A review of ARIMA vs. machine learning approaches for time series forecasting in data driven networks
VI Kontopoulou, AD Panagopoulos, I Kakkos… - Future Internet, 2023 - mdpi.com
In the broad scientific field of time series forecasting, the ARIMA models and their variants
have been widely applied for half a century now due to their mathematical simplicity and …
have been widely applied for half a century now due to their mathematical simplicity and …
Role of artificial intelligence (AI) in fish growth and health status monitoring: A review on sustainable aquaculture
Aquaculture plays a crucial role in meeting the growing global demand for seafood, but it
faces challenges in terms of fish growth and health monitoring. The advancement of artificial …
faces challenges in terms of fish growth and health monitoring. The advancement of artificial …
A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …
large amount of data to achieve exceptional performance. Unfortunately, many applications …
A comparative analysis on suicidal ideation detection using NLP, machine, and deep learning
Social networks are essential resources to obtain information about people's opinions and
feelings towards various issues as they share their views with their friends and family …
feelings towards various issues as they share their views with their friends and family …
A real-time adaptive model for bearing fault classification and remaining useful life estimation using deep neural network
Rolling element bearings are essential components of a wide variety of industrial machinery
and the leading cause of equipment failure. The prediction of Remaining Useful Life (RUL) …
and the leading cause of equipment failure. The prediction of Remaining Useful Life (RUL) …
Deep learning for Covid-19 forecasting: State-of-the-art review.
The Covid-19 pandemic has galvanized scientists to apply machine learning methods to
help combat the crisis. Despite the significant amount of research there exists no …
help combat the crisis. Despite the significant amount of research there exists no …
Integration of deep learning into the iot: A survey of techniques and challenges for real-world applications
The internet of things (IoT) has emerged as a pivotal technological paradigm facilitating
interconnected and intelligent devices across multifarious domains. The proliferation of IoT …
interconnected and intelligent devices across multifarious domains. The proliferation of IoT …
Chronobridge: a novel framework for enhanced temporal and relational reasoning in temporal knowledge graphs
The task of predicting entities and relations in Temporal Knowledge Graph (TKG)
extrapolation is crucial and has been studied extensively. Mainstream algorithms, such as …
extrapolation is crucial and has been studied extensively. Mainstream algorithms, such as …
A novel approach for COVID-19 infection forecasting based on multi-source deep transfer learning
COVID-19 is a contagious disease; so, predicting its future infections in a provincial region
requires the consideration of the related data (ie, rates of infection, mortality and recovery …
requires the consideration of the related data (ie, rates of infection, mortality and recovery …
Novel production prediction model of gasoline production processes for energy saving and economic increasing based on AM-GRU integrating the UMAP algorithm
J Liu, L Chen, W Xu, M Feng, Y Han, T Xia, Z Geng - Energy, 2023 - Elsevier
Gasoline, as an extremely important petroleum product, is of great significance to ensure
people's living standards and maintain national energy security. In the actual gasoline …
people's living standards and maintain national energy security. In the actual gasoline …