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IEEE/ICACT20250017 Slide.12        [Big Slide]       [YouTube]	Oral Presentation
	Our methodology encompasses sophisticated data preprocessing techniques: Converting protein sequences into numerical representations suitable for machine learning Using techniques like label encoding of amino acids, dimensionality reduction with SVD, and row averaging for pattern extraction We implement various AI models: Machine Learning Algorithms: KNN, Decision Trees, Random Forest - lightweight options suitable for small-scale datasets Deep Learning Architectures: CNN-LSTM combines convolutional networks and sequence modeling; Convolutional Denoising Autoencoder fills gaps and corrects errors; Transformer and GPT-2 models capture long-range dependencies for full-sequence predictions

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