Title: Advances in Artificial Intelligence: Reviews

  Editor: Sergey Y. Yurish

  Publisher: International Frequency Sensor Association (IFSA) Publishing, S. L.

  Formats: hardcover (print book) and printable pdf Acrobat (e-book), 332 pages

  Price: 90.00 EUR for print book in hardcover

  Delivery time for print book: 7-17 days. Please contact us for priority (5-9 days), ground (3-8 days) and express (2-3 days) delivery options by e-mail

  Pubdate: 15 May 2019

  ISBN: 978-84-09-09016-7

  e-ISBN: 978-84-09-09015-0

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 Book Description

According to recent market study, the artificial intelligence market was valued at USD 16.06 billion in 2017 and is expected to reach USD 190.61 billion by 2025, at a CAGR of 36.62% during the forecast period. Artificial Intelligences currently transforming the manufacturing industry. Virtual reality, automation, Internet of Things (IoT), and robotics are some important features of AI that are benefitting the manufacturing industry.

Artificial intelligence has been one of the fastest-growing technologies in recent years. The market growth is mainly driven by factors such as the increasing adoption of cloud-based applications and services, growing big data, and increasing demand for intelligent virtual assistants. Various end-use industries have also employed artificial intelligence such as retail and business analysis that has also boosted the demand in this market. The major restraint for the market is the limited number of artificial intelligence technology experts. The Book Series on ‘Advances in Artificial Intelligence: Reviews’ has been launched with the aim to fill-in this gap. It is the 10^th ‘Advances’ Book Series published by IFSA Publishing in various research areas.

The first book volume from the ‘Advances in Artificial Intelligence: Reviews’  Book Series  contains 11 chapters written by 21 contributors from academia and industry from 10 countries: Algeria, Germany, India, Iran, Israel, Russia, Slovenia, South Africa, Tunisia and USA.

This book covers many different timely topics related to artificial intelligence and its applications. All chapters have the same structure: first, an introduction to specific topic under study; second, particular field description including sensing or/and measuring applications. Each of chapter is ending by well selected list of references with books, journals, conference proceedings and web sites.

Contents:

Contents
Preface
Contributors

 1. Learning General Constraints in C

1.1. Introduction
1.2. Background
1.2.1. Essentials of HCSP
1.2.2. Conflict Analysis
1.2.3. Clausal Explanations
1.3. Non-clausal Inference: Requirements
1.4. Non-clausal Inference: Rules and Their Proofs
1.4.1. A Generic Inference Rule: Combine
1.4.2. Selected Rules Based on Instantiating Combine
1.4.3. Selected Rules not Based on Combine
1.5. Experimental Results
1.6. Conclusion and Future Work
References

2. Sub-dimensional Surrogates to Solve High Dimensional Optimization Problems in Machine Learning

	
2.1. Introduction	
2.2. Sub-dimensional Surrogates	
2.2.1. Sub-dimensional Greedy Surrogates
2.3. Numerical Study Online
2.4. Results
2.4.1. Ackley Results
2.4.2. Sum of Squares Results
2.4.3. Discussion on Higher Versus Lower Dimensional Searches on Initial Performance
2.5. Lessons Learned and Sensible Heuristics
2.5.1. Surrogate Dimensionality Heuristic
2.5.2. Sampling Dimensions Heuristic or Strategy
2.5.3. Sampling Quantity Heuristic
2.5.4. Sampling Domain Heuristic
2.6. Neural Network Train
2.7. Conclusions
Acknowledgments
References

3. Reusing Strategies for Decision Support in Disaster Management – A Case-based High-level Petri Net Approach

3.1. Introduction
3.2. Petri Nets and Their Application in Disaster Management
3.3. Research Questions
3.4. Case- and HLPN-based Decision Support
3.5. Strategy Model
3.6. Reuse of Prepared Strategies for Decision Support
3.7. Example
3.8. Discussion and Future Work
Acknowledgements
References

4. Towards Human-like Behavior Generation Through Understating of the Complex Interplay Between Verbal and Non-verbal Signals in Multiparty 

    and Informal Interactions by Utilizing Information Fusion Approach

4.1. Introductio
4.2. Embodied Language Processing (ELU)
4.3. Fusion-based Co-verbal Behavior Generation Model
4.3.1. A Co-verbal Behavior Model’s Data Source – EVA Corpus
4.3.2. Segmentation and Quantification of Conversational Noise 
into Conversational signals – the EVA Annotation Scheme
4.4. A Framework for Analytics of Verbal  and Non-verbal Behavior Signals
4.5. A Signal-fusion System for Automatic Co-verbal Behavior Generation
4.6. Conclusion
Acknowledgements
References

5. A Survey of Temporal Extensions  to Resource Description Framework (RDF)

5.1. Introduction
5.2. RDF
5.3. Temporal Extensions to RDF
5.3.1. Reification
5.3.2. Temporal RDF
5.3.3. Named Graph
5.3.4. 4D Fluents
5.3.5. RDF*
5.3.6. Singleton Property
5.3.7. N-ary Relations	
5.3.8. Annotated RDF
5.4. Taxonomy of Temporal Extensions to RDF
5.5. Conclusion
Acknowledgements
References

6. Bee Algorithms

6.1. A Review of the Bee Algorithms
6.1.1. The Queen Bee Algorithm (QB)
6.1.2. The Artificial Bee Colony Algorithm (ABC)
6.1.3. The Discrete Artificial Bee Colony Algorithm (DABC)
6.1.4. The Fast Marriage in Honey Bee Optimization Algorithm (FMBO)
6.1.5. The Discrete Fast Marriage in Honey Bee Optimization
6.1.6. Modified Fast Marriage in Honey Bee Optimization Algorithm
6.2. The Benchmark Functions
6.2.1. Sphere Function
6.2.2. Schwefel Function
6.2.3. Rastrigin Function
6.2.4. Griewank Function
6.3. The Simulation Results and Analysis
6.4. Designing of the PID Controllers
6.5. Conclusion
References

7. AI-based Tools for Performance  and Monitoring of Sustainable Built  and Natural Environments and the Climate

7.1. Overall Background
7.2. The Built Infrastructure Construction
7.2.1. Literature Review
7.2.2. Description of the Systems
7.3. Environmental Protection
7.3.1. Literature Review
7.3.2. Description of the Systems
7.4. Climate Change
7.4.1. Literature Review
7.4.2. Description of the System
7.5. Conclusions
References

8. Fuzzy Circle and AI

8.1. Artificial Intelligence
8.2. Need for Fuzzy Logic
8.3. Fuzzy Architecture
8.4. Fuzzy Logic in AI
8.5. Space, Orientation and Distance: Spatial Reasoning
8.5.1. Fuzzy Circle – A Literature Review
8.5.2. Fuzzy Trigonometric Functions – A Literature Review
8.5.3. Fuzzy Particle Swarm Optimization – A Literature Review
8.6. Gaussian Membership Function with Fuzzy Qualitative Trigonometry
8.6.1. Gaussian Qualitative Coordinates
8.6.2. The Fuzzy Centre and Position of a Point
8.6.3. Fuzzy PSO with Fuzzy Matrices and Gaussian Membership Function
8.7. Conclusion
Acknowledgements
References

9. Intelligent Methods and Models for Big Data Analysis. Application for Smart Quality Control in Steel Plant

9.1. Overview of Big Data Analysis for Smart Quality Control
9.1.1. Principle of SVR Algorithm [19-21]
9.1.2. ANN Algorithm
9.2. Proposed Regression Model Using Data Mining
9.2.1. General Formalism
9.2.2. Least Square PCA Based Prediction (LS – PCA)
9.3. Application
9.3.1. Process Description
9.3.2. Metallurgical Reactions
9.3.3. Model Input/Output
9.3.4. Prediction and Result Analysis
9.4. Conclusion
References

10. Aspects of Program Design  for Electromagnetic Environment Visualization Near Cellular Antennas

10.1. Rationale for Electromagnetic Environment Visualization
10.2. Requirements for Electromagnetic Environment Visualization 
for Socially Oriented Monitoring
10.3. Specification of the Presentation Requirements for the Electromagnetic Environment Assessment Results
10.4. Dividing Calculation Space into Characteristic Regions with an Account to the Reflective Properties of the Roof
10.4.1. Horizontally Oriented Rooftop
10.4.2. Pitched Rooftop
10.5. Dividing Calculation Space into Characteristic Regions with an Account to Diffraction Phenomena on Antenna Structure
10.6. Conventional Software Analysis
10.6.1. A Model Considering the Reflection from a Flat Surface
10.6.2. A Model Considering Diffraction Phenomena on Antenna Structure
10.6.3. Notes on the Choice of Programming Language
10.7. Conclusions
Acknowledgements

11. Artificial Intelligence Approaches Applied to Arabic Digits, Letters and Isolated Words Recognition

11.1. Introduction
11.2. Theoretical Background
11.2.1. Cepstral Coefficients
11.2.2. Mel Frequency Cepstral Coefficients (MFCC)
11.2.3. The First-order Temporal Derivative Coefficients of MFCCs (ΔMFCCs)
11.2.4. Linear Prediction Coding (LPC)
11.2.5. Linear Predictive Cepstral Coefficients: LPCC
11.2.6. The Perceptually Based Linear Prediction Analysis (PLP and Rasta-PLP)
11.2.7. The First-order Temporal Derivative Coefficients  of PLPs (∆PLPs)
11.2.8. Vector Quantization (VQ)	
11.2.9. Applying PCA	
11.3. Arabic Digits Recognition
11.3.1. Speech Recording and Preprocessing
11.3.2. Applying FFBPNN
11.3.3. Results and Discussion
11.4. Arabic Letters Recognition
11.4.1. Corpus Preparation
11.4.2. Features Extraction
11.4.3. Experimental Results and Discussion
11.5. Arabic Words Recognition
11.5.1. Design of the Experimental Corpora
11.5.2. Applying PCA
11.5.3. Features Extraction
11.5.4. Applying FFBPNN
11.5.5. Experimental Results
11.5.6. Results and Discussion
References

Index

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