Deep learning techniques for automation and industrial applications

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Başlık:

Yazar:

Rathore, Pramod Singh, 1988- editor.

ISBN:

9781394234271

9781394234264

9781394234257

Fiziksel Tanımlama:

1 online resource (280 p.)

Genel Not:

Description based upon print version of record.

6.4 Results and Discussions

İçerik:

Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Text Extraction from Images Using Tesseract -- 1.1 Introduction -- 1.1.1 Areas -- 1.1.2 Why Text Extraction? -- 1.1.3 Applications of OCR -- 1.2 Literature Review -- 1.3 Development Areas -- 1.3.1 React JavaScript (JS) -- 1.3.2 Flask -- 1.4 Existing System -- 1.5 Enhancing Text Extraction Using OCR Tesseract -- 1.6 Unified Modeling Language (UML) Diagram -- 1.6.1 Use Case Diagram -- 1.6.2 Model Architecture -- 1.6.3 Pseudocode -- 1.7 System Requirements -- 1.7.1 Software Requirements

1.7.2 Hardware Requirements -- 1.8 Testing -- 1.9 Result -- 1.10 Future Scope -- 1.11 Conclusion -- References -- Chapter 2 Chili Leaf Classification Using Deep Learning Techniques -- 2.1 Introduction -- 2.2 Objectives -- 2.3 Literature Survey -- 2.4 About the Dataset -- 2.5 Methodology -- 2.6 Result -- 2.7 Conclusion and Future Work -- References -- Chapter 3 Fruit Leaf Classification Using Transfer Learning Techniques -- 3.1 Introduction -- 3.2 Literature Review -- 3.3 Methodology -- 3.3.1 Image Preprocessing -- 3.3.2 Data Augmentation -- 3.3.3 Deep Learning Models -- 3.3.4 Accuracy Chart

3.3.5 Accuracy and Loss Graph -- 3.4 Conclusion and Future Work -- References -- Chapter 4 Classification of University of California (UC), Merced Land-Use Dataset Remote Sensing Images Using Pre-Trained Deep Learning Models -- 4.1 Introduction -- 4.2 Motivation and Contribution -- 4.2.1 Related Work -- 4.3 Methodology -- 4.3.1 Pre-Trained Models -- 4.3.2 Dataset -- 4.3.3 Training Processes -- 4.4 Experiments and Results -- 4.4.1 VGG Family -- 4.4.2 ResNet Family -- 4.4.2.1 ResNet101 -- 4.4.2.2 ResNet152 -- 4.4.3 MobileNet Family -- 4.4.4 Inception Family -- 4.4.5 Xception Family

4.4.6 DenseNet Family -- 4.4.7 NasNet Family -- 4.4.8 EfficientNet Family -- 4.4.9 ResNet Version 2 -- 4.5 Conclusion -- References -- Chapter 5 Sarcastic and Phony Contents Detection in Social Media Hindi Tweets -- 5.1 Introduction -- 5.1.1 Sarcasm in Social Media Hindi Tweets -- 5.2 Literature Review -- 5.2.1 Literature Review of Sarcasm Detection Based on Data Analysis Without Machine Learning Algorithms -- 5.2.1.1 Other Related Works without Machine Learning Algorithms for Sarcasm Detection

5.2.2 Literature Review of Sarcasm Detection with Machine Learning Algorithms and Based on Manual Feature Engineering Approach -- 5.3 Research Gap -- 5.4 Objective -- 5.5 Proposed Methodology -- 5.6 Expected Outcomes -- References -- Chapter 6 Removal of Haze from Synthetic and Real Scenes Using Deep Learning and Other AI Techniques -- 6.1 Introduction -- 6.2 Formation of a Haze Model -- 6.3 Different Techniques of Single-Image Dehazing -- 6.3.1 Contrast Enhancement -- 6.3.2 Dark Channel Prior -- 6.3.3 Color Attenuation Prior -- 6.3.4 Fusion Techniques -- 6.3.5 Deep Learning

Özet:

This book provides state-of-the-art approaches to deep learning in areas of detection and prediction, as well as future framework development, building service systems and analytical aspects in which artificial neural networks, fuzzy logic, genetic algorithms, and hybrid mechanisms are used. Deep learning algorithms and techniques are found to be useful in various areas, such as automatic machine translation, automatic handwriting generation, visual recognition, fraud detection, and detecting developmental delays in children. "Deep Learning Techniques for Automation and Industrial Applications" presents a concise introduction to the recent advances in this field of artificial intelligence (AI). The broad-ranging discussion covers the algorithms and applications in AI, reasoning, machine learning, neural networks, reinforcement learning, and their applications in various domains like agriculture, manufacturing, and healthcare. Applying deep learning techniques or algorithms successfully in these areas requires a concerted effort, fostering integrative research between experts from diverse disciplines from data science to visualization. This book provides state-of-the-art approaches to deep learning covering detection and prediction, as well as future framework development, building service systems, and analytical aspects. For all these topics, various approaches to deep learning, such as artificial neural networks, fuzzy logic, genetic algorithms, and hybrid mechanisms, are explained. Audience The book will be useful to researchers and industry engineers working in information technology, data analytics network security, and manufacturing. Graduate and upper-level undergraduate students in advanced modeling and simulation courses will find this book very useful.

Notlar:

John Wiley and Sons

Konu Terimleri:

Automation.

Automatisation.

Intelligence (AI) & Semantics.

COMPUTERS.

Tür:

Electronic books.

Yazar Ek Girişi:

Rathore, Pramod Singh, 1988-

Ahuja, Sachin.