Intelligent Hybrid model to Enhance Time Series Models for Predicting Network Traffic, the proposed research has used the clustering approach to handle the ambiguity from the entire network data for enhancing the existing time series models.
This dataset contains multispectral high resolution 1627 image patches of size 10 x 10 pixels with each pixel size of 10mx10m. These patches are generated from the Sentinel-2 (A/B) satellite images acquired during the period of October 2018 to May 2019. It covered one life cycle (12 months) of the sugarcane crop in the region of the Karnataka, India. Many parameters like plantation season, soil type, plantation type, crop variety and irrigation type that affects the growth of the sugarcane crop are considered while generating the samples.
Three month Coffee Leaf Rust dataset generated by the Cyber Physical Data Collection System.
Deep facial features with identity generated from CelebA dataset using facenet network (128 real-valued features). Dataset contains:
- full dataset
- training dataset
- validation dataset
Link to CelebA dataset: http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html
Music and animal's basic emotions associated with acoustic signals.
Files associated with animals’ sounds mainly were based on the records from Volodins Bioacoustic Group Homepage
http://www.bioacoustica.org/index_eng.html
http://www.bioacoustica.org/gallery/gallery_rus.html
More details:
Giemsa-stained thin blood smear slides from 150 P. falciparum-infected and 50 healthy patients were collected and photographed at Chittagong Medical College Hospital, Bangladesh. The smartphone’s built-in camera acquired images of slides for each microscopic field of view. Initially, the images were manually annotated by an expert slide reader at the Mahidol-Oxford Tropical Medicine Research Unit in Bangkok, Thailand (the originals can be found at NLM, ftp://lhcftp.nlm.nih.gov/Open-Access-Datasets/Malaria/).
The dataset contains medical signs of the sign language including different modalities of color frames, depth frames, infrared frames, body index frames, mapped color body on depth scale, and 2D/3D skeleton information in color and depth scales and camera space. The language level of the signs is mostly Word and 55 signs are performed by 16 persons two times (55x16x2=1760 performance in total).
We build an original dataset of thermal videos and images that simulate illegal movements around the border and in protected areas and are designed for training machines and deep learning models. The videos are recorded in areas around the forest, at night, in different weather conditions – in the clear weather, in the rain, and in the fog, and with people in different body positions (upright, hunched) and movement speeds (regu- lar walking, running) at different ranges from the camera.
Mosquito bites result in the deaths of more than 1 million people every year. Certain species of mosquitos like Aedes are the main vector of arboviruses that cause Dengue, Malaria and Yellow fever. Image based mosquito species classification can be helpful to implement strategies to prevent the spread of mosquito borne disease. Automated mosquito species classification can aid in laborious and time consuming task of entomologists besides enhancing accuracy.
