The areas of interest of the conference include but are not limited to:
- Pure Mathematics
- Applied Mathematics
- Engineering Mathematics
- Mathematics Education
- Computational Mathematics
- Mathematics & Multimedia
- Statistics
- Optimization
- Operational Research
- Actuarial Science
- Finance
- Quality & Productivity
- Data Science
Specific scopes for the proceedings are as follows:
- Pure Mathematics: Algebra, Algebra & Mathematical Logic, Algebraic Geometry, Approximation Theory, Analysis, Combinatorics, Complex Analysis, Cryptography, Differential Geometry, Fractals, Functional Analysis, Fuzzy, Game Theory, Geometry, Graph Theory, Matrix Theory, Number Theory, Set Theory, Topology.
- Applied Mathematics: Differential Equations (ODEs and PDEs),Dynamical System & Chaos, Engineering Mathematics, Fluid Mechanics, Heat and Mass Transfer, Mathematical Computational Techniques, Mathematical Physics, Mathematics & Multimedia, Numerical Analysis.
- Statistics: Bayesian Analysis, Bioinformatics, Biostatistics, Business Statistics, Demography, Distribution Theory, Econometrics, Engineering Statistics, Environmental Statistics, Epidemiology, Forecasting, Prediction Analysis, Probability Models, Reliability Theory, Robust Methods, Sampling, Statistical Computing, Statistical Inference, Statistical Theory, Stochastic Process, Survival Analysis, Time Series.
- Operational Research: Decision Analysis, Linear and Nonlinear Programming, Logistics, Mathematical Modeling, Multi Criteria Decision Making, Optimization, Project Management, Simulation, Supply Chain Management, Timetabling, Scheduling and Queuing, Transportation and Traffic.
- Actuarial Science: Financial Mathematics, Insurance, Investment and Assets Management, Mortality, Pensions
- Quality and Productivity: Benchmarking and Best Practices, Excellence Model, Lean Enterprise, Performance Measurement, Productivity Analysis, Quality Management, Quality Tools and Techniques, Reliability, Six Sigma, Statistical Quality Control, Service Quality.
- Data Science: Big Data, Machine Learning, Data Network, Database, Data Mining.
- Mathematics Education: Application of Soft Skills, Continuous Improvement, Curriculum Development, Effective Teaching and Learning, Futuristic Innovation In Teaching and Learning, Measurement and Evaluation.