scholarly journals Driver Distraction Identification with an Ensemble of Convolutional Neural Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Hesham M. Eraqi ◽  
Yehya Abouelnaga ◽  
Mohamed H. Saad ◽  
Mohamed N. Moustafa
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Traffic Accidents ◽  
Ad Hoc ◽  
Road Traffic ◽  
Driver Distraction ◽  
World Health ◽  
Skin Segmentation ◽  
Visual Elements ◽  
Health Organization

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.

scholarly journals Analysis of Separability of COVID-19 and Pneumonia in Chest X-ray Images by Means of Convolutional Neural Networks

Proceedings ◽  
2020 ◽  
Vol 54 (1) ◽  
pp. 31
Author(s):  
Joaquim de Moura ◽  
Lucía Ramos ◽  
Plácido L. Vidal ◽  
Jorge Novo ◽  
Marcos Ortega
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Clinical Symptoms ◽  
Early Stage ◽  
World Health ◽  
Complete Analysis ◽  
Screening Process ◽  
X Ray ◽  
Chest X Ray ◽  
Health Organization

The new coronavirus (COVID-19) is a disease that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). On 11 March 2020, the coronavirus outbreak has been labelled a global pandemic by the World Health Organization. In this context, chest X-ray imaging has become a remarkably powerful tool for the identification of patients with COVID-19 infections at an early stage when clinical symptoms may be unspecific or sparse. In this work, we propose a complete analysis of separability of COVID-19 and pneumonia in chest X-ray images by means of Convolutional Neural Networks. Satisfactory results were obtained that demonstrated the suitability of the proposed system, improving the efficiency of the medical screening process in the healthcare systems.

Comparative Analysis of Convolutional Neural Networks Applied in the Detection of Pneumonia Through X-Ray Images of Children

2021 ◽  
Vol 18 (2) ◽  
pp. 4-15
Author(s):  
Luan Oliveira Silva ◽  
◽  
Leandro dos Santos Araújo ◽  
Victor Ferreira Souza ◽  
Raimundo Matos Barros Neto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Standard Of Care ◽  
Classification Problem ◽  
World Health ◽  
X Rays ◽  
Medical Problems ◽  
Learning Techniques ◽  
Incidence And Mortality ◽  
Health Organization

Pneumonia is one of the most common medical problems in clinical practice and is the leading fatal infectious disease worldwide. According to the World Health Organization, pneumonia kills about 2 million children under the age of 5 and is constantly estimated to be the leading cause of infant mortality, killing more children than AIDS, malaria, and measles combined. A key element in the diagnosis is radiographic data, as chest x-rays are routinely obtained as a standard of care and can aid to differentiate the types of pneumonia. However, a rapid radiological interpretation of images is not always available, particularly in places with few resources, where childhood pneumonia has the highest incidence and mortality rates. As an alternative, the application of deep learning techniques for the classification of medical images has grown considerably in recent years. This study presents five implementations of convolutional neural networks (CNNs): ResNet50, VGG-16, InceptionV3, InceptionResNetV2, and ResNeXt50. To support the diagnosis of the disease, these CNNs were applied to solve the classification problem of medical radiographs from people with pneumonia. InceptionResNetV2 obtained the best recall and precision results for the Normal and Pneumonia classes, 93.95% and 97.52% respectively. ResNeXt50 achieved the best precision and f1-score results for the Normal class (94.62% and 94.25% respectively) and the recall and f1-score results for the Pneumonia class (97.80% and 97.65%, respectively).

scholarly journals Forecasting road traffic accident deaths in India using seasonal autoregressive integrated moving average model

2018 ◽  
Vol 5 (9) ◽  
pp. 3962
Author(s):  
Manikandan M. ◽  
Vishnu Prasad R. ◽  
Amit Kumar Mishra ◽  
Rajesh Kumar Konduru ◽  
Newtonraj A.
Keyword(s):  
Traffic Accidents ◽  
Time Series Data ◽  
Road Traffic ◽  
Moving Average ◽  
Arima Model ◽  
World Health ◽  
Series Data ◽  
Road Traffic Accidents ◽  
Increasing Trend ◽  
Health Organization

Background: As per World Health Organization (WHO) report 1.24 million people die each year as a result of road traffic accidents (RTA) globally. A vast majority of 20-50 million people suffer from non-fatal injuries, many of them ultimately end in disability. Forecasting RTA deaths could help in planning the intervention at the right time in an effective way.Methods: An attempt was made to forecast the RTA deaths in India with seasonal auto regressive integrated moving average (SARIMA) model. ARIMA model is one of the common methods which are used for forecasting variables as the method is very easy and requires only long time series data. The method of selection of appropriate ARIMA model has been explained in detail. Month wise RTA deaths for previous years data was collected from Govt. of India website. Data for 12 years (2001 to 2012) was extracted and appropriate ARIMA model was selected. Using the validated ARIMA model the RTA deaths are forecasted for 8 years (2013-2020).Results: The appropriate SARIMA (1,0,0) (2,1,0) 12 model was selected based on minimal AIC and BIC values. The forecasted RTA deaths show increasing trend overtime.Conclusions: There is an increasing trend in the forecasted numbers of road traffic accidental deaths and it also shows seasonality of RTA deaths with more number of accidents during the month of April and May in every years. It is recommended that the policy makers and transport authority should pay more attention to road traffic accidents and plan some effective intervention to reduce the burden of RTA deaths.

scholarly journals Modelling Deaths Associated with Road Traffic Accidents and other Factors on Great North Road in Zambia between the Years 2010 and 2016 Using Poisson Models

2019 ◽  
Vol 12 (1) ◽  
pp. 68-77
Author(s):  
Ronald Fisa ◽  
Chola Nakazwe ◽  
Charles Michelo ◽  
Patrick Musonda
Keyword(s):  
Incidence Rate ◽  
Traffic Accidents ◽  
Negative Binomial ◽  
Road Traffic ◽  
World Health ◽  
P Value ◽  
Road Traffic Accidents ◽  
Poisson Models ◽  
Health Organization ◽  
Best Fit

Background: According to the World Health Organization (WHO), 1.24 million people die annually on the world’s roads, with 20-50 million sustaining non-fatal injuries. More than 85% (1.05 million) of the global deaths due to injuries occur in the developing world. Road traffic deaths and injuries are a major but neglected public health challenge that requires concerted efforts for effective and sustainable prevention. The objectives of the study were to estimate the incidence rate of death from RTAs, to determine factors associated with serious and fatal Road Traffic Accidents (RTAs) and to determine which of the poisson models fit the count data better. Methods: Data was collected from Zambia Police (ZP), Traffic Division on accidents that occurred on the Great North Road (GNR) highway between Lusaka and Kapiri-Mposhi in Zambia from January 1, 2010 to December 31, 2016. Results from standard Poisson regression were compared to those obtained using the Negative Binomial (NB), Zero-Truncated Negative Binomial (ZTNB) and the Zero-Truncated Poisson (ZTP) regression models. Diagnostic tests were used to determine the best fit model. The data was analysed using STATA software, version 14.0 SE (Stata Corporation, College Station, TX, USA). Results: A total of 1, 023 RTAs were analysed in which 1, 212 people died. Of these deaths, 82 (7%) were Juveniles and 1, 130 (93%) were adults. Cause of accident such as pedestrians crossing the road accounted for 30% (310/1,023) while 29% (295/1,023) were as a result of driver’s excessive speed. The study revealed that driving in the early hours of the day (1AM-6AM) as compared to driving in the night (7PM-12AM) had a significant increase in the incidence rate of death from RTAs, Incidence Rate Ratio (IRR) of 2.1, (95% CI={1.01-4.41}), p-value=0.048. Results further showed that public transport as compared to private transport had an increased incidence rate of death from RTAs (IRR=5.65, 95% CI={2.97-10.73}), p-value<0.0001. The two competing models were the ZTP and the ZTNB. The ZTP had AIC=1304.55, BIC= 1336.55, whereas the ZTNB had AIC=742.25 and BIC=819.69. This indicated that the ZTNB with smaller AIC and BIC was the best fit model for the data. Conclusion: There is a reduced incidence of dying if one is using a private vehicle as compared to a public vehicle. Driving in the early hours of the day (1AM and 6AM) had an increased incidence of death from RTAs. This study suggests that when dealing with counts in which there are a few zeros observed such as in serious and fatal RTAs, ZTNB fits the data well as compared to other models.

scholarly journals Medical Image Classification for Coronavirus Disease (COVID-19) Using Convolutional Neural Networks

2021 ◽  
pp. 2740-2747
Author(s):  
Ehsan Ali Al-Zubaidi ◽  
Maad M. Mijwil
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Medical Image ◽  
Early Stage ◽  
World Health ◽  
Elapsed Time ◽  
The World ◽  
Medical Image Classification ◽  
Serious Disease ◽  
Health Organization

     The coronavirus is a family of viruses that cause different dangerous diseases that lead to death. Two types of this virus have been previously found: SARS-CoV, which causes a severe respiratory syndrome, and MERS-CoV, which causes a respiratory syndrome in the Middle East. The latest coronavirus, originated in the Chinese city of Wuhan, is known as the COVID-19 pandemic. It is a new kind of coronavirus that can harm people and was first discovered in Dec. 2019. According to the statistics of the World Health Organization (WHO), the number of people infected with this serious disease has reached more than seven million people from all over the world. In Iraq, the number of people infected has reached more than twenty-two thousand people until April 2020. In this article, we have applied convolutional neural networks (ConvNets) for the detection of the accuracy of computed tomography (CT) coronavirus images that assist medical staffs in hospitals on categorization chest CT-coronavirus images at an early stage. The ConvNets are able to automatically learn and extract features from the medical image dataset. The objective of this study is to train the GoogleNet ConvNet architecture, using the COVID-CT dataset, to classify 425 CT-coronavirus images. The experimental results show that the validation accuracy of GoogleNet in training the dataset is 82.14% with an elapsed time of 74 minutes and 37 seconds.

scholarly journals Improving Pre-hospital Care of Road Traffic Accident's Victims with Smartphone Technology

2018 ◽  
Vol 12 (2) ◽  
pp. 130 ◽  
Author(s):  
Osman A. Abdellah ◽  
Majed M. Aborokbah ◽  
Abbdelrahman Osman Elfaki
Keyword(s):  
Traffic Accidents ◽  
Success Factors ◽  
Road Traffic ◽  
Emergency Situation ◽  
Healthcare Services ◽  
World Health ◽  
Viable Solution ◽  
The World ◽  
Information And Communication ◽  
Health Organization

<p class="0abstract">One of the most causes to lose millions of lives around the world is Road Traffic Accidents (RTAs). According to the world health organization (WHO) report, 1.25 million people are killed each year as a result of RTAs, 20 to 50 million people were injured, and the number of killed people by RTAs is expected to increase further by 2020. The recent studies conclude that patient survival during a health emergency situation depends on the effective pre-hospital healthcare services, while the effective communication between the paramedics and prehospital staff is one of the important healthcare success factors. With the rapid growing of information and communication technology (ICT), wireless technologies and mobile services can provide viable solution to overcome the pre-hospital healthcare problems. The aim of this research is to improve the quality of prehospital emergency healthcare services at KSA by developing and implementing a mobile based emergency system. The proposed application is moving the diagnosis time to be started during traveling time witch accelerate the treatment. The proposed system shows satisfactory results in term of effectiveness and satisfaction</p>

scholarly journals Pedestrians’ Injury Severity in Traffic Accidents in Spain: A Pedestrian Actions Approach

2021 ◽  
Vol 13 (11) ◽  
pp. 6439
Author(s):  
Juan Diego Febres ◽  
Miguel Ángel Mariscal ◽  
Sixto Herrera ◽  
Susana García-Herrero
Keyword(s):  
High Speed ◽  
Traffic Accidents ◽  
Injury Severity ◽  
Motor Vehicle ◽  
Road Traffic ◽  
World Health ◽  
The Road ◽  
The World ◽  
Unsafe Behavior ◽  
Health Organization

Road traffic accidents are currently between the seventh and tenth leading cause of death in the world, with approximately 1.35 million people killed per year. Despite extensive efforts by governments, according to the World Health Organization, road accidents still cause far too many deaths, especially among pedestrians, cyclists and two-wheel motor vehicle riders, who together account for almost 50% of road traffic fatalities. In particular, Spain had 410,974 traffic accidents between 2016 and 2019, involving 722,516 vehicles and 61,177 pedestrians with varying degrees of injury. This study uses the Bayesian network method to understand how the pedestrians’ responsibility and actions at the time of the traffic accident affect the injury suffered by said pedestrian, also considering the variables of the road infrastructure and vehicles at the accident site. The results confirm that the variables linked to the unsafe behavior of pedestrians, and their responsibility in traffic accidents, increase the risk of suffering serious or fatal injuries during an accident; for example, if a pedestrian is distracted this increases his/her probability of suffering a severe injury (27.86%) with respect to not being distracted (20.73%). Conditions related to traffic in high-speed areas, areas with no or poor lighting, and areas lacking sidewalks, also record increases in pedestrian injury, as is the case in the age group of pedestrians over 60 years of age.

scholarly journals Numerical Assessment of a Safety System to Minimize Injuries during a Cyclist Run-Over

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
E. H. López-García ◽  
M. F. Carbajal-Romero ◽  
J. A. Flores-Campos ◽  
C. R. Torres-SanMiguel
Keyword(s):  
Traffic Accidents ◽  
Road Traffic ◽  
Safety System ◽  
Security System ◽  
World Health ◽  
Passive Safety ◽  
Passive Safety System ◽  
Comparative Results ◽  
Health Organization ◽  
The Impact

Background. The World Health Organization has reported that 1.35 million people die on the roads every year due to road traffic accidents. This paper focuses on exploring a passive safety system that reduces lesions in the overtaking run-over scenario. Methods. Head Injury Criterion (HIC) and Combined Thoracic Index (CTI) were evaluated through numerical simulations using LS-Dyna®; in order to compare the computed results, three different speed scenarios were carried out (velocity of running over 40, 50, 60 km/h). Results. The computed results were divided into groups, A for the run-over test without a passive security system and B for the run-over test with a passive security system. For case A.1, the HIC15 was 3325. For case A.2, the HIC15 was 1510, and for case A.3, the HIC 15 was 1208. For case B.1, the HIC15 2605, for case B.2, the HIC15 was 1282, and for case B.3, the HIC was 730. Conclusion. The comparative results show that the passive safety system installed on the bicycle has an increased benefit impact on the severity of the injury on vulnerable road users, decreasing the probability of cranioencephalic lesions in all study cases. In addition, the thorax injuries are cut down only in the impact scenario at a speed of 40 km/h.

scholarly journals Identification of Risk Factors Influencing Road Traffic Accidents

2018 ◽  
Vol 8 (1) ◽  
pp. 2417-2421 ◽  
Author(s):  
M. Touahmia
Keyword(s):  
Traffic Safety ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Public Awareness ◽  
Seat Belt ◽  
World Health ◽  
Road Traffic Accidents ◽  
The World ◽  
Traffic Law Enforcement ◽  
Health Organization

Road traffic accidents (RTAs) are becoming a major problem around the world, incurring enormous losses of human and economic resources. Recent reports from the World Health Organization (WHO) reveal that each year more than 1.25 million people are killed and 50 million are injured in road traffic accidents worldwide. In Saudi Arabia, statistics show that at least one traffic accident occurs every minute, causing up to 7,000 deaths and over 39,000 injuries annually. In this study, the main causes of RATs in the province of Hail are examined. The data was collected through the use of a survey which was developed to evaluate the effect of influencing parameters on RTA rate. The results show that 67% of RTAs result from human factors, 29% from road conditions and 4% from vehicle defects. Excessive speed and violation of traffic rules and regulations were found to be the main causes of RATs. Low rates of compliance with speed limit signs and seat-belt regulations were also observed. These findings highlight the need of strengthening effective traffic law enforcement alongside with improving traffic safety and raising public awareness.

scholarly journals Pneumonia Detection Using Convolutional Neural Networks

2021 ◽  
Vol 7 (01) ◽  
pp. 77-80
Author(s):  
Puneet Gupta
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Large Scale ◽  
Disease Classification ◽  
World Health ◽  
X Rays ◽  
X Ray ◽  
Life Threatening ◽  
Chest X Ray ◽  
Health Organization

Abstract— Pneumonia is a life-threatening infectious disease affecting one or both lungs in humans commonly caused by bacteria called Streptococcus pneumoniae. One in three deaths in India is caused due to pneumonia as reported by World Health Organization (WHO). Chest X-Rays which are used to diagnose pneumonia, need expert radiotherapists for evaluation. Thus, developing an automatic system for detecting pneumonia would be beneficial for treating the disease without any delay particularly in remote areas. Due to the success of deep learning algorithms in analyzing medical images, Convolutional Neural Networks (CNNs) have gained much attention for disease classification. In addition, features learned by pre-trained CNN models on large-scale datasets are much useful in image classification tasks. In this work, we appraise the functionality of pre-trained CNN models utilized as feature-extractors followed by different classifiers for the classification of abnormal and normal chest X-Rays. We analytically determine the optimal CNN model for the purpose. Statistical results obtained demonstrates that pretrained CNN models employed along with supervised classifier algorithms can be very beneficial in analyzing chest X-ray images, specifically to detect Pneumonia. In this project Transfer learning and a CNN Model is used to detect whether the person has pneumonia or not using chest x-ray.

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