| 000 -LEADER |
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| fixed length control field |
nam a22 7a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
180309b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781138198692 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
338.9270285 |
| Item number |
YUT |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Yu, Ting |
| 245 ## - TITLE STATEMENT |
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| Title |
Computational intelligent data analysis for sustainable development |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of publisher, distributor, etc |
CRC Press, |
| Place of publication, distribution, etc |
2013 |
| Date of publication, distribution, etc |
Boca Raton: |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xviii, 414 p. |
| Other physical details |
ill. |
| Dimensions |
24 cm. |
| 365 ## - TRADE PRICE |
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| Price type code |
GBP |
| Price amount |
38.99, Rs. 3696.25 |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
Going beyond performing simple analyses, researchers involved in the highly dynamic field of computational intelligent data analysis design algorithms that solve increasingly complex data problems in changing environments, including economic, environmental, and social data. Computational Intelligent Data Analysis for Sustainable Development presents novel methodologies for automatically processing these types of data to support rational decision making for sustainable development. Through numerous case studies and applications, it illustrates important data analysis methods, including mathematical optimization, machine learning, signal processing, and temporal and spatial analysis, for quantifying and describing sustainable development problems. With a focus on integrated sustainability analysis, the book presents a large-scale quadratic programming algorithm to expand high-resolution input-output tables from the national scale to the multinational scale to measure the carbon footprint of the entire trade supply chain. It also quantifies the error or dispersion between different reclassification and aggregation schemas, revealing that aggregation errors have a high concentration over specific regions and sectors. The book summarizes the latest contributions of the data analysis community to climate change research. A profuse amount of climate data of various types is available, providing a rich and fertile playground for future data mining and machine learning research. The book also pays special attention to several critical challenges in the science of climate extremes that are not handled by the current generation of climate models. It discusses potential conceptual and methodological directions to build a close integration between physical understanding, or physics-based modeling, and data-driven insights. The book then covers the conservation of species and ecologically valuable land. A case study on the Pennsylvania Dirt and Gravel Roads Program demonstrates that multiple-objective linear programming is a more versatile and efficient approach than the widely used benefit targeting selection process. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Data Communications |
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| Topical term or geographic name as entry element |
Climate informatics |
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| Topical term or geographic name as entry element |
Mathematical programming application |
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| Topical term or geographic name as entry element |
Wind resource assessment |
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| Topical term or geographic name as entry element |
Criminal offense record |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Chawla, Nitesh V. |
|
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| Personal name |
Simoff, Simeon J. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
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| Item type |
Books |