Shu Gao^a,b,*, Zhen Wang^a, Liangchen Chen^a

Intelligent Automation & Soft Computing, Vol.25, No.4, pp. 683-689, 2019, DOI:10.31209/2018.100000061

Abstract The explosive growth of data produced by internet of things has contributed to the abundance of data. Since then, efficient indexing and querying techniques for data retrieval has become a major challenge. Bitmap index and its extension techniques, which involve a bit sequence that represents a specified property and indicates the data items that satisfies this property, are well-known methods to improve processing time for complex and interactive queries on the read-mostly or append-only data. This paper proposes an improved bitmap index technique, named Sliced-Interval Bitmap Index (SI Bitmap Index), which is efficient in both More >

Alberto Fernández Oliva¹, Francisco Maciá Pérez², José Vicente Berná-Martinez^2,*, Miguel Abreu Ortega³

Computer Systems Science and Engineering, Vol.34, No.3, pp. 131-144, 2019, DOI:10.32604/csse.2019.34.131

Abstract This study presents a method for the detection of outliers based on the Variable Precision Rough Set Model (VPRSM). The basis of this model is the generalisation of the standard concept of a set inclusion relation on which the Rough Set Basic Model (RSBM) is based. The primary contribution of this study is the improvement in detection quality, which is achieved due to the generalisation allowed by the classification system that allows a certain degree of uncertainty. From this method, a computationally efficient algorithm is proposed. The experiments performed with a real scenario and a More >

Preeti Tiwari^{1, *}, Piyush Shukla¹

Journal on Artificial Intelligence, Vol.1, No.2, pp. 45-58, 2019, DOI:10.32604/jai.2019.04444

Abstract The prediction of crop yield is one of the important factor and also challenging, to predict the future crop yield based on various criteria’s. Many advanced technologies are incorporated in the agricultural processes, which enhances the crop yield production efficiency. The process of predicting the crop yield can be done by taking agriculture data, which helps to analyze and make important decisions before and during cultivation. This paper focuses on the prediction of crop yield, where two models of machine learning are developed for this work. One is Modified Convolutional Neural Network (MCNN), and the… More >

Pingshui Wang^1,*, Tao Chen^1,2, Zecheng Wang¹

Journal of Information Hiding and Privacy Protection, Vol.1, No.2, pp. 61-68, 2019, DOI:10.32604/jihpp.2019.05943

Abstract In recent years, with the explosive development in Internet, data storage and data processing technologies, privacy preservation has been one of the greater concerns in data mining. A number of methods and techniques have been developed for privacy preserving data mining. This paper provided a wide survey of different privacy preserving data mining algorithms and analyzed the representative techniques for privacy preservation. The existing problems and directions for future research are also discussed. More >

A. Joshuva¹, V. Sugumaran²

Structural Durability & Health Monitoring, Vol.13, No.2, pp. 181-203, 2019, DOI:10.32604/sdhm.2019.00287

Abstract Wind turbine blades are generally manufactured using fiber type material because of their cost effectiveness and light weight property however, blade get damaged due to wind gusts, bad weather conditions, unpredictable aerodynamic forces, lightning strikes and gravitational loads which causes crack on the surface of wind turbine blade. It is very much essential to identify the damage on blade before it crashes catastrophically which might possibly destroy the complete wind turbine. In this paper, a fifteen tree classification based machine learning algorithms were modelled for identifying and detecting the crack on wind turbine blades. The More >

Chen Yu^∗, Qinmin Hong, Dezhong Yao, Hai Jin

Computer Systems Science and Engineering, Vol.33, No.2, pp. 87-94, 2018, DOI:10.32604/csse.2018.33.087

Abstract The rapid expansion of GPS-embedded devices has showed the emerging new look of location-based services, enabling such offerings as travel guide services and location-based social networks. One consequence is the accumulation of a rich supply of GPS trajectories, indicating individuals’ historical position. Based on these data, we aim to mine the hot route by using a collaborative tensor calculation method. We present an efficient trajectory data processing model for mining the hot route. In this paper, we rst model the individual’s trajectory log, extract sources and destinations, use map matching to get the corresponding road More >

Zheng Xu, Zhiguo Yan

Intelligent Automation & Soft Computing, Vol.24, No.3, pp. 517-518, 2018, DOI:10.31209/2018.100000018

Abstract This article has no abstract. More >

Qian Wang^a,b,c, Jiadong Ren^a,b, Darryl N Davis^c, Yongqiang Cheng^c

Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 399-404, 2018, DOI:10.1080/10798587.2017.1340135

Abstract Frequent pattern mining usually requires much run time and memory usage. In some applications, only the patterns with top frequency rank are needed. Because of the limited pattern numbers, quality of the results is even more important than time and memory consumption. A Frequent Pattern algorithm for mining Top-rank-K patterns, FP_TopK, is proposed. It is based on a Node-list data structure extracted from FTPP-tree. Each node is with one or more triple sets, which contain supports, preorder and postorder transversal orders for candidate pattern generation and top-rank-k frequent pattern mining. FP_ TopK uses the minimal 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 >

A.S. Koyuncugil¹, N. Ozgulbas²

The International Conference on Computational & Experimental Engineering and Sciences, Vol.11, No.2, pp. 39-46, 2009, DOI:10.3970/icces.2009.011.039

Abstract In this paper, a data mining model for detecting financial and operational risk indicators by CHAID Decision Tree is presenting. The identification of the risk factors by clarifying the relationship between the variables defines the discovery of knowledge from the financial and operational variables. Automatic and estimation oriented information discovery process coincides the definition of data mining. During the formation of model; an easy to understand, easy to interpret and easy to apply utilitarian model that is far from the requirement of theoretical background is targeted by the discovery of the implicit relationships between the More >