A Neural Network Model for Time-Series Forecasting

The paper presents some aspects regarding the use of pattern recognition techniques and neural networks for the activity evolution diagnostication and prediction by means of a set of indicators. Starting from the indicators set there is defined a measure on the patterns set, measure representing a scalar value that characterizes the activity analyzed at each time moment. A pattern is defined by the values of the indicators set at a given time. Over the classes set obtained by means of the classification and recognition techniques is defined a relation that allows the representation of the evolution from negative evolution towards positive evolution. For the diagnostication and prediction the following tools are used: pattern recognition and multilayer perceptron. The paper also presents the REFORME software written by the authors and the results of the experiment obtained with this software for macroeconomic diagnostication and prediction during the years 2003-2010.time-series, pattern recognition, neural networks, multilayer perceptron, diagnostication, forecasting

ANALISIS KEMAMPUAN COMPUTATIONAL THINKING SISWA DALAM MENYELESAIKAN SOAL HOTS OPEN ENDED DITINJAU DARI GAYA KOGNITIF REFLEKTIF DAN IMPULSIF

The ability of computational thinking is the ability needed to balance the development in the 21st century digitalization era. The purpose of this study was to describe the ability of students' computational thinking in solving open ended HOTS problems in terms of reflective and impulsive cognitive styles. This research is a descriptive research with qualitative approach with the research subjects are four students of class VIII SMP Muhammadiyah 1 Sumberpucung, namely two students with reflective cognitive style and two students with impulsive cognitive style. The results of this study found that the ability of computational thinking students with reflective cognitive style is in the high category while students with impulsive cognitive style in the medium category. R1 can fulfill the aspects of decomposition, pattern recognition, abstraction, and algorithm design but the solution obtained is not correct because it is less careful when working. R2 also fulfills all four aspects and the solution obtained is correct. I1 and I2 both could not fulfill the algorithm design aspect, but could fulfill the aspects of decomposition, pattern recognition, and abstraction even though they were still less precise

Trends in Pattern Recognition and Machine Learning

This paper is tutorial in nature introducing the statistical and syntactic pattern recognition technique. The problem of pattern recognition has special reference with image analysis and some aspects of modern methods and application of the area of shape analysis and detection of objects included

Interactive Pattern Analysis and Recognition

I want to stress the procedural aspects of solving pattern recognition problems. What are the tools, what are the thought processes, and what are the algorithms that you may need to do an efficient job of creating your pattern recognition design? I hope after this presentation you\u27ll appreciate why we have spent many years in developing tools for creating pattern recognition and their designs. These are mainly computer-based, display oriented, large systems that have a comprehensive assortment of algorithms that can be easily accessed by experimenters

Adaptive expert decision making: Skilled chess players search more and deeper

Previous research has suggested that depth of search in chess does not increase much as a function of skill. We submitted players to a problem-solving task with complex positions. We found a strong skill effect in depth of search, rate of search, and number of nodes generated. At the level of strong masters, the absolute values of these variables were much higher than in previous studies (sometimes 10 times higher). Supplementary data on memory, practice, reaction times, and time-constrained decision making (a maximum decision time of 10 seconds) indicated that players’ behaviour was consistent with the behaviour of players previously studied in the literature and with predictions of theories based on pattern recognition. Beyond adding support to the hypothesis that both the ability to search and pattern recognition are relevant aspects of expert thinking, these results are important in showing that previous research has vastly underestimated experts’ search potential. We conclude that long-term memory knowledge allows both extensive search and rapid evaluation when making decisions under time pressure. Players adaptively use either problem-solving method depending on the demands of the task