A5092035196- Parallel Computing and Optimization Techniques
- Advanced Data Storage Technologies
- SARS-CoV-2 and COVID-19 Research
- COVID-19 epidemiological studies
- Neural Networks and Applications
- Energy Load and Power Forecasting
- Advanced Neural Network Applications
- Computational Physics and Python Applications
- COVID-19 diagnosis using AI
- Model Reduction and Neural Networks
Middle Tennessee State University
2023
Data preprocessing is a fundamental stage in deep learning modeling and serves as the cornerstone of reliable data analytics. These models require significant amounts training to be effective, with small datasets often resulting overfitting poor performance on large datasets. One solution this problem parallelization modeling, which allows model fit more effectively, leading higher accuracy sets overall. In research, we developed novel approach that effectively deployed tools such MPI MPI4Py...
Infectious disease epidemics are challenging for medical and public health practitioners. They require prompt treatment, but it is to recognize define in real time. Knowing the prediction of an infectious epidemic can evaluate prevent disease’s impact. Mathematical models that work time important tools preventing disease, data-driven deep learning enables practical algorithms identifying parameters mathematical models. In this paper, SIR model was reduced a logistic differential equation...
Artificial neural networks have changed many fields by giving scientists a strong way to model complex phenomena. They are also becoming increasingly useful for solving various difficult scientific problems. Still, people keep trying find faster and more accurate ways simulate dynamic systems. This research explores the transformative capabilities of physics-informed networks, specialized subset artificial in modeling dynamical systems with enhanced speed accuracy. These incorporate known...
Data preprocessing is a fundamental stage in deep learning modeling and serves as the cornerstone of reliable data analytics. These models require significant amounts training to be effective, with small datasets often resulting overfitting poor performance on large datasets. One solution this problem parallelization modeling, which allows model fit more effectively, leading higher accuracy sets overall. In research, we developed novel approach that effectively deployed tools such MPI MPI4Py...