| Description |
1 online resource (ix, 514 pages) : illustrations (some color) |
| Series |
Studies in big data, 2197-6511 ; volume 134 |
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Studies in big data ; v. 134. 2197-6511
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| Contents |
Intro -- Preface -- Contents -- Analytics-Oriented Applications -- Recursive Multi-step Time-Series Forecasting for Residual-Feedback Artificial Neural Networks: A Survey -- 1 Introduction -- 2 Residual-Feedback ANNs: A Systematic Review -- 2.1 Systematic Review Planning and Execution -- 2.2 Overview of the Systematic Review Findings -- 3 The Existing Recursive Multi-step Forecast Strategy Solution -- 4 Limitation -- 5 Conclusions and Future Works -- References -- Feature Selection: Traditional and Wrapping Techniques with Tabu Search -- 1 Introduction -- 2 Related Work -- 3 Methodology |
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3.1 Data Description -- 3.2 Entropy-Based Feature Selection -- 3.3 Feature Selection Using Principal Component Analysis -- 3.4 Correlation-Based Feature Selection -- 4 Tabu Search -- 4.1 Initial Solution -- 4.2 Neighborhood -- 4.3 Objective Function -- 4.4 Memory Structures -- 5 Results -- 6 Discussion -- 7 Conclusions and Future Work -- References -- Pattern Classification with Holographic Neural Networks: A New Tool for Feature Selection -- 1 Introduction -- 2 Holographic Neural Networks -- 2.1 Basic Theory -- 2.2 Learning and Prediction Methods |
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2.3 red Explainability and Optimization of Holographic Models -- 3 Feature Selection with Holographic Neural Neworks -- 3.1 Previous Works -- 3.2 Pythagorean Membership Grades -- 4 Pattern Classification -- 4.1 Iris Dataset -- 4.2 red NIPS Feature Selection Challenge -- 5 red Conclusions and Future Works -- References -- Reusability Analysis of K-Nearest Neighbors Variants for Classification Models -- 1 Introduction -- 2 The K-Nearest Neighbors Algorithm -- 3 The Parameter K -- 4 Closeness Metrics -- 5 Analysis of KNN Variants -- 5.1 Heuristics for Class Assignment |
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5.2 Reduction of Dataset Records -- 5.3 Estimation of Dataset Variables -- 5.4 Discussion -- 6 Conclusions -- References -- Speech Emotion Recognition Using Deep CNNs Trained on Log-Frequency Spectrograms -- 1 Introduction -- 2 Literature Survey -- 2.1 Motivation -- 2.2 Contributions -- 3 Proposed Methodology -- 3.1 Data Augmentation -- 3.2 Extraction of Log-Frequency Spectrograms -- 3.3 Motivation Behind Using Spectrograms -- 3.4 Log-Frequency Spectrogram Extraction -- 3.5 Understanding What a Spectrogram Conveys -- 4 The Deep Convolutional Neural Network -- 4.1 Architecture -- 4.2 Training |
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5 Observations -- 5.1 Dataset Used -- 5.2 Performance Metrics Used -- 5.3 Results Obtained -- 5.4 Comparison Study -- 6 Conclusion -- References -- Text Classifier of Sensationalist Headlines in Spanish Using BERT-Based Models -- 1 Introduction -- 2 Background -- 2.1 Sensationalism -- 2.2 BERT-Based Models -- 3 Related Work -- 4 Dataset and Methods -- 4.1 Data Gathering and Data Labeling -- 4.2 Data Analysis -- 4.3 Model Generation and Fine-Tuning -- 5 Results -- 6 Conclusion -- References -- Arabic Question-Answering System Based on Deep Learning Models -- 1 Introduction |
| Summary |
In recent years, significant progress has been made in achieving artificial intelligence (AI) with an impact on students, managers, scientists, health personnel, technical roles, investors, teachers, and leaders. This book presents numerous successful applications of AI in various contexts. The innovative implications covered fall under the general field of machine learning (ML), including deep learning, decision-making, forecasting, pattern recognition, information retrieval, and interpretable AI. Decision-makers and entrepreneurs will find numerous successful applications in health care, sustainability, risk management, human activity recognition, logistics, and Industry 4.0. This book is an essential resource for anyone interested in challenges, opportunities, and the latest developments and real-world applications of ML. Whether you are a student, researcher, practitioner, or simply curious about AI, this book provides valuable insights and inspiration for your work and learning |
| Notes |
Online resource; title from PDF title page (SpringerLink, viewed October 9, 2023) |
| Subject |
Machine learning.
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Deep learning (Machine learning)
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Deep learning (Machine learning)
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Machine learning
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| Form |
Electronic book
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| Author |
Rivera, Gilberto, editor.
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Rosete, Alejandro, editor.
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Dorronsoro, Bernabé, editor.
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Rangel-Valdez, Nelson, editor.
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| ISBN |
9783031406881 |
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3031406885 |
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