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A novel convolutional neural network architecture for efficient tumor classification by integrating multi-scale fusion techniques with dual-path attention is presented, making it a great fit for resource-constrained medical devices that need to be deployed in real-time.
The results of the framework will validate the radical combination of the structural and the temporal elements of a complex cyber attack MODEL as a possible answer to the challenge of providing a simple, dynamic, and effective scalable solution to the detection of sophisticated cyber disasters in realistic and complex...
This study evaluates four encoding schemes—one-hot, k-mer (substring-based encoding), embeddings, and Position-Specific Scoring Matrix (PSSM) using Convolutional Neural Networks (CNNs) and Long Short-Term Memories (LSTMs) and shows that k-mer encoding achieved the highest accuracy.
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