Artificial Intelligence

We uploaded the raw collected fish image data used for model training. These data were divided into those in simple and complex background. In total, there are more than 800 images.

This dataset includes real-world Channel Quality Indicator (CQI) values from UEs connected to real commercial LTE networks in Greece. Channel Quality Indicator (CQI) is a metric posted by the UEs to the base station (BS). It is linked with the allocation of the UE’s modulation and coding schemes and ranges from 0 to 15 in values. This is from no to 64 QAM modulation, from zero to 0.93 code rate, from zero to 5.6 bits per symbol, from less than 1.25 to 20.31 SINR (dB) and from zero to 3840 Transport Block Size bits.

This cherry tree disease detection dataset is a multimodal, multi-angle dataset which was constructed for monitoring the growth of cherry trees, including stress analysis and prediction. An orchard of cherry trees is considered in the area of Western Macedonia, where 577 cherry trees were recorded in a full crop season starting from Jul. 2021 to Jul. 2022. The dataset includes a) aerial / Unmanned Aerial Vehicle (UAV) images, b) ground RGB images/photos, and c) ground multispectral images/photos.

This dataset contains bone scan image and its segmentation mask. The segmentation mask is made for the purpose of detecting metastases on bone scan images

We create a thermal infrared face dataset (TIF) for fever screening. TIF is collected at the entrance of our university’s engineering building. The infrared face images are captured by an infrared camera under different environmental conditions.

This dataset is about UAV signals.

Both indoor and outdoor experiments are conducted.

the summary datasets in aspect-based sentiment analysis task cotain many social reviews.

Description

Forest environmental sound classification is one use case of ESC which has been widely experimenting to identify illegal activities inside a forest. With the unavailability of public datasets specific to forest sounds, there is a requirement for a benchmark forest environment sound dataset. With this motivation, the FSC22 was created as a public benchmark dataset, using the audio samples collected from FreeSound org.

Nowadays, more and more machine learning models have emerged in the field of sleep staging. However, they have not been widely used in practical situations, which may be due to the non-comprehensiveness of these models' clinical and subject background and the lack of persuasiveness and guarantee of generalization performance outside the given datasets. Meanwhile, polysomnogram (PSG), as the gold standard of sleep staging, is rather intrusive and expensive. In this paper, we propose a novel automatic sleep staging architecture called TinyUStaging using single-lead