Machine Learning
Since meteorological satellites can observe the Earth’s atmosphere from a spatial perspective at a large scale, in this paper, a dust storm database is constructed using multi-channel and dust label data from the Fengyun-4A (FY-4A) geosynchronous orbiting satellite, namely, the Large-Scale Dust Storm database based on Satellite Images and Meteorological Reanalysis data (LSDSSIMR), with a temporal resolution of 15 minutes and a spatial resolution of 4 km from March to May of each year during 2020–2022.
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The process of dataset generation comprises three integral components: "Account Profiles," responsible for creating detailed account representations; "Transaction Generation," which simulates a diverse range of financial transactions; and the "Generation of Fraud Scenarios," which introduces predefined templates for identifying potential fraudulent transactions based on various criteria. Together, these components collaboratively construct a dynamic and realistic dataset, mirroring real-world financial systems.
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Acute myocardial infarction (AMI) is the main cause of death in developed and developing countries. AMI is a serious medical problem that necessitates hospitalization and sometimes results in death. Patients hospitalized in the emergency department (ED) should therefore receive an immediate diagnosis and treatment. Many studies have been conducted on the prognosis of AMI with hemogram parameters. However, no study has investigated potential hemogram parameters for the diagnosis of AMI using an interpretable artificial intelligence-based clinical approach.
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The dataset consists of measurements of four different stages of degradation in low-voltage contactors used for industrial purposes. The measurements were obtained with fiber Bragg grating (FBG) sensors that detect the dynamic deformation generated in switching under different internal components. The measurements were processed and features from PSD, FFT and TSFEL python library were extracted. The features of PSD and FFT were acquired in 40 sliding windows of 50Hz from the signal.
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This data set contains:
- Training dataset: 271 CT-scans of inner ears used for optimization and training of the model.
- Validation dataset: 70 CT-scans of inner ears used for external validation.
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457 Views
Dataset1
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328 Views
Nowadays, how to non-destructively obtain the bending strength of the bronze-based matrix fabricated by low-temperature hot pressing sintering (HPS) is still difficult. The main contribution of our research is a proposed visual quantization model based on microstructure features of metallographic microscopy images and machine learning to predict the bending strength of bronze-based material. Exploring the interrelationship between microstructure features and mechanical properties will guide the modulation of grinding wheel composition and HPS parameters.
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Data on 2355 COVID-19 cases by date of July to December 2021 were extracted from a data set recorded by COVID-19 referral centers at Qazvin province in Iran. We recorded a wide range of clinical characteristics including age, sex, previous diseases, and hospitalization time. Moreover, we collected data about the different consumed medications including Atrovastatin 20 mg, Atrovastatin 40 mg, Ivermectin 3 mg, Ivermectin 40 mg, Dexamethasone, Kaletra, Favipiravir, Famotidine 40 mg, Interferon, Remdesivir, Hydroxychloroquine.
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During our research in generating or optimizing molecules to be drug candidates by extending deep reinforcement learning and graph neural networks algorithms, we used GEOM data [1], and we had an idea to make a dataset obtained from molecules from GEOM to predit the activity towards COVID and the drug linkeness. We calculated over 200 descriptors for the molecules using RDKit [2]. We hope you enjoy using it.
References:
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377 Views