A review on extreme learning machine
Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward
neural network (SLFN), which converges much faster than traditional methods and yields …
neural network (SLFN), which converges much faster than traditional methods and yields …
Do natural resources, urbanization, and value-adding manufacturing affect environmental quality? Evidence from the top ten manufacturing countries
Environmental pollutants have become a problem throughout the world. In the past few
years, investigations of ecological footprints and their determining factors have been at the …
years, investigations of ecological footprints and their determining factors have been at the …
Prediction of photovoltaic power output based on similar day analysis, genetic algorithm and extreme learning machine
Recently, many machine learning techniques have been successfully employed in
photovoltaic (PV) power output prediction because of their strong non-linear regression …
photovoltaic (PV) power output prediction because of their strong non-linear regression …
Application of extreme learning machine for short term output power forecasting of three grid-connected PV systems
The power output (PO) of a photovoltaic (PV) system is highly variable because of its
dependence on solar irradiance and other meteorological factors. Hence, accurate PO …
dependence on solar irradiance and other meteorological factors. Hence, accurate PO …
Modelling reference evapotranspiration using a new wavelet conjunction heuristic method: wavelet extreme learning machine vs wavelet neural networks
O Kisi, M Alizamir - Agricultural and forest meteorology, 2018 - Elsevier
Evapotranspiration is an important parameter in linking ecosystem functioning, climate and
carbon feedbacks, agricultural management, and water resources. This study investigates …
carbon feedbacks, agricultural management, and water resources. This study investigates …
Appeal of word of mouth: influences of public opinions and sentiment on ports in corporate choice of import and export trade in the post-COVID-19 era
With the advent of the post-COVID-19 era, corporate managers of import and export trade
are now more sensitive in their daily work, and their decisions are more likely to be …
are now more sensitive in their daily work, and their decisions are more likely to be …
GDP responses to supply chain disruptions in a post-pandemic era: Combination of DL and ANN outputs based on Google Trends
U Shahzad, KS Mohammed, N Schneider… - … Forecasting and Social …, 2023 - Elsevier
Abstract With the recent Russian-Ukraine conflict, the frequency and intensity of disruptive
shocks on major supply chains have risen, causing increasing food and energy security …
shocks on major supply chains have risen, causing increasing food and energy security …
Modelling long-term groundwater fluctuations by extreme learning machine using hydro-climatic data
M Alizamir, O Kisi… - Hydrological sciences …, 2018 - Taylor & Francis
The ability of the extreme learning machine (ELM) is investigated in modelling groundwater
level (GWL) fluctuations using hydro-climatic data obtained for Hormozgan Province …
level (GWL) fluctuations using hydro-climatic data obtained for Hormozgan Province …
A big data analytics based methodology for strategic decision making
M Özemre, O Kabadurmus - Journal of Enterprise Information …, 2020 - emerald.com
Purpose The purpose of this paper is to present a novel framework for strategic decision
making using Big Data Analytics (BDA) methodology. Design/methodology/approach In this …
making using Big Data Analytics (BDA) methodology. Design/methodology/approach In this …
Assessing the impact of hydropower projects in Brazil through data envelopment analysis and machine learning
M Bortoluzzi, M Furlan, JF dos Reis Neto - Renewable Energy, 2022 - Elsevier
The aim of this study was to assess the environmental impact of hydroelectric power
generation projects and classify them according to their scale of environmental impact. To …
generation projects and classify them according to their scale of environmental impact. To …