenbing Zhao⁵, Jinsong Wu⁶

Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 973-987, 2020, DOI:10.32604/iasc.2020.010129

Abstract The past decade has witnessed the rapid advancements of geological data analysis techniques, which facilitates the development of modern agricultural systems. However, there remains some technical challenges that should be addressed to fully exploit the potential of those geological big data, while gathering massive amounts of data in this application field. Generally, a good representation of correlation in the geological big data is critical to making full use of multi-source geological data, while discovering the relationship in data and mining mineral prediction information. Then, in this article, a scheme is proposed towards intelligent mining of More >

Guorong Cui, Hao Li, Yachuan Zhang, Rongjing Bu, Yan Kang^*, Jinyuan Li, Yang Hu

Journal of Quantum Computing, Vol.2, No.2, pp. 85-95, 2020, DOI:10.32604/jqc.2020.09717 - 19 October 2020

Abstract The traditional K-means clustering algorithm is difficult to determine the cluster number, which is sensitive to the initialization of the clustering center and easy to fall into local optimum. This paper proposes a clustering algorithm based on self-organizing mapping network and weight particle swarm optimization SOM&WPSO (Self-Organization Map and Weight Particle Swarm Optimization). Firstly, the algorithm takes the competitive learning mechanism of a self-organizing mapping network to divide the data samples into coarse clusters and obtain the clustering center. Then, the obtained clustering center is used as the initialization parameter of the weight particle swarm… More >

He Ni^1,*, Yongqiao Wang¹, Buyun Xu²

Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 227-236, 2020, DOI:10.31209/2019.100000068

Abstract The paper presents a probabilistic clustering approach based on self-organizing learning algorithm and recursive Bayesian estimation. The model is built upon the principle that the market data space is multimodal and can be described by a mixture of Gaussian distributions. The model parameters are approximated by a stochastic recursive Bayesian learning: searches for the maximum a posterior solution at each step, stochastically updates model parameters using a “dualneighbourhood” function with adaptive simulated annealing, and applies profile likelihood confidence interval to avoid prolonged learning. The proposed model is based on a number of pioneer works, such More >

Xiang Cao^1,2,3, Haichun Yu^1,3, Hongbing Sun^1,3

Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 25-34, 2019, DOI:10.31209/2018.100000038

Abstract For cooperative hunting by a multi-AUV (multiple autonomous underwater vehicles) team, not only basic problems such as path planning and collision avoidance should be considered but also task assignments in a dynamic way. In this paper, an integrated algorithm is proposed by combining the self-organizing map (SOM) neural network and the Glasius Bio-Inspired Neural Network (GBNN) approach to improve the efficiency of multi-AUV cooperative hunting. With this integrated algorithm, the SOM neural network is adopted for dynamic allocation, while the GBNN is employed for path planning. It deals with various situations for single/multiple target(s) hunting More >

Amin Alqudah^1,2, Hussein R. Al-Zoubi², Mahmood A. Al-Khassaweneh^2,3, Mohammed Al-Qodah¹

Intelligent Automation & Soft Computing, Vol.24, No.3, pp. 493-505, 2018, DOI:10.31209/2018.100000005

Abstract Handwritten numeral recognition is one of the most popular fields of research in automation because it is used in many applications. Indeed, automation has continually received substantial attention from researchers. Therefore, great efforts have been made to devise accurate recognition methods with high recognition ratios. In this paper, we propose a method for integrating the correlation coefficient with a Self-Organizing Maps (SOM)-based technique to recognize offline handwritten Arabic decimal digits. The simulation results show very high recognition rates compared with the rates achieved by other existing methods. More >

Marco Götz^1,*, Ferenc Leichsenring¹, Thomas Kropp², Peter Müller², Tobias Falk², Wolfgang Graf¹, Michael Kaliske¹, Welf-Guntram Drossel²

CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 387-423, 2018, DOI:10.31614/cmes.2018.04112

Abstract Numerical mechanical models used for design of structures and processes are very complex and high-dimensionally parametrised. The understanding of the model characteristics is of interest for engineering tasks and subsequently for an efficient design. Multiple analysis methods are known and available to gain insight into existing models. In this contribution, selected methods from various fields are applied to a real world mechanical engineering example of a currently developed clinching process. The selection of introduced methods comprises techniques of machine learning and data mining, in which the utilization is aiming at a decreased numerical effort. The More >