Detecting anomalies in the injection molding process remains a challenging task,
demanding significant resources, data, and expertise due to their impact on cost and time …

Air pollution is a pressing concern that the entire world is striving to combat. Among air
pollutants, particulate matter poses a significant threat to human health. The Sustainable …

This paper aims to explore the relationship between deep learning and forecasting within
the context of the Sustainable Development Goals (SDGs). The primary objective is to …

Abstract Fine particulate matter (PM 2.5) is a major air pollutant affecting human survival,
development and health. By predicting the spatial distribution concentration of PM 2.5 …

Accurately predicting the spatial-temporal distribution of PM2. 5 is challenging due to
missing data and selecting an appropriate modeling method. Effective imputation of missing …

This research addresses the basic concern of inconsistency location in control frameworks
through an in-depth investigation of time arrangement examination procedures, with a …

The increase in pollutant concentrations in the atmosphere is one of the most important
environmental challenges that must be addressed. Researchers employed a range of …

The demand for dependable workload prediction models has surged in the ever-evolving
domain of cloud computing, especially across renowned platforms such as AWS, Google …

One of the key challenges with respect to the environment is the rise of concentration of
pollutants in air, which needs to be addressed. For the prediction of pollutants, researchers …

This work aims to determine if the deep learning models can predict air quality using
spectral imaging of the time series data. Spectrograms provide an insight into the frequency …