Early Stage Fire Source Classification in Building using Artificial Intelligence

2018 ◽  
Author(s):  
A. M. Andrew ◽  
A.Y.M. Shakaff ◽  
A. Zakaria ◽  
R. Gunasagaran ◽  
E. Kanagaraj ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Early Stage ◽  
Fire Source

The European Union Artificial Intelligence Act: Potential implications for Australia

2021 ◽  
pp. 1037969X2110523
Author(s):  
Dan Svantesson
Keyword(s):  
Artificial Intelligence ◽  
European Union ◽  
Early Stage ◽  
The European Union ◽  
Key Features ◽  
Proposed Regulation

The European Union (EU) published its proposed Regulation laying down harmonised rules for Artificial Intelligence (the Artificial Intelligence Act) on 21 April 2021. Once it comes into force, this Act will impact upon Australia. It is therefore important that Australians take note of the proposal at this relatively early stage. This article brings attention to the key features of the EU’s proposed Artificial Intelligence Act. However, the main aim is to highlight why it is important for Australia and to examine, in some detail, the rules that will determine when the Act applies to Australians.

Early Stage Detection of Psoriasis Using Artificial Intelligence and Image Processing

2020 ◽  
pp. 1199-1208
Author(s):  
D. R. Kalbande ◽  
Uday Khopkar ◽  
Avinash Sharma ◽  
Neil Daftary ◽  
Yash Kokate ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Early Stage

Fuzzy K-Nearest Neighbour (FkNN) Based Early Stage Fire Source Classification in Building

2018 ◽  
Author(s):  
A. M. Andrew ◽  
A. Y. M. Shakaff ◽  
A. Zakaria ◽  
R. Gunasagaran ◽  
E. Kanagaraj ◽  
...  
Keyword(s):  
Early Stage ◽  
Nearest Neighbour ◽  
Fire Source

Tie me to the mast: artificial intelligence & reputation risk management

2018 ◽  
Vol 39 (1) ◽  
pp. 61-64 ◽  
Author(s):  
Peter Buell Hirsch
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Risk Management ◽  
Thought Experiment ◽  
Early Stage ◽  
Stress Test ◽  
Risky Behaviors ◽  
Content Type ◽  
Early Signs ◽  
Reputation Risk

Purpose Artificial intelligence and machine learning have spread rapidly across every aspect of business and social activity. The purpose of this paper is to examine how this rapidly growing field of analytics might be put to use in the area of reputation risk management. Design/methodology/approach The approach taken was to examine in detail the primary and emerging applications of artificial intelligence to determine how they could be applied to preventing and mitigating reputation risk by using machine learning to identify early signs of behaviors that could lead to reputation damage. Findings This review confirmed that there were at least two areas in which artificial intelligence could be applied to reputation risk management – the use of machine learning to analyze employee emails in real time to detect early signs of aberrant behavior and the use of algorithmic game theory to stress test business decisions to determine whether they contained perverse incentives leading to potential fraud. Research limitations/implications Because of the fact that this viewpoint is by its nature a thought experiment, the authors have not yet tested the practicality or feasibility of the uses of artificial intelligence it describes. Practical implications Should the concepts described be viable in real-world application, they would create extraordinarily powerful tools for companies to identify risky behaviors in development long before they had run far enough to create major reputation risk. Social implications By identifying risky behaviors at an early stage and preventing them from turning into reputation risks, the methods described could help restore and maintain trust in the relationship between companies and their stakeholders. Originality/value To the best of the author’s knowledge, artificial intelligence has never been described as a potential tool in reputation risk management.

scholarly journals Artificial intelligence in paediatric radiology: Future opportunities

2021 ◽  
Vol 94 (1117) ◽  
pp. 20200975
Author(s):  
Natasha Davendralingam ◽  
Neil J Sebire ◽  
Owen J Arthurs ◽  
Susan C Shelmerdine
Keyword(s):  
Artificial Intelligence ◽  
High Performance ◽  
Early Stage ◽  
Image Interpretation ◽  
Paediatric Radiology ◽  
Radiology Service ◽  
Clinical Radiology ◽  
The Media ◽  
Performance Statistics ◽  
Image Pattern

Artificial intelligence (AI) has received widespread and growing interest in healthcare, as a method to save time, cost and improve efficiencies. The high-performance statistics and diagnostic accuracies reported by using AI algorithms (with respect to predefined reference standards), particularly from image pattern recognition studies, have resulted in extensive applications proposed for clinical radiology, especially for enhanced image interpretation. Whilst certain sub-speciality areas in radiology, such as those relating to cancer screening, have received wide-spread attention in the media and scientific community, children’s imaging has been hitherto neglected. In this article, we discuss a variety of possible ‘use cases’ in paediatric radiology from a patient pathway perspective where AI has either been implemented or shown early-stage feasibility, while also taking inspiration from the adult literature to propose potential areas for future development. We aim to demonstrate how a ‘future, enhanced paediatric radiology service’ could operate and to stimulate further discussion with avenues for research.

scholarly journals Can computers understand words like humans do? Comparable semantic representation in neural and computer systems

2019 ◽  
Author(s):  
Linmin Zhang ◽  
Lingting Wang ◽  
Jinbiao Yang ◽  
Peng Qian ◽  
Xuefei Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Word Processing ◽  
Semantic Processing ◽  
Computational Models ◽  
Time Window ◽  
Early Stage ◽  
Semantic Representation ◽  
Deep Understanding ◽  
Joint Force ◽  
Strong Artificial Intelligence

AbstractSemantic representation has been studied independently in neuroscience and computer science. A deep understanding of human neural computations and the revolution to strong artificial intelligence appeal for a joint force in the language domain. We investigated comparable representational formats of lexical semantics between these two complex systems with fine temporal resolution neural recordings. We found semantic representations generated from computational models significantly correlated with EEG responses at an early stage of a typical semantic processing time window in a two-word semantic priming paradigm. Moreover, three representative computational models differentially predicted EEG responses along the dynamics of word processing. Our study provided a finer-grained understanding of the neural dynamics underlying semantic processing and developed an objective biomarker for assessing human-like computation in computational models. Our novel framework trailblazed a promising way to bridge across disciplines in the investigation of higher-order cognitive functions in human and artificial intelligence.

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