Commercial aircraft propulsion and energy systems research : reducing global carbon emissions / / The National Academies of Sciences, Engineering, and Medicine

• Record Nr.: UNINA9910820348403321
• Pubbl/distr/stampa: Washington, District of Columbia : , : The National Academies Press, , 2016; ©2016
• ISBN: 0-309-44099-8; 0-309-44097-1
• Descrizione fisica: 1 online resource (123 pages) : illustrations (some color)
• Disciplina: 363.7387
• Soggetti: Carbon dioxide - Environmental aspects; Aeronautics, Commercial - Environmental aspects; Atmospheric carbon dioxide
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Aircraft-propulsion integration -- Aircraft gas turbine engines -- Electric propulsion -- Sustainable alternative jet fuels -- Findings, recommendations, roles, and resources.
• Sommario/riassunto: "This report focuses on propulsion and energy technologies for reducing carbon emissions from large, commercial aircraft--single -aisle and twin-aisle aircraft that carry 100 or more passengers--because such aircraft account for more than 90 percent of global emissions from commercial aircraft."--P. 1.

Machine Learning for Industrial Applications

• Record Nr.: UNINA9911019499103321
• Autore: Prakash Kolla Bhanu
• Pubbl/distr/stampa: Newark : , : John Wiley & Sons, Incorporated, , 2024; ©2024
• ISBN: 9781394268993; 1394268998; 9781394268986; 139426898X
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (341 pages)
• Collana: Next-generation computing and communication engineering
• Disciplina: 006.3/1
• Soggetti: Machine learning - Industrial applications
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Includes index.
• Nota di contenuto: Cover -- Series Page -- Title Page -- Copyright Page -- Dedication Page -- Contents -- Preface -- Chapter 1 Overview of Machine Learning -- 1.1 Introduction -- 1.2 Sorts of Machine Learning -- 1.3 Regulated Gaining Knowledge of Dog and Human -- 1.4 Solo Learning -- 1.5 Support Mastering -- 1.6 Bundles or Applications of Machine Learning -- 1.6.1 Photograph Reputation -- 1.6.2 Discourse Recognition -- 1.6.3 Traffic Prediction -- 1.6.4 Item Recommendations -- 1.6.5 Self-Using Vehicles -- 1.6.6 Electronic Mail Unsolicited Mail And Malware Filtering -- 1.6.7 Computerized Private Assistant -- 1.6.8 Online Fraud Detection -- 1.6.9 Securities Exchange Buying and Selling -- 1.6.10 Clinical Prognosis -- 1.6.11 Computerized Language Translation -- 1.6.12 Online Media Features -- 1.6.13 Feeling Evaluation -- 1.6.14 Robotizing Employee Get Right of Entry to Manipulate -- 1.6.15 Marine Flora and Fauna Protection -- 1.6.16 Anticipate Potential Coronary Heart Failure -- 1.6.17 Directing Healthcare Efficiency and Scientific Offerings -- 1.6.18 Transportation and Commuting (Uber) -- 1.6.19 Dynamic Pricing -- 1.6.19.1 How Does Uber Decide the Cost of Your Excursion? -- 1.6.20 Online Video Streaming (Netflix) -- 1.7 Challenges in Machine Learning -- 1.8 Limitations of Machine Learning -- 1.9 Projects in Machine Learning -- ; References -- Chapter 2 Machine Learning Building Blocks -- 2.1 Data Collection -- 2.1.1 Importing the Data from CSV Files -- 2.2 Data Preparation -- 2.2.1 Data Exploration -- 2.2.2 Data Pre-Processing -- 2.3 Data Wrangling -- 2.4 Data Analysis -- 2.5 Model Selection -- 2.6 Model Building -- 2.7 Model Evaluation -- 2.7.1 Classification Metrics -- 2.7.1.1 Accuracy -- 2.7.1.2 Precision -- 2.7.1.3 Recall -- 2.7.2 Regression Metrics -- 2.7.2.1 Mean Squared Error -- 2.7.2.2 Root Mean Squared Error -- 2.7.2.3 Mean Absolute Error -- 2.8 Deployment.; 2.8.1 Machine Learning Projects -- 2.8.2 Spam Detection Using Machine Learning -- 2.8.3 Spam Detection for YouTube Comments Using Naïve Bayes Classifier -- 2.8.4 Fake News Detection -- 2.8.5 House Price Prediction -- 2.8.6 Gold Price Prediction -- Bibliography -- Chapter 3 Multilayer Perceptron (in Neural Networks) -- 3.1 Multilayer Perceptron for Digit Classification -- 3.1.1 Implementation of MLP using TensorFlow for Classifying Image Data -- 3.2 Training Multilayer Perceptron -- 3.3 Backpropagation -- References -- Chapter 4 Kernel Machines -- 4.1 Different Kernels and Their Applications -- 4.2 Some Other Kernel Functions -- 4.2.1 Gaussian Radial Basis Function (RBF) -- 4.2.2 Laplace RBF Kernel -- 4.2.3 Hyperbolic Tangent Kernel -- 4.2.4 Bessel Function of the First-Kind Kernel -- 4.2.5 ANOVA Radial Basis Kernel -- 4.2.6 Linear Splines Kernel in One Dimension -- 4.2.7 Exponential Kernel -- 4.2.8 Kernels in Support Vector Machine -- References -- Chapter 5 Linear and Rule-Based Models -- 5.1 Least Squares Methods -- 5.2 The Perceptron -- 5.2.1 Bias -- 5.2.2 Perceptron Weighted Sum -- 5.2.3 Activation Function -- 5.2.3.1 Types of Activation Functions -- 5.2.4 Perceptron Training -- 5.2.5 Online Learning -- 5.2.6 Perceptron Training Error -- 5.3 Support Vector Machines -- 5.4 Linearity with Kernel Methods -- References -- Chapter 6 Distance-Based Models -- 6.1 Introduction -- 6.1.1 Distance-Based Clustering -- 6.2 K-Means Algorithm -- 6.2.1 K-Means Algorithm Working Process -- 6.3 Elbow Method -- 6.4 K-Median -- 6.4.1 Algorithm -- 6.5 K-Medoids, PAM (Partitioning Around Medoids) -- 6.5.1 Advantages -- 6.5.2 Drawbacks -- 6.5.3 Algorithm -- 6.6 CLARA (Clustering Large Applications) -- 6.6.1 Advantages -- 6.6.2 Disadvantages -- 6.7 CLARANS (Clustering Large Applications Based on Randomized Search) -- 6.7.1 Advantages -- 6.7.2 Disadvantages.; 6.7.3 Algorithm -- 6.8 Hierarchical Clustering -- 6.9 Agglomerative Nesting Hierarchical Clustering (AGNES) -- 6.10 DIANA -- References -- Chapter 7 Model Ensembles -- 7.1 Bagging -- 7.1.1 Advantages -- 7.1.2 Disadvantages -- 7.1.3 Bagging Workage -- 7.1.4 Algorithm -- 7.2 Boosting -- 7.2.1 Types of Boosting -- 7.2.2 Advantages -- 7.2.3 Disadvantages -- 7.2.4 Algorithm -- 7.3 Stacking -- 7.3.1 Architecture of Stacking -- 7.3.2 Stacking Ensemble Family -- References -- Chapter 8 Binary and Beyond Binary Classification -- 8.1 Binary Classification -- 8.2 Logistic Regression -- 8.3 Support Vector Machine -- 8.4 Estimating Class Probabilities -- 8.5 Confusion Matrix -- 8.6 Beyond Binary Classification -- 8.7 Multi-Class Classification -- 8.8 Multi-Label Classification -- Reference -- Chapter 9 Model Selection -- 9.1 Model Selection Considerations -- 9.1.1 What Do We Care Approximately When Choosing the Final Version? -- 9.2 Model Selection Strategies -- 9.3 Types of Model Selection -- 9.3.1 Methods of Re-Sampling -- 9.3.2 Random Separation -- 9.3.3 Time Divide -- 9.3.4 K-Fold Cross-Validation -- 9.3.5 Stratified K-Fold -- 9.3.6 Bootstrap -- 9.3.7 Possible Steps -- 9.3.8 Akaike Information Criterion (AIC) -- 9.3.9 Bayesian Information Criterion (BIC) -- 9.3.10 Minimum Definition Length (MDL) -- 9.3.11 Building Risk Reduction (SRM) -- 9.3.12 Excessive Installation (Overfitting) -- 9.4 The Principle of ; Parsimony -- 9.5 Examples of Model Selection Criterions -- 9.6 Other Popular Properties -- 9.7 Key Considerations -- 9.8 Model Validation -- 9.8.1 Why is Model Validation Important? -- 9.8.2 How to Validate the Model -- 9.8.3 What is a Model Validation Test? -- 9.8.4 Benefits of Modeling Validation -- 9.8.5 Model Validation Traps -- 9.8.6 Data Verification -- 9.8.7 Model Performance and Validation -- 9.9 Self-Driving Cars -- 9.10 K-Fold Cross Validation.; 9.11 No One-Size-Fits-All Model Validation -- 9.12 Validation Strategies -- 9.13 K-Fold Cross-Validation -- 9.14 Capture Confirmation Using Hold-Out Validation -- 9.15 Comparison of Validation Strategy -- References -- Chapter 10 Support Vector Machines -- 10.1 History -- 10.2 Model -- 10.3 Kinds of Support Vector Machine -- 10.3.1 Straight SVM -- 10.3.2 Non-Direct SVM -- 10.3.3 Benefits of Help Vector Machines -- 10.3.4 The Negative Marks of Help Vector Machines -- 10.3.5 Applications -- 10.4 Hyperplane and Support Vectors Inside the SVM Set of Rules -- 10.4.1 Hyperplane -- 10.5 Support Vectors -- 10.6 SVM Kernel -- 10.7 How Can It Function? -- 10.7.1 See the Right Hyperplane (Circumstance 1) -- 10.7.2 See the Appropriate Hyperplane (Situation 2) -- 10.7.3 Distinguish the Right Hyper-Airplane (Situation 3) -- 10.7.4 Would We Have the Option to Organize Models (Circumstance 4)? -- 10.7.5 Track Down the Hyperplane to Isolate Into Guidelines (Situation 5) -- 10.8 SVM for Classification -- 10.9 SVM for Regression -- 10.10 Python Implementation of Support Vector Machine -- 10.10.1 Data Pre-Taking Care of Step -- 10.10.2 Fitting the SVM Classifier to the Readiness Set -- 10.10.2.1 Outcome -- 10.10.3 Anticipating the Investigated Set Final Product -- 10.10.3.1 Yield -- 10.10.4 Fostering the Disarray Lattice -- 10.10.5 Picturing the Preparation Set Outcome -- 10.10.5.1 Yield -- 10.10.6 Imagining the Investigated Set Outcome -- 10.10.6.1 Yield -- 10.10.7 Part or Kernel -- 10.10.8 Support Vector Machine (SVM) Code in Python -- 10.10.9 Intricacy of SVM -- References -- Chapter 11 Clustering -- 11.1 Example -- 11.2 Kinds of Clustering -- 11.2.1 Hard Clustering -- 11.2.2 Delicate Clustering -- 11.2.2.1 Dividing Clustering or Partitioning Clustering -- 11.2.2.2 Thickness Essentially Based Clustering or Density Fundamentally Based Clustering.; 11.2.2.3 Transport Model-Based Clustering or Distribution Model-Based Clustering -- 11.2.2.4 Progressive Clustering or Hierarchical Clustering -- 11.2.2.5 Fluffy Clustering or Fuzzy Clustering -- 11.3 What are the Utilization of Clustering? -- 11.4 Models -- 11.5 Uses of Clustering -- 11.5.1 In Character of Most Tumor Cells -- 11.5.2 In Web Crawlers Like Google -- 11.5.3 Shopper Segmentation -- 11.5.4 In Biology -- 11.5.5 In Land Use -- 11.6 Bunching Algorithms or Clustering Algorithms -- 11.6.1 K-Means Clustering -- 11.6.2 Mean-Shift Clustering -- 11.6.3 Thickness or Density-Based Spatial Clustering of Application with Noise (DBSCAN) -- 11.6.4 Assumption Maximization Clustering Utilizing Gaussian Combination Models -- 11.6.5 Agglomerative Hierarchical Clustering -- 11.7 Instances of Clustering Algorithms -- 11.7.1 Library Setup -- 11.7.2 Grouping or Clustering Dataset -- 11.7.3 Fondness or Affinity Propagation -- 11.7.4 Agglomerative Clustering -- 11.7.5 BIRCH -- 11.7.6 DBSCAN -- 11.7.7 K-Means -- 11.7.8 Mini-Batch K-Means -- 11.7.9 Mean Shift -- 11.7.10 OPTICS -- 11.7.11 Unearthly or Spectral Clustering -- 11.7.12 Gaussian Mixture Model -- 11.8 Python Implementation of K-Means -- 11.8.1 Stacking the Data -- 11.8.2 Plotting the Information -- 11.8.3 Choosing the Component -- 11.8.4 Clustering -- 11.8.5 Clustering Results -- 11.8.6 WCSS and Elbow Technique -- 11.8.7 Uses of K-Mean Bunching -- 11.8.8 Benefits of K-Means -- 11.8.9 Bad Marks of K-MEAN -- References -- Chapter 12 Reinforcement Learning -- 12.1 ; Model -- 12.2 Terms Utilized in Reinforcement Learning -- 12.3 Key Elements of Reinforcement Learning -- 12.4 Instances of Reinforcement Learning -- 12.5 Advantages of Reinforcement Learning -- 12.6 Challenges with Reinforcement Learning -- 12.7 Sorts of Reinforcement -- 12.7.1 Positive -- 12.7.2 Negative.; 12.8 What are the Useful Utilizations of Reinforcement Learning?.
• Sommario/riassunto: The main goal of the book is to provide a comprehensive and accessible guide that empowers readers to understand, apply, and leverage machine learning algorithms and techniques effectively in real-world scenarios. Welcome to the exciting world of machine learning! In recent years, machine learning has rapidly transformed from a niche field within computer science to a fundamental technology shaping various aspects of our lives. Whether you realize it or not, machine learning algorithms are at work behind the scenes, powering recommendation systems, autonomous vehicles, virtual assistants, medical diagnostics, and much more. This book is designed to serve as your comprehensive guide to understanding the principles, algorithms, and applications of machine learning. Whether a student diving into this field for the first time, a seasoned professional looking to broaden your skillset, or an enthusiast eager to explore cutting-edge advancements, this book has something for you. The primary goal of Machine Learning for Industrial Applications is to demystify machine learning and make it accessible to a wide audience. It provides a solid foundation in the fundamental concepts of machine learning, covering both the theoretical underpinnings and practical applications. Whether you're interested in supervised learning, unsupervised learning, reinforcement learning, or innovative techniques like deep learning, you'll find comprehensive coverage here. Throughout the book, a hands-on approach is emphasized. As the best way to learn machine learning is by doing, the book includes numerous examples, exercises, and real-world case studies to reinforce your understanding and practical skills. Audience The book will enjoy a wide readership as it will appeal to all researchers, students, and technology enthusiasts wanting a hands-on guide to the new advances in machine learning.