Driver Distraction Identification with an Ensemble of Convolutional Neural Networks

The World Health Organization (WHO) reported 1.25 million deaths yearly due to road traffic accidents worldwide and the number has been continuously increasing over the last few years. Nearly fifth of these accidents are caused by distracted drivers. Existing work of distracted driver detection is concerned with a small set of distractions (mostly, cell phone usage). Unreliable ad-hoc methods are often used.In this paper, we present the first publicly available dataset for driver distraction identification with more distraction postures than existing alternatives. In addition, we propose a reliable deep learning-based solution that achieves a 90% accuracy. The system consists of a genetically-weighted ensemble of convolutional neural networks, we show that a weighted ensemble of classifiers using a genetic algorithm yields in a better classification confidence. We also study the effect of different visual elements in distraction detection by means of face and hand localizations, and skin segmentation. Finally, we present a thinned version of our ensemble that could achieve 84.64% classification accuracy and operate in a real-time environment.Comment: arXiv admin note: substantial text overlap with arXiv:1706.0949

CIFAR-10: KNN-based Ensemble of Classifiers

In this paper, we study the performance of different classifiers on the CIFAR-10 dataset, and build an ensemble of classifiers to reach a better performance. We show that, on CIFAR-10, K-Nearest Neighbors (KNN) and Convolutional Neural Network (CNN), on some classes, are mutually exclusive, thus yield in higher accuracy when combined. We reduce KNN overfitting using Principal Component Analysis (PCA), and ensemble it with a CNN to increase its accuracy. Our approach improves our best CNN model from 93.33% to 94.03%

Caracterización molecular y desarrollo de métodos de PCR en tiempo real para evaluar y cuantificar genes de virulencia de enterococos en alimentos fermentados y no fermentados, y "Bacillus sporothermodurans" en leche UHT

La calidad y seguridad alimentaria es un asunto de vital importancia hoy en día en el campo de la alimentación tanto para los investigadores como para los consumidores. A pesar de los estudios realizados en la última decada en este campo, existen pocos datos disponibles acerca de la influencia del tipo de alimento en la transferencia de genes entre la microbiota presente en los mismos y en la dinámica de la generación de resistencias a antibióticos y los factores de virulencia durante los procesos de fermentación, como en la producción de queso y productos cárnicos elaborados. Por lo tanto, es de gran importancia el desarrollo de técnicas analíticas rápidas y específicas que permitan detectar y cuantificar genes que codificantes de factores de virulencia en los microorganismos presentes en los alimentos. Por otro lado, es asimismo importante el desarrollo de un método rápido y específico para detectar y cuantificar Bacillus sporothermodurans, debido a que desde mediados de los 80 ha estado implicado en contaminaciones masivas de de leche UHT. Los resultados obtenidos han puesto de manifiesto la importancia que los productos lácteos y la carne que pueden tener en la difusión de factores de virulencia y resistencia a antimicrobianos a través de los enterococos de la cadena alimentaria. Pudo también comprobarse que los factores de virulencia fueron más prevalentes dentro de las cepas aisladas a partir de los alimentos fermentados, mientras que la resistencia a los antibióticos no presentó variaciones dependientes del proceso de fermentación. Además, este estudio presenta métodos eficientes que pueden utilizarse directamente en los productos alimenticios para la cuantificación rápida y la monitorización de los genes codificantes de factores de virulencia así como para la cuantificación de B. sporothermodurans. Por otra parte, esta es la primera vez que se cuantifican directamente en muestras de alimentos factores de virulencia o B. sporothermodurans en muestras de alimentos o leche UHT respectivamente mediante ensayos RTi-PCR utilizando sondas Taqman®

The Use of Information Systems to Improve Academic Supervision in Colleges

A supervisory service known as academic advice aims to familiarize the student with the university and its scientific departments, the domains in which graduates work, the facets of care, and the services the university offers to its students. The academic advising service assists students in adjusting to the university environment and taking advantage of the opportunities available to them by equipping them with fundamental knowledge and skills that raise their educational attainment. Academic advising is an important link in guiding students to achieve the best performance during the teaching and learning processes, to obtain the best educational outcomes and the best possible academic achievement. Exam anxiety, academic pressures, low achievement, a lack of study time, weak motivation to learn, low self-concept, social and economic pressures, and other issues are common during the university stage and prevent students from adjusting to the university environment. As a result, it becomes urgently necessary to have an advanced academic advising system to address all of these issues and ensure its capacity to achieve psychological harmony. By considering the factors of the student's academic level and university specialty, this study seeks to shed light on the reality of the Faculty of Management Academic Guidance Unit from the perspectives of students and faculty members. The statistical analysis results from the use of various statistical approaches demonstrate that students are generally satisfied with the many dimensions of the questionnaire on the caliber of academic extension services offered by the institution

The effect of whole-body vibration training on selected breast cancer risk factors in obese postmenopausal women: A randomized controlled trial

Menopause is associated with various hormonal changes leading to many complications such as obesity, hot flashes and increased liability for breast cancer, that intractably alter female quality of life. This study was conducted to determine the effect of whole-body vibration training (WBVT) on body mass index and serum prolactin concentration, as risk factors for breast cancer and severity of hot flashes in obese postmenopausal women. A prospective, randomized, parallel group, active controlled study with a 1:1 allocation ratio was carried out. A total of 40 obese postmenopausal women (aged 50 to 60 years, postmenopausal for at least 3 years, with a body mass index BMI between 30-39.9 kg/m2) were randomly assigned into two equal groups (group A and B). Group A (WBVT, n = 20) received WBVT, 3 sessions per week for 3 months, while group B (control group, n = 20) was asked to retrain their usual lifestyle pattern. They were all instructed about healthy dieting. Statistical analysis was performed utilizing SPSS for windows, version 18 (SPSS, Inc., Chicago, IL). The results of our study showed that there was a statistically significant reduction in all measured variables in group A in post-study (p 0.05). Comparison of the two groups after treatment showed a statistically significant decrease in the measured variables, in favor of the study group A (p < 0.05). From the obtained results, it was concluded that whole body vibration is effective in decreasing BMI and waist circumference, in addition to reducing serum prolactin concentration and the severity of hot flashes in obese postmenopausal women. Therefore, it could be used to decrease the risk of breast cancer in obese postmenopausal women

Spatial ecology of a wastewater network defines the antibiotic resistance genes in downstream receiving waters

Wastewater treatment plants (WWTPs) are an effective barrier in the protection of human and environment health around the world, although WWTPs also are suggested to be selectors and-or reservoirs of antibiotic resistance genes (ARGs) before entering the environment. The dogma about WWTPs as “ARG selectors” presumes that biotreatment compartments (e.g., activated sludge; AS) are single densely populated ecosystems with elevated horizontal gene transfer. However, recent work has suggested WWTP biotreatment compartments may be different than previously believed relative to antibiotic resistance (AR) fate, and other process factors, such as bacterial separation and specific waste sources, may be key to ARGs released to the environment. Here we combined 16S rRNA metagenomic sequencing and high-throughput qPCR to characterise microbial communities and ARGs across a wastewater network in Spain that includes both community (i.e., non-clinical urban) and hospital sources. Contrary to expectations, ARGs found in downstream receiving waters were not dominated by AS biosolids (RAS), but more resembled raw wastewater sources. In fact, ARGs and microbial communities in liquid-phase WWTP effluents and RAS were significantly different (Bray–Curtis dissimilarity index = 0.66 ± 0.11), with a consequential fraction of influent ARGs and organisms passing directly through the WWTP with limited association with RAS. Instead, ARGs and organisms in the RAS may be more defined by biosolids separation and biophysical traits, such as flocculation, rather than ARG carriage. This explains why RAS has significantly lower ARG richness (47 ± 4 ARGs) than liquid-phase effluents (104 ± 5 ARGs), and downstream water column (135 ± 4 ARGs) and river sediments (120 ± 5 ARGs) (Tukey's test, p < 0.001). These data suggest RAS and liquid-phase WWTP effluents may reflect two parallel ecosystems with potentially limited ARG exchange. As such, ARG mitigation in WWTPs should more focus on removing bacterial hosts from the liquid phase, AR source reduction, and possibly disinfection to reduce ARG releases to the environment.Work within this manuscript was primarily funded byMERMAID; An Initial Training Network in the People Programme(Marie Skłodowska-Curie Actions) of the European Union's SeventhFramework Programme FP7/2007e2013/under REA grant agree-ment n 607492. Additional funding support was provided by the UK Medical Research Council (MR/P028195/1)S

Susceptible exposed infectious recovered-machine learning for COVID-19 prediction in Saudi Arabia

Susceptible exposed infectious recovered (SEIR) is among the epidemiological models used in forecasting the spread of disease in large populations. SEIR is a fitting model for coronavirus disease (COVID-19) spread prediction. Somehow, in its original form, SEIR could not measure the impact of lockdowns. So, in the SEIR equations system utilized in this study, a variable was included to evaluate the impact of varying levels of social distance on the transmission of COVID-19. Additionally, we applied artificial intelligence utilizing the deep neural network machine learning (ML) technique. On the initial spread data for Saudi Arabia that were available up to June 25th, 2021, this improved SEIR model was used. The study shows possible infection to around 3.1 million persons without lockdown in Saudi Arabia at the peak of spread, which lasts for about 3 months beginning from the lockdown date (March 21st). On the other hand, the Kingdom's current partial lockdown policy was estimated to cut the estimated number of infections to 0.5 million over nine months. The data shows that stricter lockdowns may successfully flatten the COVID-19 graph curve in Saudi Arabia. We successfully predicted the COVID-19 epidemic's peaks and sizes using our modified deep neural network (DNN) and SEIR model

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Driver Distraction Identification with an Ensemble of Convolutional Neural Networks

The World Health Organization (WHO) reported 1.25 million deaths yearly due to road traffic accidents worldwide and the number has been continuously increasing over the last few years. Nearly fifth of these accidents are caused by distracted drivers. Existing work of distracted driver detection is concerned with a small set of distractions (mostly, cell phone usage). Unreliable ad hoc methods are often used. In this paper, we present the first publicly available dataset for driver distraction identification with more distraction postures than existing alternatives. In addition, we propose a reliable deep learning-based solution that achieves a 90% accuracy. The system consists of a genetically weighted ensemble of convolutional neural networks; we show that a weighted ensemble of classifiers using a genetic algorithm yields a better classification confidence. We also study the effect of different visual elements in distraction detection by means of face and hand localizations, and skin segmentation. Finally, we present a thinned version of our ensemble that could achieve 84.64% classification accuracy and operate in a real-time environment