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Machine learning algorithms for engineering applications
להגדלת הטקסט להקטנת הטקסט- ספר
"Machine learning is a vital part of numerous academic and financial applications, in areas ranging from health care and treatment to finding relevant information in social networks. Large organizations thoughtfully apply machine learning algorithms with extensive research teams. The purpose of this book is to provide an intellectual introduction to statistical or machine learning (ML) techniques for those that would not normally be exposed to such approaches during their typical required statistical exercise. Statistical analysis is an integral part of machine learning and can be described as a form of it, often even utilizing well-known and familiar techniques, that has a different focus than traditional analytical practice in applied disciplines. The key notion is that flexible, automatic approaches are used to detect patterns within the data, with a primary focus on making predictions on future data"-- Provided by publisher.
| כותר |
Machine learning algorithms for engineering applications : future trends and research directions / edited by Prasenjit Chatterjee. |
|---|---|
| מוציא לאור |
New York, New York : Nova Science Publishers |
| שנה |
[2022] |
| הערות |
Description based upon print version of record. Includes bibliographical references and index. |
| הערת תוכן ותקציר |
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. 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. |
| סדרה |
Advances in Data Science and Computing Technologies |
| היקף החומר |
1 online resource (234 pages) |
| שפה |
אנגלית |
| שנת זכויות יוצרים |
©2022 |
| מספר מערכת |
997012634566105171 |
תצוגת MARC
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