Methodologies and Applications of Computational Statistics for Machine Intelligence 1st Edition by Debabrata Samanta, Raghavendra Rao Althar, Sabyasachi Pramanik 1799877019 9781799877011
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Authors:Samanta, Debabrata; Rao Althar, Raghavendra; Pramanik, Sabyasachi; Dutta, Soumi , Series:IT & Computer [377] , Tags:Computers; Artificial Intelligence; General; Data Science; Machine Learning; Mathematics; Probability & Statistics , Author sort:Samanta, Debabrata & Rao Althar, Raghavendra & Pramanik, Sabyasachi & Dutta, Soumi , Ids:Google; 9781799877035 , Languages:Languages:eng , Published:Published:Jun 2021 , Publisher:IGI Global , Comments:Comments:With the field of computational statistics growing rapidly, there is a need for capturing the advances and assessing their impact. Advances in simulation and graphical analysis also add to the pace of the statistical analytics field. Computational statistics play a key role in financial applications, particularly risk management and derivative pricing, biological applications including bioinformatics and computational biology, and computer network security applications that touch the lives of people. With high impacting areas such as these, it becomes important to dig deeper into the subject and explore the key areas and their progress in the recent past.Methodologies and Applications of Computational Statistics for Machine Intelligence serves as a guide to the applications of new advances in computational statistics. This text holds an accumulation of the thoughts of multiple experts together, keeping the focus on core computational statistics that apply to all domains. Covering topics including artificial intelligence, deep learning, and trend analysis, this book is an ideal resource for statisticians, computer scientists, mathematicians, lecturers, tutors, researchers, academic and corporate libraries, practitioners, professionals, students, and academicians.
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ISBN 10: 1799877019
ISBN 13: 9781799877011
Author: Debabrata Samanta, Raghavendra Rao Althar, Sabyasachi Pramanik
Methodologies and Applications of Computational Statistics for Machine Intelligence 1st Table of contents:
Chapter 1: Hybrid Feature Vector-Assisted Action Representation for Human Action Recognition Using Support Vector Machines
ABSTRACT
I. INTRODUCTION
II. LITERATURE SURVEY
III. PROPOSED METHOD
IV. SIMULATION EXPERIMENTS
V. CONCLUSION
REFERENCES
Chapter 2: Unsupervised Summarization Approach With Computational Statistics of Microblog Data
ABSTRACT
INTRODUCTION
LITERATURE REVIEW
PROPOSED METHODOLOGY
EXPERIMENTS AND RESULTS
CONCLUSION
REFERENCES
Chapter 3: Machine Intelligence of Pi From Geometrical Figures With Variable Parameters Using SCILab
ABSTRACT
INTRODUCTION
BACKGROUND
PROBLEM STATEMENT
SOLUTION
FUTURE RESEARCH DIRECTIONS
CONCLUSION
REFERENCES
Chapter 4: Computational Statistics-Based Prediction Algorithms Using Machine Learning
ABSTRACT
INTRODUCTION
WHAT IS MACHINE LEARNING?
PREDICTIVE MODELING
WHAT IS INDUSTRY 4.0
APPLICATIONS OF ML (PREDICTIVE MODELING) IN VARIOUS FIELDS OF INDUSTRY 4.0
EXISTING SYSTEM AND ITS LIMITATIONS
MACHINE LEARNING USECASES
CONCLUSION
REFERENCES
Chapter 5: Computational Statistics of Data Science for Secured Software Engineering
ABSTRACT
INTRODUCTION
FOCUS OF THE CHAPTER
EXPLORATION OF SECURED SOFTWARE ENGINEERING
FUTURE RESEARCH DIRECTIONS
CONCLUSION
REFERENCES
Chapter 6: Quantitative and Visual Exploratory Data Analysis for Machine Intelligence
ABSTRACT
INTRODUCTION
QUANTITATIVE AND GRAPHICAL EXPLORATORY DATA ANALYSIS
PLOTTING SYSTEM USING R
RESEARCH AND FUTURE TRENDS OF EDA
CONCLUSION
REFERENCES
Chapter 7: Continuous Autoregressive Moving Average Models
ABSTRACT
INTRODUCTION
AUTOREGRESSIVE MODELS
MOVING AVERAGE MODELS
ARMA MODELS
CONTINUOUS ARMA MODELS
REFERENCES
Chapter 8: A Computational Statistics Review for Low Complexity Clutter Cancellation for Passive Bi-Static Radar
ABSTRACT
INTRODUCTION
SIGNAL MODEL
ADAPTIVE DISTURBANCE CANCELLATION TECHNIQUE
RESULTS
CONCLUSION
REFERENCES
Chapter 9: Machine Intelligence-Based Trend Analysis of COVID-19 for Total Daily Confirmed Cases in Asia and Africa
ABSTRACT
INTRODUCTION
LITERATURE REVIEW
DATA AND METHODOLOGY
RESULTS
CONCLUSION
REFERENCES
Chapter 10: Application of Machine Intelligence-Based Knowledge Graphs for Software Engineering
ABSTRACT
INTRODUCTION
FOCUS OF THE CHAPTER
EXPLORATION OF KNOWLEDGE GRAPHS FOR SOFTWARE ENGINEERING
FUTURE RESEARCH DIRECTIONS
CONCLUSION
REFERENCES
Chapter 11: A Comprehensive Study on Artificial Intelligence and Robotics for Machine Intelligence
ABSTRACT
INTRODUCTION
DEEP AI
ROBOTICS
CLASSIFICATION
PAST AND PRESENT
HUMAN-ROBOT INTERACTION (HRI)
LIMITATIONS
ARE AI AND ROBOTICS THE SAME THING?
AI TO ROBOTS
LOOKING TO THE FUTURE
CASE STUDY: HOW COMPANIES USING AI IN ROBOTICS
CONCLUSION
REFERENCES
Chapter 12: Innovation and Creativity for Data Mining Using Computational Statistics
ABSTRACT
INTRODUCTION
DATA MINING PRINCIPLES
STATISTICS
DIFFERENCES OF DATA MINING AND STATISTICS
STATISTICAL ANALYTICS AND TYPES
DATA SCIENTIST
DATA VISUALIZATION
CONCLUSION
REFERENCES
Compilation of References
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