Mathematical Modeling for Big Data Analytics

Dr. Passent M El-Kafrawy is a Full Professor in the Department of Math and Computer Science at Effat University, Saudi Arabia, and a Full Professor of Artificial Intelligence in the Math and Computer Science Department, Faculty of Science, at Menoufia University Egypt. Dr. El-Kafrawy received her Ph.D. from the University of Connecticut, USA in Computer Science and Engineering, in the field of Computational Geometry as a branch of Artificial Intelligence. She has taught at Eastern State University, Connecticut, USA, and at Nile University, Egypt. She has been an organizing member of conferences including Signal Processing and Information Technology, the International Conference of Language Engineering, and the International Learning and Technology conference, and was co-Editor of the Signal Processing and Information Technology conference proceedings, published by Springer.Dr. Mohamed F. El-Amin is a Full Professor of Applied Mathematics and Computational Sciences at Effat University, Saudi Arabia. He is also a Visiting Professor at King Abdullah University of Science and Technology, Saudi Arabia, and is a Full Professor at Aswan University, Egypt. As a mathematician, he has over 25 years of research experience in the field of computational sciences, applied mathematics, transport in porous media, heat/mass transfer, fluid dynamics, turbulence, reservoir simulation, and other aspects of complex systems. After obtaining his PhD in 2001, he held research positions in several universities including South Valley University (Egypt), Stuttgart University (Germany), Kyushu University (Japan), and KAUST. Dr. El-Amin is the editor of several journal special issues and the editor of books including Numerical Modeling of Nanoparticle Transport in Porous Media: MATLAB/Python Approach, Elsevier.Dr. El-Amin's key areas of research are computational mathematics and fluid flow modeling, with applications in several areas - including but not limited to reservoir simulation, transport phenomena, nanofluids flow, multiphase flow, transport in porous media, heat and mass transfer, hydrogen energy, boundary layer flow, magnetohydrodynamics, and non-Newtonian fluids.

• Part I: Theoretical Foundation1. An Overview of Big Data Analytics2. Mathematical and Statistical Concepts Underlying Big Data Analytics3. Qualitative Analytics Techniques4. Quantitative Analytics Techniques5. An Introduction to Digital Twins and their Use in Big Data Analytics6. Exploration of Machine Learning Techniques7. On Deep Learning Techniques8. Optimization Techniques for Big Data Analytics9. Visualization in Big Data Analytics10. Ethical Considerations for Big Data AnalyticsPart II: Data-Specific Application11. Text Analytics Techniques12. Network Analytics Techniques13. Spatial Analytics Techniques14. Timeseries and Sound Analytics Techniques15. IoT based data Analytics