THERE was once an Emperor who had a horse shod with gold. He had a golden shoe on each foot, and why was this? He was a beautiful creature. He had slender legs, bright, intelligent eyes, and a mane that hung down over his neck like a veil.
This data set is from the pictures collected at the industrial production site of the power adapter.The dataset contains surface defects such as scratches, glue spills, dirt, dirty spots, and off-frame labels that occur during the production process of the power adapter. The original dataset contains 235 images with a height and width of 2448 and 2048, which are annotated in VOC2007 format. These defects were categorized into five classes: label, mark, scratch, smudge, and spill, with each class containing 32, 38, 58, 80, and 32 images.
An AI-based Ancient Hebrew Language Translator aims to revive Ancient Hebrew by constructing a comprehensive dataset with contemporary and ancient Hebrew samples. Seamless integration of the Google Vision API facilitates Optical Character Recognition (OCR) for image processing. The translation process initiates in English through the model, leading to a multilingual interface. This initiative represents a crucial step in preserving ancient languages in the digital age.
Completing the docking between two multi-rotor unmanned aerial vehicles (UAVs) in the air has great significance for expanding the application scenarios of multiple UAVs and improving their endurance capability. In this regard, this paper first designs a safe and autonomous docking scheme and constructs a sub-mother UAV system.
In order to conform to our proposed network, we processed the skeleton data in NTU-RGB+D, which is divided into two files: skeleton.npy shaped as (52678,3,80,25,2), representing 52678 actions; skeleton_label.pkl shaped as (52678,2), representing 52678. The IDs of the actions and the original file names. Ids from 0 to 59 represent the 60 different types of actions. The raw file name is the action file with a skeleton extension.
Predicting future events always comes with uncertainty, but traditional non-probabilistic methods cannot distinguish certain from uncertain predictions. In survival analysis, probabilistic methods applied to state-of-the-art solutions in the healthcare and biomedical field are still novel and their implications have not been fully evaluated. In this paper, we study the benefits of modeling uncertainty in deep neural networks for survival analysis with a focus on prediction and calibration performance.
This is a dataset on normal and early stage (stage I and II) endometrial cancer, comprising a total of 300 MRI images of patients (100 normal, 100 stage I and II), 207 patients (77 healthy, 100 stage IA (50 stage IA, 50 stage IB), and 30 stage II patients. From January 1, 2018 to December 31, 2020, he underwent 1.5-T MRI in Fujian Maternal and Child Health Hospital, with an average age of 55.7 years. Patient age The images in this dataset were all provided by the Radiology Department of Fujian Provincial Maternal and Child Health Care Hospital and may contain privacy concerns.
The Nematode Detection Dataset is a comprehensive collection of 1,368 high-quality microscope images specifically curated for the advancement of agricultural pest management through machine learning. This dataset has been meticulously assembled to aid in the detection, identification, and analysis of four key types of nematodes that are critical to global agriculture: Meloidogyne (Root-knot nematodes), Globodera pallida (Potato cyst nematodes), Pratylenchus (Root-lesion nematodes), and Ditylenchus (Stem nematodes).
These datasets are gathered from an array of four gas sensors to be used for the odor detection and recognition system. The smell inspector Kit IX-16 used to create the dataset. each of 4 sensor has 16 channels of readings. Odors of different 12 samples are taken from these six sensors
1- Natural Air
2- Fresh Onion
3- Fresh Garlic
4- Black Lemon
5- Tomato
6- Petrol
7- Gasoline
8- Coffee
9- Orange
These datasets are gathered from an array of six gas sensors to be used for the odor recognition system. The sensors those used to create the data set are; Df-NH3, MQ-136, MQ-135, MQ-8, MQ-4, and MQ-2.
odors of different 10 samples are taken from these six sensors
1- Natural Air
2- Fresh Onion
3- Fresh Garlic
4- Fresh Lemon
5- Tomato
6- Petrol
7- Gasoline
8- Coffee 1,2
9- Orange
10- Colonia Perfume
