An ensemble multi-stream classifier for infant needs detection.

In this paper, we propose a novel multi-stream video classifier for infant needs detection. The proposed system is an ensemble-based system that combines several machine learning to improve the overall result of the state-of-the-art algorithms. It is a multi-stream in the sense that it combines the output predictions of both audio and images of infants from every single classifier employed in the system for a unified result. This produces better performance and results compared to the previous other research techniques, which relied on only one of these modalities. For training and testing the proposed system, from the Dunstan Baby Language video collection, we built three separate datasets for videos, images, and sounds encompassing the five primary infant needs that require predicting. These are: hunger, have wind, uncomfortable (require diaper change), wants to burp or tired, with a total of 3348 samples. We used four different ensemble algorithms for the best reachable performance. The proposed algorithm improves the overall accuracies of each single classifier from a low of 51% to a high of 99%. The proposed method also improves the accuracy of the classification process by about 9% compared to the state-of-the-art approaches, which was 90%.

Implementation of a low-cost mobile devices to support medical diagnosis.

Medical imaging has become an absolutely essential diagnostic tool for clinical practices; at present, pathologies can be detected with an earliness never before known. Its use has not only been relegated to the field of radiology but also, increasingly, to computer-based imaging processes prior to surgery. Motion analysis, in particular, plays an important role in analyzing activities or behaviors of live objects in medicine. This short paper presents several low-cost hardware implementation approaches for the new generation of tablets and/or smartphones for estimating motion compensation and segmentation in medical images. These systems have been optimized for breast cancer diagnosis using magnetic resonance imaging technology with several advantages over traditional X-ray mammography, for example, obtaining patient information during a short period. This paper also addresses the challenge of offering a medical tool that runs on widespread portable devices, both on tablets and/or smartphones to aid in patient diagnostics.