SoC based IoT sensor network hub for activity recognition using ML.net framework

Alexan, Alexandru; Alexan, Anca; Oniga, Stefan

doi:10.1109/siitme50350.2020.9292278

Cited by 3 publications

(1 citation statement)

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“…ML.Net is a machine learning framework developed by Microsoft for the new ".Net" platform and provides a low-code development tool called "Model Builder", an intuitive graphical Visual Studio extension for generating, training and deploying custom machine learning models [18]. Therefore, for ".Net" platform developers, using the ML.Net machine learning framework is an excellent choice in terms of ease of use, performance and accuracy [19] ResNet-50 is a residual network that uses a shortcut connection to connect the inputs directly to the outputs (as shown in Figure 3A), which effectively solves the problem of performance degradation due to the deepening of the network as the shortcut connection does not increase the amount of computation [20].…”

Section: Machine Learning Modelmentioning

confidence: 99%

Tailings Pond Classification Based on Satellite Images and Machine Learning: An Exploration of Microsoft ML.Net

Zahidi

2023

Mathematics

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Mine pollution from mining activities is often widely recognised as a serious threat to public health, with mine solid waste causing problems such as tailings pond accumulation, which is considered the biggest hidden danger. The construction of tailings ponds not only causes land occupation and vegetation damage but also brings about potential environmental pollution, such as water and dust pollution, posing a health risk to nearby residents. If remote sensing images and machine learning techniques could be used to determine whether a tailings pond might have potential pollution and safety hazards, mainly monitoring tailings ponds that may have potential hazards, it would save a lot of effort in tailings ponds monitoring. Therefore, based on this background, this paper proposes to classify tailings ponds into two categories according to whether they are potentially risky or generally safe and to classify tailings ponds with remote sensing satellite images of tailings ponds using the DDN + ResNet-50 machine learning model based on ML.Net developed by Microsoft. In the discussion section, the paper introduces the environmental hazards of mine pollution and proposes the concept of “Healthy Mine” to provide development directions for mining companies and solutions to mine pollution and public health crises. Finally, we claim this paper serves as a guide to begin a conversation and to encourage experts, researchers and scholars to engage in the research field of mine solid waste pollution monitoring, assessment and treatment.

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Section: Machine Learning Modelmentioning

confidence: 99%