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.

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.

MSD is a infrared and visible image dataset collected in real-world road environments. It comprises aligned infrared and visible images captured in multiple scenarios, such as dense fog, dusk, nighttime, intersection, and tunnel.

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.

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.

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.

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.

Real-time tracking of electricians in distribution rooms is essential for ensuring operational safety. Traditional GPS-based methods, however, are ineffective in such environments due to complex non-line-of-sight (NLOS) conditions caused by dense cabinets and thick walls that obstruct satellite signals. Existing solutions, such as video-based systems, are prone to inaccuracies due to NLOS effects, while wearable devices often prove inconvenient for workers.

This dataset contains anonymized responses from 600 Egyptian citizens collected in March 2025 to assess public perceptions of artificial intelligence (AI) and deepfake technologies used in the animation of ancient pharaonic statues and symbols. The survey was conducted as part of a broader research study titled "Animating the Sacred: The Ethical and Cultural Implications of AI-Powered Awakening of Pharaonic Symbols Using Deepfake Techniques."

The diameter of the rivet hole is 5mm. In the experiments at the Cooperative Institute, the AE sensor spacing was set to 130mm, where the centers of sensor 1 and sensor 2 were 90mm from each end of the test piece. The waveform flow data obtained in the experiment only retained the information from 30 minutes before the crack initiation to the fracture of the test piece, and the image data of the test piece during this period were recorded.