Efficient prediction of cardiovascular disease using machine learning algorithms with relief and LASSO feature selection techniques
Cardiovascular diseases (CVD) are among the most common serious illnesses affecting
human health. CVDs may be prevented or mitigated by early diagnosis, and this may reduce …
human health. CVDs may be prevented or mitigated by early diagnosis, and this may reduce …
A comprehensive survey on computational methods of non-coding RNA and disease association prediction
The studies on relationships between non-coding RNAs and diseases are widely carried out
in recent years. A large number of experimental methods and technologies of producing …
in recent years. A large number of experimental methods and technologies of producing …
Carbon emission prediction models: A review
Y Jin, A Sharifi, Z Li, S Chen, S Zeng, S Zhao - Science of the Total …, 2024 - Elsevier
Amidst growing concerns over the greenhouse effect, especially its consequential impacts,
establishing effective Carbon Emission Prediction Models (CEPMs) to comprehend and …
establishing effective Carbon Emission Prediction Models (CEPMs) to comprehend and …
Multiple-input deep convolutional neural network model for short-term photovoltaic power forecasting
With the fast expansion of renewable energy system installed capacity in recent years, the
availability, stability, and quality of smart grids have become increasingly important. The …
availability, stability, and quality of smart grids have become increasingly important. The …
Novel enhanced-grey wolf optimization hybrid machine learning technique for biomedical data computation
Today, a significant number of biomedical data is generated continuously from various
biomedical equipment and experiments due to rapid technological improvements in medical …
biomedical equipment and experiments due to rapid technological improvements in medical …
A data-driven machine learning approach for the 3D printing process optimisation
ABSTRACT 3D printing has become highly applicable in modern life recently. The industry
has brought a facelift to most others. However, this technology still exists some …
has brought a facelift to most others. However, this technology still exists some …
Graph correlated attention recurrent neural network for multivariate time series forecasting
X Geng, X He, L Xu, J Yu - Information Sciences, 2022 - Elsevier
Multivariate time series (MTS) forecasting is an urgent problem for numerous valuable
applications. At present, attention-based methods can relieve recurrent neural networks' …
applications. At present, attention-based methods can relieve recurrent neural networks' …
Multi-energy load forecasting for regional integrated energy systems considering temporal dynamic and coupling characteristics
S Wang, S Wang, H Chen, Q Gu - Energy, 2020 - Elsevier
Accurate multi-energy load forecasting (MELF) is the key to realize the balance between
supply and demand in regional integrated energy systems (RIES). To this end, a hybrid …
supply and demand in regional integrated energy systems (RIES). To this end, a hybrid …
A comparative study of different machine learning tools in detecting diabetes
A significant proportion of people around the world are currently suffering from the harmful
effects of diabetes and a considerable number of them not being identified at an early stage …
effects of diabetes and a considerable number of them not being identified at an early stage …
Predicting cycle-level traffic movements at signalized intersections using machine learning models
Predicting accurate traffic parameters is fundamental and cost-effective in providing traffic
applications with required information. Many studies adopted various parametric and …
applications with required information. Many studies adopted various parametric and …