Computational Analysis and Deep Learning for Medical Care: Principles, Methods, and Applications
- 7h 16m
- Amit Kumar Tyagi
- John Wiley & Sons (US)
- 2021
The book presents the various research areas dealing with the application of different sustainable technologies for enhancing the dyeing and comfort properties of textile materials with substantial reduction in wastewater problems.
Increasing environmental and health concerns in the textile industry and fashion sector about the use of large quantities of water and hazardous chemicals in conventional textile finishing processes, has led to the design and development of new dyeing strategies and technologies. Effluents produced from the textile wet processing industry are very diverse in chemical composition, ranging from inorganic finishing agents, surfactants, chlorine compounds, salts and total phosphate to polymers and organic products. This has forced Western countries to exploit their high technical skills for the advancement of textile materials with high quality technical performances, and the development of cleaner production technologies for cost-effective and value-added textile materials.
Sustainable Practices in the Textile Industry focuses on the sophisticated methods for improving dye extraction and dyeing properties which will minimize the use of bioresource products. This book also brings out the innovative ways of wet chemical processing to alleviate the environmental impacts arising from this sector. The book also discusses innovations in eco-friendly methods for textile wet processes and applications of enzymes in textiles in addition to the advancements in the use of nanotechnology for wastewater remediation.
Audience
Researchers, engineers in the textile industry, textile chemistry and dyeing, chemical engineering, environmental science, and materials science as well as graduate and postgraduate students, will find this book invaluable.
In this Book
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CNN: A Review of Models, Application of IVD Segmentation
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Location-Aware Keyword Query Suggestion Techniques With Artificial Intelligence Perspective
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Identification of a Suitable Transfer Learning Architecture for Classification: A Case Study with Liver Tumors
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Optimization and Deep Learning–Based Content Retrieval, Indexing, and Metric Learning Approach for Medical Images
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Deep Learning for Clinical and Health Informatics
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Biomedical Image Segmentation by Deep Learning Methods
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Multi-Lingual Handwritten Character Recognition Using Deep Learning
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Disease Detection Platform Using Image Processing Through OpenCV
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Computer-Aided Diagnosis of Liver Fibrosis in Hepatitis Patients Using Convolutional Neural Network
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Lung Cancer Prediction in Deep Learning Perspective
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Lesion Detection and Classification for Breast Cancer Diagnosis Based on Deep CNNs from Digital Mammographic Data
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Health Prediction Analytics Using Deep Learning Methods and Applications
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Ambient-Assisted Living of Disabled Elderly in an Intelligent Home Using Behavior Prediction—A Reliable Deep Learning Prediction System
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Early Diagnosis Tool for Alzheimer's Disease Using 3D Slicer
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Deep Learning for Medical Healthcare: Issues, Challenges, and Opportunities
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A Perspective Analysis of Regularization and Optimization Techniques in Machine Learning
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Deep Learning-Based Prediction Techniques for Medical Care: Opportunities and Challenges
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Machine Learning and Deep Learning: Open Issues and Future Research Directions for the Next 10 Years
