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Intelligent Environmental Data Monitoring for Pollution Management discusses evolving novel intelligent algorithms and their applications in the area of environmental data-cent… Read more
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Immediately download your ebook while waiting for your print delivery. No promo code is needed.
Intelligent Environmental Data Monitoring for Pollution Management discusses evolving novel intelligent algorithms and their applications in the area of environmental data-centric systems guided by batch process-oriented data. Thus, the book ushers in a new era as far as environmental pollution management is concerned. It reviews the fundamental concepts of gathering, processing and analyzing data from batch processes, followed by a review of intelligent tools and techniques which can be used in this direction. In addition, it discusses novel intelligent algorithms for effective environmental pollution data management that are on par with standards laid down by the World Health Organization.
Academics and researchers interested in joining interdisciplinary projects on intelligent environmental data management
1. Batch Adsorption Process in Water Treatment
2. Removal of heavy metals from industrial effluents by using Biochar
3. Nanoparticles: A new tool for control of mosquito larvae
4. Biosorption driven green technology for the treatment of heavy metal (loids) contaminated effluents
5. A Comprehensive Review of Glyphosate Adsorption with Factors Influencing Mechanism: Kinetics, Isotherms, Thermodynamics Study
6. Dyes and their removal technologies from wastewater
7. An Intelligent Estimation Model for Water Quality Parameters Assesment at Periyakulam Lake, South India
8. Recent Trends in Air Quality Prediction: An Artificial Intelligence Perspective
9. Optimisation of absorption process for exclusion of Carbaryl from aqueous environment using natural adsorbents
10. Artificial Neural Network: An Alternative Approach for Assessment of Biochemical Oxygen Demand of the Damodar River, West Bengal, India
11. Co-design to improve IAQ awareness in classrooms
12. Data Perspective on Environmental Mobile Crowd Sensing
13. A Survey of Adsorption Process Parameter Optimization related to Degradation of Environmental Pollutants
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Jan Platos received a Ph.D. in computer science in 2010. He became a Full professor in 2021 at the Department of Computer Science. Since 2021, he has been Dean of the Faculty of Electrical Engineering and Computer Science, VSB-TUO. He has co-authored more than 240 scientific articles published in proceedings and journals. His primary fields of interest are machine learning, artificial intelligence, industrial data processing, text processing, data compression, bioinspired algorithms, information retrieval, data mining, data structures, and data prediction.
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