Driver Identification Using Statistical Features of Motor Activity and Genetic Algorithms.

Driver identification refers to the process whose primary purpose is identifying the person behind the steering wheel using collected information about the driver him/herself. The constant monitoring of drivers through sensors generates great benefits in advanced driver assistance systems (ADAS), to learn more about the behavior of road users. Currently, there are many research works that address the subject in search of creating intelligent models that help to identify vehicle users in an efficient and objective way. However, the different methodologies proposed to create these models are based on data generated from sensors that include different vehicle brands on routes established in real environments, which, although they provide very important information for different purposes, in the case of driver identification, there may be a certain degree of bias due to the different situations in which the route environment may change. The proposed method seeks to intelligently and objectively select the most outstanding statistical features from motor activity generated in the main elements of the vehicle with genetic algorithms for driver identification, this process being newer than those established by the state-of-the-art. The results obtained from the proposal were an accuracy of 90.74% to identify two drivers and 62% for four, using a Random Forest Classifier (RFC). With this, it can be concluded that a comprehensive selection of features can greatly optimize the identification of drivers.

Metabolomic Selection in the Progression of Type 2 Diabetes Mellitus: A Genetic Algorithm Approach.

According to the World Health Organization (WHO), type 2 diabetes mellitus (T2DM) is a result of the inefficient use of insulin by the body. More than 95% of people with diabetes have T2DM, which is largely due to excess weight and physical inactivity. This study proposes an intelligent feature selection of metabolites related to different stages of diabetes, with the use of genetic algorithms (GA) and the implementation of support vector machines (SVMs), K-Nearest Neighbors (KNNs) and Nearest Centroid (NEARCENT) and with a dataset obtained from the Instituto Mexicano del Seguro Social with the protocol name of the following: "Análisis metabolómico y transcriptómico diferencial en orina y suero de pacientes pre diabéticos, diabéticos y con nefropatía diabética para identificar potenciales biomarcadores pronósticos de daño renal" (differential metabolomic and transcriptomic analyses in the urine and serum of pre-diabetic, diabetic and diabetic nephropathy patients to identify potential prognostic biomarkers of kidney damage). In order to analyze which machine learning (ML) model is the most optimal for classifying patients with some stage of T2DM, the novelty of this work is to provide a genetic algorithm approach that detects significant metabolites in each stage of progression. More than 100 metabolites were identified as significant between all stages; with the data analyzed, the average accuracies obtained in each of the five most-accurate implementations of genetic algorithms were in the range of 0.8214-0.9893 with respect to average accuracy, providing a precise tool to use in detections and backing up a diagnosis constructed entirely with metabolomics. By providing five potential biomarkers for progression, these extremely significant metabolites are as follows: "Cer(d18:1/24:1) i2", "PC(20:3-OH/P-18:1)", "Ganoderic acid C2", "TG(16:0/17:1/18:1)" and "GPEtn(18:0/20:4)".

Two-Dimensional Convolutional Neural Network for Depression Episodes Detection in Real Time Using Motor Activity Time Series of Depresjon Dataset.

Depression is a common illness worldwide, affecting an estimated 3.8% of the population, including 5% of all adults, in particular, 5.7% of adults over 60 years of age. Unfortunately, at present, the ways to evaluate different mental disorders, like the Montgomery-Åsberg depression rating scale (MADRS) and observations, need a great effort, on part of specialists due to the lack of availability of patients to obtain the necessary information to know their conditions and to detect illness such as depression in an objective way. Based on data analysis and artificial intelligence techniques, like Convolutional Neural Network (CNN), it is possible to classify a person, from the mental status examination, into two classes. Moreover, it is beneficial to observe how the data of these two classes are similar in different time intervals. In this study, a motor activity database was used, from which the readings of 55 subjects of study (32 healthy and 23 with some degree of depression) were recorded with a small wrist-worn accelerometer to detect the peak amplitude of movement acceleration and generate a transient voltage signal proportional to the rate of acceleration. Motor activity data were selected per patient in time-lapses of one day for seven days (one week) in one-minute intervals. The data were pre-processed to be given to a two-dimensional convolutional network (2D-CNN), where each record of motor activity per minute was represented as a pixel of an image. The proposed model is capable of detecting depression in real-time (if this is implemented in a mobile device such as a smartwatch) with low computational cost and accuracy of 76.72% In summary, the model shows promising abilities to detect possible cases of depression, providing a helpful resource to identify the condition and be able to take the appropriate follow-up for the patient.