Machine Learning
This is an observation data for water quality monitoring.
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This dataset was used in the article "Dias-Audibert FL, Navarro LC, de Oliveira DN, Delafiori J, Melo CFOR, Guerreiro TM, Rosa FT, Petenuci DL, Watanabe MAE, Velloso LA, Rocha AR and Catharino RR (2020) Combining Machine Learning and Metabolomics to Identify Weight Gain Biomarkers. Front. Bioeng. Biotechnol. 8:6. doi: 10.3389/fbioe.2020.00006", open access available at: https://doi.org/10.3389/fbioe.2020.00006.
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This dataset was created for research on blockchain anomaly and fraud detection. And donated to IEEE data port online community.
https://github.com/epicprojects/blockchain-anomaly-detection
Files:
bitcoin_hacks_2010_2013.csv: Contains known hashes of bitcoin theft/malicious transactions from 2010-2013
malicious_tx_in.csv: Contains hashes of input transactions flowing into malicious transactions.
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Information:
This dataset was created for research on blockchain anomaly and fraud detection. And donated to IEEE data port online community.
Research experiments for this dataset can be found at https://github.com/epicprojects/blockchain-anomaly-detection
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Information:
This dataset was created for research on blockchain anomaly and fraud detection. And donated to IEEE data port online community.
https://github.com/epicprojects/blockchain-anomaly-detection
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Normal
0
false
false
false
EN-US
X-NONE
AR-SA
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The orchid flower dataset was selected from the northern part of Thailand. The dataset contains Thai native orchid flowers, and each class contains at least 20 samples. The orchid dataset including 52 species and the visual characteristics of the flower are varying in terms of shape, color, texture, size, and the other parts of the orchid plant like a leaf, inflorescence, roots, and surroundings. All images are taken from many devices such as a digital camera, a mobile phone, and other equipment. The orchids dataset contains 3,559 images from 52 categories.
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