Hybrid approach integrating deep learning-autoencoder with statistical process control chart for anomaly detection: Case study in injection molding process
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 …
demanding significant resources, data, and expertise due to their impact on cost and time …
Deep learning approach to forecast air pollution based on novel hourly index
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 …
pollutants, particulate matter poses a significant threat to human health. The Sustainable …
[HTML][HTML] Prediction of PM2. 5 concentration based on a CNN-LSTM neural network algorithm
X Bai, N Zhang, X Cao, W Chen - PeerJ, 2024 - peerj.com
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 …
development and health. By predicting the spatial distribution concentration of PM 2.5 …
A new attention-based CNN_GRU model for spatial–temporal PM2.5 prediction
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 …
missing data and selecting an appropriate modeling method. Effective imputation of missing …
[HTML][HTML] Exploring the Role of Deep Learning in Forecasting for Sustainable Development Goals: A Systematic Literature Review
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 …
the context of the Sustainable Development Goals (SDGs). The primary objective is to …
Time Series Analysis for Power Grid Anomaly Detection using LSTM Networks
This research addresses the basic concern of inconsistency location in control frameworks
through an in-depth investigation of time arrangement examination procedures, with a …
through an in-depth investigation of time arrangement examination procedures, with a …
Machine Learning Models for daily AQI Prediction: An In-depth Analysis
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 …
environmental challenges that must be addressed. Researchers employed a range of …
[PDF][PDF] COMPARATIVE ANALYSIS OF PREDICTIVE MODELS FOR WORKLOAD SCALING IN IAAS CLOUDS: A STUDY ON MODEL EFFECTIVENESS AND …
SN POTHU, DRS KAILASAM - Journal of Theoretical and Applied …, 2023 - jatit.org
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 …
domain of cloud computing, especially across renowned platforms such as AWS, Google …
Comparative Analysis of Prediction Models for Particulate Matter (PM2.5) Prediction
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 …
pollutants in air, which needs to be addressed. For the prediction of pollutants, researchers …
Deep learning approaches for air quality prediction using spectrogram images of time series
M Shakir, U Kumaran, N Rakesh - Challenges in Information …, 2025 - taylorfrancis.com
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 …
spectral imaging of the time series data. Spectrograms provide an insight into the frequency …