In the classification process, cross validation was used to confirm the accuracy obtained.
📌 3 Key Takeaways
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Objective & Hypothesis:The study is related to the classification of the videos of the UCF101 dataset obtained from kaggle with the help of artificial intelligence and machine learning.
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Key Findings & Data:The ucf 101 dataset has six classes and 155 videos in each class, each of which has approximately 150 picture frames. and with 3 different preprocessing algorithms, features were obtained from each picture frame, and 3 different accuracies were obtained by sending them to the LSTM classifier and the obtained results were compared with each other.
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Conclusion & Impact:In the classification process, cross validation was used to confirm the accuracy obtained.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Artificial Intelligence · Classification · Video Processing
Abstract
The study is related to the classification of the videos of the UCF101 dataset obtained from kaggle with the help of artificial intelligence and machine learning. The ucf 101 dataset has six classes and 155 videos in each class, each of which has approximately 150 picture frames. and with 3 different preprocessing algorithms, features were obtained from each picture frame, and 3 different accuracies were obtained by sending them to the LSTM classifier and the obtained results were compared with each other. In the classification process, cross validation was used to confirm the accuracy obtained.
AKARSU, E., KARACALI, T. (2023). Video Classification Results with Artificial Intelligence and Machine Learning. International Journal of Innovative Research and Reviews, Cilt: 7 Sayı: 1, 22–26. https://ajindex.com/articles/952329
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