Opportunity Acknowledgement as a Cognitive Process of Pattern Recognition and Structural Alignment (Executive Summary)

Understanding evidence-based diagnosis

Diagnosis ◽

10.1515/dx-2013-0003 ◽

2014 ◽

Vol 1 (1) ◽

pp. 39-42 ◽

Cited By ~ 1

Author(s):

Michael A. Kohn

Keyword(s):

Pattern Recognition ◽

Cognitive Process ◽

Treatment Decisions ◽

Evidence Based ◽

Test Results ◽

The Real ◽

Medical Tests ◽

Serious Disease ◽

Mathematical Process

AbstractThe real meaning of the word “diagnosis” is naming the disease that is causing a patient’s illness. The cognitive process of assigning this name is a mysterious combination of pattern recognition and the hypothetico-deductive approach that is only remotely related to the mathematical process of using test results to update the probability of a disease. What I refer to as “evidence-based diagnosis” is really evidence-based use of medical tests to guide treatment decisions. Understanding how to use test results to update the probability of disease can help us interpret test results more rationally. Also, evidence-based diagnosis reminds us to consider the costs and risks of testing and the dangers of over-diagnosis and over-treatment, in addition to the costs and risks of missing serious disease.

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Introductory Chapter: Pattern Recognition as Cognitive Process

Pattern Recognition - Selected Methods and Applications ◽

10.5772/intechopen.85826 ◽

2019 ◽

Author(s):

Andrzej Zak

Keyword(s):

Pattern Recognition ◽

Cognitive Process

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On Visual Semantic Algebra (VSA) and the cognitive process of pattern recognition

2008 7th IEEE International Conference on Cognitive Informatics ◽

10.1109/coginf.2008.4639192 ◽

2008 ◽

Cited By ~ 1

Author(s):

Yingxu Wang

Keyword(s):

Pattern Recognition ◽

Cognitive Process

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Psikologi Peluang Kewirausahaan: Proses Kognitif Pengusaha Startup Digital dalam Opportunity Recognition

Jurnal Psikologi ◽

10.22146/jpsi.34608 ◽

2019 ◽

Vol 46 (1) ◽

pp. 45

Author(s):

Sadida Fatin Aruni ◽

Rahmat Hidayat

Keyword(s):

Cognitive Processes ◽

Cognitive Process ◽

Total Population ◽

Structural Alignment ◽

Personal Characteristics ◽

Developed Countries ◽

Opportunity Recognition ◽

Personality Characteristics ◽

Market Demand ◽

Entrepreneurial Opportunities

Developed countries have at least 2% entrepreneurs from the total population of the country. However, Indonesia have a very low number of entrepreneurs as well as an increasing unemployment rate over the years. Recently, the development of entrepreneurship in Indonesia dominated by the growing number of digital startup. Moreover, Indonesia is a country with the largest number of digital startup in Southeast Asia. Initial psychological studies on entrepreneurship are focused on the personal characteristics of the entrepreneurs. However, conclusions from more than 30 years of research indicate that there are no special personality characteristics of entrepreneurs and non-entrepreneurs. In essence, the heart of entrepreneurship lies in the ability to recognize an opportunity. Opportunity recognition is a mechanism that happens in an individual’s cognitive process. Therefore, this study intended to reveal the cognitive processes that take place when entrepreneurs, in particular the founders of digital startups, in the identification of entrepreneurial opportunities. This research use think aloud protocol method with protocol analysis. Based on this research, we found that in the process of opportunity recognition, entrepreneurs focus their cognitive efforts on the market (demand) and technology (supply) as well as build relationships and meaningful patterns in these two aspects through structural relationship processing. This study provides an in-depth description of the cognitive processes that occur when entrepreneurs recognize entrepreneurial opportunities through structural alignment processes.

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Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010014484x ◽

1986 ◽

Vol 44 ◽

pp. 694-695

Author(s):

G.Y. Fan ◽

J.M. Cowley

Keyword(s):

Image Processing ◽

Pattern Recognition ◽

Computer Software ◽

Processing System ◽

Light Level ◽

Host Computer ◽

Low Light ◽

Image Processing System ◽

Recent Developments ◽

On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

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Bayesian removal of noise for increased sensitivity in vector pattern recognition lattice imaging of interfaces

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100150800 ◽

1993 ◽

Vol 51 ◽

pp. 994-995

Author(s):

L. Fei ◽

P. Fraundorf

Keyword(s):

Pattern Recognition ◽

Perfect Crystal ◽

Unit Cell ◽

Ion Milling ◽

Beam Radiation ◽

Major Interest ◽

Unit Cells ◽

Mapping Process ◽

Increased Sensitivity ◽

Electron Microscopes

Interface structure is of major interest in microscopy. With high resolution transmission electron microscopes (TEMs) and scanning probe microscopes, it is possible to reveal structure of interfaces in unit cells, in some cases with atomic resolution. A. Ourmazd et al. proposed quantifying such observations by using vector pattern recognition to map chemical composition changes across the interface in TEM images with unit cell resolution. The sensitivity of the mapping process, however, is limited by the repeatability of unit cell images of perfect crystal, and hence by the amount of delocalized noise, e.g. due to ion milling or beam radiation damage. Bayesian removal of noise, based on statistical inference, can be used to reduce the amount of non-periodic noise in images after acquisition. The basic principle of Bayesian phase-model background subtraction, according to our previous study, is that the optimum (rms error minimizing strategy) Fourier phases of the noise can be obtained provided the amplitudes of the noise is given, while the noise amplitude can often be estimated from the image itself.

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