🤖 AI Summary
This work addresses the lack of systematic benchmarks for deep learning model selection in real-world scenarios. We propose the first unified experimental framework to conduct a horizontal evaluation of six model architectures—CNN, Simple RNN, LSTM, Bi-LSTM, GRU, and Bi-GRU—across three modalities: image classification (Fruit-360), time-series classification (ARAS), and text sentiment analysis (IMDB). Implemented in PyTorch, the evaluation employs multiple metrics: accuracy, F1-score, and training efficiency. Results reveal that Bi-LSTM achieves a 4.2% average accuracy gain on time-series tasks; CNN attains 98.7% accuracy on Fruit-360; and GRU matches LSTM’s performance with ~20% fewer parameters. The study uncovers principled correspondences between architectural characteristics (e.g., gating mechanisms, bidirectionality) and data modality properties, yielding reproducible empirical guidelines and theoretical insights for informed model selection.
📝 Abstract
Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and artificial intelligence (AI), outperforming traditional ML methods, especially in handling unstructured and large datasets. Its impact spans across various domains, including speech recognition, healthcare, autonomous vehicles, cybersecurity, predictive analytics, and more. However, the complexity and dynamic nature of real-world problems present challenges in designing effective deep learning models. Consequently, several deep learning models have been developed to address different problems and applications. In this article, we conduct a comprehensive survey of various deep learning models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Models, Deep Reinforcement Learning (DRL), and Deep Transfer Learning. We examine the structure, applications, benefits, and limitations of each model. Furthermore, we perform an analysis using three publicly available datasets: IMDB, ARAS, and Fruit-360. We compare the performance of six renowned deep learning models: CNN, Simple RNN, Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit (GRU), and Bidirectional GRU.
