[HTML][HTML] Data science in economics: comprehensive review of advanced machine learning and deep learning methods
This paper provides a comprehensive state-of-the-art investigation of the recent advances in
data science in emerging economic applications. The analysis is performed on the novel …
data science in emerging economic applications. The analysis is performed on the novel …
Deep learning and machine learning in hydrological processes climate change and earth systems a systematic review
Artificial intelligence methods and application have recently shown great contribution in
modeling and prediction of the hydrological processes, climate change, and earth systems …
modeling and prediction of the hydrological processes, climate change, and earth systems …
Digital twin: Values, challenges and enablers from a modeling perspective
Digital twin can be defined as a virtual representation of a physical asset enabled through
data and simulators for real-time prediction, optimization, monitoring, controlling, and …
data and simulators for real-time prediction, optimization, monitoring, controlling, and …
Review of machine learning for hydrodynamics, transport, and reactions in multiphase flows and reactors
Artificial intelligence (AI), machine learning (ML), and data science are leading to a
promising transformative paradigm. ML, especially deep learning and physics-informed ML …
promising transformative paradigm. ML, especially deep learning and physics-informed ML …
Predicting standardized streamflow index for hydrological drought using machine learning models
S Shamshirband, S Hashemi, H Salimi… - Engineering …, 2020 - Taylor & Francis
Hydrological droughts are characterized based on their duration, severity, and magnitude.
Among the most critical factors, precipitation, evapotranspiration, and runoff are essential in …
Among the most critical factors, precipitation, evapotranspiration, and runoff are essential in …
[HTML][HTML] Deep learning for detecting building defects using convolutional neural networks
Clients are increasingly looking for fast and effective means to quickly and frequently survey
and communicate the condition of their buildings so that essential repairs and maintenance …
and communicate the condition of their buildings so that essential repairs and maintenance …
Advances in machine learning modeling reviewing hybrid and ensemble methods
The conventional machine learning (ML) algorithms are continuously advancing and
evolving at a fast-paced by introducing the novel learning algorithms. ML models are …
evolving at a fast-paced by introducing the novel learning algorithms. ML models are …
Digital twin: Values, challenges and enablers
A digital twin can be defined as an adaptive model of a complex physical system. Recent
advances in computational pipelines, multiphysics solvers, artificial intelligence, big data …
advances in computational pipelines, multiphysics solvers, artificial intelligence, big data …
State of the art survey of deep learning and machine learning models for smart cities and urban sustainability
Deep learning (DL) and machine learning (ML) methods have recently contributed to the
advancement of models in the various aspects of prediction, planning, and uncertainty …
advancement of models in the various aspects of prediction, planning, and uncertainty …
Sustainability implications of artificial intelligence in the chemical industry: A conceptual framework
Artificial intelligence (AI) is an emerging technology that has great potential in reducing
energy consumption, environmental burdens, and operational risks of chemical production …
energy consumption, environmental burdens, and operational risks of chemical production …