| Description |
1 online resource (317 p.) |
| Series |
Smart Engineering Systems Ser |
|
Smart Engineering Systems Ser
|
| Contents |
Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Editors -- Contributors -- Chapter 1 A Review of Automated Sleep Apnea Detection Using Deep Neural Network -- 1.1 Introduction -- 1.2 Materials and Methods -- 1.3 Signal and Dataset -- 1.3.1 Based on Pulse Oxygen Saturation Signal -- 1.3.2 Based on Electrocardiogram (ECG) -- 1.3.3 Based on Airflow (AF) -- 1.3.4 Based on Sound -- 1.4 Data Preprocessing -- 1.4.1 Raw Signal -- 1.4.2 Filtered Signal -- 1.4.3 Signal Normalization -- 1.4.4 Spectrogram -- 1.4.5 Feature Analyses |
|
1.5 Performance Metrics -- 1.6 Classifiers -- 1.6.1 CNN -- 1.6.1.1 D1CNN -- 1.6.1.2 D2CNN -- 1.6.2 RNN -- 1.6.2.1 LSTM -- 1.6.2.2 GRU -- 1.6.3 Deep Vanilla Neural Network (DVNN) -- 1.6.3.1 MHLNN -- 1.6.3.2 SSAE -- 1.6.3.3 DBN -- 1.6.4 Combined DNN Approach -- 1.7 Discussion -- 1.8 Conclusion -- References -- Chapter 2 Optimization of Tool Wear Rate Using Artificial Intelligence-Based TLBO and Cuckoo Search Approach -- 2.1 Introduction -- 2.2 Artificial Intelligence -- 2.3 Electric Discharge Machining (EDM) -- 2.4 Analysis of Variance (ANOVA) -- 2.5 Optimization -- 2.5.1 Cuckoo Search Algorithm |
|
2.5.2 Teaching-Learning-Based Optimization -- 2.6 Experimental Details and Results -- 2.7 Conclusion -- References -- Chapter 3 Lung Tumor Segmentation Using a 3D Densely Connected Convolutional Neural Network -- 3.1 Introduction -- 3.2 Literature Survey -- 3.2.1 Traditional vs Deep Learning Approaches -- 3.2.2 Lung Nodule Detection -- 3.2.3 Lung Tumor Detection -- 3.3 Related Work -- 3.3.1 U-Net Segmentation Model -- 3.3.2 DenseNet Model -- 3.4 Proposed Methodology -- 3.4.1 Dataset -- 3.4.1.1 Dataset Description -- 3.4.1.2 Data Preprocessing -- 3.4.2 Segmentation Model |
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3.4.2.1 Model Architecture -- 3.4.2.2 Model Training -- 3.5 Experimental Results -- 3.5.1 Evaluation Criteria -- 3.5.2 Results -- 3.6 Discussion -- 3.7 Conclusion and Future Scope -- Acknowledgment -- References -- Chapter 4 Day-Ahead Solar Power Forecasting Using Artificial Neural Network with Outlier Detection -- 4.1 Introduction -- 4.2 Literature Review -- 4.3 Electrical Characteristics of a PV Module -- 4.3.1 Correlation of Temperature and Irradiance to the Output Power of a PV Module -- 4.3.2 Variation of Current and Voltage with Irradiance and Temperature -- 4.3.3 Studied PV System and Data |
|
4.3.4 Data Pre-Processing -- 4.4 Overview to ANN -- 4.5 Methodology -- 4.5.1 Interpolation for Imputation of Missing Values -- 4.5.2 Exponential Smoothing for Imputation of Missing Values -- 4.5.3 Design of ANN Structure -- 4.5.4 Evaluation of the Forecasting Model -- 4.6 Results and Discussion -- 4.7 Conclusion -- Acknowledgement -- References -- Chapter 5 Fuzzy-Inspired Three-Dimensional DWT and GLCM Framework for Pixel Characterization of Hyperspectral Images -- 5.1 Introduction -- 5.2 Experimentation -- 5.2.1 3D DWT and 3D GLCM-Based Approach for Hyperspectral Image Classification |
| Notes |
Description based upon print version of record |
|
5.2.1.1 3D DWT Decomposition |
| Subject |
Artificial intelligence -- Industrial applications -- Congresses
|
|
Smart materials
|
|
Electrical engineering -- Data processing
|
| Form |
Electronic book
|
| Author |
J. Karande, Kailash
|
|
G. Deshmukh, Sampat
|
| ISBN |
9781000653564 |
|
1000653560 |
|
