The abstract summarizes the key findings and performance indicators of the customized
model that is intended for the categorization of illnesses affecting Gingerleaves. Utilizing …

The abstract offers a thorough analysis of the ground-breaking casting defect prediction
system, demonstrating its remarkable efficacy over a wide range of defect types. All defect …

This research presents a novel approach to detect and classify different threat levels of
guava leaf diseases using Convolutional Neural Networks (CNNs). The proposed CNN …

The research is briefly summarized in the abstract, which includes metrics and important
conclusions from the assessment of the CNN-SVM hybrid model for welding defect …

This study investigates the creation of a sophisticated system for classifying hair types
through the use of a hybrid technique that combines Random Forest algorithms and …

The rice variety classification model has impressive performance, highlighted in this
summary. Using accuracy measures like precision and recall, it effectively distinguishes five …

This study presents the development and evaluation of a road surface categorization model
designed to classify surfaces into four categories: dry, wet, snowy, and muddy roads. The …

Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) were expertly
combined in this study to create a powerful hybrid model for the automated diagnosis of …

The research findings on hair loss classification using a machine learning model are
summarized in the abstract and detailed in Table 1. The model achieved high precision …

The results of research assessing the effectiveness of a defect classification model applied
to several casting defect classes are briefly summarized in the abstract. For every defect …