A comprehensive review of the load forecasting techniques using single and hybrid predictive models
A Al Mamun, M Sohel, N Mohammad… - IEEE …, 2020 - ieeexplore.ieee.org
Load forecasting is a pivotal part of the power utility companies. To provide load-shedding
free and uninterrupted power to the consumer, decision-makers in the utility sector must …
free and uninterrupted power to the consumer, decision-makers in the utility sector must …
[HTML][HTML] Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis
Electroencephalography (EEG) is an important tool for studying the human brain activity and
epileptic processes in particular. EEG signals provide important information about …
epileptic processes in particular. EEG signals provide important information about …
Optimal deep learning lstm model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches
Background: With the development of smart grids, accurate electric load forecasting has
become increasingly important as it can help power companies in better load scheduling …
become increasingly important as it can help power companies in better load scheduling …
Deep neural network based demand side short term load forecasting
In the smart grid, one of the most important research areas is load forecasting; it spans from
traditional time series analyses to recent machine learning approaches and mostly focuses …
traditional time series analyses to recent machine learning approaches and mostly focuses …
Signal processing techniques for vibration-based health monitoring of smart structures
JP Amezquita-Sanchez, H Adeli - Archives of Computational Methods in …, 2016 - Springer
Signal processing is the key component of any vibration-based structural health monitoring
(SHM). The goal of signal processing is to extract subtle changes in the vibration signals in …
(SHM). The goal of signal processing is to extract subtle changes in the vibration signals in …
Forecasting of natural gas consumption with artificial neural networks
J Szoplik - Energy, 2015 - Elsevier
In this study, the results of forecasting of the gas demand obtained with the use of artificial
neural networks are presented. Design and training of MLP (multilayer perceptron model) …
neural networks are presented. Design and training of MLP (multilayer perceptron model) …
A novel machine learning model for estimation of sale prices of real estate units
Predicting the price of housing is of paramount importance for near-term economic
forecasting of any nation. This paper presents a novel and comprehensive model for …
forecasting of any nation. This paper presents a novel and comprehensive model for …
Autism: cause factors, early diagnosis and therapies
Autism spectrum disorder (ASD) is a complex neurobiological disorder characterized by
neuropsychological and behavioral deficits. Cognitive impairment, lack of social skills, and …
neuropsychological and behavioral deficits. Cognitive impairment, lack of social skills, and …
Nature inspired computing: an overview and some future directions
N Siddique, H Adeli - Cognitive computation, 2015 - Springer
This paper presents an overview of significant advances made in the emerging field of
nature-inspired computing (NIC) with a focus on the physics-and biology-based approaches …
nature-inspired computing (NIC) with a focus on the physics-and biology-based approaches …
Current methods in electrocardiogram characterization
The Electrocardiogram (ECG) is the P-QRS-T wave depicting the cardiac activity of the heart.
The subtle changes in the electric potential patterns of repolarization and depolarization are …
The subtle changes in the electric potential patterns of repolarization and depolarization are …