Soft Computing in Context-Sensitive Multidimensional Ranking

2006 ◽  
pp. 1374-1381
Author(s):  
Weber Martins ◽  
Lauro Eugênio Guimarães Nalini ◽  
Marco Antonio Assfalk de Oliveira ◽  
Leonardo Guerra de Rezende Guedes
Keyword(s):  
Soft Computing ◽  
Context Sensitive

Soft Computing in Electromagnetics

2015 ◽  
Author(s):  
Balamati Choudhury ◽  
Rakesh Mohan Jha
Keyword(s):  
Soft Computing

Neuropsychology in the Real World

2014 ◽  
Vol 25 (4) ◽  
pp. 233-238 ◽  
Author(s):  
Martin Peper ◽  
Simone N. Loeffler
Keyword(s):  
Neuropsychological Assessment ◽  
Real World ◽  
Ecological Validity ◽  
Real Life ◽  
Emotional States ◽  
Context Sensitive ◽  
Traditional Assessment ◽  
Life Data ◽  
Real Life Data ◽  
Assessment And Treatment

Current ambulatory technologies are highly relevant for neuropsychological assessment and treatment as they provide a gateway to real life data. Ambulatory assessment of cognitive complaints, skills and emotional states in natural contexts provides information that has a greater ecological validity than traditional assessment approaches. This issue presents an overview of current technological and methodological innovations, opportunities, problems and limitations of these methods designed for the context-sensitive measurement of cognitive, emotional and behavioral function. The usefulness of selected ambulatory approaches is demonstrated and their relevance for an ecologically valid neuropsychology is highlighted.

Context -Sensitive Adjustment of Cognitive Control in Dual -Task Performance

2013 ◽  
Author(s):  
Rico Fischer ◽  
Caroline Gottschalk ◽  
Gesine Dreisbach
Keyword(s):  
Cognitive Control ◽  
Task Performance ◽  
Dual Task ◽  
Context Sensitive ◽  
Dual Task Performance

MACHINE LEARNING FOR SOFTWARE

2019 ◽  
pp. 251-255
Author(s):  
Shafagat Mahmudova
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Soft Computing ◽  
Computing Technology

The study machine learning for software based on Soft Computing technology. It analyzes Soft Computing components. Their use in software, their advantages and challenges are studied. Machine learning and its features are highlighted. The functions and features of neural networks are clarified, and recommendations were given.

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