Machine Learning Methods for Engineering Application Development

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. 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 This book is a quick review of machine learning methods for engineering applications. It provides an introduction to the principles of machine learning and common algorithms in the first section. Proceeding chapters summarize and analyze the existing scholarly work and discuss some general issues in this field. Next, it offers some guidelines on applying machine learning methods to software engineering tasks. Finally, it gives an outlook into some of the future developments and possibly new research areas of machine learning and artificial intelligence in general.Techniques highlighted in the book include: Bayesian models, support vector machines, decision tree induction, regression analysis, and recurrent and convolutional neural network. Finally, it also intends to be a reference book.Key Features:- Describes real-world problems that can be solved using machine learning- Explains methods for directly applying machine learning techniques to concrete real-world problems- Explains 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 meant to be an introduction to artificial intelligence (AI), machine earning, and its applications in Industry 4.0. It explains the basic mathematical principles but is intended to be understandable for readers who do not have a background in advanced mathematics.