Combining Fuzzy Deep Learning with HPC to Classify Images

In this study, High Performance Computing (HPC) is used to train and run the sophisticated model that combines fuzzy logic and deep learning for data categorization. The suggested method aims to enhance categorization while utilizing HPC to control workload and enhance time performance. The fuzzy deep neural network model is essential for processing different characteristics of each unit. The data is gathered and preprocessed by the input layer in preparation for further processing. The following layer detects ambiguity in gathered data. Fuzzification is used in that layer. The model can successfully handle ambiguous and uncertain input due to combination of two approaches. Deep neural networks are used in the feature extraction layer to obtain high-level information from the dataset. The learning process for these neural networks can be computationally taxing, but HPC dramatically speeds up the learning rate, making it possible to handle massive datasets. Fuzzification results integrated with deep learning results in the last layer called fusion. This fusion approach improves the entry data by utilizing the complementing characteristics of both two approaches. The last layer classifies merged dataset to get final results. Tests are conducted using the CIFAR-10 dataset to evaluate the proposed model's effectiveness. Compared to conventional approaches and standalone techniques, effect of combined approach is more prominent when utilized by using high-performance computing. This powerful combination raises major developments possibility in machine learning and data processing techniques. This model is a potential approach for tackling complicated issues and elevating the state-of-the-art in AI and classification approaches thanks to its enhanced accuracy, robustness, and interpretability.