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001431810 0247_ $$a10.1007/978-981-15-6695-0$$2doi
001431810 0248_ $$a10.1007/978-981-15-6
001431810 035__ $$aSP(OCoLC)1191243074
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001431810 050_4 $$aQA76.9.N37$$bB56 2021
001431810 08204 $$a006.3$$223
001431810 24500 $$aBio-inspired algorithms for data streaming and visualization, big data management, and fog computing /$$cSimon James Fong, Richard C. Millham, editors.
001431810 264_1 $$aSingapore :$$bSpringer,$$c[2021]
001431810 300__ $$a1 online resource
001431810 336__ $$atext$$btxt$$2rdacontent
001431810 337__ $$acomputer$$bc$$2rdamedia
001431810 338__ $$aonline resource$$bcr$$2rdacarrier
001431810 347__ $$atext file
001431810 347__ $$bPDF
001431810 4901_ $$aSpringer tracts in nature-inspired computing
001431810 504__ $$aIncludes bibliographical references.
001431810 5050_ $$aChapter 1. The Big Data Approach Using Bio-Inspired Algorithms: Data Imputation -- Chapter 2. Parameter Tuning onto Recurrent Neural Network and Long Short Term Memory (RNN-LSTM) Network for Feature Selection in Classification of High-dimensional Bioinformatics Datasets -- Chapter 3. Data Stream Mining in Fog Computing Environment with Feature Selection Using Ensemble of Swarm Search Algorithms -- Chapter 4. Pattern Mining Algorithms -- Chapter 5. Extracting Association Rules: Meta-Heuristic and Closeness Preference Approach -- Chapter 6. Lightweight Classifier-based Outlier Detection Algorithms from Multivariate Data Stream -- Chapter 7. Comparison of Contemporary Meta-heuristic Algorithms for Solving Economic Load Dispatch Problem -- Chapter 8. The paradigm on fog computing with bio-inspired search methods and the '5Vs of big data -- Chapter 9. Approach for sentiment analysis on social media sites -- Chapter 10. Data Visualisation techniques and Algorithms -- Chapter 11. Business Intelligence -- Chapter 12. Big Data Tools for Tasks.
001431810 506__ $$aAccess limited to authorized users.
001431810 520__ $$aThis book aims to provide some insights into recently developed bio-inspired algorithms within recent emerging trends of fog computing, sentiment analysis, and data streaming as well as to provide a more comprehensive approach to the big data management from pre-processing to analytics to visualization phases. The subject area of this book is within the realm of computer science, notably algorithms (meta-heuristic and, more particularly, bio-inspired algorithms). Although application domains of these new algorithms may be mentioned, the scope of this book is not on the application of algorithms to specific or general domains but to provide an update on recent research trends for bio-inspired algorithms within a specific application domain or emerging area. These areas include data streaming, fog computing, and phases of big data management. One of the reasons for writing this book is that the bio-inspired approach does not receive much attention but shows considerable promise and diversity in terms of approach of many issues in big data and streaming. Some novel approaches of this book are the use of these algorithms to all phases of data management (not just a particular phase such as data mining or business intelligence as many books focus on); effective demonstration of the effectiveness of a selected algorithm within a chapter against comparative algorithms using the experimental method. Another novel approach is a brief overview and evaluation of traditional algorithms, both sequential and parallel, for use in data mining, in order to provide an overview of existing algorithms in use. This overview complements a further chapter on bio-inspired algorithms for data mining to enable readers to make a more suitable choice of algorithm for data mining within a particular context. In all chapters, references for further reading are provided, and in selected chapters, the author also include ideas for future research.
001431810 588__ $$aOnline resource; title from digital title page (viewed on October 07, 2020).
001431810 650_0 $$aNature-inspired algorithms.
001431810 650_0 $$aBig data.
001431810 650_6 $$aAlgorithmes inspirés par la nature.
001431810 650_6 $$aDonnées volumineuses.
001431810 655_0 $$aElectronic books.
001431810 7001_ $$aFong, Simon,$$eeditor.
001431810 7001_ $$aMillham, Richard C.,$$eeditor.
001431810 77608 $$iPrint version:$$tBio-inspired algorithms for data streaming and visualization, big data management, and fog computing.$$dSingapore : Springer, [2021]$$z9811566941$$z9789811566943$$w(OCoLC)1156994752
001431810 830_0 $$aSpringer tracts in nature-inspired computing.
001431810 852__ $$bebk
001431810 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-15-6695-0$$zOnline Access$$91397441.1
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