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Improve Unsupervised Machine Learning Model on Fruits by Using VGG-16

https://doi.org/10.54097/39xpbb68
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1038/s41598-023-29453-8openalex-snapshot
Random embedded calibrated statistical blind steganalysis using cross validated support vector machine and support vector machine with particle swarm optimization
inferredcites →https://doi.org/10.3390/agriengineering5020050openalex-snapshot
A Case Study toward Apple Cultivar Classification Using Deep Learning
inferredcites →https://doi.org/10.3390/agronomy13061633openalex-snapshot
Comparing Inception V3, VGG 16, VGG 19, CNN, and ResNet 50: A Case Study on Early Detection of a Rice Disease
inferredcites →https://doi.org/10.3390/computers12050091openalex-snapshot
Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions
inferredcites →https://doi.org/10.3390/life13030691openalex-snapshot
Robust Classification and Detection of Big Medical Data Using Advanced Parallel K-Means Clustering, YOLOv4, and Logistic Regression
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition

What points at this work

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inferred← citeshttps://doi.org/10.2139/ssrn.4890491openalex-extracted
Retail Product Recognition: A Combination of Yolov8 and Modified Self-Supervised Learning
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