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

Dynamic Conditional Imitation Learning for Autonomous Driving

Conditional imitation learning (CIL) trains deep neural networks, in an end-to-end manner, to mimic human driving. This approach has demonstrated suitable vehicle control when following roads, avoiding obstacles, or taking specific turns at intersections to reach a destination. Unfortunately, performance dramatically decreases when deployed to unseen environments and is inconsistent against varying weather conditions. Most importantly, the current CIL fails to avoid static road blockages. In this work, we propose a solution to those deficiencies. First, we fuse the laser scanner with the regular camera streams, at the features level, to overcome the generalization and consistency challenges. Second, we introduce a new efficient Occupancy Grid Mapping (OGM) method along with new algorithms for road blockages avoidance and global route planning. Consequently, our proposed method dynamically detects partial and full road blockages, and guides the controlled vehicle to another route to reach the destination. Following the original CIL work, we demonstrated the effectiveness of our proposal on CARLA simulator urban driving benchmark. Our experiments showed that our model improved consistency against weather conditions by four times and autonomous driving success rate generalization by 52%. Furthermore, our global route planner improved the driving success rate by 37%. Our proposed road blockages avoidance algorithm improved the driving success rate by 27%. Finally, the average kilometers traveled before a collision with a static object increased by 1.5 times. The main source code can be reached at https://heshameraqi.github.io/dynamic_cil_autonomous_driving.Comment: 14 pages, 11 figures, 7 table

GPC3 gene expression and allelic discrimination of FZD7 gene in Egyptian patients with hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related deaths worldwide, and especially in Egypt. Early diagnosis of HCC greatly improves the survival and prognosis of patients. Low sensitivity and specificity of alpha-fetoprotein (AFP) has led to the demand for novel biomarkers of HCC. The aim of the present study was to evaluate the validity of frizzled-7 (FZD7) and glypican-3 (GPC3) gene expression as potential biomarkers for HCC early diagnosis, and to investigate the association between FZD7 rs2280509 polymorphism and HCC risk. Materials and methods: Quantification of FZD7 and GPC3 gene expression by real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) assay, and genotyping FZD 7 (rs2280509 SNP) gene polymorphism using RT-PCR. Results: The current results revealed that FZD7 gene expression had a greater area under the curve (AUC) for identifying HCC than GPC3 gene expression and AFP levels. The combination of the three markers as a panel showed a better diagnostic performance with a greater AUC than any of the single markers alone (p < 0.05). The FZD7 rs2280509 polymorphism (CT) was found to be significantly associated with an increased risk of HCC. The CT genotype and T allele were significantly more prevalent in the HCC group compared to either the cirrhosis (p = 0.03) or control groups (p = 0.0009 and 0.002; respectively). Conclusion: FZD7 and GPC3 gene expressions have a complementary role in early HCC detection, with a greater diagnostic sensitivity and accuracy than AFP. In addition, FZD7 rs2280509 polymorphism is significantly associated with an increased risk of HCC in the Egyptian population

The impact of e-banking service quality on the sustainable customer satisfaction: Evidence from the Saudi Arabia commercial banking sector

The banking sector around the globe has witnessed a huge development in its services and products. The electronic banking services are considered as a competitive advantage for the banking sector. The purpose of this paper is to evaluate the effectiveness of e-banking service quality on customer satisfaction in the context of Saudi Arabian commercial banks. Both quantitative and qualitative research methods were used in the study. A sample of 308 customers from the banking sector participated in this study. The researchers have developed a self-structured questionnaire to collect the relevant data. In addition, secondary data was gathered from published sources, including websites, journal papers, and publications of the chosen commercial banks. The findings of this study show that the eight service quality dimensions; reliability, transactional efficiency, customer support, service security, ease of use, performance, satisfaction with service quality and service content have a significant impact on the level of user's satisfaction with e-banking in the Saudi Arabian commercial banks

Symptomatic Acute Hepatitis C in Egypt: Diagnosis, Spontaneous Viral Clearance, and Delayed Treatment with 12 Weeks of Pegylated Interferon Alfa-2a

The aim of this study was to estimate the proportion of spontaneous viral clearance (SVC) after symptomatic acute hepatitis C and to evaluate the efficacy of 12 weeks of pegylated interferon alfa-2a in patients who did not clear the virus spontaneously.Patients with symptomatic acute hepatitis C were recruited from two "fever hospitals" in Cairo, Egypt. Patients still viremic three months after the onset of symptoms were considered for treatment with 12 weeks of pegylated interferon alfa-2a (180 microg/week).Between May 2002 and February 2006, 2243 adult patients with acute hepatitis were enrolled in the study. The SVC rate among 117 patients with acute hepatitis C was 33.8% (95%CI [25.9%-43.2%]) at three months and 41.5% (95%CI [33.0%-51.2%]) at six months. The sustained virological response (SVR) rate among the 17 patients who started treatment 4-6 months after onset of symptoms was 15/17 = 88.2% (95%CI [63.6%-98.5%]).Spontaneous viral clearance was high (41.5% six months after the onset of symptoms) in this population with symptomatic acute hepatitis C. Allowing time for spontaneous clearance should be considered before treatment is initiated for symptomatic acute hepatitis C