Multivariate Radiological-Based Models for the Prediction of Future Knee Pain: Data from the OAI

In this work, the potential of X-ray based multivariate prognostic models to predict the onset of chronic knee pain is presented. Using X-rays quantitative image assessments of joint-space-width (JSW) and paired semiquantitative central X-ray scores from the Osteoarthritis Initiative (OAI), a case-control study is presented. The pain assessments of the right knee at the baseline and the 60-month visits were used to screen for case/control subjects. Scores were analyzed at the time of pain incidence (T-0), the year prior incidence (T-1), and two years before pain incidence (T-2). Multivariate models were created by a cross validated elastic-net regularized generalized linear models feature selection tool. Univariate differences between cases and controls were reported by AUC, C-statistics, and ODDs ratios. Univariate analysis indicated that the medial osteophytes were significantly more prevalent in cases than controls: C-stat 0.62, 0.62, and 0.61, at T-0, T-1, and T-2, respectively. The multivariate JSW models significantly predicted pain: AUC = 0.695, 0.623, and 0.620, at T-0, T-1, and T-2, respectively. Semiquantitative multivariate models predicted paint with C-stat = 0.671, 0.648, and 0.645 at T-0, T-1, and T-2, respectively. Multivariate models derived from plain X-ray radiography assessments may be used to predict subjects that are at risk of developing knee pain

Feature Selection Using Genetic Algorithms for the Generation of a Recognition and Classification of Children Activities Model Using Environmental Sound

In the area of recognition and classification of children activities, numerous works have been proposed that make use of different data sources. In most of them, sensors embedded in children’s garments are used. In this work, the use of environmental sound data is proposed to generate a recognition and classification of children activities model through automatic learning techniques, optimized for application on mobile devices. Initially, the use of a genetic algorithm for a feature selection is presented, reducing the original size of the dataset used, an important aspect when working with the limited resources of a mobile device. For the evaluation of this process, five different classification methods are applied, k-nearest neighbor (k-NN), nearest centroid (NC), artificial neural networks (ANNs), random forest (RF), and recursive partitioning trees (Rpart). Finally, a comparison of the models obtained, based on the accuracy, is performed, in order to identify the classification method that presents the best performance in the development of a model that allows the identification of children activity based on audio signals. According to the results, the best performance is presented by the five-feature model developed through RF, obtaining an accuracy of 0.92, which allows to conclude that it is possible to automatically classify children activity based on a reduced set of features with significant accuracy.In the area of recognition and classification of children activities, numerous works have been proposed that make use of different data sources. In most of them, sensors embedded in children’s garments are used. In this work, the use of environmental sound data is proposed to generate a recognition and classification of children activities model through automatic learning techniques, optimized for application on mobile devices. Initially, the use of a genetic algorithm for a feature selection is presented, reducing the original size of the dataset used, an important aspect when working with the limited resources of a mobile device. For the evaluation of this process, five different classification methods are applied, k-nearest neighbor (k-NN), nearest centroid (NC), artificial neural networks (ANNs), random forest (RF), and recursive partitioning trees (Rpart). Finally, a comparison of the models obtained, based on the accuracy, is performed, in order to identify the classification method that presents the best performance in the development of a model that allows the identification of children activity based on audio signals. According to the results, the best performance is presented by the five-feature model developed through RF, obtaining an accuracy of 0.92, which allows to conclude that it is possible to automatically classify children activity based on a reduced set of features with significant accuracy

Comparison of Convolutional Neural Network Architectures for Classification of Tomato Plant Diseases

Tomato plants are highly affected by diverse diseases. A timely and accurate diagnosis plays an important role to prevent the quality of crops. Recently, deep learning (DL), specifically convolutional neural networks (CNNs), have achieved extraordinary results in many applications, including the classification of plant diseases. This work focused on fine-tuning based on the comparison of the state-of-the-art architectures: AlexNet, GoogleNet, Inception V3, Residual Network (ResNet) 18, and ResNet 50. An evaluation of the comparison was finally performed. The dataset used for the experiments is contained by nine different classes of tomato diseases and a healthy class from PlantVillage. The models were evaluated through a multiclass statistical analysis based on accuracy, precision, sensitivity, specificity, F-Score, area under the curve (AUC), and receiving operating characteristic (ROC) curve. The results present significant values obtained by the GoogleNet technique, with 99.72% of AUC and 99.12% of sensitivity. It is possible to conclude that this significantly success rate makes the GoogleNet model a useful tool for farmers in helping to identify and protect tomatoes from the diseases mentioned

Desarrollo de un Modelo 3D para un Sistema Infotainment

At present, we are witnessing the constant advance in the development of technologies for the automotive industry. Every day, there are significant advances and improvements to provide greater comfort to passengers. One of the vehicle technologies that are especially under development is designing specific user interfaces for infotainment systems and different types of interaction, for example, the implementation of 3D user interfaces to provide feedback to the user. These interfaces can be created from technologies available on the market, both free and private software for designing 3D models, and the development of Human Machine Interfaces used in the automotive industry. This article describes the process followed to develop a functional interface from technologies for 3D modeling and interface design. This interface provides visual feedback to the user that allows understanding of what is happening in the vehicle.En la actualidad somos testigos del constante avance en el desarrollo de tecnologías para la industria automotriz donde cada día hay avances y mejoras significativas para brindar mayor comodidad a los pasajeros. Una de las tecnologías del vehículo que está especialmente en desarrollo es el diseño de interfaces de usuario específicas para sistemas infotainment y diferentes tipos de interacción, por ejemplo, la implementación de interfaces de usuario 3D para proporcionar retroalimentación al usuario. Estas interfaces se pueden crear a partir de tecnologías disponibles en el mercado, tanto de software libre como privado para el diseño de modelos 3D y para el desarrollo de Interfaces Humano Máquina utilizados en la industria automotriz. Este artículo describe el proceso que se llevó a cabo para el desarrollo de una interfaz funcional a partir de tecnologías para el modelado 3D y diseño de interfaces. Dicha interfaz proporciona una retroalimentación visual al usuario que permite comprender lo que sucede en el vehículo

Infrastructure-Less Indoor Localization Using the Microphone, Magnetometer and Light Sensor of a Smartphone

In this paper, we present the development of an infrastructure-less indoor location system (ILS), which relies on the use of a microphone, a magnetometer and a light sensor of a smartphone, all three of which are essentially passive sensors, relying on signals available practically in any building in the world, no matter how developed the region is. In our work, we merge the information from those sensors to estimate the user’s location in an indoor environment. A multivariate model is applied to find the user’s location, and we evaluate the quality of the resulting model in terms of sensitivity and specificity. Our experiments were carried out in an office environment during summer and winter, to take into account changes in light patterns, as well as changes in the Earth’s magnetic field irregularities. The experimental results clearly show the benefits of using the information fusion of multiple sensors when contrasted with the use of a single source of information

A Similarity Analysis of Audio Signal to Develop a Human Activity Recognition Using Similarity Networks

Human Activity Recognition (HAR) is one of the main subjects of study in the areas of computer vision and machine learning due to the great benefits that can be achieved. Examples of the study areas are: health prevention, security and surveillance, automotive research, and many others. The proposed approaches are carried out using machine learning techniques and present good results. However, it is difficult to observe how the descriptors of human activities are grouped. In order to obtain a better understanding of the the behavior of descriptors, it is important to improve the abilities to recognize the human activities. This paper proposes a novel approach for the HAR based on acoustic data and similarity networks. In this approach, we were able to characterize the sound of the activities and identify those activities looking for similarity in the sound pattern. We evaluated the similarity of the sounds considering mainly two features: the sound location and the materials that were used. As a result, the materials are a good reference classifying the human activities compared with the location

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

Tecnologías de infoentretenimiento basadas en inteligencia artificial: Tendencias actuales de investigación y direcciones futuras

Objective. To identify the main research, development and innovation themes associated with infotainment systems and artificial intelligence from 2001 to 2022. In addition, to identify the performance and impact of these areas of knowledge using the Scopus database. Design/Methodology/Approach. The research is based on a two-phase analysis. First is an analysis of performance and impact through evaluating the main bibliometric indicators. Secondly, a science mapping of the publications identified within the areas of knowledge with SciMAT. The combination of both phases allows for analyzing the initial and current state of the knowledge areas, identifying the main themes and stakeholders, thereby establishing a framework of reference. Results/Discussion. In general, 44 documents related to infotainment and artificial intelligence systems have been identified in the Scopus database. From 2001 to 2022, 57 research, development, and innovation themes have been identified and ordered, among which the main themes are: HUMAN-COMPUTER-INTERIORATION-(HCI), 5G-MOBILE-COMMUNICATION-SYSTEMS, ACCIDENT-PREVENTION, WIRELESS-AND-MOBILE-COMMUNICATIONS, and VEHICULAR-ADHOC-NETWORKS-(VANETS). A relevant aspect is that two areas are visualized, software and hardware, which are articulated towards the objectives of these technologies, where the user and connectivity are key pillars in its evolution. Conclusions. In terms of performance, the dimension and weight of the literature related to infotainment systems and artificial intelligence has been exposed since its first publication in 2001. Considering the volume of publications and citations recorded growing, it is expected that the development and application of the same will increase in the coming years. Current research shows that infotainment systems and artificial intelligence are growing areas of research with different approaches. In addition, some lines of future research, development, and innovation are needed to deepen these systems, their evolution, and their relationship with other topics from the point of view of hardware or concrete applications. Originality/Value. Both infotainment technologies and artificial intelligence are growing fields, so having a reference framework will be useful to define and develop the most appropriate R&D and innovation lines according to the needs.Objetivo. Identificar los principales temas de investigación, desarrollo e innovación asociados a los sistemas de infoentretenimiento e inteligencia artificial durante el periodo: 2001-2022, incluyendo su rendimiento e impacto, tomando como referencia la información disponible en la base de datos Scopus. Diseño/Metodología/Enfoque. La investigación se basó en dos fases. En la primera fase, se hizo un análisis de rendimiento e impacto a través de la evaluación de los principales indicadores bibliométricos. En la segunda fase, se hizo un mapeo científico de las publicaciones utilizando el software SciMAT. La combinación de ambas fases permitió analizar el estado inicial y actual de las áreas de conocimiento, sus temas y agentes. Resultados/Discusión. Se identificaron 44 documentos relacionados con los sistemas de infoentretenimiento e inteligencia artificial en Scopus. Se identificaron 57 temas de investigación, desarrollo e innovación, destacándose: HUMAN-COMPUTER-INTERACION-(HCI), 5G-MOBILE-COMMUNICATION-SYSTEMS, ACCIDENT-PREVENTION, WIRELESS-AND-MOBILE-COMMUNICATIONS, y VEHICULAR-ADHOC-NETWORKS-(VANETS). Se visualizan dos ámbitos, el de software y hardware, los cuales se articulan hacia los principios de estas tecnologías, donde el usuario y la conectividad son pilares claves en su evolución. Conclusiones. Teniendo en cuenta con volumen de publicaciones y citas registradas en crecimiento, se espera que el desarrollo y la aplicación de éstos se incrementen en los próximos años. La investigación actual pone de manifiesto que los sistemas de infoentretenimiento y la inteligencia artificial son un área de investigación en crecimiento con diferentes enfoques. Además, son necesarias algunas líneas de investigación, desarrollo e innovación futuras para profundizar en estos sistemas, su evolución y su relación con otros temas desde el punto de vista de hardware u aplicaciones concretas. Originalidad/Valor. Tanto las tecnologías de infoentretenimiento como la inteligencia artificial son ámbitos en crecimiento, por lo que, disponer de un marco de referencia ayudará en la definición y desarrollo de líneas de investigación, desarrollo e innovación más acordes a las necesidades

Identification of Diabetic Patients through Clinical and Para-Clinical Features in Mexico: An Approach Using Deep Neural Networks

Diabetes is a chronic and noncommunicable but preventable disease that is affecting the Mexican population at worrying levels, being the first place in prevalence worldwide. Early diabetes detection has become important to prevent other health conditions that involve low organ yield until the patient death. Based on this problem, this work proposes the architecture of an Artificial Neural Network (ANN) for the automated classification of healthy patients from diabetics patients. The analysis was performed used a set of 19 para-clinical features to determine the health status of the patients. The developed model was evaluated through a statistical analysis based on the calculation of the loss function, accuracy, area under the curve (AUC) and receiving operating characteristics (ROC) curve. The results obtained present statistically significant values, with accuracy of 0.94 and AUC values of 0.98. Based on these results, it is possible to conclude that the ANN implemented in this work can classify patients with presence of diabetes from controls with significant accuracy, presenting preliminary results for the development of a diagnostic tool that can be supportive for health specialists