Scientific Simulation & Statistical Data Analysis Services
Overcome computational bottlenecks and statistical errors in your Ph.D. and Technical Systems Specialist research. Jagmohan Kurmi provides rigorous simulation implementation, mathematical modeling, and empirical data processing across engineering, management, and scientific disciplines. Complete source code, publication-grade figures, and viva defense walkthroughs provided.
Computational Environments
- Python ML & Deep Learning: PyTorch, TensorFlow, CNNs, Transformers, NLP models, and computer vision classification.
- MATLAB / Simulink: Power systems, PV-grid integration, motor control, bio-signal processing, PSO optimization.
- SPSS & AMOS: Mediation and moderation analysis, CFA, SEM model fit indices, hypothesis testing for survey data.
- Network Simulation: NS-2 and NS-3 wireless routing protocols, MANET/VANET performance evaluation.
Complete Deliverables Package
You receive everything needed to defend your implementation with 100% confidence.
Clean Source Code
Fully documented .py, .m, or .tcl script files with zero dependencies issues and clear execution commands.
High-Res Vector Figures
300 DPI EPS/TIFF graphs and confusion matrices formatted specifically for IEEE, Elsevier and Springer manuscripts.
1-on-1 Code Walkthrough
Remote screen-sharing session explaining model architectures, mathematical formulas, and simulation workflows.
Simulation & Data Analysis FAQ
Do you provide fully commented source code with the simulation results?
Yes. Every MATLAB, Python, or NS2/NS3 model includes clean, modular, fully commented code files, dataset preprocessing scripts, and an execution walkthrough via video or remote session so you can explain every function to your thesis evaluation committee.
Can you run Structural Equation Modeling (SEM) in AMOS and SPSS for management scholars?
Yes. We perform confirmatory factor analysis (CFA), composite reliability, discriminant validity (Fornell-Larcker criterion and HTMT), model fit indices (CMIN/DF, CFI, RMSEA), and path regression analysis formatted for high-ranking journals.
Can you generate comparative baseline tables for IEEE papers?
Yes. We benchmark your proposed algorithm against classical and recent state-of-the-art models on standardized performance metrics (Accuracy, Precision, Recall, F1-Score, PSNR, MSE, Execution Time).
Validate Your Experimental Methodology
Discuss your dataset, simulation environment, and expected performance benchmarks.