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Found 10 papers, 1 papers with code
FinBERT-BiLSTM: A Deep Learning Model for Predicting Volatile Cryptocurrency Market Prices Using Market Sentiment Dynamics
Time series forecasting is a key tool in financial markets, helping to predict asset prices and guide investment decisions.
Mining Weighted Sequential Patterns in Incremental Uncertain Databases
Several algorithms exist to mine frequent patterns and weighted sequences from incremental databases.
Augmenting Deep Learning Adaptation for Wearable Sensor Data through Combined Temporal-Frequency Image Encoding
To overcome these challenges, researchers have proposed encoding of wearable temporal sensor data in images using recurrent plots.
Unbiased Pain Assessment through Wearables and EHR Data: Multi-attribute Fairness Loss-based CNN Approach
The combination of diverse health data (IoT, EHR, and clinical surveys) and scalable-adaptable Artificial Intelligence (AI), has enabled the discovery of physical, behavioral, and psycho-social indicators of pain status.
Enabling Heterogeneous Domain Adaptation in Multi-inhabitants Smart Home Activity Learning
Domain adaptation for sensor-based activity learning is of utmost importance in remote health monitoring research.
Semi-Supervised Domain Adaptation with Auto-Encoder via Simultaneous Learning
We present a new semi-supervised domain adaptation framework that combines a novel auto-encoder-based domain adaptation model with a simultaneous learning scheme providing stable improvements over state-of-the-art domain adaptation models.
Pseudo value-based Deep Neural Networks for Multi-state Survival Analysis
To address these limitations, we propose a new class of pseudo-value-based deep learning models for multi-state survival analysis, where we show that pseudo values - designed to handle censoring - can be a natural replacement for estimating the multi-state model quantities when derived from a consistent estimator.
FedPseudo: Pseudo value-based Deep Learning Models for Federated Survival Analysis
To overcome the challenges of existing federated survival analysis methods, we leverage the predictive accuracy of the deep learning models and the power of pseudo values to propose a first-of-its-kind, pseudo value-based deep learning model for federated survival analysis (FSA) called FedPseudo.
PALMAR: Towards Adaptive Multi-inhabitant Activity Recognition in Point-Cloud Technology
With the advancement of deep neural networks and computer vision-based Human Activity Recognition, employment of Point-Cloud Data technologies (LiDAR, mmWave) has seen a lot interests due to its privacy preserving nature.
A Gray Box Interpretable Visual Debugging Approach for Deep Sequence Learning Model
In this work, we have developed a visual interactive web application, namely d-DeVIS, which helps to visualize the internal reasoning of the learning model which is trained on the audio data.
