Search Results for author:
Found 16 papers, 10 papers with code
WaveCatBoost for Probabilistic Forecasting of Regional Air Quality Data
Accurate and reliable air quality forecasting is essential for protecting public health, sustainable development, pollution control, and enhanced urban planning.
Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission
The calibration of DeepSurv (IBS: 0. 041) performed the best, followed by RSF (IBS: 0. 042) and GBM (IBS: 0. 0421), all using the full variables.
When Geoscience Meets Generative AI and Large Language Models: Foundations, Trends, and Future Challenges
Generative Artificial Intelligence (GAI) represents an emerging field that promises the creation of synthetic data and outputs in different modalities.
Forecasting CPI inflation under economic policy and geo-political uncertainties
This study proposes a novel filtered ensemble wavelet neural network (FEWNet) that can produce reliable long-term forecasts for CPI inflation.
Skew Probabilistic Neural Networks for Learning from Imbalanced Data
This paper introduces an imbalanced data-oriented approach using probabilistic neural networks (PNNs) with a skew normal probability kernel to address this major challenge.
Thompson sampling for zero-inflated count outcomes with an application to the Drink Less mobile health study
Mobile health (mHealth) technologies aim to improve distal outcomes, such as clinical conditions, by optimizing proximal outcomes through just-in-time adaptive interventions.
Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-art
Since their inception in 2014, Generative Adversarial Networks (GANs) have rapidly emerged as powerful tools for generating realistic and diverse data across various domains, including computer vision and other applied areas.
Prediction of Transportation Index for Urban Patterns in Small and Medium-sized Indian Cities using Hybrid RidgeGAN Model
These synthetic urban universes mimic global urban patterns and evaluating their landscape structures through spatial pattern analysis can aid in comprehending landscape dynamics, thereby enhancing sustainable urban planning.
An ensemble neural network approach to forecast Dengue outbreak based on climatic condition
In comparison with statistical, machine learning, and deep learning methods, our proposed XEWNet performs better in 75% of the cases for short-term and long-term forecasting of dengue incidence.
Knowledge-based Deep Learning for Modeling Chaotic Systems
Our proposed KDL can learn the complex patterns governing chaotic systems by jointly training on real and simulated data directly from the dynamics and their differential equations.
W-Transformers : A Wavelet-based Transformer Framework for Univariate Time Series Forecasting
Deep learning utilizing transformers has recently achieved a lot of success in many vital areas such as natural language processing, computer vision, anomaly detection, and recommendation systems, among many others.
Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting Epidemics
Unfortunately, most of these past epidemics exhibit nonlinear and non-stationary characteristics due to their spreading fluctuations based on seasonal-dependent variability and the nature of these epidemics.
Probabilistic AutoRegressive Neural Networks for Accurate Long-range Forecasting
In this study, we introduce the Probabilistic AutoRegressive Neural Networks (PARNN), capable of handling complex time series data exhibiting non-stationarity, nonlinearity, non-seasonality, long-range dependence, and chaotic patterns.
Optimized ensemble deep learning framework for scalable forecasting of dynamics containing extreme events
This study develops an optimized ensemble deep learning (OEDL) framework wherein these two machine learning techniques are jointly used to achieve synergistic improvements in model accuracy, stability, scalability, and reproducibility prompting a new wave of applications in the forecasting of dynamics.
Superensemble Classifier for Improving Predictions in Imbalanced Datasets
Regularity conditions for universal consistency and the idea of parameter optimization of the proposed model are provided.
Imbalanced Ensemble Classifier for learning from imbalanced business school data set
Private business schools in India face a common problem of selecting quality students for their MBA programs to achieve the desired placement percentage.
