Direct construction of optical linear transform and its application on arbitrary optical complex data generation

Correlativity of perception is defined as a capacity to discover similar configurations of stimuli and to form high- level configurations from them. It is equivalent to describing information in terms of generative elements and their transformations. Such a representation saves memory and reveals causality in data generation. This approach is implemented in a model of artificial perception wherein data are selforganized in order to segregate patterns before recognizing them. Input information is described as generative patterns and their transformations. The least complex data representation that leads to a causally related semantic description is chosen, with (Kolmogorov) complexity defined by the amount of memory store required. The model is applied to voice separation and to rhythm/tempo tracking. Chord spectra are described by generative subspectra, which correspond to tonal spectra, and by their translations, which coincide with the intervals of the chord. Time events are also described by generative rhythmic patterns. Tempo and rhythm interdependence is overcome by the optimal sharing of complexity between representations of rhythmic patterns and tempo curve. The model explains the function of interval hearing, certain statements of music theory, and some effects of rhythm perception. Applications to image processing and modeling of abstract thinking are also discussed.

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Test Data Generation for Complex Data Types Using Imprecise Model Constraints and Constraint Solving Techniques

Information Technology And Control ◽

10.5755/j01.itc.42.2.1855 ◽

2013 ◽

Vol 42 (2) ◽

Author(s):

Sarunas Packevicius ◽

Greta Krivickaite ◽

Dominykas Barisas ◽

Robertas Jasaitis ◽

Tomas Blazauskas ◽

...

Keyword(s):

Test Data ◽

Constraint Solving ◽

Complex Data ◽

Test Data Generation ◽

Data Generation ◽

Data Types

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Implementation of data mining as a support of business application strategy

Journal of Applied Information, Communication and Technology ◽

10.33555/ejaict.v5i1.49 ◽

2018 ◽

Vol 5 (1) ◽

pp. 47-55

Author(s):

Florensia Unggul Damayanti

Keyword(s):

Data Mining ◽

Random Forest ◽

Business Strategy ◽

Input Parameter ◽

Data Mining Algorithm ◽

Complex Data ◽

Business Decision ◽

Marketing Department ◽

Business Application ◽

Complex Data Sets

Data mining help industries create intelligent decision on complex problems. Data mining algorithm can be applied to the data in order to forecasting, identity pattern, make rules and recommendations, analyze the sequence in complex data sets and retrieve fresh insights. Yet, increasing of technology and various techniques among data mining availability data give opportunity to industries to explore and gain valuable information from their data and use the information to support business decision making. This paper implement classification data mining in order to retrieve knowledge in customer databases to support marketing department while planning strategy for predict plan premium. The dataset decompose into conceptual analytic to identify characteristic data that can be used as input parameter of data mining model. Business decision and application is characterized by processing step, processing characteristic and processing outcome (Seng, J.L., Chen T.C. 2010). This paper set up experimental of data mining based on J48 and Random Forest classifiers and put a light on performance evaluation between J48 and random forest in the context of dataset in insurance industries. The experiment result are about classification accuracy and efficiency of J48 and Random Forest , also find out the most attribute that can be used to predict plan premium in context of strategic planning to support business strategy.

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