Search Results

Now showing 1 - 2 of 2

Some of the metrics are blocked by your
consent settings
Mobiloscope: a technological solution for early mastitis detection in dairy cattle
(Institute of Electrical and Electronics Engineers Inc., 2021-12-23)
One of the most critical challenges in dairy farms is the Mastistis condition causing economic losses associated with milk production reduction and veterinary treatment expenses. Although it exists different methodologies for diagnosing animals with mastitis, these tests are usually indirect; others require laboratory analysis taking a lot of time to obtain the result, limiting its viability and monitoring in the field. To solve this problem, we propose a Mobiloscope, which is a portable, practical, effective, and low-cost diagnostic system for sub-clinical mastitis. Hence, this device provides an early detection in-situ and at a low cost to cover farmers' unsatisfied demand for having innovative tools that allow them to carry out better sub-clinical mastitis early detection. Our system comprises four components: (i) the holder for the electronic device and the screen to display the graphic interface; (ii) a part where the battery for the micro-computer will be housed; (iii) a dedicated part for the microscope and sample holder; and (iv) a holder for the light source. Despite the need to validate the prototype for commercial purposes, our prototype is able to estimate the number of somatic cells. Therefore, our mobiloscope could help the farmers to make an in-situ analysis of milk quality at a low-cost.
Some of the metrics are blocked by your
consent settings
An approach to growth delimitation of straight line segment classifiers based on a minimum bounding box
(MDPI, 2021-11-01)
Several supervised machine learning algorithms focused on binary classification for solving daily problems can be found in the literature. The straight-line segment classifier stands out for its low complexity and competitiveness, compared to well-knownconventional classifiers. This binary classifier is based on distances between points and two labeled sets of straight-line segments. Its training phase consists of finding the placement of labeled straight-line segment extremities (and consequently, their lengths) which gives the minimum mean square error. However, during the training phase, the straight-line segment lengths can grow significantly, giving a negative impact on the classification rate. Therefore, this paper proposes an approach for adjusting the placements of labeled straight-line segment extremities to build reliable classifiers in a constrained search space (tuned by a scale factor parameter) in order to restrict their lengths. Ten artificial and eight datasets from the UCI Machine Learning Repository were used to prove that our approach shows promising results, compared to other classifiers. We conclude that this classifier can be used in industry for decision-making problems, due to the straightforward interpretation and classification rates.
3

Producción científica

Browse

Filters

Settings

Sort By

Results per page

Search Results