Comparison of Multilayer Neural Network Models in Terms of Success of Classifications Based on EmguCV, ML.NET and Tensorflow.Net

Magdin, Martin; Benc, Juraj; Koprda, Štefan; Balogh, Zoltán; Tuček, Daniel

doi:10.3390/app12083730

Cited by 4 publications

(3 citation statements)

References 42 publications

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“…To program neural networks, we used the Python language with the connection of Numpy, Keras and TensorFlow libraries (Hoijtink and Planqué-Van Hardeveld, 2022;Magdin et al, 2022). The Numpy library helps develop a simple neural network that solves a prediction problem.…”

Section: Methodsmentioning

confidence: 99%

Application of Machine Learning Technology to Management Methods

Lisitsyn,

Chernukha,

Xiao

et al. 2023

Meat Technology

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Multicomponent meat system -brine injected pork and cooked sausage "Doctorskaya" were was analyzed using neural network technologies and the conditions of uncertainty and risk of human error in a decision-making process in time domain were identified. The formation of a situational classifier (digital image-based histology -meat sample sections with a detailed description) and the system's knowledge base was described. The general steps of a histological section image processing are: 1) preprocessing of section images (noise removal, palette optimization, etc.); 2) color segmentation based on palette minimization; 3) approximation of boundaries of the areas highlighted in the image; 4) area size determination; 5) particle shape determination; 6) particle color determination; 7) identification of the presence of counterfeits; 8) results' output regarding the determination of the presence of counterfeits. The Jupyter Notebook and Colaboratory software environment was used to study and compare the influence of several activation functions (ReLu, tanH, eLu, sigmoid, softPlus, softSign) on the generated DataSet. The best result was obtained when with ReLu (0.9843) activation function, followed by SoftPlus (0.9765) and eLu (0.9687) activation functions. This stage of the study considered a kind of convolutional neural network (CNN) architecture with two layers of convolution (Convolutional, C-Layer) and pooling (Subsampling, S-Layer). An algorithm of the Error Back Propagation gradient was applied to train CNN. This is the first research stage for convolutional neural network applications in solution management.

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Section: Methodsmentioning

confidence: 99%

Application of Machine Learning Technology to Management Methods

Lisitsyn,

Chernukha,

Xiao

et al. 2023

Meat Technology

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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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“…The HAR is very important in real-time systems that include smart gadgets and deep learning techniques [18,19]. To recognize activities from a real environment with continuous data streaming remains a major challenge, as shown in [20].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Machine Learning for Human Activities Recognition Based on Wrist-Worn Wearable Devices

Alexan,

Oniga

2023

Applied Sciences

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Wearable technologies have slowly invaded our lives and can easily help with our day-to-day tasks. One area where wearable devices can shine is in human activity recognition, as they can gather sensor data in a non-intrusive way. We describe a real-time activity recognition system based on a common wearable device: a smartwatch. This is one of the most inconspicuous devices suitable for activity recognition as it is very common and worn for extensive periods of time. We propose a human activity recognition system that is extensible, due to the wide range of sensing devices that can be integrated, and that provides a flexible deployment system. The machine learning component recognizes activity based on plot images generated from raw sensor data. This service is exposed as a Web API that can be deployed locally or directly in the cloud. The proposed system aims to simplify the human activity recognition process by exposing such capabilities via a web API. This web API can be consumed by small-network-enabled wearable devices, even with basic processing capabilities, by leveraging a simple data contract interface and using raw data. The system replaces extensive pre-processing by leveraging high performance image recognition based on plot images generated from raw sensor data. We have managed to obtain an activity recognition rate of 94.89% and to implement a fully functional real-time human activity recognition system.

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Comparison of Multilayer Neural Network Models in Terms of Success of Classifications Based on EmguCV, ML.NET and Tensorflow.Net

Cited by 4 publications

References 42 publications

Application of Machine Learning Technology to Management Methods

Application of Machine Learning Technology to Management Methods

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

Real-Time Machine Learning for Human Activities Recognition Based on Wrist-Worn Wearable Devices

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