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METHOD FOR IDENTIFYING THE EQUIVALENT FRAME OF A SOLUTION BY ANALYZING TITRATION VIDEO IMAGES USING AN AUTOMATIC APPROACH
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Determination of equivalence in inorganic chemistry can be achieved by various methods, such as colorimetric titration, conductimetric titration and pH-metric titration. However, these traditional methods often require repetition to guarantee reliable results, increasing the time and cost of the experiment. In response to this limitation, some authors have suggested a semi-automatic approach based on colorimetric titration, although this also has subjective aspects. The aim of this article is to propose a new method based on intuitive observation to identify the equivalent frame in a titration video. This approach relies on the use of the KNN classifier to automate the detection of the reference frame within a predefined confidence interval. To facilitate this automation, a dataset was built up from experiments at the Groupe Chimie de lEau et Substances Naturelles (GCESNA) laboratory at the Institut National Polytechnique HouphouetBoigny (INP-HB).The performance of the KNN algorithm was effectively assessed by evaluating it against the performance indicators of precision, recall and F-measure. The results obtained are as follows: Precision: 92.2%, Recall: 91.7% and F-measure: 92.0%. These experimental results demonstrate the effectiveness of the KNN algorithm in frame classification.
International Journal Of Advanced Research
Title: METHOD FOR IDENTIFYING THE EQUIVALENT FRAME OF A SOLUTION BY ANALYZING TITRATION VIDEO IMAGES USING AN AUTOMATIC APPROACH
Description:
Determination of equivalence in inorganic chemistry can be achieved by various methods, such as colorimetric titration, conductimetric titration and pH-metric titration.
However, these traditional methods often require repetition to guarantee reliable results, increasing the time and cost of the experiment.
In response to this limitation, some authors have suggested a semi-automatic approach based on colorimetric titration, although this also has subjective aspects.
The aim of this article is to propose a new method based on intuitive observation to identify the equivalent frame in a titration video.
This approach relies on the use of the KNN classifier to automate the detection of the reference frame within a predefined confidence interval.
To facilitate this automation, a dataset was built up from experiments at the Groupe Chimie de lEau et Substances Naturelles (GCESNA) laboratory at the Institut National Polytechnique HouphouetBoigny (INP-HB).
The performance of the KNN algorithm was effectively assessed by evaluating it against the performance indicators of precision, recall and F-measure.
The results obtained are as follows: Precision: 92.
2%, Recall: 91.
7% and F-measure: 92.
0%.
These experimental results demonstrate the effectiveness of the KNN algorithm in frame classification.
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