Artificial intelligence, Internet of Things (IoT) and smart materials for energy applications. (2022)

• Record Type: Book
• Further Information: Note: Edited by Mohan Lal Kolhe, Kailash J. Karande, Sampat G. Deshmukh.
• Editors: Lal Kolhe, Mohan; Karande, Kailash Jagannath; Deshmukh, Sampat G
• Contents: Chapter 01 - A Review of Automated Sleep Apnea Detection Using Deep Neural Network; Praveen Kumar Tyagi, Dheeraj Agarwal, Pushyamitra Mishra Abstract 1.1 Introduction 1.2 Materials and methods; 1.2.1 Signal and data set; 1.2.2 Based on pulse Oxygen Saturation signal; 1.2.3 Based on electrocardiogram (ECG); 1.2.4 Based on Airflow (AF) 1.3 Data Pre-Processing; 1.3.1 Raw signal; 1.3.2 Filtered Signal; 1.3.3 Signal Normalization; 1.3.4 Spectrogram; 1.3.5 Feature Analyses; 1.4 Performance metrics; 1.5 Classifiers; 1.5.1 CNN 1.5.2 D1CNN 1.5.3 D2CNN; 1.5.4 RNN; 1.5.5 LSTM; 1.5.6 GRU; 1.5.7 Deep Vanilla Neural Network (DVNN); 1.5.8 MHLNN; 1.5.9 SSAE; 1.5.10 DBN; 1.5.11 Combined DNN approach; 1.6 Discussion; 1.7 Conclusion; 1.8 References Chapter 02 - Optimization of Tool Wear Rate using Artificial Intelligence based TLBO and Cuckoo Search Approach; Vishal Parashar, Shubam Jain, P.M.Mishra Abstract 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 2.8 References ; Chapter 03 - Lung Tumor Segmentation using a 3D Densely Connected Convolutional Neural Network; Shweta Tyagi, Sanjay N. Talbar Abstract 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 3.4.2 Segmentation model 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 3.8 Acknowledgement 3.9 References Chapter 04 -Day Ahead Solar Power Forecasting Using Artificial Neural Network with Outlier Detection; D. J. K. Dassanayake, M. H. M. R. S. Dilhani, K. M. S. Y. Konara, Mohan Lal Kolhe Abstract 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 Preprocessing 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 4.8 Acknowledgement 4.9 References Chapter 05-Fuzzy Inspired Three Dimensional DWT and GLCM Framework for Pixel Characterisation of Hyperspectral Images; K.Kavitha, D.Sharmila Banu ; Abstract 5.1 Introduction; 5.2 Experimentation; 5.2.1 3D DWT and 3D GLCM based Approach for Hyperspectral Image Classification; 5.2.1.1 3D DWT Decomposition; 5.2.1.2 3D GLCM Feature Extraction; 5.2.2 Support Vector Machines (SVM); 5.2.2.1 SVM for Non-Linear and Non-Separable Classes; 5.2.3 3D DWT and 3D GLCM Based Hyperspectral Image Classification Method 5.2.4 Proposed Fuzzy Inspired Image Classification Method 5.2.4.1 Mixed Pixel Identification 5.2.4.2 Fuzzification 5.2.4.3 Membership Function 5.2.4.4 Reclassification; 5.2.4.5 Fuzzy inspired Process; 5.3 Results and Discussion; 5.3.1 Results obtained for simple 3D DWT and GLCM Method; 5.3.2 Results obtained for Fuzzy inspired 3D DWT and 3D GLCM Method; 5.4 Conclusion; 5.5 Scope; 5.6 References Chapter 06- Painless Machine learning approach to estimate blood glucose level of Non-Invasive device; Altaf O. Mulani, Makarand M. Jadhav, Mahesh Seth ; Abstract; 6.1 Introduction; 6.2 Types of Glucose Monitoring Techniques; 6.2.1 Invasive method for glucose measurement 6.2.2 Non-Invasive method for glucose measurement 6.3 Painless Non-Invasive Glucometer using machine learning approach 6.4 Results and Discussion 6.4.1 Channel Estimation for finding Glucose Level 6.4.2 Model Validation; 6.4.3 Fast-Tree Regression Machine learning technique; 6.5 Conclusion 6.6 References Chapter 07 - Artificial Intelligence and Machine Learning in Biomedical Applications; Vaibhav.V. Dixit, Mayuresh B. Gulame Abstract; 7.1 Introduction 7.1.1 Innovations of technology 7.2 Challenges and Issues 7.2.1 Data Collection 7.2.2 Poor quality of data 7.2.3 Interpretability 7.2.4 Domain complexity 7.2.5 Feature enrichment 7.2.6 Temporal modeling 7.2.7 Balancing model accuracy and interpretability; 7.2.8 Legal issues 7.3 Artificial intelligence and machine learning applications in Biomedical 7.3.1 Precision medicine 7.3.2 Genetics-based solutions 7.3.3 Drug Improvement and discovery 7.3.4 Prediction of protein structure 7.3.5 Medical image recognition 7.3.6 Health monitoring and wearable’s 7.3.7 Minimally invasive surgery (MIS); 7.3.8 Monitoring by Biosensor 7.4 Success elements for AI in Biomedical Engineering; 7.4.1 Assessment of condition; 7.4.2 Managing complications 7.4.3 Patient-care assistance 7.4.4 Medical research 7.5 Conclusion 7.6 References ; Chapter 08- The use of Artificial Intelligence based models for Biomedical Application; Sharad Mulik, Nilesh Dhobale, Kanchan Pujari and Kailash Karande Abstract 8.1 Introduction 8.2 AI Methods and Applications 8.2.1 Machine learning (ML) 8.2.2 Natural language processing (NLP) 8.2.3 Neural network (NN) 8.2.4 Deep learning (DL) 8.2.5 Machine vision/computer vision (MV/CV) 8.3 Robotic-Assisted Surgical Systems (RASS) and Computer-Assisted Surgery (CAS); 8.4 Virtual nurse assistants (VNAs) for healthcare; 8.4.1 Medication management and medication error reduction (MMMER) 8.4.2 Improving medical safety 8.4.3 Monitoring medication non adherence 8.4.4 Clinical trial participation (CTP) 8.5 Preliminary diagnosis and prediction (PDP); 8.5.1 Diabetes prediction 8.5.2 Cancer prediction 8.5.3 Tube
• Edition: 1st
• Publisher Details: Boca Raton : CRC Press
• Publication Date: 2022
• Extent: 1 online resource
• Subjects: 621.310285; Electric power systems -- Control; Electric power systems -- Data processing; Electrical engineering; Energy conversion -- Materials; Artificial intelligence -- Industrial applications; Internet of things -- Industrial applications; Smart materials
• Languages: English
• ISBNs: 9781000653625; 9781000653564; 9781003220176
• Related ISBNs: 9781032115023
• Notes: Note: Description based on CIP data; resource not viewed.
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