CNN-LSTM: An efficient hybrid deep learning architecture for predicting short-term photovoltaic power production
Climate change is pushing an increasing number of nations to use green energy resources,
particularly solar power as an applicable substitute to traditional power sources. However …
particularly solar power as an applicable substitute to traditional power sources. However …
Detection and classification of cardiac arrhythmias by a challenge-best deep learning neural network model
Electrocardiograms (ECGs) are widely used to clinically detect cardiac arrhythmias (CAs).
They are also being used to develop computer-assisted methods for heart disease …
They are also being used to develop computer-assisted methods for heart disease …
[HTML][HTML] The Free-movement pattern Y-maze: A cross-species measure of working memory and executive function
M Cleal, BD Fontana, DC Ranson, SD McBride… - Behavior research …, 2021 - Springer
Numerous neurodegenerative and psychiatric disorders are associated with deficits in
executive functions such as working memory and cognitive flexibility. Progress in developing …
executive functions such as working memory and cognitive flexibility. Progress in developing …
Time series big data: a survey on data stream frameworks, analysis and algorithms
Big data has a substantial role nowadays, and its importance has significantly increased
over the last decade. Big data's biggest advantages are providing knowledge, supporting …
over the last decade. Big data's biggest advantages are providing knowledge, supporting …
A novel validation framework to enhance deep learning models in time-series forecasting
Time-series analysis and forecasting is generally considered as one of the most challenging
problems in data mining. During the last decade, powerful deep learning methodologies …
problems in data mining. During the last decade, powerful deep learning methodologies …
Time series analysis and modeling to forecast: A survey
F Dama, C Sinoquet - arXiv preprint arXiv:2104.00164, 2021 - arxiv.org
Time series modeling for predictive purpose has been an active research area of machine
learning for many years. However, no sufficiently comprehensive and meanwhile …
learning for many years. However, no sufficiently comprehensive and meanwhile …
A novel forecasting strategy for improving the performance of deep learning models
IE Livieris - Expert Systems with Applications, 2023 - Elsevier
In this research, a new strategy is introduced for the development of robust, efficient and
reliable deep learning time-series models, which is based on a sophisticated algorithmic …
reliable deep learning time-series models, which is based on a sophisticated algorithmic …
A novel multi-step forecasting strategy for enhancing deep learning models' performance
IE Livieris, P Pintelas - Neural Computing and Applications, 2022 - Springer
Multi-step forecasting is considered as an open challenge in time-series analysis. Although
several approaches were proposed to address this complex prediction problem, none of …
several approaches were proposed to address this complex prediction problem, none of …
[HTML][HTML] Impact of the splitting of the German–Austrian electricity bidding zone on investment in a grid-scale battery energy storage system deployed for price arbitrage …
A Hurta, M Žilka, F Freiberg - Energy Reports, 2022 - Elsevier
The splitting of the German–Austrian electricity bidding zone in October 2018 established
limits for cross-border exchanges of power between Germany and Austria, which led to the …
limits for cross-border exchanges of power between Germany and Austria, which led to the …
Mitigating drift in time series data with noise augmentation
T Fields, G Hsieh, J Chenou - 2019 International Conference …, 2019 - ieeexplore.ieee.org
Machine leaning (ML) models must be accurate to produce quality AI solutions. There must
be high accuracy in the data and with the model that is built using the data. Online machine …
be high accuracy in the data and with the model that is built using the data. Online machine …