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This is a list of datasets for machine learning research. It is part of the list of datasets for machine-learning research. These datasets consist primarily of images or videos for tasks such as object detection, facial recognition, and multi-label classification.
On the Evaluation of Unsupervised Outlier Detection: Measures, Datasets, and an Empirical Study Most data files are adapted from UCI Machine Learning Repository data, some are collected from the literature. treated for missing values, numerical attributes only, different percentages of anomalies, labels 1000+ files ARFF: Anomaly detection
Decisions may be based on decision-support models (crop simulation models and recommendation models) based on big data, but in the final analysis it is up to the farmer to decide in terms of business value and impacts on the environment- a role being taken over by artificial intelligence (AI) systems based on machine learning and artificial ...
Perturb-seq (also known as CRISP-seq and CROP-seq) refers to a high-throughput method of performing single cell RNA sequencing (scRNA-seq) on pooled genetic perturbation screens. [ 1 ] [ 2 ] [ 3 ] Perturb-seq combines multiplexed CRISPR mediated gene inactivations with single cell RNA sequencing to assess comprehensive gene expression ...
Evolutionary support vector machine (ESVM) based classifier with automatic selection from a large set of physicochemical composition (PCC) features to design an accurate system for predicting protein subnuclear localization.
Hugging Face, Inc. is a Franco-American company that develops computation tools for building applications using machine learning. It is known for its transformers library built for natural language processing applications.
Satellite crop monitoring technology allows to perform online crop monitoring on different fields, located in different areas, regions, even countries and on different continents. The technology's advantage is a high automation level of sown area condition and its interpretation in an interactive map which can be read by different groups of users.
Shogun is a free, open-source machine learning software library written in C++. It offers numerous algorithms and data structures for machine learning problems. It offers interfaces for Octave, Python, R, Java, Lua, Ruby and C# using SWIG. It is licensed under the terms of the GNU General Public License version 3 or later.