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| Main Author: | |
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| Format: | Recurso digital |
| Language: | English, Old (ca. 450-1100) |
| Published: |
Zenodo
2024
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| Online Access: | https://doi.org/10.5281/zenodo.11219624 |
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Table of Contents:
- <p>Digital Image Processing (DIP) is a very interesting and challenging research area for researchers and scientists. Broadly, DIP refers to the use of several algorithmic approaches to perform processing on digital images for specific tasks. DIP has a wide range of applications such as image classification, segmentation, visualization, information extraction, pattern recognition, etc. Machine Learning (ML) has become one of the most widely used artificial intelligence techniques for several applications. ML algorithms can interpret images the same way our brains do. It plays an important role to study the behaviour of data and process it to provide the required output to the users. The ML techniques are extended to deep learning (DL) approaches to extend the input size and hardware configuration, and process in an optimized manner in the current scenario. ML techniques can be mainly classified as supervised and unsupervised ML techniques. In this work, an attempt has been made to solve five DIP problems using ML based approaches. These problems are mainly focused on the alzheimer disease, breast ultrasound, tomato leaf disease, weather analysis, and human face with mask and without mask. Finally, simulations have been performed to implement and evaluate the performance of ML based models such as Logistic Regression, Decision Tree, Support Vector Machine, Neural Network, Random Forest, Naive Bayes, etc. along with the hybridized model to validate the work.</p>