scholarly journals The Potential of the SP System in Machine Learning and Data Analysis for Image Processing

2021 ◽  
Vol 5 (1) ◽  
pp. 7
Author(s):  
J. Gerard Wolff
Keyword(s):  
Pattern Recognition ◽  
Human Learning ◽  
Multiple Alignment ◽  
Scene Analysis ◽  
Syntactic Parsing ◽  
Information Compression ◽  
Levels Of Abstraction ◽  
Theory Of Intelligence ◽  
The Face ◽  
Multiple Levels

This paper aims to describe how pattern recognition and scene analysis may with advantage be viewed from the perspective of the SP system (meaning the SP theory of intelligence and its realisation in the SP computer model (SPCM), both described in an appendix), and the strengths and potential of the system in those areas. In keeping with evidence for the importance of information compression (IC) in human learning, perception, and cognition, IC is central in the structure and workings of the SPCM. Most of that IC is achieved via the powerful concept of SP-multiple-alignment, which is largely responsible for the AI-related versatility of the system. With examples from the SPCM, the paper describes: how syntactic parsing and pattern recognition may be achieved, with corresponding potential for visual parsing and scene analysis; how those processes are robust in the face of errors in input data; how in keeping with what people do, the SP system can “see” things in its data that are not objectively present; the system can recognise things at multiple levels of abstraction and via part-whole hierarchies, and via an integration of the two; the system also has potential for the creation of a 3D construct from pictures of a 3D object from different viewpoints, and for the recognition of 3D entities.

Learning at Multiple Levels of Abstraction: The Case of Verb Argument Constructions

2007 ◽  
Author(s):  
Amy Perfors ◽  
Charles Kemp ◽  
Elizabeth Wonnacott ◽  
Joshua B. Tenenbaum
Keyword(s):  
Levels Of Abstraction ◽  
Multiple Levels

scholarly journals A Model for Analysing the Collective Dynamic Behaviour and Characterising the Exploitation of Population-Based Algorithms

2014 ◽  
Vol 22 (1) ◽  
pp. 159-188 ◽  
Author(s):  
Mikdam Turkey ◽  
Riccardo Poli
Keyword(s):  
Population Dynamics ◽  
Dynamic Behaviour ◽  
Fitness Landscape ◽  
Population Based ◽  
Levels Of Abstraction ◽  
Collective Dynamic ◽  
Model Studies ◽  
Proposed Model ◽  
Wide Range ◽  
Multiple Levels

Several previous studies have focused on modelling and analysing the collective dynamic behaviour of population-based algorithms. However, an empirical approach for identifying and characterising such a behaviour is surprisingly lacking. In this paper, we present a new model to capture this collective behaviour, and to extract and quantify features associated with it. The proposed model studies the topological distribution of an algorithm's activity from both a genotypic and a phenotypic perspective, and represents population dynamics using multiple levels of abstraction. The model can have different instantiations. Here it has been implemented using a modified version of self-organising maps. These are used to represent and track the population motion in the fitness landscape as the algorithm operates on solving a problem. Based on this model, we developed a set of features that characterise the population's collective dynamic behaviour. By analysing them and revealing their dependency on fitness distributions, we were then able to define an indicator of the exploitation behaviour of an algorithm. This is an entropy-based measure that assesses the dependency on fitness distributions of different features of population dynamics. To test the proposed measures, evolutionary algorithms with different crossover operators, selection pressure levels and population handling techniques have been examined, which lead populations to exhibit a wide range of exploitation-exploration behaviours.

A Novel Artificial Face Mask based Nanofibers with Special Intelligent Engineered Nanocomposite Against Covid-19

2021 ◽  
pp. PP. 21-22
Author(s):  
Ahmed A. Elngar ◽  
◽  
◽  
S.I. El El-Dek
Keyword(s):  
Artificial Intelligence ◽  
Resource Allocation ◽  
Pattern Recognition ◽  
Disease Management ◽  
Face Mask ◽  
Vital Role ◽  
Mobile Unit ◽  
Disease Clusters ◽  
Infected People ◽  
The Face

We introduce our idea about a new face mask against Covid-19. Herein our novel face mask is a polymeric matrix of nanofibers. These nanofibers are decorated with special engineered nanocomposite. The later possesses antiviral, antimicrobial. A well-established IR temperature biosensor will be implanted in the face mask and connected to the mobile phone using App (Seek thermal) to allow temperature monitoring. Artificial Intelligence can play a vital role in the fight against COVID-19. AI is being successfully used in the identification of disease clusters, monitoring of cases, prediction of the future outbreaks, mortality risk, diagnosis of COVID-19, disease management by resource allocation, facilitating training, record maintenance and pattern recognition for studying the disease trend. Therefore, AI is used as a type of alarm which be connected through Global Position System (GPS) to a central networking system to monitor the crowded areas of probable infections. In this case, the hospital in this neighborhood will be charged to let a mobile unit of assessment travel quickly to the infected people areas.

Introduction

1990 ◽  
Author(s):  
Steven Kim
Keyword(s):  
New York ◽  
Difficult Problem ◽  
Two Dimensions ◽  
Three Dimensions ◽  
High Yield ◽  
Acceptable Solution ◽  
Theory Of Intelligence ◽  
Creative Solutions ◽  
The World ◽  
The Face

The world around us abounds with problems requiring creative solutions. Some of these are naturally induced, as when an earthquake levels a city or an epidemic decimates a population. Others are products of our own creation, as in the “need” to curb pollution, to develop a theory of intelligence, or to compose works of art. Still others are a combination of both, as in the development of high-yield grains to feed an overpopulated planet, or the maintenance of health in the face of ravaging diseases. The word problem is used in a general sense to refer to any mental activity having some recognizable goal. The goal itself may not be apparent beforehand. Problems may be characterized by three dimensions relating to domain, difficulty, and size. These attributes are depicted in Figure 1.1. The domain refers to the realm of application. These realms may relate to the sciences, technology, arts, or social crafts. The dimension of difficulty pertains to the conceptual challenge involved in identifying an acceptable solution to the problem. A difficult problem, then, is one that admits no obvious solution, nor even a well-defined approach to seeking it. The size denotes the magnitude of work or resources required to develop a solution and implement it. This attribute differs from the notion of difficulty in that it applies to the stage that comes after a solution has been identified. In other words, difficulty refers to the prior burden in defining a problem or identifying a solution, while size describes the amount of work required to implement or realize the solution once it has jelled conceptually. For convenience in representation on a 2-dimensional page, the domain axis may be compressed into the plane of other attributes. The result is Figure 1.2, which presents sample problems to illustrate the two dimensions of difficulty and size. Cleaning up spilled milk is a trivial problem having numerous simple solutions. In contrast, refacing the subway trains in New York City with a fresh coat of paint is a formidable task that could require hundreds of workyears of effort.

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