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Technological Frontier on Hybrid Deep Learning Paradigm for Global Air Quality Intelligence
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Author(s): S. Silvia Priscila (Bharath Institute of Higher Education and Research, India), D. Celin Pappa (Dhaanish Ahmed College of Engineering, India), M. Shagar Banu (Dhaanish Ahmed College of Engineering, India), Edwin Shalom Soji (Bharath Institute of Higher Education and Research, India), A. T. Ashmi Christus (Dhaanish Ahmed College of Engineering, India)and Venkata Surendra Kumar (Intellect Business, USA)
Copyright: 2024
Pages: 19
Source title:
Cross-Industry AI Applications
Source Author(s)/Editor(s): P. Paramasivan (Dhaanish Ahmed College of Engineering, India), S. Suman Rajest (Dhaanish Ahmed College of Engineering, India), Karthikeyan Chinnusamy (Veritas, USA), R. Regin (SRM Instıtute of Science and Technology, India)and Ferdin Joe John Joseph (Thai-Nichi Institute of Technology, Thailand)
DOI: 10.4018/979-8-3693-5951-8.ch010
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Abstract
This hybrid deep-learning study focuses on pollutant concentration. It illuminates convolutional neural networks (CNN) and long short-term memory in hybrid deep learning methods (LSTM). CNNs are essential to deep learning, especially image processing. They are ideal for pollution concentration analysis because they extract complex data features. LSTM is another important tool for this study. LSTMs are recurrent neural networks (RNNs) that can process and store data sequences. Time-series data analysis, common in pollution concentration research, benefits from them. Understanding deep learning and hybrid learning's impact on pollutant concentration issues. It investigates a hybrid CNN-LSTM model that combines CNN feature extraction with LSTM sequence processing. This fusion lets the model make smart predictions from input data sequences. PCA is key to this investigation. PCA dimensionality reduction finds variables with significant relationships.
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