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010   $a981-9683-62-9
024 7 $a10.1007/978-981-96-8362-8
035   $a(CKB)44770003400041
035   $a(MiAaPQ)EBC32484362
035   $a(Au-PeEL)EBL32484362
035   $a(OCoLC)1569920088
035   $a(DE-He213)978-981-96-8362-8
035   $a(EXLCZ)9944770003400041
100   $a20260102d2025      u|         0
101 0 $aeng
135   $aur|||||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aData Science and Cases in Sustainability $ePattern Recognition and Machine Learning /$fby Ashish Ghosh
205   $a1st ed. 2025.
210  1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025.
215   $a1 online resource (0 pages)
225 1 $aMathematics for Sustainable Developments,$x3004-9024
311 08$a981-9683-61-0 
327   $aChapter 1. Evolution of Data Science -- Chapter 2. LearningDimensionality Reduction -- Chapter 3. Types of Data -- Chapter 4. Pre-processing of Data -- Chapter 5. Dimensionality Reduction -- Chapter 6. Pattern Recognition System -- Chapter 7. Classification -- Chapter 8. Classifiers -- Chapter9. Combination of Classifiers -- Chapter10. Clustering -- Chapter 11. Clustering Algorithms -- Chapter 12. Outliers -- Chapter 13. Fuzzy Set Theoretic Approach to Pattern Recognition -- Chapter 14. Rule of Thumb -- Chapter 15. Artificial Neural Networks -- Chapter 16. Multilayer Perceptron -- Chapter 17. Evolutionary Computing for Machine Learning -- Chapter 18. Support Vector Machine -- Chapter 19. Kernel Machines -- Chapter 20. Extreme Learning Machines -- Chapter 21. Deep Learning.
330   $aThis book discusses the fascinating world of data science and cases in sustainability focusing on topics related to pattern recognition and machine learning, emphasizing applications that directly address topics related to SDG 9 (Industry, Innovation and Infrastructure). Recognizing the sustainable applications of big data, this text emphasizes the shift from traditional statistical analyses to more sophisticated methods. Each of these techniques?pattern recognition and machine learning?plays a crucial role in extracting hidden knowledge from vast amount of data. Targeted to students, researchers and professionals, it highlights the multidisciplinary and sustainable nature of the field and showcasing real-world applications and equips the readers to navigate the data-driven future. The first of the two volumes, the book highlights the multidisciplinary nature of data science in the fields of computer science, statistics, physics and economics. It meticulously guides its readers through the data science workflow, covering data collection, preparation, storage, analysis, management and visualization. It highlights specific techniques and algorithms used in each of the above-mentioned stages and offers explanations of major learning mechanisms: dimensionality reduction, classification, clustering and outlier analysis. Additionally, it sheds light on the modern field of deep learning and unfolds the complexity of its mechanism with explanation. Case studies showcase the practical applications and successes of data science across various domains.
410  0$aMathematics for Sustainable Developments,$x3004-9024
606   $aArtificial intelligence$xData processing
606   $aQuantitative research
606   $aMachine learning
606   $aImage processing$xDigital techniques
606   $aComputer vision
606   $aData Science
606   $aData Analysis and Big Data
606   $aMachine Learning
606   $aComputer Imaging, Vision, Pattern Recognition and Graphics
615  0$aArtificial intelligence$xData processing.
615  0$aQuantitative research.
615  0$aMachine learning.
615  0$aImage processing$xDigital techniques.
615  0$aComputer vision.
615 14$aData Science.
615 24$aData Analysis and Big Data.
615 24$aMachine Learning.
615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics.
676   $a338.9270285
700   $aGhosh$b Ashish$01346642
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9911049090403321
996   $aData Science and Cases in Sustainability$94530827
997   $aUNINA