A Comparative Study on Supervised Machine Learning Algorithms for Copper Recovery Quality Prediction in a Leaching Process

Flores, Víctor Manuel Heredia; Leiva, Claudio

doi:10.37247/pasen.2.2021.6

Cited by 5 publications

(1 citation statement)

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“…According to the analysis of the occlusion detection algorithm, it can be seen that the occlusion detection mechanism determines that there is currently occlusion, and at this time, the machine learning algorithm is executed, and the candidate frame is not generated. However, if the current index parameters are selected too much, the response value of the target will be mistakenly detected as occlusion, resulting in inaccurate tracking [22].…”

Section: Tennis Video Target Extraction Experimentsmentioning

confidence: 99%

Tennis Video Target Tracking Based on Mobile Network Communication and Machine Learning Algorithm

Meng

2022

International Transactions on Electrical Energy Systems

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The application of information technology has realized the transformation of people’s production and lifestyle and also promoted the development of the sports industry. At present, the application of informatization in the field of sports is becoming more and more powerful. By using the advanced methods and technologies of its information display, this paper aimed to realize the research on the tracking of tennis sports video objects in the mobile network environment. It is helpful to analyze and solve the objectivity problems such as most of the loopholes in the single research and traditional methods of tennis video target tracking research. By drawing on the principles and rules of machine learning algorithms, the tennis video target tracking research is carried out, and the informatization and dataization of tennis are realized. In the experiment of the target tracking algorithm, 12 tracking videos have higher tracking accuracy than other parameters. The overall tracking accuracy of the video sequence under grayscale feature was 0.694; the tracking accuracy was the highest, but the tracking speed was the lowest compared to other parameters in the experiment. Therefore, it is very important to study the target tracking of tennis sports in the tennis field.

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Section: Tennis Video Target Extraction Experimentsmentioning

confidence: 99%

Tennis Video Target Tracking Based on Mobile Network Communication and Machine Learning Algorithm

Meng

2022

International Transactions on Electrical Energy Systems

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A machine learning framework for urban mining: A case study on recovery of copper from printed circuit boards

Daware

Chandel

Rai

2022

Minerals Engineering

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On the Challenges of Applying Machine Learning in Mineral Processing and Extractive Metallurgy

et al. 2023

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The application of Machine Learning in Mineral Processing and Extractive Metallurgy has important benefits in terms of increasing the predictability and controllability of the processes, optimizing their performance, and improving maintenance. However, this application has significant implementation challenges. This paper analyzes these challenges and proposes ways of addressing them. Among the main identified challenges are data scarcity and the difficulty in characterizing abnormal events/conditions as well as modeling processes, which require the creative use of different learning paradigms as well as incorporating phenomenological models in the data analysis process, which can make the learning process more efficient. Other challenges are related to the need of developing reliable in-line sensors, adopting interoperability data models and tools, and implementing the continuous measurement of critical variables. Finally, the paper stresses the need for training of advanced human capital resources with the required skills to address these challenges.

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A Comparative Study on Supervised Machine Learning Algorithms for Copper Recovery Quality Prediction in a Leaching Process

Cited by 5 publications

References 0 publications

Tennis Video Target Tracking Based on Mobile Network Communication and Machine Learning Algorithm

Tennis Video Target Tracking Based on Mobile Network Communication and Machine Learning Algorithm

A machine learning framework for urban mining: A case study on recovery of copper from printed circuit boards

On the Challenges of Applying Machine Learning in Mineral Processing and Extractive Metallurgy

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