Physics Maths Engineering
Hierarchical fuzzy deep learning system for various classes of images
Shashank Kamthan
Peer Reviewed
Related Subjects
Abstract
There has been an increasing interest in the development of deep-learning models for the large data processing such as images, audio, or video. Image processing has made breakthroughs in addressing important problems such as genome-wide biological networks, map interactions of genes and proteins, network, etc. With the increase in sophistication of the system, and other areas such as internet of things, social media, web development, etc., the need for classification of image data has been felt more than ever before. It is more important to develop intelligent approaches that can take care of the sophistication of systems. Several researchers are working on the real-time images to solve the problems related to the classification of images. The algorithms to be developed will have to meet the large image datasets. In this paper, the generalized hierarchical fuzzy deep learning approach is discussed and developed to meet such demands. The objective is to design the algorithm for image classification so that it results in high accuracy. The approach is for real-life intelligent systems and the classification results have been shared for large image datasets such as the YaleB database. The accuracy of the algorithm has been obtained for various classes of images using image thresholding. The development of learning algorithms has been validated on corrupted and noisy data and results of various classes of images are presented.
Key Questions
What is generalized hierarchical fuzzy deep learning?
Generalized hierarchical fuzzy deep learning is an advanced approach that combines fuzzy logic with deep learning to handle complex and noisy data. It is designed to improve image classification accuracy, especially for large and sophisticated datasets.
Why is image classification important in modern systems?
Image classification is crucial for applications like genome-wide biological networks, social media analysis, IoT, and web development. It helps in organizing and interpreting large datasets, enabling smarter decision-making in real-time systems.
How does fuzzy logic improve deep learning for image classification?
Fuzzy logic helps handle uncertainty and noise in data, making deep learning models more robust. By integrating fuzzy logic, the model can better classify images with imperfections, such as corrupted or noisy data.
What datasets were used to test the proposed algorithm?
The algorithm was tested on large image datasets, including the YaleB database. It was also validated on corrupted and noisy data to demonstrate its effectiveness in real-world scenarios.
What is the accuracy of the proposed image classification algorithm?
The algorithm achieves high accuracy for various classes of images, even when dealing with noisy or corrupted data. Specific accuracy results are shared for different datasets, showcasing its reliability.
How does the algorithm handle noisy or corrupted data?
The algorithm uses fuzzy logic to manage uncertainty and noise, ensuring accurate classification even when the data is imperfect. This makes it suitable for real-life systems where data quality can vary.
What are the applications of this image classification approach?
This approach can be used in fields like bioinformatics (e.g., gene and protein interaction mapping), IoT, social media analysis, and web development. It is particularly useful for systems requiring high accuracy with large, complex datasets.
How does this approach compare to traditional deep learning methods?
The proposed approach outperforms traditional deep learning methods by integrating fuzzy logic, which improves handling of noisy data and enhances classification accuracy. It is specifically designed for sophisticated, real-world systems.
What is image thresholding, and how is it used in this study?
Image thresholding is a technique used to simplify image data by converting it into binary form. In this study, it helps improve classification accuracy by focusing on key features of the images.
Why is this approach suitable for real-life intelligent systems?
The approach is designed to handle the complexity and noise often found in real-world data. Its ability to classify images accurately, even in challenging conditions, makes it ideal for intelligent systems in various industries.
What are the future directions for this research?
Future research could focus on applying this approach to other types of data, such as audio or video, and further optimizing the algorithm for specific applications like healthcare or autonomous systems.
