 
BA 2023 : Business Analytics: Theory and Practice  
 
Call For Papers  
BUSINESS ANALYTICS: THEORY AND PRACTICE
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; NonLinear Regression; Logistic Regression; Path Model; Discriminant Analysis; Principal Component Analysis; Factor Analysis; Cluster Analysis; Structural Equation Modeling. Time Series Modeling; Moving Averages; BoxJenkins Method – ARMA, ARIMA, Others; StateSpace Representation; Kalman Filter; Periodogram; Autocovariance Functions; White Noise; Partial Correlation; LevinsonDurbin Recursion. Mathematical Optimization Techniques – Linear Programming; Overview of Advanced Linear Programming; Integer Programming; Dynamic Programming; Markov Analysis; Information Theory; NonLinear 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 MultiInput Neuron; Layer of Neuron; Perceptron Network; Hamming Network; Hopfield Network; Backpropagation; Learning Rules – Supervised Hebbian Learning; WidrawHoff Learning; Associative Learning. Descriptive Analytics  Association Rule; Sequence Rule; Segmentation and Hierarchical Clustering; KMeans 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; MultiTask 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: bandyopadhyaysusmita2010@gmail.com with subject line, "Abstract for Business Analytics" (for abstract) or "Full Chapters for Business Analytics". [The chapters will be checked for Plagiarism issue] 
