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Found 10 papers, 0 papers with code
AI Recommendation System for Enhanced Customer Experience: A Novel Image-to-Text Method
Existing fashion recommendation systems encounter difficulties in using visual data for accurate and personalized recommendations.
Reshaping Smart Energy Transition: An analysis of human-building interactions in Qatar Using Machine Learning Techniques
The study has important implications for developing targeted strategies aimed at improving the efficacy of energy policies and sustainability performance indicators.
Spatiotemporal Tensor Completion for Improved Urban Traffic Imputation
To recover the missing data, we propose an enhanced CANDECOMP/PARAFAC (CP) completion approach that considers the urban and temporal aspects of the traffic.
Dairy Cow rumination detection: A deep learning approach
Cattle activity is an essential index for monitoring health and welfare of the ruminants.
Predicting COVID-19 cases using Bidirectional LSTM on multivariate time series
Conclusion: Using data of multiple countries in addition to lockdown measures improve accuracy of the forecast of daily cumulative COVID-19 cases.
Deep-Gap: A deep learning framework for forecasting crowdsourcing supply-demand gap based on imaging time series and residual learning
This allows anticipating the supply-demand gap and redirecting crowdsourced service providers towards target areas.
Cluster validity index based on Jeffrey divergence
These indexes are in general constructed by combining a measure of compactness and a measure of separation.
A Deep Learning Spatiotemporal Prediction Framework for Mobile Crowdsourced Services
This papers presents a deep learning-based framework to predict crowdsourced service availability spatially and temporally.
Multimodal deep learning approach for joint EEG-EMG data compression and classification
In this paper, we present a joint compression and classification approach of EEG and EMG signals using a deep learning approach.
Multispectral image denoising with optimized vector non-local mean filter
In our contribution, we propose an optimization framework where we dynamically fine tune the NLM filter parameters and attenuate its computational complexity by considering only pixels which are most similar to each other in computing a restored pixel.
