Deep Learning Techniques and Optimization Strategies in Big Data Analytics

Thomas ,, J. Joshua
Karagöz, Pınar
Ahmad, Bazeer
Vasant, Pandian


Deep neural networks for faster nonparametric regression
Kaygusuz, Mehmet Ali; Purutçuoğlu Gazi, Vilda (2021-07-19)
Deep Learning Based Speed Up of Fluid Dynamics Solvers
Acar, Deniz Alper; Uzol, Oğuz; Department of Aerospace Engineering (2022-9-8)
In this thesis, two distinct deep learning-based methods for the speed-up of fluid dynamics solvers are proposed. The first method called Parametric Encoded Physics informed neural network (PEPINN), is utilized to solve transient fluid dynamics problems. PEPINN is an alternative to the Physics informed neural networks (PINN) and is based on the parametric encoding of the problem domain. In PEPINN the automatic differentiation for calculation of the problem residual is replaced with finite difference kernel...
Hybrid statistical and machine learning modeling of cognitive neuroscience data
Çakar, Serenay; Gökalp Yavuz, Fulya (2023-01-01)
The nested data structure is prevalent for cognitive measure experiments due to repeatedly taken observations from different brain locations within subjects. The analysis methods used for this data type should consider the dependency structure among the repeated measurements. However, the dependency assumption is mainly ignored in the cognitive neuroscience data analysis literature. We consider both statistical, and machine learning methods extended to repeated data analysis and compare distinct algorithms ...
Deep Learning with Multivariate Adaptive Regression Spline with Bagging Methods
Kaygusuz, Mehmet Ali; Somuncuoğlu, Abdullah Nuri; Purutçuoğlu Gazi, Vilda (2021-09-01)
Structured neural networks for modeling and identification of nonlinear mechanical systems
Kılıç, Ergin; Dölen, Melik; Koku, Ahmet Buğra; Department of Mechanical Engineering (2012)
Most engineering systems are highly nonlinear in nature and thus one could not develop efficient mathematical models for these systems. Artificial neural networks, which are used in estimation, filtering, identification and control in technical literature, are considered as universal modeling and functional approximation tools. Unfortunately, developing a well trained monolithic type neural network (with many free parameters/weights) is known to be a daunting task since the process of loading a specific pat...
Citation Formats
Thomas, P. Karagöz, B. Ahmad, and P. Vasant, Deep Learning Techniques and Optimization Strategies in Big Data Analytics. 2020.