Machine Learning Algorithms for Engineering Applications

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Bibliographic Details
Main Author Chatterjee, Prasenjit
Format eBook
LanguageEnglish
Published New York Nova Science Publishers, Incorporated 2022
Edition1
Subjects
Artificial intelligence
Engineering-Data processing
Machine learning
Online AccessGet full text
ISBN9781685074494
1685074499

Cover

Table of Contents:
  • 3.4. Feature Reduction -- 3.5. Feature Reduction -- 4. Results -- 5. Wearable Real-Time Heart Attack Detection -- 6. Real-Time Smart-Digital Stethoscope System -- 7. Wearable System -- 8. Preprocessing -- 9. Feature Reduction -- 10. Results -- 11. Discussion -- Conclusion -- Conflict of Interest -- References -- Chapter 7 -- The Role of Machine Learning in the Food Industry -- Abstract -- Introduction -- Machine Learning for Evaluation of Food Quality -- Machine Learning in Supply Chain Management -- Application of Machine Learning in Management of Food Waste -- Machine Learning in Restaurant Business -- Conclusion -- References -- Chapter 8 -- Phrase Based Machine Translation of English Text to Indian Sign Language Using Order Scoring Algorithm -- Abstract -- 1. Introduction -- 1.1. Indian Sign Language -- 1.2. Machine Translation -- 2. Machine Translation of Sign Languages -- 2.1. Rule Based Approach -- 2.1.1. Direct Transfer -- 2.1.2. Semantic Transfer -- 2.2. Statistical Approach -- 3. Phrase Based Translation Model for ISL -- 3.1. Phrase Extraction -- 3.2. Phrase Reordering -- 4. Proposed Order Scoring Model -- 5. Experimental Results -- Conclusion -- References -- Chapter 9 -- Stem Cell and Tissue Engineering: Application of Machine Learning -- Abstract -- Introduction -- Machine Learning Driven Stem Cell and Tissue Engineering -- Machine Learning Application in Induced Pluripotent Stem Cells -- Application of Machine Learning in Organ Modeling -- Practices Machine Learning in Tissue Engineering -- Conclusion -- References -- Chapter 10 -- Reconstruction of 3D Point Cloud-Based on the Sequence of Images -- Abstract -- 1. Introduction -- 1.1. Accidents in Industries and Increasing Use of VR (Virtual Reality) Training -- 1.2. Three-Dimensional Models -- 1.3. Automated Algorithms for 3-d Modelling -- 2. Literature Review
  • 3. Algorithm Design -- 3.1. Extracting Frames -- 3.2. Correspondence Search -- 3.2.1. Feature Extraction -- 3.2.2. Matching -- 3.2.3. Geometric Verification -- 3.2.4. Fundamental Matrix -- 3.3. Incremental Reconstruction -- 3.3.1. Initialization -- 3.3.2. Image Registration -- 3.3.3. Triangulation -- 3.3.4. Bundle Adjustment -- 4. Results -- 4. ET Dataset -- 5. Kermit Dataset -- Conclusion and Future Work -- Acknowledgment -- References -- Chapter 11 -- Application of Machine Learning in Financial Services -- Abstract -- 1. Introduction -- 2. Fraud Prevention -- 3. Risk Management -- 4. Portfolio Management -- 4.1. Hedge Fund Management -- 4.2. Sentimental Analysis -- 5. Algorithmic Trading -- 6. Customer Service -- 7. Marketing -- 8. Network Security -- 9. Process Automation -- 10. Content Creation -- 11. Custom Machine Learning Solutions -- Conclusion -- References -- Chapter 12 -- Eye-Tracking Data as a Way to Detect Sleep Deprivation in an Individual, Based on Attention, Mental Agility, and Problem-Solving -- Abstract -- 1. Introduction -- 2. Literature Review -- Attention (Pashler 2016) -- Mental Agility (Rognum et al. 1986) -- Problem Solving (Kjellberg 1975) -- Current Measures of Sleep Deprivation -- Eye Movement Data as a Basis of the Measure of Sleepiness -- 3. Experiment Design -- 4. Data Collection -- 5. Data Processing -- 6. Model Training -- 8. Result -- 9. Discussion -- Conclusion -- Acknowledgment -- References -- Editor Contact Information -- Index -- Blank Page -- Blank Page
  • Intro -- Dedication -- Contents -- Preface -- Acknowledgments -- Chapter 1 -- Tools for Machine Learning -- Abstract -- 1. Introduction -- 1.1. Machine Learning -- 1.2. Types of Machine Learning -- 1.2.1. Supervised Machine Learning -- 1.2.2. Unsupervised Machine Learning -- 1.2.3. Reinforcement Machine Learning -- 1.3. Machine Learning Languages -- 1.4. General Machine Learning Applications -- 2. Machine Learning Tools -- 2.1. Scikit-Learn -- 2.2. PyTorch -- Packed for Production -- Distributed Training -- Cloud Support -- 2.3. TensorFlow -- 2.4. Weka -- 2.5. KNIME -- 2.5. Colab -- 2.6. Apache Mahout -- 2.7. Accord.Net Network -- Science Informatics -- Signal and Image Processing -- Supporting Libraries -- 2.8. Shogun -- Shogun is Available -- Shogun is a State of the Art -- Shogun's Open Source -- 2.9. Keras.io -- 2.10. Rapid Miners -- 2.11. Apache Singa -- 2.12. Apache Spark MLlib -- 3. Comparison Analysis of Machine Learning Software Tools -- References -- Chapter 2 -- Comprehensive Review on Applications of Machine Learning in Healthcare -- Abstract -- 1. Introduction -- 1. High Availability of Medical Data -- 2. Development of Complex Algorithms -- 2. Motivation -- 3. Literature Review -- 4. ML in Healthcare -- 4.1. Imaging and Diagnosis -- Supervised Learning -- Support Vector Machine Classifier -- 4.2. Data Collection and Follow-Ups -- 4.3. Drug Discovery and Experimentation -- 4.4. Radiology and Radiotherapy -- 4.5. Medical Image Segmentation -- 4.6. Medical Image Registration -- 4.7. Computer-Aided Detection and Diagnosis -- 4.8. Brain Function Examination and Clinical Diagnosis for -- FMR Images -- 5. Discussion -- Conclusion -- References -- Chapter 3 -- Retinal Vessel Enhancement Evaluation on DRIVE Dataset from CNN Performance -- Abstract -- 1. Introduction -- 2. Literature Survey -- 3. Image Enhancement Methods
  • 3.1. Contrast Limited Adaptive Histogram Equalization -- 3.2. Adaptive Gamma Correction -- 3.3. Tophat Transformation -- 4. Evaluation Metrics -- 4.1. Sensitivity -- 4.2. Accuracy -- 4.3. AUC -- 5. Drive Dataset -- 6. Convolutional Neural Network -- 6.1. Architecture -- 6.2. Training -- 6.3. Implementation Requirements -- 7. Experimental Results -- 7.1. Applications of Enhancement Techniques -- 7.2. Training Phase -- 7.2.1. Evaluation on DRIVE Test Set -- 8. Discussion -- Conclusion -- References -- Chapter 4 -- Machine Learning for Network Analysis -- Abstract -- 1. Introduction -- 2. Machine Learning -- 2.1. Characteristic Methods in ML -- 2.2. Algorithms of ML -- 2.2.1. Regression Algorithms -- 2.2.2. Clustering Algorithms -- 2.2.3. Decision Tree Algorithms -- 2.2.4. Bayesian Algorithms -- 2.2.5. Artificial Neural Network Algorithms -- 2.3. ML Breaks Out -- 3. Basic Workflow for MLN -- 3.1. Problem Formulation -- 3.2. Data Collection -- 3.3. Data Analysis -- 3.4. Model Construction -- 3.5. Model Validation -- 3.6. Deployment and Inference -- 4. Machine Learning and Networking -- 4.1. Wireless Network -- 4.2. Energy-Aware Communications in WSN -- Conclusion -- References -- Chapter 5 -- Machine Learning in Hydrology -- Abstract -- 1. Introduction -- 2. A Brief History of Hydrological Modeling -- 3. Types of Hydrological Models -- 3.1. Classification of Hydrological Models -- 3.2. Lumped and Distributed Hydrological Models -- 4. Selection of Proper Models -- 5. Main Physical Processes -- 6. Global Database of River Flow -- References -- Chapter 6 -- Machine Learning Applications in Smart Sensors -- Abstract -- 1. Introduction -- 2. Smart Insoles for GAIT Analysis -- 3. Blood Pressure Estimation from the Photo Plethysmo Gram (PPG) Signal -- 3.1. Normalization and Filtration -- 3.2. Baseline Correction -- 3.3. Feature Extraction