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Design and implementation of image based object recognition

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The training algorithm improved robustness for object recognition, achieving about 98% accuracy.
Using the AlexNet model on the Cifar-10 dataset, a 70/30 split was employed for training and testing.
Different network architectures facilitated enhanced performance in recognizing various object images.
Feed-forward neural networks showed comparable performance to advanced recognition systems.

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Implementation of a convolutional neural network demonstrates 98% accuracy in object recognition, highlighting advancements in training algorithms.

Omar et al. (2020) studied this question.

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Implementation of a convolutional neural network demonstrates 98% accuracy in object recognition, highlighting advancements in training algorithms.