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IEEE/ICACT20240043 Slide.10        [Big Slide]	Oral Presentation
	This slide presents two neural network architectures for processing sequential or time-series data: CNN-LSTM Architecture: This combines Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The architecture takes an input, processes it through CNN layers to extract spatial features, which are then downsampled by pooling layers. The data is flattened and passed through LSTM layers to capture temporal dependencies before a final dense layer outputs the results. Proposed CNN Architecture: The second architecture is our proposed CNN model, consisting of alternating convolutional and pooling layers that process the input, extract features, and downsample them to reduce dimensionality. This is followed by a dense layer for output generation. The most important thing is the input of this architecture. Our inputs consist of four images, not only one image, which enables us to represent time series data.

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