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.
Author(s): Prasad Lokulwar, Basant Verma, N. Thillaiarasu, Kailash Kumar, Mahip Bartere, Dharam Singh
Publisher: Bentham Science Publishers
Year: 2022
Language: English
Pages: 238
City: Singapore
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
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
Ritu Aggarwal1,* and Suneet Kumar2
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
Mangala Madankar1,* and Manoj Chandak2
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
M. Chandraprabha1 and Rajesh Kumar Dhanaraj1,*
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
Mangala Hiwarkar1,*, Mangala S. Madankar1 and T.P. Girish Kumar1
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
Md. Alimul Haque1,*, Shameemul Haque2, Samah A. Alhazmi3 and D.N. Pandit4
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
Makram Soui1,*, Karthik Srinivasan1,* and Abdulaziz Albesher1,*
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
T. Saravanan1 and P. Sasikumar1,*
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
Ritu Aggarwal1,* and Suneet Kumar2
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
Shruti J. Sapra Thakur1,2 and Avinash S. Kapse3,*
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
Sana Zeba1,*, Md. Alimul Haque2, Samah A. Alhazmi3 and Shameemul Haque4
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
Subject Index
Back Cover
