Machine Learning Methods for Engineering Application Development -  - E-Book

Machine Learning Methods for Engineering Application Development E-Book

0,0
65,35 €

-100%
Sammeln Sie Punkte in unserem Gutscheinprogramm und kaufen Sie E-Books und Hörbücher mit bis zu 100% Rabatt.
Mehr erfahren.
Beschreibung

This book is a quick review of machine learning methods for engineeringapplications. It provides an introduction to the principles of machine learningand common algorithms in the first section. Proceeding chapters summarize andanalyze the existing scholarly work and discuss some general issues in this field.Next, it offers some guidelines on applying machine learning methods to softwareengineering tasks. Finally, it gives an outlook into some of the futuredevelopments and possibly new research areas of machine learning and artificialintelligence in general.Techniques highlighted in the book include: Bayesian models, supportvector machines, decision tree induction, regression analysis, and recurrent andconvolutional neural network. Finally, it also intends to be a reference book. Key Features:Describes real-world problems that can be solved using machine learningExplains methods for directly applying machine learning techniques to concrete real-world problemsExplains concepts used in Industry 4.0 platforms, including the use and integration of AI, ML, Big Data, NLP, and the Internet of Things (IoT). It does not require prior knowledge of the machine learning This book is meantto be an introduction to artificial intelligence (AI), machine earning, and itsapplications in Industry 4.0. It explains the basic mathematical principlesbut is intended to be understandable for readers who do not have a backgroundin advanced mathematics.

Sie lesen das E-Book in den Legimi-Apps auf:

Android
iOS
von Legimi
zertifizierten E-Readern

Seitenzahl: 316

Veröffentlichungsjahr: 2003

Bewertungen
0,0
0
0
0
0
0
Mehr Informationen
Mehr Informationen
Legimi prüft nicht, ob Rezensionen von Nutzern stammen, die den betreffenden Titel tatsächlich gekauft oder gelesen/gehört haben. Wir entfernen aber gefälschte Rezensionen.



Table of Contents
BENTHAM SCIENCE PUBLISHERS LTD.
End User License Agreement (for non-institutional, personal use)
Usage Rules:
Disclaimer:
Limitation of Liability:
General:
FOREWORD
PREFACE
Key Features
List of Contributors
Cutting Edge Techniques of Adaptive Machine Learning for Image Processing and Computer Vision
Abstract
INTRODUCTION
Techniques for Improvising Images
Spatial-Domain Method
Frequency-Domain Method
TRANSFORMS: IMAGE IMPROVEMENT
Wavelet-Transform Oriented Image Improvement
Scaling and Translation
IMAGE IMPROVEMENT WITH FILTERS
DENOISING OF IMAGES
Frontward Transform
IMAGE IMPROVEMENT WITH PRINCIPAL COMPONENT PCA FOR 2D
Implementing 2D-PCA
SELECTION AND EXTRACTION OF FEATURES
Criteria for Selecting Features
Linear Criteria for Extracting Features
Discontinuity Handling
Integration Part: Limitations
Alteration of Smoothness Terminology
CONCLUSION
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
References
Algorithm For Intelligent Systems
Abstract
INTRODUCTION
Reinforcement Learning
Q-Learning
Game Theory
Machine Learning
Decision Tree
Logistic Regression
K-Means Clustering
Artificial Neural Network (ANN)
Swarm Intelligence
Swarm Robots
Swarm Intelligence in Decision Making Algorithm
Natural Language Processing
CONCLUSION
FUTURE SCOPE
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENTS
REFERENCES
Clinical Decision Support System for Early Prediction of Congenital Heart Disease using Machine learning Techniques
Abstract
INTRODUCTION
RELATED WORK
PROPOSED METHODOLOGY AND DATASET
STEPS FOR TRAINING AND TESTING THE DATASET
MACHINE LEARNING ALGORITHMS FOR PREDICTION
SUPPORT VECTOR MACHINE
RANDOM FOREST
MULTILAYER PERCEPTRON
INPUT LAYER
HIDDEN LAYER
OUTPUT LAYER
K- NEAREST NEIGHBOR (K-NN)
EXPERIMENTS AND RESULTS
Comparison Results
CONCLUSION
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENTS
REFERENCES
A Review on Covid-19 Pandemic and Role of Multilingual Information Retrieval and Machine Translation for Managing its Effect
Abstract
INTRODUCTION
RELATED WORK
OUTBREAK STAGE OF COVID 19
Travel History from Infected Countries
Local Transmission
Geographical Cluster of Cases
Community Transmission
CURRENT SITUATION IN INDIA
TREATMENT
ILLNESS SEVERITY
ANTIBODY AND PLASMA THERAPY
VACCINE
PREVENTIVE MEASURE
Myths
EMERGING TECHNOLOGY FOR MITIGATING THE EFFECT OF THE COVID-19 PANDEMIC
Infodemic and Natural Language Processing
Arogya Setu App
Issues of Languages all Over the World and Machine Translation
Difficulties in Accessing Data in the Native Language
INFORMATION RETRIEVAL SYSTEM FOR COVID-19
New Information Retrieval System for COVID-19: TREC COVID
CO-Search: COVID-19 Information Retrieval
COVID-19 Dataset Search System
Role of Cross-lingual and Multilingual Information Retrieval in COVID-19 Pandemic
Challenges in Machine Translation, Information Retrieval and MLIR System
CONCLUSION
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
An Empirical View of Genetic Machine Learning based on Evolutionary Learning Computations
Abstract
INTRODUCTION
Preamble of Evolutionary Algorithms (EA)
Contextual Parameters of EA
CLASSIFICATION OF EVOLUTIONARY ALGORITHMS
The Family of Evolutionary Algorithms
FITNESS FUNCTION & PROBABILITY
SHORT-TERM MEMORY THRESHOLDING (STM)
INCLUSION OF PROBABILISTIC AND STOCHASTIC PROCESSES (PSP) IN EA
OPTIMIZING EAS
Imitation
Innovation
FUNCTIONALITY OF GA
SAMPLE CODE OF EA TO FIND OPTIMAL RESULT OF A TEST
CONCLUSION
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
References
High-Performance Computing for Satellite Image Processing Using Apache Spark
Abstract
INTRODUCTION
Parallel Computing
Distributed Computing
Virtual Machine Software (VMware Workstation Pro)
Apache Spark
Features of Apache Spark
• Speed
• Supports multiple languages
• Reusability
Components Of Spark
• Apache Spark Core
• Spark SQL
• Spark Streaming
• MLlib (Machine Learning Library)
• GraphX
Spark Architecture Overview
Resilient Distributed Dataset (RDD)
Methodology
NDVI (Normalized Difference Vegetation Index)
Proposed Plan Work
RESULT
CONCLUSION
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
Artificial Intelligence and Covid-19: A Practical Approach
Abstract
INTRODUCTION
Background
Clinical Features
Transmission Mechanism
Organization
OTHER RELATED PAPERS
EFFECT OF THE COVID-19 PANDEMIC ON THE GLOBAL ECONOMY
Effects on the Lives of People
Effects on Employment
Employment Misfortune
TREATMENT AND VACCINE DEVELOPMENT
Vaccine Development
MODERNA'S mRNA-1273
PittCoVacc
Vaccine from Johnson & Johnson
CEPI Multiple Efforts
Potential Drugs
PREVENTIVE MEASURES
EMERGING TECHNOLOGIES TO MITIGATE THE COVID-19 PANDEMIC EFFECT
Artificial Intelligence (AI) and COVID-19
Applications of AI in COVID-19 Pandemic
Early Detection and Diagnosis of the Infection
Monitoring the Treatment
Contact Tracing of SARS Cov-2 Individual
Development of Drugs and Vaccines
Reducing the Workload of Healthcare Workers
Prevention of the Disease
Summary of AI Applications for Covid-19
FUTURE SCOPE OF THE STUDY AND CONCLUSION
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
Intelligent Personalized E-Learning Platform using Machine Learning Algorithms
Abstract
INTRODUCTION
RELATED WORK
Machine Learning Approach
Rule-based Approach
BACKGROUD
Feature Selection Techniques
SFS
SBS
SFFS
Machine learning Algorithms
K-Nearest Neighbor (KNN)
Support Vector Machine (SVM)
Random Forest (RF)
AdaBoost
Gradient Boosting
XGBoost
Motivation Example
PROPOSED APPROACH
Preprocessing
Standard scalar
Random oversamplng
SFS
Classification Phase
VALIDATION
Description of the Experimental Database
Evaluation Metrics
Results for Research Question 1
Experimental Results With Full Dataset
Experimental results with Filtered Dataset
CONCLUSION
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
AcknowledgmentS
References
Automated Systems using AI in the Internet of Robotic Things: A New Paradigm for Robotics
Abstract
INTRODUCTION
Need for MRS
Major Gaps in MRS
EFFECTUAL COORDINATION-ALGORITHMS FOR MRS
Context of the Software Utilization
Top-Level Design (TLD)
An UCF Central Algorithm
UCF Token Passing with a Weakly Centralized Approach
OPTIMIZATION OF MULTI-ROBOT TASK PROVISION (MTRP)
MRTP With Cuckoo-Search Rule
Algorithm: Cuckoo-Search
Terminologies of CSA
Parameter Optimizing in CSA
ROBOT MANIPULATORS: MODELLING AND SIMULATION
Bond Graph Modelling Simulation
CONCLUSION
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
References
Missing Value Imputation and Estimation Methods for Arrhythmia Feature Selection Classification Using Machine Learning Algorithms
Abstract
INTRODUCTION
Literature Review
Materials and Methods
MEAN/ MODE IMPUTATION
K-NN IMPUTATION METHOD
MICE
Algorithm
Procedure:
GENETIC ALGORITHM
MACHINE LEARNING CLASSIFIERS
KNN CLASSIFIER
NAÏVE BAYES CLASSIFIER
4.3. RANDOM FOREST
MLP (MULTILAYER PERCEPTRON)
EXPERIMENTS AND RESULTS
IMPLEMENTATION RESULTS IN HIGHER DIMENSIONAL VALUE
CONCLUSIONS AND FUTURE SCOPE
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
Analysis of Abstractive Text Summarization with Deep Learning Technique
Abstract
INTRODUCTION
Historical Development
Area of Research and its Contribution
Trends in Area of Research
Current Challenges in the Area of Research
KEY CHALLENGES IN DEEP LEARNING
Deep Learning Needs Enough Quality Data
AI and Expectations
Becoming Production-Ready
Deep Learning Does not Understand Context Very Well
Deep Learning Security
Closing Thoughts
TensorFlow
What is a Text Summarization?
Challenges in Abstractive summarization
Importance of Text Summarization
Examples of Text Summaries
Types of Masses Benefited
Aim
Objectives
Literature Review
Research Issues
Gaps in Research Issue
Motivation
Scope
Current Technologies Used
Python, Jupyter Notebook
Apache Kafka and KSQL
Kafka and Python and Jupyter to resolve the abstract Technical Dept. in the proposed model:
Tools
Database
EXISTING METHODOLOGY/TECHNOLOGIES AND ANALYSIS
Structure-based Abstractive Summarization Methods
Semantic-based Abstractive Summarization Methods
Methods for Abstractive Summarization are Written Below
IMPLICATIONS
CONCLUSION
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
Advanced Topics in Machine Learning
Abstract
INTRODUCTION
LITERATURE REVIEW
TYPES OF MACHINE LEARNING ALGORITHM
Supervised Learning
Unsupervised Learning
Semi-supervised Learning
Reinforcement Learning
ADVANCED MACHINE LEARNING ALGORITHMS
Linear Regression
Logistic Regression
KNN (K-nearest neighbor) algorithm
SVM (Support vector machines) algorithm
Naive Bayes algorithm
Decision tree
K-means
Random Forest algorithm
Classification and Regression Trees (CART)
Apriori
PCA (Principal Component Analysis)
Boosting with AdaBoost
Comparison of Various Advanced Machine Learning
FUTRUE ROAD MAP
CONCLUSION
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
Machine Learning Methods for Engineering Application Development
Edited by
Prasad Lokulwar
Associate Professor Department of Computer Science and Engineering, G H Raisoni College of Engineering, Nagpur, India
Basant Verma
Professor in Emerging Technology (AI/ML, Data Science, Cyber Security) Panipat Institute of Engineering and Technology, India
N. Thillaiarasu
Associate Professor School of Computing and Information technology REVA University, Banglore, India
Kailash Kumar
Assistant Professor College of Computing and Informatics Saudi Electronic University, Riyadh, Kingdom of Saudi Arabia
Mahip Bartere
Department of Computer Science and Engineering, P. R. Pote Patil College of Engineering and Management, India
&
Dharam Singh
Senior Developer, Machine Learning and BI, Wallmart, USA

BENTHAM SCIENCE PUBLISHERS LTD.

End User License Agreement (for non-institutional, personal use)

This is an agreement between you and Bentham Science Publishers Ltd. Please read this License Agreement carefully before using the ebook/echapter/ejournal (“Work”). Your use of the Work constitutes your agreement to the terms and conditions set forth in this License Agreement. If you do not agree to these terms and conditions then you should not use the Work.

Bentham Science Publishers agrees to grant you a non-exclusive, non-transferable limited license to use the Work subject to and in accordance with the following terms and conditions. This License Agreement is for non-library, personal use only. For a library / institutional / multi user license in respect of the Work, please contact: [email protected].

Usage Rules:

All rights reserved: The Work is the subject of copyright and Bentham Science Publishers either owns the Work (and the copyright in it) or is licensed to distribute the Work. You shall not copy, reproduce, modify, remove, delete, augment, add to, publish, transmit, sell, resell, create derivative works from, or in any way exploit the Work or make the Work available for others to do any of the same, in any form or by any means, in whole or in part, in each case without the prior written permission of Bentham Science Publishers, unless stated otherwise in this License Agreement.You may download a copy of the Work on one occasion to one personal computer (including tablet, laptop, desktop, or other such devices). You may make one back-up copy of the Work to avoid losing it.The unauthorised use or distribution of copyrighted or other proprietary content is illegal and could subject you to liability for substantial money damages. You will be liable for any damage resulting from your misuse of the Work or any violation of this License Agreement, including any infringement by you of copyrights or proprietary rights.

Disclaimer:

Bentham Science Publishers does not guarantee that the information in the Work is error-free, or warrant that it will meet your requirements or that access to the Work will be uninterrupted or error-free. The Work is provided "as is" without warranty of any kind, either express or implied or statutory, including, without limitation, implied warranties of merchantability and fitness for a particular purpose. The entire risk as to the results and performance of the Work is assumed by you. No responsibility is assumed by Bentham Science Publishers, its staff, editors and/or authors for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products instruction, advertisements or ideas contained in the Work.

Limitation of Liability:

In no event will Bentham Science Publishers, its staff, editors and/or authors, be liable for any damages, including, without limitation, special, incidental and/or consequential damages and/or damages for lost data and/or profits arising out of (whether directly or indirectly) the use or inability to use the Work. The entire liability of Bentham Science Publishers shall be limited to the amount actually paid by you for the Work.

General:

Any dispute or claim arising out of or in connection with this License Agreement or the Work (including non-contractual disputes or claims) will be governed by and construed in accordance with the laws of Singapore. Each party agrees that the courts of the state of Singapore shall have exclusive jurisdiction to settle any dispute or claim arising out of or in connection with this License Agreement or the Work (including non-contractual disputes or claims).Your rights under this License Agreement will automatically terminate without notice and without the need for a court order if at any point you breach any terms of this License Agreement. In no event will any delay or failure by Bentham Science Publishers in enforcing your compliance with this License Agreement constitute a waiver of any of its rights.You acknowledge that you have read this License Agreement, and agree to be bound by its terms and conditions. To the extent that any other terms and conditions presented on any website of Bentham Science Publishers conflict with, or are inconsistent with, the terms and conditions set out in this License Agreement, you acknowledge that the terms and conditions set out in this License Agreement shall prevail.

Bentham Science Publishers Pte. Ltd. 80 Robinson Road #02-00 Singapore 068898 Singapore Email: [email protected]

FOREWORD

As I reviewed the manuscript prior to writing this Foreword, I was fascinated by many unique features that I would like to share with you. The book can best be described as concise yet detailed. There is more useful information packed into its 12 chapters than seen in most books twice its size. Therefore, it gives me great pleasure to contribute to this foreword.

This book is an enthusiastic celebration of many Machine Learning Techniques for Engineering Applications. It is also a unique tribute to many academicians and researchers who were involved in their study and contributed to society. Still another element is provided by many interesting technical details and an abundance of illustrations in the form of figures and tables. On top of that, there are innumerable machine learning algorithms for Intelligent Systems, Computational Linguistics, Natural Language Processing, Information Retrieval, Neural Networks, Social Networks, Recommender Systems, etc., indeed, to anyone with a fascination with the world of machine learning. This book can be read on two different levels. First, it may be read by ordinary people with a limited, if any, scientific background. Throughout, the book has been written with this audience in mind. The second group of readers will be represented by professionals from academia, government agencies and researchers. I do feel that everybody in the scientific community agrees with the content and ideas put forth in this book, and I hope that the information and knowledge presented will become a useful guideline for the research community and scholars.

This book contains so much useful information, and the chapters contain many pearls. I hope that this book will become a primer for teachers, teacher educators, and professional developers, helping teachers across the world to learn, teach, and practice machine learning techniques for various applications.

Sangeeta Sonania Software Developer Sydney Australia

PREFACE

Machine learning deals with the issue of how to build programs that improve their performance at some tasks through experience. Machine learning (ML) plays a major role in the fourth industrial revolution, and we see a lot of evolution in various machine learning methodologies. AI techniques are widely used by practicing engineers to solve real-world problems. Industry 4.0 refers to the introduction of digital technologies and the development of skills, resources, and high-tech for the evolution of Industrial Factories. The concepts of Artificial Intelligence (AI), Machine Learning, and its applications in Industry 4.0 are popular among researchers. Several industrial applications are being designed and deployed. Herein, we share a few examples of machine learning that we use every day and perhaps have no idea that they are driven by ML-like Virtual Personal Assistants, Predictions, Videos Surveillance, Social Media Services, Email Spam and Malware Filtering, Online Customer Support, Search Engine Result Refining, Product Recommendations, and Online Fraud Detection. Besides, numerous researchers from diversified domains are working towards the amalgamation of these technologies.

The chapters of this book are organized into five parts, Machine Learning Essentials, Applied Machine Learning, Surveillance Systems, Machine Learning in IoT and Cyber Security, and Intelligent Systems. Machine learning algorithms have proven to be of great practical value in a variety of application domains. Not surprisingly, the field of software engineering turns out to be a fertile ground where many software development and maintenance tasks could be formulated as learning problems and approached in terms of learning algorithms. This book deals with the subject of applying machine learning methods and engineering. In these books, we first provide the characteristics and applicability of some frequently utilized machine learning algorithms. We then summarize and analyze the existing work and discuss some general issues in this niche area. Finally, we offer some guidelines on applying machine learning methods to software engineering tasks.

This book describes the most common Artificial Intelligence (AI), Machine Learning and its applications in Industry 4.0, including Bayesian models, support vector machines, decision tree induction, regression analysis, and recurrent and convolutional neural networks. It first introduces the principles of machine learning; it then covers the basic methods, including the mathematical foundations. The biggest part of the book provides common machine learning algorithms and their applications. Finally, the book gives an outlook into some of the future developments and possibly new research areas of machine learning and artificial intelligence in general.

This book is meant to be an introduction to Artificial Intelligence (AI), Machine Learning, and its applications in Industry 4.0. It does not require prior knowledge in this area. It covers some of the basic mathematical principles but intends to be understandable even without a background in mathematics. It can be read chapter-wise and intends to be comprehensible, even when not starting in the beginning. Finally, it also intends to be a reference book.

Key Features

• Describes real-world problems that can be solved using Machine Learning.

• Provides methods for directly applying Machine Learning techniques to concrete real-world problems.

• Research outputs require working in Industry 4.0 platforms, including the use and integration of AI, ML, Big Data, NLP, and the Internet of Things (IoT).

• We welcome new developments in statistics, mathematics, and computing that are relevant to the machine learning perspective, including foundations, systems, innovative applications, and other research contributions related to the overall design of machine learning and models and algorithms that are relevant for AI.

Prasad Lokulwar Associate Professor Department of Computer Science and Engineering, G H Raisoni College of Engineering, Nagpur, IndiaBasant Verma Professor in Emerging Technology (AI/ML, Data Science, Cyber Security) Panipat Institute of Engineering and Technology, IndiaN. Thillaiarasu Associate Professor School of Computing and Information technology REVA University, Banglore, IndiaKailash Kumar Assistant Professor College of Computing and Informatics Saudi Electronic University, Riyadh, Kingdom of Saudi ArabiaMahip Bartere Department of Computer Science and Engineering, P. R. Pote Patil College of Engineering and Management, India &Dharam Singh Senior Developer, Machine Learning and BI, Wallmart, USA

List of Contributors

Abdulaziz AlbesherCollege of Computing and Informatics, Saudi Electronic University, Saudi Arabia,Avinash S. KapseAnuradha College of Engineering, Chikhli, Amravati University, India,D.N. PanditDepartment of Zoology, Veer Kunwar Singh University, Ara, IndiaH.R. DeshmukhShri R. R. Lahoti Science College, IndiaKarthik SrinivasanCollege of Computing and Informatics, Saudi Electronic University, , Saudi Arabia,M. ChandraprabhaGalgotias College of Engineering and Technology, Galgotias University, Greater Noida, IndiaMd. Alimul HaqueDepartment of Computer Science, Veer Kunwar Singh University, Ara, IndiaMakram SouiCollege of Computing and Informatics, Saudi Electronic University, , Saudi Arabia,Mangala S. MadankarAssistant Professor, Department of Computer Science and Engineering, G H Raisoni College of Engineering, Nagpur, IndiaManoj ChandakRamdeobaba College of Engineering and Management, Nagpur, India,P. SasikumarMalla Reddy Institute of Engineering & Technology, Secunderabad, IndiaPallavi HiwarkarAssistant Professor, Department of Computer Science and Engineering, G H Raisoni Institute of Engineering & Technology, Nagpur, IndiaPawan BhalandhareG H Raisoni College of Engineering, Nagpur, IndiaPranay SarafG H Raisoni College of Engineering, Nagpur, IndiaPratik DhokeG H Raisoni College of Engineering, Nagpur, IndiaRahul AgrawalG H Raisoni College of Engineering, Nagpur, IndiaRajesh Kumar DhanarajGalgotias College of Engineering and Technology, Galgotias University, Greater Noida, IndiaRitu AggarwalMaharishi Markendeshwar Engineering College, Maharishi Markendeshwar Institute of Computer Technology and Business Management Mullana, Ambala, Haryana, India, 133207Samah AlhazmiCollege of Computing and Informatics, Saudi Electronic University, Riyadh, Kingdom of Saudi ArabiaSana ZebaJamia Millia Islamia University, New Delhi, IndiaShameemul HaqueAl-Hafeez College, Ara, IndiaShruti J. Sapra ThakurDepartment of Computer Science and Engineering, Amravati University, IndiaSuneet KumarMaharishi Markendeshwar Engineering College, Maharishi Markendeshwar Institute of Computer Technology and Business Management Mullana, Ambala, Haryana, India, 133207T. SaravananGITAM University, Bengaluru, IndiaYogadhar PandeyAssociate Professor, Technocrats Institute of Technology (Excellence), India

Cutting Edge Techniques of Adaptive Machine Learning for Image Processing and Computer Vision

P. Sasikumar1,*,T. Saravanan2
1 Malla Reddy Institute of Engineering & Technology, St. Martin's Engineering College, Secunderabad, India
2 GITAM University, Bengaluru, India

Abstract

Computers, systems, applications, and technology, in general, are becoming more commonly used, advanced, scalable, and thus effective in modern times. Because of its widespread use, it undergoes various advancements on a regular basis. A fast-paced life is also associated with modern times. This way of life necessitates that our systems behave similarly. Adaptive Machine Learning (AML) can do things that conventional machine learning cannot. It will easily adjust to new information and determine the significance of that information. Adaptive machine learning uses a variety of data collection, grouping, and analysis methods due to its single-channeled structure. It gathers, analyses, and learns from the information. That is why it is adaptive: as long as new data is presented, the system can learn and update. This single-channeled device acts on any piece of input it receives in order to improve potential forecasts and outcomes. Furthermore, since the entire process happens in real-time, it can immediately adjust to new actions. High efficiency and impeccably precise accuracy are two of AML's main advantages. The system does not become outdated or redundant because it is constantly running in real-time. So, incorporating the three core concepts of agility, strength, and efficiency better explains AML.

Agility helps systems to respond rapidly and without hesitation. The systems achieve new levels of proficiency and accuracy as a result of their power, and they can find new ways to operate flawlessly at lower costs as a result of their performance. This chapter covers the preparation, regularisation, and structure of deep neural networks such as convolutional and generative adversarial networks. New information in the reinforcement learning chapter includes a description of t-SNE, a standard dimensionality reduction approach, as well as multilayer perceptrons on auto encoders and the word2vec network. As a consequence, these suggestions will assist readers in applying what they have learned.

Keywords: Autoencoders, Automatic Learning, Contourlet and orthogonal transforms, Disparity, Domain Methods, Stereo Face Images.
*Corresponding author Sasikumar P: Malla Reddy Institute of Engineering & Technology, Secunderabad, India; Tel: +91- 8883209920; E-mail: [email protected]