However, the Bayesian paradigm suggested differences even for triglycerides and TC:HDL ratio across gender. This clearly suggests that even when sample size is small, the Bayesian paradigm closely approximates our prior knowledge of lipid profile as the risk factor for CAD occurrence. The Bayesian paradigm unraveled the importance of clinical parameters (triglyceride and TC:HDL), which remained hidden under the classical t-test.
Chapter 35 proposes an architecture of cascaded convolutional long short-term memory (ConvLSTM) that uses the idea of combining the patch-based dictionary learning approach for reconstruction of dynamic MRI. K-space data of T2-weighted dynamic MRI sequences obtained from ADNI database is undersampled for accelerating the acquisition process using Cartesian masks of different undersampling rates. Proposed architecture is later used to reconstruct this undersampled sequence. Results are compared with state-of-the-art 2-dimensional cascaded convolutional neural network (CNN)-based reconstruction for all standardmetrics. The proposed methodology is capable of preserving anatomical structure modality even after manifold undersampling.
Chapter 36 presents a multispectral imaging-based gender classification with images collected in nine narrow spectrums across VIS and NIR spectrum. The authors also experimentally presented the comparative performance analysis study on gender classification using affine hull algorithm and wavelet averaging fusion. The experimental results are obtained on the multispectral facial database of 145 subjects corresponding to 78300 sample spectral band images. The extensive experimental results are carried out across six different illuminations using three different feature extraction methods for gender classification. The average classification obtained indicates the superiority of the wavelet fusion method over the affine hull subspace learning method in successfully extracting the unique characteristic information from spectral bands for improved performance.
Chapter 37 analyzes the performance of various deep learning models for polyp detection. Various image enhancement techniques, such as max, min, sobel and canny filters, are applied to improve the performance efficiency of the training networks, which further helps to increase the rate of polyp detection. The concept of transfer learning and fine tuning is implemented to improve the efficiency of VGG16 and VGG19 deep neural networks. The system model is tested to detect polyps and the results of the system are described using different performance metrics like accuracy, loss, precision and recall. This work concludes that VGG19 deep neural networks are more suitable for polyp detection than other methods.
Chapter 38 proposes a method based on a combined approach of extrinsic content sensors and ab-initio signal sensors to predict boundary exons in human sequences. Here, a homology-based exon prediction method is used which utilizes external information from sources like transcript and protein databases. The method is evaluated at a nucleotide as well as exon level. The experimental results indicate that the proposed method is appropriate for predicting boundary exons with a significant level of accuracy. It also demonstrated superior performance when compared with existing protein-coding gene prediction methods.
Chapter 39 discusses a blood glucose monitoring system with 5-fixed LED wavelengths in near-infrared (NIR) region at 2.12 μm, 2.24 μm, 2.27 μm, 2.31 μm and 2.33 μm as a source of excitation. The Jetson Nano board having ARM Cortex A57 is used to control these LED sources. The authors recorded 57 spectra on laboratory samples prepared to resemble blood, having proportions as per the major constituents (glucose, ascorbate, urea, lactate, and alanine) present in the blood. Out of 57spectra, 53 were used for calibration set and 4 were used for validating the model. Partial least square regression (PLSR) prediction algorithms are developed in Python and run on Jetson Nano board.With PLSR, the result of glucose prediction gave a root mean square error (RMSE) of 12.01mg/dL, determination coefficient R2 = 0.97 and accuracy of 90.14%. A back propagation–artificial neural network (BP-ANN) model is developed on Jetson Nano board for accuracy. This BP-ANN model is used to train the same 53 sample data sets and 4 for validating the model. The system is validated with Clarke error grid analysis (CEGA) and Bland–Altman analysis.
Chapter 40 envisions the potential of geographic information systems (GIS) in combating COVID-19. These systems play an important role in many areas, including quick mapping of epidemic information, spatial monitoring of cases reported, forecasting of district transmission of epidemic hazard and mitigation, and social-emotional assistance for decision-making and control. The authors have collected the state-level variation of COVID-19 pandemic prevalence spreading across the Gujarat state of India. In the present study, they prepared four maps, namely, confirmed positive cases, cases tested for COVID-19, patients recovered, people under quarantine, and total deaths as per data collected from the government. Cluster zones can be easily identified by the GIS-based mapping approach. Outcomes of the study demonstrated that Ahmedabad city in Gujarat has suffered more as a result of this pandemic.
Chapter 41 proposes a mobile-based medical alert system for a COVID-19 detection system using ZigBee technology. The health report of the user will be sent to the caretaker or doctor via a cloud computing network for analysis. The real-time monitoring of body temperature and symptoms of COVID-19 and data transmission via remote sensing is also realized.
In conclusion, this book highlights the important role artificial intelligence playsin smart living and sustainable development along with the critical need for more research in the field. It provides a comprehensive overview of the latest breakthroughs and recent progress in intelligent technologies; and highlights relevant sustainable intelligent computing technologies, uses, and techniques across various industries. We hope that readers will significantly benefit from this book academically, scientifically and societally; and that it will expand opportunities and open new scientific paths to foster the discovery of knowledge and its applications.
Mukhdeep Singh Manshahia Punjabi University, Patiala, Punjab, India Valeriy Kharchenko Federal Scientific Agroengineering Center, VIM, Moscow, Russia Elias Munapo Department of Statistics & Operations Research, NWU, Mafikeng Campus, South Africa J. Joshua Thomas UOW Malaysia KDU Penang University College, Malaysia Pandian Vasant Universiti Teknologi PETRONAS, Malaysia
Acknowledgment
The editors wish to acknowledge the support of everyone involved in creating this book. We would like to sincerely thank the researchers for their wonderful help and support in reviewing all the chapters with total dedication. Our sincere thanks go out to the authors of the chapters who contributed their time and expertise to this book. Some of the authors also served as referees; we greatly appreciate their double task.
The editors are grateful to Professor Gerhard-Wilhelm Weber for the excellent foreword to this book and his precious assistance. The editors are confident that this book will certainly be useful to a wide circle of readers, from students to specialists in the field.
Our sincere thanks also goes out to the entire staff of Scrivener Publishing for their invaluable support in completing this book. In particular, special thanks to Mr. Martin Scrivener, Scrivener Publishing, and Dr. Prasenjit Chatterjee, series editor, for their guidance and cooperation.
Moreover, we would also like to thank our organizations for providing us with the facilities required to accomplish this project.
Finally, we express our heartfelt gratitude and reverence to our family members for their boundless love, support and motivation during the entire journey of this project.
Mukhdeep Singh Manshahia Punjabi University, Patiala, Punjab, India Valeriy Kharchenko Federal Scientific Agroengineering Center, VIM, Moscow, Russia Elias