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Found 14 papers, 1 papers with code
Data-driven building energy efficiency prediction using physics-informed neural networks
On top of this neural network, a function, based on physics equations, calculates the energy consumption of the building based on heat losses and enhances the loss function of the deep learning model.
Transfer learning for day-ahead load forecasting: a case study on European national electricity demand time series
Short-term load forecasting (STLF) is crucial for the daily operation of power grids.
Multimodal Detection of Social Spambots in Twitter using Transformers
Next, we propose a multimodal approach, where we use TwHIN-BERT for getting the textual representation of the user description field and employ VGG16 for acquiring the visual representation for the image modality.
DeepTSF: Codeless machine learning operations for time series forecasting
DeepTSF automates key aspects of the ML lifecycle, making it an ideal tool for data scientists and MLops engineers engaged in machine learning (ML) and deep learning (DL)-based forecasting.
Multitask learning for recognizing stress and depression in social media
People use social media to express their feelings.
Calibration of Transformer-based Models for Identifying Stress and Depression in Social Media
To resolve the above issues, we present the first study in the task of depression and stress detection in social media, which injects extra linguistic information in transformer-based models, namely BERT and MentalBERT.
Context-aware attention layers coupled with optimal transport domain adaptation and multimodal fusion methods for recognizing dementia from spontaneous speech
Alzheimer's disease (AD) constitutes a complex neurocognitive disease and is the main cause of dementia.
Targeted demand response for flexible energy communities using clustering techniques
The k-means algorithm with dynamic time warping distance considering 14 clusters exhibits the highest performance with a PPS of 0. 689.
A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers
Short-term load forecasting (STLF) is vital for the effective and economic operation of power grids and energy markets.
Neural Architecture Search with Multimodal Fusion Methods for Diagnosing Dementia
To the best of our knowledge, there is no prior work exploiting a NAS approach and these fusion methods in the task of dementia detection from spontaneous speech.
A Multimodal Approach for Dementia Detection from Spontaneous Speech with Tensor Fusion Layer
However, existing state-of-the-art works proposing multimodal approaches do not take into consideration the inter- and intra-modal interactions and propose early and late fusion approaches.
Comparison of Missing Data Imputation Methods using the Framingham Heart study dataset
Cardiovascular disease (CVD) is a class of diseases that involve the heart or blood vessels and according to World Health Organization is the leading cause of death worldwide.
Detecting Dementia from Speech and Transcripts using Transformers
Concurrently, little work has been done in terms of both the usage of transformer networks and the way the two modalities, i. e., speech and transcripts, are combined in a single neural network.
Explainable Identification of Dementia from Transcripts using Transformer Networks
Alzheimer's disease (AD) is the main cause of dementia which is accompanied by loss of memory and may lead to severe consequences in peoples' everyday life if not diagnosed on time.
