DataSet I includes images of Lymphocyte, Monocyte and Neutrophil. The numbers of these three groups of images are 3209, 15058 and 6203 respectively.
DataSet II comprises 300 precisely annotated semantic segmentation images of leukocytes.
This dataset contains high-resolution images of almond (Prunus dulcis) varieties collected for research on machine learning-based classification and varietal identification. The images were captured under controlled lighting conditions using a consistent setup to ensure uniform quality and minimal variability caused by external factors. Each image corresponds to a specific almond variety, and annotations include class labels representing the respective types.
This dataset contains raw Inertial Measurement Unit (IMU) recordings for human activity recognition in strength training exercises, collected using a custom wearable device based on the Arduino Nano 33 BLE. The device was worn on the wrist and equipped with a 6-axis IMU (accelerometer and gyroscope), sampled at 100 Hz. Data was collected from five exercises commonly used in fitness training: chest press, chest fly, lat pulldown, tricep extension, and seated row.
Accurate identification of nutrients and micronutrients in papaya plants is essential for farmers to ensure healthy growth and improve the quality and taste of the fruit. Traditional manual methods exist for this purpose, but they are often time-consuming and less reliable. In contrast, machine learning models can perform the same task more efficiently and with higher accuracy by analyzing images of the plant leaf.
The clinical diagnosis of cardiovascular diseases from echocardiographic images requires experts to monitor different heart views. However, current datasets lack complete views of the heart for clinical diagnosis. This article introduces a novel publicly available dataset for multi-view echocardiogram classification: the cardiac multi-view echo dataset (CAMEO). The CAMEO dataset comprises high-quality cardiac ultrasound images collected from 9 standard echocardiographic views, including the apical, parasternal long axis, parasternal short axis, and subcostal views.
The datasets include UCI, image, face, and handwritten.
This dataset comprises 32-bit floating-point SAR images in TIFF format, capturing coastal regions. It includes corresponding ground truth masks that differentiate between land and water areas. The covered regions include the Netherlands, London, Ireland, Spain, France, Lisbon, the USA, India, Africa, and Italy. The SAR images were acquired in Interferometric Wide (IW) mode with dual polarization at a spatial resolution of 10m × 10m.
This dataset provides measurements of cerebral blood flow using Radio Frequency (RF) sensors operating in the Ultra-Wideband (UWB) frequency range, enabling non-invasive monitoring of cerebral hemodynamics. It includes blood flow feature data from two arterial networks, Arterial Network A and Arterial Network B. Statistical features were manually extracted from the RF sensor data, while autonomous feature extraction was performed using a Stacked Autoencoder (SAE) with architectures such as 32-16-32, 64-32-16-32-64, and 128-64-32-16-32-64-128.
As various modalities of genomic data are accumulating, methods to integrate across multi-omics datasets are becoming important. Error-correcting output codes (ECOC) is an ensemble learning strategy for solving a multiclass problem thru a decoding process that aggregates the predictions of multiple classifiers. Thus, it lends itself naturally to aggregating predictions across multiple views as well. We applied the ECOC to multi-view learning to see if this strategy can enhance classifier performance as compared to traditional techniques.
This dataset consists of high-dimensional data streams collected from a cyber-physical 118-bus power system, offering a valuable resource for fault diagnosis and classification in large-scale smart grids.
