1D Convolutional

One-dimensional convolutional neural networks (1D-CNNs) are a powerful tool for analyzing sequential data, finding applications in diverse fields ranging from speech recognition and signature verification to medical diagnosis and cybersecurity. Current research emphasizes the effectiveness of 1D-CNNs, often in hybrid architectures with other models like transformers or within federated learning frameworks, for achieving high accuracy and efficiency in classification and other tasks. This approach offers advantages in computational cost and data privacy, leading to improved performance and broader applicability across various domains.