Cognitive data models for sustainable environment

Cognitive data models for sustainable environment

"Cognitive Models for Sustainable Environment reviews the fundamental concepts of gathering, processing and analyzing data from batch processes, along with a review of intelligent and cognitive tools that can be used. The book is centered on evolving novel intelligent/cognitive models and algor...

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Detalles Bibliográficos
Otros Autores: Bhattacharyya, Siddhartha, 1975- editor (editor), Sangaiah, Arun Kumar, 1981- editor, Mondal, Naba Kumar, editor, Mondal, Koushik, editor, Singh, Jyoti Prakash, editor, Prakash, Kolla Bhanu, editor
Formato: Libro electrónico
Idioma:Inglés
Publicado: London, England : Academic Press [2022]
Colección:Cognitive data science in sustainable computing.
Materias:
Environmental monitoring > Data processing.
Pollution > Measurement > Data processing.
Soft computing.
Environmental Monitoring
Environmental Pollution > prevention & control
Models, Theoretical
Soft Computing
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009835435206719
Tabla de Contenidos:
  • Front Cover
  • Cognitive Data Models for Sustainable Environment
  • Cognitive Data Models for Sustainable Environment: Cognitive Data Science in Sustainable Computing
  • Copyright
  • Dedication
  • Contents
  • Contributors
  • Preface
  • 1 - Multidimensional controlling properties of biofabricated silver-nanoparticles on different mosquito species
  • 1. Introduction
  • 2. Silver nanoparticles synthesis
  • 2.1 Plant-mediated synthesis
  • 2.2 Microorganism-mediated synthesis
  • 2.3 Animal products - mediated synthesis
  • 3. Silver nanoparticles application on mosquito
  • 3.1 AgNPs application on mosquito larvae
  • 3.2 AgNPs application on mosquito pupae
  • 3.3 AgNPs application on mosquito egg and adult
  • 4. Research gaps
  • 5. Conclusion
  • Appendix A. Supplementary data
  • References
  • 2 - Machine learning-enabled cognitive approaches for handling IoT-based environmental data
  • 1. Introduction
  • 2. Cognitive IoT data-processing framework
  • 3. Solution and technology overview
  • 3.1 Data collection
  • 3.2 Data storage
  • 3.3 Data flow orchestration
  • 3.4 Data analysis
  • 3.5 Data visualization
  • 3.6 Action
  • 4. Experimental results and discussion
  • 5. Last note
  • 6. Future directions
  • Appendix A. Supplementary data
  • References
  • 3 - Evolution of sustainable environment: a cognitive outlook
  • 1. Introduction
  • 2. Classification of smart materials
  • 2.1 Piezoelectric substances
  • 2.2 Thermosensitive substances
  • 2.3 pH-sensitive substances
  • 2.4 Chromogenic substances
  • 2.5 Hydrogels
  • 2.6 Magnetoresponsive substances
  • 2.7 Optical fiber
  • 2.8 Active and passive substances
  • 3. Properties of smart materials
  • 4. Application of smart materials
  • 4.1 Application in the field of nanotechnology and acoustics
  • 4.2 Application in the field of piezoelectric and electrochromic device.
  • 4.3 Application in field of civil engineering
  • 4.4 Application in field of electronics
  • 4.5 Application in field of medical
  • 4.6 Application of smart material in the field of aerospace
  • 4.7 Application of smart material in the field of biosensors
  • 4.8 Application of smart material in the field of dentistry
  • 5. Impact and aspect of smart material
  • 6. Impact of smart material on the sustainability
  • 7. Comparative analysis for the evaluation of smart material in sustainable environment
  • 7.1 Future challenges and benefits
  • 8. Conclusion
  • Appendix A. Supplementary data
  • References
  • 4 - Application of nanotechnology in pesticides adsorption with statistical optimization and modeling
  • 1. Introduction
  • 2. Different techniques of pesticide removal
  • 3. Nanomaterial synthesis and characterization
  • 4. Nanoadsorbent materials and properties
  • 4.1 Carbon-based nanoadsorbents
  • 4.2 Miscellaneous nanomaterials
  • 4.3 Nanocomposite
  • 4.4 Nanoclay
  • 5. Nanomaterials application toward pesticides adsorption and modeling
  • 6. Statistical optimization-related synthesis of precursors and adsorption
  • 6.1 RSM-based optimization
  • 6.2 AI-based optimization
  • 7. Conclusion and future perspectives
  • Appendix A. Supplementary data
  • References
  • 5 - Sustainability issues in upcoming wastewater treatment plants at Patna
  • 1. Introduction
  • 2. Scenario of existing wastewater treatment plants in Patna
  • 2.1 Sanitation and wastewater collection system
  • 2.2 Wastewater treatment plants
  • 2.3 Reasons for failure of the existing wastewater treatment plants (WWTPs)
  • 2.3.1 Poor operation and maintenance
  • 2.3.2 Inadequate supply of power
  • 2.3.3 Lack of trained manpower
  • 2.3.4 Poor wastewater collection system
  • 2.3.5 Lack of coordination and good governance
  • 3. Upcoming environmental infrastructure projects.
  • 3.1 Status of projects pertaining to sewage treatment infrastructure in India
  • 3.2 Overview on sewage treatment infrastructure projects undertaken in Bihar and Patna
  • 4. Sustainability measures taken in upcoming projects
  • 4.1 Appropriate cost analysis of the projects
  • 4.2 Adoption of alternate power generation sources
  • 4.3 Appointment of skilled and trained operators
  • 4.4 Simultaneous development of wastewater collection network
  • 4.5 Development of a proper administrative structure
  • 4.6 Smooth transfer to urban local body
  • 5. Sustainability analysis of the upcoming projects
  • 5.1 Environmental sustainability
  • 5.2 Economic sustainability
  • 5.3 Social sustainability
  • 6. Provisions to be included in upcoming projects
  • 6.1 Selection of treatment technology during the inception of the projects
  • 6.2 Resource recovery
  • 6.3 Public participation
  • 7. Conclusion
  • Appendix A. Supplementary data
  • References
  • 6 - Community approach toward disaster resilience
  • 1. Introduction
  • 2. Hazards, vulnerability, and resilience
  • 3. Community-based disaster management (CBDM) approach
  • 4. Outline of Total Disaster Risk Management (TDRM)
  • 5. Disaster reduction cycle
  • 6. Case studies
  • 6.1 Community-based disaster management approach in Bangladesh
  • 6.2 Empowering community for disaster risk reduction in Nepal
  • 6.3 Reporting on community-based disaster management in Indonesia
  • 6.4 India's community-based disaster risk reduction plan
  • 6.5 Japan's disaster risk reduction plan
  • 7. Risk mitigation analysis
  • 8. Conclusion
  • Appendix A. Supplementary data
  • References
  • 7 - ZnO nanoparticles: a facile synthesized agent for removing dye from aqueous solution in an ecofriendly way
  • 1. Introduction
  • 2. Synthesis of ZnO nanoparticles
  • 2.1 Synthesis of ZnO nanoparticle using plant parts.
  • 2.2 ZnO nanoparticles synthesis using microbes
  • 3. Different characterization techniques of ZnO nanoparticles
  • 4. Effect of ZnO nanoparticles on dye solution
  • 4.1 Photocatalytic activity of ZnO NPs for dye degradation in wastewater
  • 4.2 ZnO NPs as an adsorbent for effective removal of dyes
  • 5. Conclusion
  • 6. Future perspectives
  • Appendix A. Supplementary data
  • Acknowledgments
  • References
  • 8 - Optimization of rural indoor kitchen structure and minimizing the pollution load: a sustainable environmental m ...
  • 1. Introduction
  • 2. Material and methods
  • 2.1 Area of study and research design
  • 2.2 Study period
  • 2.3 Measurement of indoor air quality
  • 2.4 Kitchen and living room ventilation pattern
  • 2.5 About Response Surface Methodology (RSM) and Central Composite Design (CCD)
  • 2.6 Experimental model design
  • 2.7 Ethical permission
  • 3. Results and discussion
  • 3.1 Nonlinear regression models
  • 3.2 Descriptive statistics and ANOVA analysis of the response variables
  • 3.3 Optimization of the desirable condition by using RSM
  • 3.4 Predicted versus actual plots of indoor variables deriving through RSM
  • 4. Model improvements
  • 5. Conclusions
  • 6. Limitation of the study
  • Appendix A. Supplementary data
  • List of abbreviations
  • Acknowledgments
  • References
  • 9 - IoT-based health care data analytical paradigm using blockchain technology
  • 1. Introduction
  • 1.1 Overview of Internet of Things and its challenges
  • 1.2 Technical analysis of blockchain and its key characteristics
  • 2. Role of IoT and blockchain in health care
  • 2.1 Integration of IoT and blockchain
  • 2.2 Reference architecture of IoT and blockchain
  • 2.3 IoT and blockchain-based remote patient monitoring system
  • 3. Monitoring health care data using smart contracts
  • 3.1 Health care IoT devices in remote medical care.
  • 3.2 Monitoring patient's using smart contract
  • 4. Privacy-preserving of health care data
  • 4.1 System and threat model of smart health care
  • 4.2 Design objectives to achieve privacy-preserving
  • 5. Comparative analysis of existing models to secure health care data using IoT and blockchain
  • 5.1 Comparative study
  • 6. Open research challenges and future directions
  • 7. Conclusion
  • Appendix A. Supplementary data
  • References
  • 10 - Environmental pain with human beauty: emerging environmental hazards attributed to cosmetic ingredients and pa ...
  • 1. Introduction
  • 2. Global scenario of PCCP production
  • 3. Fate of cosmetics and related hazards
  • 4. Cosmetic ingredients and their environmental impact
  • 4.1 Ingredients
  • 4.2 Environmental impact
  • 4.2.1 Biocide compounds
  • 4.2.2 UV filters (sunscreens)
  • 4.2.3 Preservatives
  • 4.2.4 Fragrances
  • 4.2.5 Surfactants
  • 4.2.6 Siloxanes
  • 4.2.7 Microplastics
  • 5. Plastic in cosmetics
  • 5.1 Microplastic
  • 5.2 Plastic packaging
  • 6. Conclusion
  • Appendix A. Supplementary data
  • List of abbreviations
  • References
  • 11 - Indian rural housing: an approach toward sustainability
  • 1. Introduction
  • 2. Concept of sustainability
  • 3. Indian rural housing
  • 3.1 Rural housing schemes and programs in India
  • 3.2 Problems of housing for the rural deprived
  • 4. Need for a separate rural housing policy
  • 5. Components of rural housing policies
  • 5.1 Land availability
  • 5.2 Housing finance
  • 6. Identification of rural areas for different aspects in housing
  • 7. Locally available building materials
  • 7.1 Predominant walling materials
  • 7.2 Predominant roofing materials
  • 7.3 Predominant flooring materials
  • 8. Construction techniques
  • 8.1 Wall construction techniques
  • 8.2 Roof construction techniques
  • 9. Settlement forms
  • 9.1 Architectural types.
  • 10. Technical intervention aspects in rural areas.

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