Editorial to the ‘pattern recognition and artificial intelligence for human behaviour analysis' special section

2013 ◽  
Vol 30 (2) ◽  
pp. 99-100 ◽  
Author(s):  
Luca Iocchi ◽  
Andrea Prati ◽  
Roberto Vezzani
Keyword(s):  
Artificial Intelligence ◽  
Pattern Recognition ◽  
Special Section ◽  
Human Behaviour ◽  
Behaviour Analysis

scholarly journals Statistical Machine Learning for Human Behaviour Analysis

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 530
Author(s):  
Thomas B. Moeslund ◽  
Sergio Escalera ◽  
Gholamreza Anbarjafari ◽  
Kamal Nasrollahi ◽  
Jun Wan
Keyword(s):  
Machine Learning ◽  
Information Theory ◽  
Pattern Recognition ◽  
Affective Computing ◽  
Human Behaviour ◽  
Statistical Machine Learning ◽  
Behaviour Analysis

Human behaviour analysis has introduced several challenges in various fields, such as applied information theory, affective computing, robotics, biometrics and pattern recognition [...]

Principles of induction and approaches to attribute based induction

1991 ◽  
Vol 6 (4) ◽  
pp. 307-333 ◽  
Author(s):  
G. Kalkanis ◽  
G. V. Conroy
Keyword(s):  
Artificial Intelligence ◽  
Pattern Recognition ◽  
Cognitive Psychology ◽  
Expert Systems ◽  
Basic Principles ◽  
Inductive Approach ◽  
Induction Process

AbstractThis paper presents a survey of machine induction, studied mainly from the field of artificial intelligence, but also from the fields of pattern recognition and cognitive psychology. The paper consists of two parts: Part I discusses the basic principles and features of the machine induction process; Part II uses these principles and features to review and criticize the major supervised attribute-based induction methods. Attribute-based induction has been chosen because it is the most commonly used inductive approach in the development of expert systems and pattern recognition models.

scholarly journals IEEE TCCN Special Section Editorial: Machine Learning and Artificial Intelligence for the Physical Layer

2021 ◽  
Vol 7 (1) ◽  
pp. 1-4
Author(s):  
Chunxiao Jiang ◽  
Guoru Ding ◽  
Aly El Gamal ◽  
Andrea Zanella ◽  
Oliver Holland ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Special Section ◽  
Physical Layer

Pattern Recognition in Musculoskeletal Imaging Using Artificial Intelligence

2020 ◽  
Vol 24 (01) ◽  
pp. 38-49 ◽  
Author(s):  
Natalia Gorelik ◽  
Jaron Chong ◽  
Dana J. Lin
Keyword(s):  
Artificial Intelligence ◽  
Pattern Recognition ◽  
Bone Tumors ◽  
Image Interpretation ◽  
Bone Age ◽  
Musculoskeletal Imaging ◽  
Radiology Workflow ◽  
Disease Response ◽  
Osseous Metastases ◽  
Applications Of Ai

AbstractArtificial intelligence (AI) has the potential to affect every step of the radiology workflow, but the AI application that has received the most press in recent years is image interpretation, with numerous articles describing how AI can help detect and characterize abnormalities as well as monitor disease response. Many AI-based image interpretation tasks for musculoskeletal (MSK) pathologies have been studied, including the diagnosis of bone tumors, detection of osseous metastases, assessment of bone age, identification of fractures, and detection and grading of osteoarthritis. This article explores the applications of AI for image interpretation of MSK pathologies.

scholarly journals Human Behaviour Analysis of Barrier Deviations Using a Benefit-Cost-Deficit Model

2009 ◽  
Vol 2009 ◽  
pp. 1-10 ◽  
Author(s):  
Philippe Polet ◽  
Frédéric Vanderhaegen ◽  
Patrick Millot
Keyword(s):  
Experimental Study ◽  
Human Behaviour ◽  
Safety Criterion ◽  
Human Errors ◽  
Deficit Model ◽  
Behaviour Analysis ◽  
Barrier Removal ◽  
Human Operators ◽  
Benefit Cost

A Benefit-Cost-Deficit (BCD) model is proposed for analyzing such intentional human errors as barrier removal, the deliberate nonrespect of the rules and instructions governing use of a given system. The proposed BCD model attempts to explain and predict barrier removal in terms of the benefits, costs, and potential deficits associated with this human behaviour. The results of an experimental study conducted on a railway simulator (TRANSPAL) are used to illustrate the advantages of the BCD model. In this study, human operators were faced with barriers that they could choose to deactivate, or not. Their decisions were analyzed in an attempt to explain and predict their choices. The analysis highlights that operators make their decisions using a balance between several criteria. Though barriers are safety-related elements, the decision to remove them is not guided only by the safety criterion; it is also motivated by such criteria as productivity, workload, and quality. Results of prediction supported by the BCD demonstrate the predictability of barrier violation

scholarly journals Serious games for the human behaviour analysis in emergency evacuation scenarios

2017 ◽  
Vol 20 (1) ◽  
pp. 707-720 ◽  
Author(s):  
João Emílio Almeida ◽  
Rosaldo J. F. Rossetti ◽  
João Tiago Pinheiro Neto Jacob ◽  
Brígida Mónica Faria ◽  
António Leça Coelho
Keyword(s):  
Serious Games ◽  
Emergency Evacuation ◽  
Human Behaviour ◽  
Behaviour Analysis
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