A practical evaluation of machine learning for classification of ultrasound images of ovarian development in channel catfish .

Machine learning is a powerful tool to improve efficiency of industrial processes, but it has not yet been well utilized in aquacultural and hatchery applications. The goal of the present study was to evaluate the feasibility of using a broad array of machine learning approaches (testing of > 200 vectorization and model combinations, reporting on 20) to classify ultrasound images spanning annual ovarian development (i.e.

Fully Automated Pipeline for Body Composition Estimation from 3D Optical Scans using Principal Component Analysis: A Shape Up Study.

The increasing prevalence and adaptability of 3D optical scan (3DO) technology has invoked many recent studies which use 3DO scanning as a convenient and inexpensive means for predicting body composition and health risks. The Shape Up studies seek a device-agnostic solution for body composition estimation based on principal component analysis (PCA). This paper reports a progress made on Shape Up's previous work which served as a criterion analysis for PCA-based body composition and health risk prediction.