Published Paper Details:

The rapid advancements in deep learning architectures have transformed various fields, enabling significant improvements in performance and efficiency. This study presents a comparative analysis of several state-of-the-art deep learning architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, and Generative Adversarial Networks (GANs). We evaluate their performance based on key metrics such as accuracy, precision, recall, F1 score, and computational efficiency. By conducting experiments on diverse datasets relevant to real-world applications, including healthcare, autonomous vehicles, and natural language processing, we identify the strengths and weaknesses of each architecture. Our findings reveal that while some architectures excel in specific tasks, others offer versatility across multiple domains. The implications of these advancements are discussed, highlighting their potential to drive innovation in various industries. This study aims to provide insights for researchers and practitioners in selecting appropriate deep learning models tailored to their specific applications, thereby advancing the field of artificial intelligence.