Artificial Intelligence
We also provide a new data set CA on chassis assembly for further research in this field. CA is mainly used to study possible logical anomalies in assembly chassis. It has a total of 364 samples for the training set and 191 samples for the test set. The training set contains only normal samples, and the test set contains 93 normal samples and 91 abnormal samples. The main causes of logical anomalies contains several types of logical anomalies, such as quantity anomalies, location anomalies, size anomalies, matching anomalies and mixed anomalies, which poses additional challenges.
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We also provide a new data set CA on chassis assembly for further research in this field. CA is mainly used to study possible logical anomalies in assembly chassis. It has a total of 364 samples for the training set and 191 samples for the test set. The training set contains only normal samples, and the test set contains 93 normal samples and 91 abnormal samples. The main causes of logical anomalies contains several types of logical anomalies, such as quantity anomalies, location anomalies, size anomalies, matching anomalies and mixed anomalies, which poses additional challenges.
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We also provide a new data set CA on chassis assembly for further research in this field. CA is mainly used to study possible logical anomalies in assembly chassis. It has a total of 364 samples for the training set and 191 samples for the test set. The training set contains only normal samples, and the test set contains 93 normal samples and 91 abnormal samples. The main causes of logical anomalies contains several types of logical anomalies, such as quantity anomalies, location anomalies, size anomalies, matching anomalies and mixed anomalies, which poses additional challenges.
- Categories:
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We also provide a new data set CA on chassis assembly for further research in this field. CA is mainly used to study possible logical anomalies in assembly chassis. It has a total of 364 samples for the training set and 191 samples for the test set. The training set contains only normal samples, and the test set contains 93 normal samples and 91 abnormal samples. The main causes of logical anomalies contains several types of logical anomalies, such as quantity anomalies, location anomalies, size anomalies, matching anomalies and mixed anomalies, which poses additional challenges.
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The use of technology in cricket has seen a significant increase in recent years, leading to overlapping computer vision-based research efforts. This study aims to extract front pitch view shots in cricket broadcasts by utilizing deep learning. The front pitch view (FPV) shots include ball delivery by the bowler and the stroke played by the batter. FPV shots are valuable for highlight generation, automatic commentary generation and bowling and batting techniques analysis. We classify each broadcast video frame as FPV and non-FPV using deep-learning models.
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The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another.
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The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another.
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ViewsThe CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another.
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Sensitivity (Se) is the proportion of correctly identified actual abnormal intelligence C&A by the models. Specificity (Sp) is the proportion of correctly identified normal intelligence C&A by the models. Positive predictive value (PV+) is the proportion of correctly identified C&A predicted to have abnormal intelligence. Negative predictive value (PV–) is the proportion of correctly identified C&A predicted to have normal intelligence. Odds ratio (OR) represents the ability of the models to distinguish between C&A with normal and abnormal intelligence.
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