scholarly journals The Effect of Drivers' Demographic Characteristics on Road Accidents in Different Seasons Using Data Mining

2017 ◽  
Vol 29 (6) ◽  
pp. 555-567 ◽  
Author(s):  
Sajjad Shokohyar ◽  
Ehsan Taati ◽  
Sara Zolfaghari
Keyword(s):  
Data Mining ◽  
Clustering Algorithms ◽  
Road Accident ◽  
World Health ◽  
Road Accidents ◽  
Accident Data ◽  
Pass Through ◽  
And Gender ◽  
Health Organization ◽  
Demographic Features

According to World Health Organization, each year, over 1.2 million people die on roads, and between 20 and 50 million suffer non-fatal injuries. Based on international reports, Iran has a high death rate caused by road accidents. The objective of this study was to extract implicit knowledge from road accident data sets on roads of Iran through data mining. In this regard, three useful data mining techniques were combined: clustering, classification and rule extraction. Following the preparation stage, data were segmented via three clustering algorithms; Kohonen, K-Means and Twostep. Two-step cluster analysis is a one-pass-through data approach which generates a fairly large number of pre-clusters. Next, the optimized algorithm and cluster were identified, after which, in the classification level and by adding the drivers' demographic features through C5.0, a classification algorithm was employed so as to make the decision tree. Ultimately, the effects of these demographic features were investigated on road accidents. The characteristics such as age, job, driving license duration and gender proved to be more important factors in accident analysis. Certain rules of accidents were then extracted in each season of the year.

scholarly journals Analysing Road Accident Criticality using Data mining

2019 ◽  
pp. 408-415
Author(s):  
Shahsitha Siddique V ◽  
Nithin Ramakrishnan
Keyword(s):  
Data Mining ◽  
Road Transport ◽  
Road Accident ◽  
Machine Learning Algorithms ◽  
Road Accidents ◽  
Decision Tree Classifier ◽  
Efficient Manner ◽  
Tree Classifier ◽  
Accident Severity ◽  
Accident Data

Road transport is one of the most vital forms of transportation system, connecting both long and short distances in our country. There are several attributes, which affect the intensity of a road accident like speed of the vehicle, road conditions, time of the accident etc. Analysing these attributes gives an idea about the factors lead to the severity of the accident. Data mining is a method to analyse huge amount of traffic data in an efficient manner, which gives the factors, affect the road accidents. Several machine learning algorithms can be used to find the relation between traffic attributes the lead to the severity of the accidents. In this work, we use three methods for predicting accident criticality. First, Naive Bayesian Classifier is used to get the accident severity based on Bayes rule. Then, Decision Tree classifier is used for same purpose for accident severity calculation. Finally K-Nearest Neighbour(KNN) classifier is employed for severity calculation. The accuracy of the algorithms are compared and it is found that KNN performs better than the other two algorithms employed. The major aim of the work is to find the accident severity. Also the work aims to reduce road accidents by giving awareness to public using the above method.

scholarly journals Safety Road Travelling System with Connected Vehicles

2021 ◽  
Vol 10 (2) ◽  
pp. 56-60
Author(s):  
B. Perumal ◽  
◽  
E. Naveen Kumar ◽  
P. Deepthi ◽  
K. Bhavana ◽  
...  
Keyword(s):  
Human Life ◽  
Road Accident ◽  
World Health ◽  
Connected Vehicles ◽  
Road Accidents ◽  
Wireless Broadcast ◽  
Vehicle Communication ◽  
Vehicle To Vehicle ◽  
Main Motive ◽  
Health Organization

Now a days we had seen so many Road accident cases are occurring and also increasing day by day. According to the statics of World Health Organization (WHO) 20-50 billion people were losing their life due to these accidents. To avoid these problems we came up with a proposed system called connected vehicles. Vehicle to Vehicle communication is a wireless broadcast that transmits the data between the connected vehicles. The main motive of this connected vehicles is safe travelling without any obstacles between the vehicles. Road accidents are the serious issues for human life for both individuals as well as the economic aspects. So our proposed system “Connected Vehicles” will reduce the accident cases by communicating with the nearer vehicles and shares the necessary information regarding the accidents cases to nearer vehicles.

scholarly journals Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction

Computers ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 157
Author(s):  
Daniel Santos ◽  
José Saias ◽  
Paulo Quaresma ◽  
Vítor Beires Nogueira
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Road Accident ◽  
Influential Factors ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Economic Losses ◽  
Learning Approaches ◽  
Road Accidents ◽  
Accident Data

Traffic accidents are one of the most important concerns of the world, since they result in numerous casualties, injuries, and fatalities each year, as well as significant economic losses. There are many factors that are responsible for causing road accidents. If these factors can be better understood and predicted, it might be possible to take measures to mitigate the damages and its severity. The purpose of this work is to identify these factors using accident data from 2016 to 2019 from the district of Setúbal, Portugal. This work aims at developing models that can select a set of influential factors that may be used to classify the severity of an accident, supporting an analysis on the accident data. In addition, this study also proposes a predictive model for future road accidents based on past data. Various machine learning approaches are used to create these models. Supervised machine learning methods such as decision trees (DT), random forests (RF), logistic regression (LR), and naive Bayes (NB) are used, as well as unsupervised machine learning techniques including DBSCAN and hierarchical clustering. Results show that a rule-based model using the C5.0 algorithm is capable of accurately detecting the most relevant factors describing a road accident severity. Further, the results of the predictive model suggests the RF model could be a useful tool for forecasting accident hotspots.

scholarly journals Lethal Violence: A Global View on Homicide

2019 ◽  
Author(s):  
Dietrich Oberwittler
Keyword(s):  
Early Modern Period ◽  
World Health ◽  
Homicide Rates ◽  
Welfare Policies ◽  
Lethal Violence ◽  
State Institutions ◽  
The Pacific ◽  
Other World ◽  
And Gender ◽  
Health Organization

As the most serious crime, homicide is both relevant and suitable for cross-national comparisons. The global homicide rate of ca. 6 per 100,000 people is an average of hugely diverging national rates ranging from 0.25 in Singapore to ca. 100 in El Salvador. The validity of global homicide statistics suffers from various differences in definitions as well as reporting and registration processes. Both criminal justice and causes of death statistics are used by the World Health Organization to construct rates, yet these are available only for a minority of countries. An overview on homicide in history and non-state societies shows that violence levels were considerably higher compared to those in today’s developed world and have dropped dramatically in Europe and North America during the early modern period. The rates first increased and then declined between ca.1960 and today in most developed nations in a synchronized manner, hinting at common influences. In recent years, homicide trends have shown a polarizing pattern, with increasing rates in Latin America and decreasing rates in most other world regions, especially East Asia and the Pacific, where rates have fallen below the European average concurrent with rising scores on the Human Development Index. Except in Eastern Europe, the frequency of homicide is strongly linked to the use of firearms, which account for 44% of homicide cases worldwide. Longitudinal studies have produced robust evidence for the pivotal role of deprivation and inequality in fostering lethal violence and of social welfare policies in reducing it. Although the transition to democratic political systems seems to increase homicide rates temporarily, the legitimacy of state institutions and the suppression of corruption are connected to lower homicide rates. Because of conceptual and methodological problems, questions concerning the generalizability of effects across space and time remain. Nevertheless, the research findings are sufficiently robust to draw important conclusions for violence prevention: reductions in poverty and income inequality, investments in welfare policies and gender equality, and improvements in the legitimacy of state institutions will help to bring homicide rates down.

Intimate partner violence

2018 ◽  
pp. 465-476
Author(s):  
Carmen Wong ◽  
Wai Ching Ng ◽  
Hua Zhong ◽  
Anne Scully-Hill
Keyword(s):  
Intimate Partner Violence ◽  
Partner Violence ◽  
Integrated Approach ◽  
Intimate Partner ◽  
World Health ◽  
Physical And Mental Health ◽  
Psychological Harm ◽  
And Gender ◽  
Health Organization ◽  
Internal And External Factors

Intimate partner violence (IPV) refers to any action that causes physical, sexual, and psychological harm by intimate partners, which includes domestic violence. This chapter gives a brief overview and details the prevalence, current theories, research, and evidence, including patriarchy and gender issues. IPV is complex, with internal and external factors relating to the victim, perpetrator, family, and the community. The long-term impacts on physical and mental health are reviewed. Recent direction by the World Health Organization describes a multi-level integrated approach, which is discussed topically in terms of individual, relational, and community prevention and intervention and its challenges. Finally, policies and laws relating to IPV are reviewed. This chapter has been written collaboratively by a multidisciplinary team of medical, social, and legal professionals.

scholarly journals Genetic Variation in Cytokine-Related Genes and Migraine Susceptibility

2013 ◽  
Vol 16 (6) ◽  
pp. 1079-1086 ◽  
Author(s):  
Shani Stuart ◽  
Bridget H. Maher ◽  
Heidi Sutherland ◽  
Miles Benton ◽  
Astrid Rodriguez ◽  
...  
Keyword(s):  
Gene Cluster ◽  
Disease Risk ◽  
Sterile Inflammation ◽  
World Health ◽  
Risk Haplotype ◽  
Nucleotide Polymorphisms ◽  
And Gender ◽  
Health Organization ◽  
Tnf Gene ◽  
Lymphotoxin Α

Migraine is classified by the World Health Organization (WHO) as being one of the top 20 most debilitating diseases. According to the neurovascular hypothesis, neuroinflammation may promote the activation and sensitisation of meningeal nociceptors, inducing the persistent throbbing headache characterized in migraine. The tumor necrosis factor (TNF) gene cluster, made up of TNFα, lymphotoxin α (LTA), and lymphotoxin β (LTB), has been implicated to influence the intensity and duration of local inflammation. It is thought that sterile inflammation mediated by LTA, LTB, and TNFα contributes to threshold brain excitability, propagation of neuronal hyperexcitability and thus initiation and maintenance of a migraine attack. Previous studies have investigated variants within the TNF gene cluster region in relation to migraine susceptibility, with largely conflicting results. The aim of this study was to expand on previous research and utilize a large case-control cohort and range of variants within the TNF gene cluster to investigate the role of the TNF gene cluster in migraine. Nine single nucleotide polymorphisms (SNPs) were selected for investigation as follows: rs1800683, rs2229094, rs2009658, rs2071590, rs2239704, rs909253, rs1800630, rs1800629, and rs3093664. No significant association with migraine susceptibility was found for any of the SNPs tested, with further testing according to migraine subtype and gender also showing no association for disease risk. Haplotype analysis showed that none of the tested haplotypes were significantly associated with migraine.

Augmenting Classifiers Performance through Clustering

2018 ◽  
Vol 8 (1) ◽  
pp. 57-68 ◽  
Author(s):  
Sachin Kumar ◽  
Prayag Tiwari ◽  
Kalitin Vladimirovich Denis
Keyword(s):  
Traffic Accident ◽  
Latent Class ◽  
Safety Concern ◽  
Health And Safety ◽  
Road Accident ◽  
Support Vector ◽  
Road Accidents ◽  
Real World Data ◽  
The Public ◽  
Accident Data

Road and traffic accident data analysis are one of the prime interests in the present era. It does not only relate to the public health and safety concern but also associated with using latest techniques from different domains such as data mining, statistics, machine learning. Road and traffic accident data have different nature in comparison to other real-world data as road accidents are uncertain. In this article, the authors are comparing three different clustering techniques: latent class clustering (LCC), k-modes clustering and BIRCH clustering, on road accident data from an Indian district. Further, Naïve Bayes (NB), random forest (RF) and support vector machine (SVM) classification techniques are used to classify the data based on the severity of road accidents. The experiments validate that the LCC technique is more suitable to generate good clusters to achieve maximum classification accuracy.

scholarly journals Water, Sanitation, and Hygiene: Linkages with Stunting in Rural Ethiopia

2019 ◽  
Vol 16 (20) ◽  
pp. 3793 ◽  
Author(s):  
Corina Shika Kwami ◽  
Samuel Godfrey ◽  
Hippolyte Gavilan ◽  
Monica Lakhanpaul ◽  
Priti Parikh
Keyword(s):  
World Health ◽  
Rural Ethiopia ◽  
Sanitation And Hygiene ◽  
Infection Pathways ◽  
Behavioural Factors ◽  
Outcomes For Children ◽  
And Gender ◽  
Health Organization ◽  
Integrated Program

Stunting is a global burden affecting nearly 160 million children younger than five years of age. Whilst the linkages between nutrition and stunting are well recognized, there is a need to explore environmental factors such as water and sanitation, which may influence feeding practices and result in potential infection pathways. This paper explores the linkages between stunting and water, sanitation and hygiene (WASH) factors in Ethiopia, which is a relatively understudied context. The research draws upon baseline data for children under the age of five from 3200 households across four regions in Ethiopia as part of a wider study and integrated program led by the United Nations Children’s Fund (UNICEF). Using World Health Organization (WHO) z-scoring, the average stunting rate in the sample is 47.5%. This paper also takes into account demographic and social behavioural factors such as the age, gender of children, and gender of the primary caregiver, in addition to handwashing behaviour and drinking water facilities. The evidence recommends efforts to improve handwashing behaviour for mothers and children with a focus on access to clean water. Higher stunting rates with an increase in the age of children highlight the need for continued interventions, as efforts to improve nutrition and WASH behaviours are most effective early on in promoting long-term health outcomes for children.

scholarly journals Prevalence of Early Childhood Caries in Children Aged 1 to 5 Years in the City of Quito, Ecuador

2019 ◽  
Vol 13 (1) ◽  
pp. 242-248
Author(s):  
Jenny E. C. Acuña ◽  
Karina M. S. Freitas ◽  
Rafael P. Henriques ◽  
Emerson F. Cruz ◽  
Maria C. R Binz Ordóñez ◽  
...  
Keyword(s):  
Early Childhood ◽  
Early Childhood Caries ◽  
Cross Sectional Study ◽  
World Health ◽  
Deciduous Teeth ◽  
Cross Sectional ◽  
Dmft Index ◽  
And Gender ◽  
Health Organization ◽  
Childhood Caries

Background: Early childhood caries is an aggressive pathology that can destroy the teeth in a short time, reaching the proximal surfaces, causing discomfort, pain and affecting the dental pulp, causing premature loss of deciduous teeth. Objective: The purpose of this research is to determine the prevalence of early childhood caries in children aged 1 to 5 years of the Metropolitan District of Quito. Materials and Methods: This is a cross-sectional study including a sample of 557 children attending child care centers in Quito, Ecuador, between May and July 2018. The sample was evaluated clinically registering the data in a simplified dental file containing the odontogram and the result of decayed, lost and sealed teeth, according to the dmft index, as well as data such as age and gender. Data were tabulated and analyzed statistically by independent t-test. Results: The prevalence of early childhood caries was found to be 59.61%. Children affected by early childhood caries presented a mean age of 2.83 years, the ones suffered the most from this pathology were those of 2 years (35.54%), followed by those of 3 years (34.94%) and 4 years (22.5%). Boys presented more early childhood caries (53.92%) in relation to the girls (46.08%). Conclusion: The early childhood caries presented a prevalence of 59.61% and a general dmft index of medium level of severity according to the classification of the World Health Organization, in children from 1 to 5 years of age in Quito, Ecuador.

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