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Found 8 papers, 0 papers with code
Explainable AI Integrated Feature Engineering for Wildfire Prediction
In our research, we conducted a thorough assessment of various machine learning algorithms for both classification and regression tasks relevant to predicting wildfires.
Exploring Music Genre Classification: Algorithm Analysis and Deployment Architecture
The proposed DSP and DL-based music genre classification algorithm and deployment architecture demonstrate a promising approach for music genre classification.
Dynamic Data Assimilation of MPAS-O and the Global Drifter Dataset
In this study, we propose a new method for combining in situ buoy measurements with Earth system models (ESMs) to improve the accuracy of temperature predictions in the ocean.
IDLat: An Importance-Driven Latent Generation Method for Scientific Data
We qualitatively and quantitatively evaluate the effectiveness and efficiency of latent representations generated by our method with data from multiple scientific visualization applications.
Information transmission in a two-step cascade: Interplay of activation and repression
We present an information-theoretic formalism to study signal transduction in four architectural variants of a model two-step cascade with increasing input population.
Relationship-aware Multivariate Sampling Strategy for Scientific Simulation Data
With the increasing computational power of current supercomputers, the size of data produced by scientific simulations is rapidly growing.
Deep Learning-Based Feature-Aware Data Modeling for Complex Physics Simulations
Data modeling and reduction for in situ is important.
Exploiting Inherent Error-Resiliency of Neuromorphic Computing to achieve Extreme Energy-Efficiency through Mixed-Signal Neurons
In this work, we will analyze, compare and contrast existing neuron architectures with a proposed mixed-signal neuron (MS-N) in terms of performance, power and noise, thereby demonstrating the applicability of the proposed mixed-signal neuron for achieving extreme energy-efficiency in neuromorphic computing.
