Edge computing and sensor-cloud: Overview, solutions, and directions
Sensor-cloud originates from extensive recent applications of wireless sensor networks and
cloud computing. To draw a roadmap of the current research activities of the sensor-cloud …
cloud computing. To draw a roadmap of the current research activities of the sensor-cloud …
Decomposition-based hybrid wind speed forecasting model using deep bidirectional LSTM networks
KU Jaseena, BC Kovoor - Energy Conversion and Management, 2021 - Elsevier
The goal of sustainable development can be attained by the efficient management of
renewable energy resources. Wind energy is attracting attention worldwide due to its …
renewable energy resources. Wind energy is attracting attention worldwide due to its …
An optimized model using LSTM network for demand forecasting
H Abbasimehr, M Shabani, M Yousefi - Computers & industrial engineering, 2020 - Elsevier
In a business environment with strict competition among firms, accurate demand forecasting
is not straightforward. In this paper, a forecasting method is proposed, which has a strong …
is not straightforward. In this paper, a forecasting method is proposed, which has a strong …
A brief survey of telerobotic time delay mitigation
There is a substantial number of telerobotics and teleoperation applications ranging from
space operations, ground/aerial robotics, drive-by-wire systems to medical interventions …
space operations, ground/aerial robotics, drive-by-wire systems to medical interventions …
LSTM based long-term energy consumption prediction with periodicity
JQ Wang, Y Du, J Wang - energy, 2020 - Elsevier
Energy consumption information is a kind of time series with periodicity in many real system,
while the general forecasting methods do not concern periodicity. This paper proposes a …
while the general forecasting methods do not concern periodicity. This paper proposes a …
[HTML][HTML] Forecasting of COVID-19 cases using deep learning models: Is it reliable and practically significant?
J Devaraj, RM Elavarasan, R Pugazhendhi… - Results in Physics, 2021 - Elsevier
The ongoing outbreak of the COVID-19 pandemic prevails as an ultimatum to the global
economic growth and henceforth, all of society since neither a curing drug nor a preventing …
economic growth and henceforth, all of society since neither a curing drug nor a preventing …
Improving time series forecasting using LSTM and attention models
H Abbasimehr, R Paki - Journal of Ambient Intelligence and Humanized …, 2022 - Springer
Accurate time series forecasting has been recognized as an essential task in many
application domains. Real-world time series data often consist of non-linear patterns with …
application domains. Real-world time series data often consist of non-linear patterns with …
Predictive analytics for demand forecasting–a comparison of SARIMA and LSTM in retail SCM
The application of predictive analytics (PA) in Supply Chain Management (SCM) has
received growing attention over the last years, especially in demand forecasting. The …
received growing attention over the last years, especially in demand forecasting. The …
Water quality prediction for smart aquaculture using hybrid deep learning models
KPRA Haq, VP Harigovindan - Ieee Access, 2022 - ieeexplore.ieee.org
Water quality prediction (WQP) plays an essential role in water quality management for
aquaculture to make aquaculture production profitable and sustainable. In this work, we …
aquaculture to make aquaculture production profitable and sustainable. In this work, we …
A novel approach based on combining deep learning models with statistical methods for COVID-19 time series forecasting
The COVID-19 pandemic has disrupted the economy and businesses and impacted all
facets of people's lives. It is critical to forecast the number of infected cases to make accurate …
facets of people's lives. It is critical to forecast the number of infected cases to make accurate …