Bridging Techniques: A Review of Deep Learning and Fuzzy Logic Applications
DOI:
https://doi.org/10.52098/airdj.20244314Keywords:
Deep learning, machine learning, fuzzy logic, artificial intelligence, neural network, optimization methodAbstract
Abstract—The modelling and prediction field boasts various practical applications, such as deep learning, which is a powerful tool used in this field. It has been proved that deep learning is a valuable technique for extracting extremely accurate predictions from complex data sources. Recursive neural networks have also demonstrated usefulness in language translation and caption production. However, convolutional neural networks remain the dominant solution for image classification tasks. In addition, deep learning, also known as deep neural networks, involves training models with multiple layers of interconnected artificial neurons. The primary idea of deep learning is to learn data representations through rising levels of abstraction. These strategies are effective but do not explain how the result is produced. Without knowing how a solution is arrived at using deep learning. In the field of artificial intelligence, deep learning and fuzzy logic are two powerful techniques. In addition, fuzzy logic combines deep learning to help deep learning select the desired features and work without supervision, making it possible to develop reliable systems with rich DL information even without hand-labelled data. Fuzzy logic that interprets these features will subsequently explain the system's choice of classification label. This survey highlights the various applications which use fuzzy logic to improve deep learning.
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