One of the approaches in pattern recognition is the use of fractal geometry. The property of self-similarity of fractals has been used as a feature in several pattern recognition methods. All fractal recognition methods use global analysis of the shape. In this paper we present some drawbacks of these methods and propose fractal local analysis using partitioned iterated function systems with division. Moreover, we introduce a new fractal recognition method based on a dependence graph obtained from the partitioned iterated function system. The proposed method uses local analysis of the shape, which improves the recognition rate. The effectiveness of our method is shown on two test databases. The first one was created by the authors and the second one is the MPEG7 CE-Shape-1 PartB database. The obtained results show that the proposed methodology has led to a significant improvement in the recognition rate.

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Partitioned iterated function systems with division and a fractal dependence graph in recognition of 2D shapes

Text classification problem receives a lot of research that are based on machine learning, statistical, and information retrieval techniques. In the last decade, the associative classification algorithms which depends on pure data mining techniques appears as an effective method for classification. In this paper, we examine associative classification approach on the Arabic language to mine knowledge from Arabic text data set. Two methods of classification using AC are applied in this study; these methods are single rule prediction and multiple rule prediction. The experimental results against different classes of Arabic data set show that multiple rule prediction method outperforms single rule prediction method with regards to their accuracy. In general, the associative classification approach is a suitable method to classify Arabic text data set, and is able to achieve a good classification performance in terms of classification time and classification accuracy.

Text associative classification approach for mining Arabic data set

The invention relates to a knowledge-based fault prediction expert system for a complex milling machine tool, which comprises a man-machine interface module, a data acquisition and preprocessing module, a knowledge acquisition module based on data mining, a self-adaptive fault prediction module, a fault diagnosis expert system, an interpreter, an inference machine, a prediction method library, an integrated knowledge library, a model library and a diagnosis module; wherein the man-machine interface module is used for transmitting real-detection data and historical data to other modules; the data acquisition and preprocessing module is used for preprocessing the received data and inputting the preprocessed data to the knowledge acquisition module for data mining; the knowledge acquisition module is connected with the fault diagnosis expert system through the fault prediction module; the fault prediction module and the fault diagnosis expert system input fault prediction and diagnosis results into the inference machine, and the inference machine search in the integrated knowledge library and the model library; and the fault prediction module establishes a selection target function, and the inference machine selects a proper prediction method for fault prediction from the prediction method library. The invention can be widely applied in the fault diagnosis of various numerically-controlled machine tools.

Knowledge-based fault prediction expert system for complex milling machine tool

Due to the mass availability of textual data on Web, text classification (TC), classifying texts into predetermined sets becomes a spotlight for researchers. A number of TC applications have been proposed yet very few studies reported an overview of TC research area in a proper and systematic manner. This paper aims to provide an overview of TC research trends and gaps by structuring and analyzing research patterns, encountered problems and problem-solving methods in TC. In other words, this study highlights problem types, data sources, choice of language of text and types of applied techniques in TC. An intensive systematic study is conducted by applying guidelines proposed by Petersen and colleagues in 2007. In this paper, ninety-six literatures from five electronic databases from 2006 to 2017 were systematically reviewed and followed each and every step properly in accordance with systematic mapping study. Nine main problems in TC research area were identified and significant findings which highlighted the evolution of TC research within the past 12 years were investigated. Different from other review articles, this paper highlighted issues and technical gaps of TC area in a useful and effective manner.

Trends and patterns of text classification techniques: a systematic mapping study

The invention provides a data mining based automatic research flow well logging evaluation expert system and belongs to the technical field of geophysical well logging. The system comprises a data element model base, a classic knowledge base, an artificial intelligent knowledge base, a user application interface module, a regional research module and a processing and interpretation evaluation module. The data mining technique and the traditional well logging evaluation technique are combined to establish a classic knowledge base model and the artificial intelligent knowledge base in flow automation, real-time comprehensive flow guide and technical support of experts are provided for users to perform rapid well logging evaluation of blocks, and the automatic research flow well logging evaluation expert system is realized.

Data mining based automatic research flow well logging evaluation expert system

The invention relates to a complicated industrial production intelligent system and a constructing method thereof. The method comprises the following steps: receiving industrial production data by a computer network; extracting, switching and loading the received industrial production data by an abnormal judgment module; carrying out data excavation and multidimensional analysis by a six-sigma subsystem; and reasoning by fuzzy expert subsystems comprising a multidimensional analysis subsystem, a data excavation subsystem, a fuzzy expert subsystem and the six-sigma subsystem penetrating through the three subsystems. The invention provides an intelligent system constructing method facing to complicated industrial production by learning a core idea of business intelligence and combining the self characteristics of the industrial production field. The application range of the business intelligence can be expanded, and the intelligent level of industrial production is improved.

Complicated industrial production intelligent system and constructing method thereof

In this paper, we provide an efficient method to generate the terrain used in games and simulation systems. We propose the midpoint displacement method to generate the heightmap of terrain, and give the procedural texture method based height value to create the texture map of terrain. This method can generate different style terrain by changing the roughness and sampling percent. The terrain being created by this method can be used in simulation system and games, so a lot of scientific research fee to buy DEM data can be saved. Firstly, this paper shows the midpoint displacement method. Then this paper provides the procedural texture method based height value. At last, this paper gives the implements. The implements prove that our method is feasible and valid. And our method has also been used in the practical projects.

Photorealistic terrain generation method based on fractal geometry theory and procedural texture

Text categorization has become one of the key techniques for handling and organizing text data. In practical text classification tasks, the ability to interpret the classification result is as important as the ability to classify exactly. Associative classifiers have many favorable characteristics such as rapid training, good classification accuracy, and excellent interpretation. In this paper, Closed-AC, which is a new associative classifier for text categorization, is proposed. Firstly, rough set is used to dimension reduction. Then, only generic rules composed of closed itemsets are used for classification. Experimental results show benefits of the proposed associative classifier.

A new associative classifier for text categorization

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Complicated industrial production intelligent system and constructing method thereof

Photorealistic terrain generation method based on fractal geometry theory and procedural texture

A new associative classifier for text categorization