Machine learning methods for engineering application development / Prasad Lokulwar [and five others].

Format:

Book

Author/Creator:

Lokulwar, Prasad, author.

Language:

English

Subjects (All):

Machine learning.

Physical Description:

1 online resource (240 pages)

Edition:

First edition.

Place of Publication:

Singapore : Bentham Science Publishers, [2022]

Summary:

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.

Contents:

Cover

Title

Copyright

End User License Agreement

Contents

Foreword

Preface

[Key Features]

Key Features

List of Contributors

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

P. Sasikumar1 and T. Saravanan1,*

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

Pratik Dhoke1,*, Pranay Saraf1, Pawan Bhalandhare1, Yogadhar Pandey1, H. R. Deshmukh1 and Rahul Agrawal1

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

FUTURE SCOPE

ACKNOWLEDGEMENTS

Clinical Decision Support System for Early Prediction of Congenital Heart Disease using Machine learning Techniques

Ritu Aggarwal1,* and Suneet Kumar2

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

A Review on Covid-19 Pandemic and Role of Multilingual Information Retrieval and Machine Translation for Managing its Effect

Mangala Madankar1,* and Manoj Chandak2

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

An Empirical View of Genetic Machine Learning based on Evolutionary Learning Computations

M. Chandraprabha1 and Rajesh Kumar Dhanaraj1,*

Preamble of Evolutionary Algorithms (EA)

Contextual Parameters of EA

CLASSIFICATION OF EVOLUTIONARY ALGORITHMS

The Family of Evolutionary Algorithms

FITNESS FUNCTION &amp

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

High-Performance Computing for Satellite Image Processing Using Apache Spark

Mangala Hiwarkar1,*, Mangala S. Madankar1 and T.P. Girish Kumar1

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

Artificial Intelligence and Covid-19: A Practical Approach

Md. Alimul Haque1,*, Shameemul Haque2, Samah A. Alhazmi3 and D.N. Pandit4

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 &amp

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

Intelligent Personalized E-Learning Platform using Machine Learning Algorithms

Makram Soui1,*, Karthik Srinivasan1,* and Abdulaziz Albesher1,*

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

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

ACKNOWLEDGMENTS

Automated Systems using AI in the Internet of Robotic Things: A New Paradigm for Robotics

T. Saravanan1 and P. Sasikumar1,*

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

Missing Value Imputation and Estimation Methods for Arrhythmia Feature Selection Classification Using Machine Learning Algorithms

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)

IMPLEMENTATION RESULTS IN HIGHER DIMENSIONAL VALUE

CONCLUSIONS AND FUTURE SCOPE

Analysis of Abstractive Text Summarization with Deep Learning Technique

Shruti J. Sapra Thakur1,2 and Avinash S. Kapse3,*

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.

Notes:

Includes bibliographical references.

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

9789815079180

9815079182

OCLC:

1355222380