|
" Data science and big data analytics : "
Durgesh Kumar Mishra, Xin-She Yang, Aynur Unal, editors.
| Document Type
|
:
|
BL
|
| Record Number
|
:
|
889148
|
| Main Entry
|
:
|
ACM-WIR (Workshop)(2018 :, Indore, India)
|
| Title & Author
|
:
|
Data science and big data analytics : : ACM-WIR 2018 /\ Durgesh Kumar Mishra, Xin-She Yang, Aynur Unal, editors.
|
| Publication Statement
|
:
|
Singapore :: Springer,, 2019.
|
| Series Statement
|
:
|
Lecture notes on data engineering and communications technologies,; volume 16
|
| Page. NO
|
:
|
1 online resource
|
| ISBN
|
:
|
9789811076411
|
|
|
:
|
: 9811076413
|
|
|
:
|
9789811076404
|
|
|
:
|
9811076405
|
| Notes
|
:
|
Includes author index.
|
| Contents
|
:
|
Intro; Preface; Contents; About the Editors; Keynote Speakers; Analysis and Evaluation of Big Data Video Quality in Wireless Networks; Publishing Ethics and Author Services; Applying Machine Learning Techniques for Big Data Analytics; Outlier Detection in Big Data; Identification and Prevention of Internet Addiction in Youth; Android: Security Issues; Tweaking Big Data Analysis for Malicious Intents; Big Data and Web Mining; Big Data and Data Science Trends: Challenges and Opportunities
|
|
|
:
|
2.4 ICESat/GLAS Data Processing3 Study Area and Materials; 4 Methodology; 5 Description of Datasets; 5.1 Satellite Data; 5.2 Field Data; 6 Genetic Approach for Optimization of LiDAR Biomass Estimation; 7 Optimization Genetic Algorithm Using R Package; 7.1 Calculate of RMSE; 8 Results and Discussions; 8.1 Analysis of GLAS Derived Waveform Parameters; 8.2 Results from Genetic Algorithm; 8.3 Biomass Equation Generated; 9 Conclusions; References
|
|
|
:
|
3 Proposed Work3.1 Problem Identification; 3.2 Methodology; 4 Performance Analysis; 4.1 Implementation; 4.2 Result Comparisons; 5 Conclusion; References; Baron-Cohen Model Based Personality Classification Using Ensemble Learning; 1 Introduction; 2 Related Work; 3 Implementation; 3.1 Data Set Details; 3.2 Hypothesis; 3.3 Machine Learning; 3.4 K-Fold Validation (K = 10); 3.5 Ensemble Learning; 4 Results; 5 Conclusion; References; Investigation of MANET Routing Protocols via Quantitative Metrics; 1 Introduction; 2 Related Work; 3 Routing Protocols; 4 Simulation Table and Results
|
|
|
:
|
A Study of the Correlation Between Internet Addiction and Aggressive Behaviour Among the Namibian University Students1 Introduction; 2 Literature Review; 3 Methodology; 3.1 Study Design and Population; 3.2 Measures and Data Collection; 3.3 Data Analysis; 4 Results and Analysis; 4.1 Descriptive Statistics; 4.2 Correlations Between Variables; 5 Conclusion; References; Genetic Algorithm Approach for Optimization of Biomass Estimation at LiDAR; 1 Introduction; 2 Review of Literature; 2.1 Biomass Estimation; 2.2 Review of Global Forest Biomass Assessment; 2.3 Spaceborne LiDAR (ICESat/GLAS)
|
|
|
:
|
E-alive: An Integrated Platform Based on Machine Learning Techniques to Aware and Educate Common People with the Current Statistics of Maternal and Child Health Care1 Introduction; 2 Motivation; 3 Preliminaries; 3.1 Maternal and Child Health Care Parameters; 3.2 Government Schemes; 3.3 Techniques of Data Mining; 4 System Implementation; 5 Results and Discussions; 6 Conclusion and Future Scope; References; An Effective TCP's Congestion Control Approach for Cross-Layer Design in MANET; 1 Introduction; 1.1 Mobile Ad Hoc Network; 1.2 Congestion Control; 1.3 Cross-Layer Design; 2 Literature Survey
|
| Abstract
|
:
|
This book presents conjectural advances in big data analysis, machine learning and computational intelligence, as well as their potential applications in scientific computing. It discusses major issues pertaining to big data analysis using computational intelligence techniques, and the conjectural elements are supported by simulation and modelling applications to help address real-world problems. An extensive bibliography is provided at the end of each chapter. Further, the main content is supplemented by a wealth of figures, graphs, and tables, offering a valuable guide for researchers in the field of big data analytics and computational intelligence.
|
| Subject
|
:
|
Big data, Congresses.
|
| Subject
|
:
|
Data mining, Congresses.
|
| Subject
|
:
|
Machine learning, Congresses.
|
| Subject
|
:
|
Big data.
|
| Subject
|
:
|
COMPUTERS-- Database-- General.
|
| Subject
|
:
|
Data mining.
|
| Subject
|
:
|
Machine learning.
|
| Dewey Classification
|
:
|
005.7
|
| LC Classification
|
:
|
QA76.9.B45
|
| Added Entry
|
:
|
Mishra, Durgesh Kumar
|
|
|
:
|
Unal, Aynur
|
|
|
:
|
Yang, Xin-She
|
| Parallel Title
|
:
|
ACM-WIR 2019
|
| |