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BA 2023 : Business Analytics: Theory and Practice


When Aug 1, 2022 - Dec 15, 2023
Where CRC Press
Abstract Registration Due Oct 15, 2022
Submission Deadline Dec 31, 2022
Notification Due Jan 20, 2023
Final Version Due Apr 30, 2023
Categories    engineering

Call For Papers

CRC PRESS; ISBN NUMBER: 9781032415574
Editor: Dr. S. Bandyopadhyay

Chapters are required for the above book on the following topics.

Introduction to Different Analytics – Descriptive Analytics; Predictive Analytics; Prescriptive Analytics; Probability – Discrete Probability Distribution; Continuous Probability Distribution; Introduction to R Software; Introduction to Python.

Basic Statistics – Central Tendency; Dispersions; Testing and Hypothesis; Statistical Inference; Sampling, ANOVA.

Multivariate Probability Distribution; Regression – Linear Regression; Multiple and Multivariate Regression; Non-Linear Regression; Logistic Regression; Path Model; Discriminant Analysis; Principal Component Analysis; Factor Analysis; Cluster Analysis; Structural Equation Modeling.

Time Series Modeling; Moving Averages; Box-Jenkins Method – ARMA, ARIMA, Others; State-Space Representation; Kalman Filter; Periodogram; Autocovariance Functions; White Noise; Partial Correlation; Levinson-Durbin Recursion.

Mathematical Optimization Techniques – Linear Programming; Overview of Advanced Linear Programming; Integer Programming; Dynamic Programming; Markov Analysis; Information Theory; Non-Linear Optimization.

Nature Based Techniques – Genetic Algorithm; Particle Swarm Optimization; Ant Colony Optimization; Artificial Immune Algorithm; Differential Evolution; Simulated Annealing; Tabu Search; Honey Bee Mating Algorithm; Frog Leaping Algorithm; Bacteria Foraging Algorithm; Firefly Algorithm; Cuckoo Search.

Neural Network - Single- and Multi-Input Neuron; Layer of Neuron; Perceptron Network; Hamming Network; Hopfield Network; Backpropagation; Learning Rules – Supervised Hebbian Learning; Widraw-Hoff Learning; Associative Learning.

Descriptive Analytics - Association Rule; Sequence Rule; Segmentation and Hierarchical Clustering; K-Means Clustering; Decision Tree – Decision Tree Rules; Decision Tree by Expected Monetary Value; Decision Induction Algorithms – ID3, C4.5, CART; Other Learning Techniques – Supervised Learning; Unsupervised Learning; Reinforcement Learning; Hybrid Learning; Multi-Task Learning; Ensemble Learning; Transfer Learning; Online Learning; Inductive, Deductive and Transductive Learning.

Discrete Event Simulation – Monte Carlo Simulation; Random Number Generator; Computer Simulation; Manufacturing Simulation; Supply Chain Simulation; Continuous Simulation; Simulation Optimization; Application of Simulation; Simulation Software.

Data Mining; Partitioning; Visualization Techniques; Dimension Reduction Techniques; Performance Metrics – Performance Metrics Based on Naïve Rule; Classification Matrix; Performance Metrics in Manufacturing; Performance Metrics in Supply Chain; Implementation of Different Methodologies with R and Python.

Send both the abstracts and full chapters to email id: with subject line, "Abstract for Business Analytics" (for abstract) or "Full Chapters for Business Analytics".
[The chapters will be checked for Plagiarism issue]

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