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Machine Learning

This dataset contains actual field/experimental data for the following environmental engineering applications, namely:

  • Concentration data generated from filtration systems which treat influents, having contaminant materials, via adsorption process.
  • Streamflow height data collated for 50 states/cities in America for the historical period between 1900-2018.
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Opportunity++ is a precisely annotated dataset designed to support AI and machine learning research focused on the multimodal perception and learning of human activities (e.g. short actions, gestures, modes of locomotion, higher-level behavior).

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CodeTiburon is a software development company and IT outsourcing provider, headquartered in Kharkiv and delivering high-end solutions to precise specifications since 2009.

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Computer vision and image processing have made significant progress in many real-world applications, including environmental monitoring and protection. Recent studies have shown that computer vision and image processing can be used to quantify water turbidity, a crucial physical parameter in water quality assessment. This paper presents a procedure to determine water turbidity using deep learning methods, specifically, convolutional neural network (CNN). At first, water samples were located inside a dark cabin before digital images of the samples were captured with a smartphone camera.

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The dataset consists of samples of DDoS attacks. The samples were generated either by dedicated tools such as Loic, Hulk, Thorshammer, or combined from publicly available source such as from DDoS Evaluation Dataset (CIC-DDoS2019).

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To train the machine learning model, a dataset was generated containing data for «Budennovskoye» field, part of which is shown in title figure. (AR and SP are given for 90 centimeter intervals, for which, in turn, the actual values K_fpo. obtained by pumping out (pump out) was determined. As a result, the input variable set consisted of 19 values, including the rock code (AR, SP). The target column isK_f_pump_out .

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Challenge Description and Goal:

The pathogenesis of COVID-19 is increasingly suggesting impairments in the respiratory system. In this light, it is natural to ask – Can sound samples serve as acoustic biomarkers of COVID-19? If yes, an acoustics based COVID-19 diagnosis can provide a fast, contactless, and inexpensive testing scheme, with potential to supplement the existing molecular testing methods, such as RT-PCR and RAT. The present Challenge is an exploration of ideas to find answers to this question.

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