scholarly journals Road Traffic Accidents Analysis Using Data Mining Techniques

2018 ◽  
Vol 14 (2) ◽  
Author(s):  
G. Janani ◽  
N. Ramya Devi
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Road Accident ◽  
Road Traffic Accidents ◽  
Road Accidents ◽  
Rule Mining ◽  
Data Mining Techniques

Road Traffic Accidents (RTAs) are a major public concern, resulting in an estimated 1.2 million deaths and 50 million injuries worldwide each year. In the developing world, RTAs are among the leading cause of death and injury. Most of the analysis of road accident uses data mining techniques which provide productive results. The analysis of the accident locations can help in identifying certain road accident features that make a road accident to occur frequently in the locations. Association rule mining is one of the popular data mining techniques that identify the correlation in various attributes of road accident. Data analysis has the capability to identify different reasons behind road accidents. In the existing system, k-means algorithm is applied to group the accident locations into three clusters. Then the association rule mining is used to characterize the locations. Most state of the art traffic management and information systems focus on data analysis and very few have been done in the sense of classification. So, the proposed system uses classification technique to predict the severity of the accident which will bring out the factors behind road accidents that occurred and a predictive model is constructed using fuzzy logic to predict the location wise accident frequency.

On Association Rule Mining for the QSAR Problem

2011 ◽  
pp. 83-86
Author(s):  
Luminita Dumitriu
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Predictive Ability ◽  
Quantitative Structure Activity Relationship ◽  
Rule Mining ◽  
Data Mining Techniques ◽  
Neuro Fuzzy ◽  
The 1960S ◽  
New Compounds

The concept of Quantitative Structure-Activity Relationship (QSAR), introduced by Hansch and co-workers in the 1960s, attempts to discover the relationship between the structure and the activity of chemical compounds (SAR), in order to allow the prediction of the activity of new compounds based on knowledge of their chemical structure alone. These predictions can be achieved by quantifying the SAR. Initially, statistical methods have been applied to solve the QSAR problem. For example, pattern recognition techniques facilitate data dimension reduction and transformation techniques from multiple experiments to the underlying patterns of information. Partial least squares (PLS) is used for performing the same operations on the target properties. The predictive ability of this method can be tested using cross-validation on the test set of compounds. Later, data mining techniques have been considered for this prediction problem. Among data mining techniques, the most popular ones are based on neural networks (Wang, Durst, Eberhart, Boyd, & Ben-Miled, 2004) or on neuro-fuzzy approaches (Neagu, Benfenati, Gini, Mazzatorta, & Roncaglioni, 2002) or on genetic programming (Langdon, &Barrett, 2004). All these approaches predict the activity of a chemical compound, without being able to explain the predicted value. In order to increase the understanding on the prediction process, descriptive data mining techniques have started to be used related to the QSAR problem. These techniques are based on association rule mining. In this chapter, we describe the use of association rule-based approaches related to the QSAR problem.

scholarly journals Road Traffic Accidents by 'Nasimon' and 'Karimon'-A Study in Faridpur

2012 ◽  
Vol 7 (1) ◽  
pp. 6-9
Author(s):  
ASMJ Chowdhury ◽  
MS Alam ◽  
SK Biswas ◽  
RK Saha ◽  
AR Mondal ◽  
...  
Keyword(s):  
Low Socioeconomic Status ◽  
Urban Areas ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Road Accident ◽  
Motor Vehicles ◽  
Road Traffic Accidents ◽  
Road Accidents ◽  
College Hospital ◽  
Medical College

Road traffic accidents in Bangladesh have been rapidly increasing with huge mortality through road accidents each year. There are many causes of road accidents in recent years; one important cause is running of locally made improvised three wheelers (flat bed tricycle) in the urban areas and also on the highways, popularly known as 'Nasimon' and 'Karimon'. This prospective study was carried out in Faridpur Medical College Hospital from January through June 2011, to study the accident patients caused by 'Nasimon' and 'Karimon'. Fifty six (12%) patients were of RTA by 'Nasimon' and 'Karimon' out of a total of 468 patients admitted into our hospital during this period. Most patients (41, 73.21%) were male, highest accidents (24, 42.86%) were observed among 21-30 years age group and most victims (33, 58.93%) were belonged to low socioeconomic status. Commonest (31, 55.36%) victims were passengers of 'Nasimon' and 'Karimon' while maximum number of accidents (46, 82.14%) took place in the urban areas and on the highways. Injury pattern of victims were similar to that found in any other road accident patients. These three wheelers 'Nasimon' and 'Karimon' are run in violating of Bangladesh Motor Vehicles Act (1983) as they are totally unfit for plying on the highways. Strict surveillance against these illegal and risky vehicles on the highways and in the urban areas by law enforcing agencies is required as a measure to reduce the burden of road accidents in our country.DOI: http://dx.doi.org/10.3329/fmcj.v7i1.10289Faridpur Med. Coll. J. 2012;7(1): 06-09

scholarly journals Crime Analysis and Prediction Using Data Mining and Machine Learning Techniques

2021 ◽  
Vol 9 (10) ◽  
pp. 1259-1270
Author(s):  
Jasmeet Kaur
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Crime Rates ◽  
Crime Analysis ◽  
Crime Trends ◽  
Outlier Analysis ◽  
Rule Mining ◽  
Data Mining Techniques ◽  
The Government

Abstract: With the increase in crime rates across the world, it has become important for the Government and crime handling agencies to control the situation as it has put every person in distress. This paper is an attempt to systematically analyze and identify the crime trends across the years, the inter-state relations based on crime rates and categories through the data available, which will help in predicting the crime trends in future and will be instrumental for the Government to take informed actions and improve the country’s situation. This paper applies various data mining techniques in order to analyze the crime records in India. The results of analysis have been compared for various algorithms in the domain of Association Rule Mining, Clustering, Outlier Analysis, Regression and Classification. The paper also attempts to predict the future occurrences of crimes using classification and regression algorithms which use data mining techniques . Keywords: Crime Analysis, Data Mining, Association Rule Mining, Clustering, outlier Analysis, Classification, Regression

scholarly journals Identification of Road Accident by using Black Spot Method Between Panthaghati to Dhalli Road

2020 ◽  
Vol 9 (2) ◽  
pp. 723-728
Keyword(s):  
Road Safety ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Black Spot ◽  
Research Paper ◽  
Road Accident ◽  
Road Traffic Accidents ◽  
Road Accidents ◽  
The Road ◽  
Black Spots

In India road accidents are very serious problem because of large population and high traffic density of vehicles. Most of the road accidents occur mainly due to the negligence of driver and poor infrastructure only a few accidents occur due to the technical error of vehicles. The main purpose of this research paper is prevention of road traffic accidents and improvement of road safety in Shimla. Road safety is very important aspect of today’s life, so it is important that everybody should aware about road safety. To do this study a section of 12km length is chosen between Panthaghati to Dhalli in district Shimla on NH 5 where accidents black spots are identified for the section by analyzing secondary data used to prevent road accidents. In this study primary data is used for observing the road conditions and secondary data is used to find accidents black spot. Black Spot is a point or a place on the road where road accident occurs repeatedly one after another which is known as accident black spot. To identify these black spots we use weighted severity index (WSI) method. It is one the most reliable and effective method for determining the most proven accidents black spots. Shimla is a hilly area and it has narrow roads, blind curve and black spots which increase the chances of road traffic accidents. In past recent years road traffic accidents are increasing in Shimla and this study deals with identification of major issues causing road traffic accidents. This research paper helps to improve the road safety in Shimla because in this study the analysis has been done to identify the major problems responsible for gradually increasing road accidents. This research paper is also used in future research paper as reference purpose and it will also provide an overview to other researchers who want do their research on similar kind of topics.

Survey on Privacy Preserving Association Rule Data Mining

2017 ◽  
Vol 4 (2) ◽  
pp. 63-80 ◽  
Author(s):  
Geeta S. Navale ◽  
Suresh N. Mali
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Performance Metrics ◽  
Relevant Information ◽  
Privacy Preserving ◽  
Sensitive Information ◽  
Rule Mining ◽  
Research Issues ◽  
Data Mining Techniques

The progress in the development of data mining techniques achieved in the recent years is gigantic. The collative data mining techniques makes the privacy preserving an important issue. The ultimate aim of the privacy preserving data mining is to extract relevant information from large amount of data base while protecting the sensitive information. The togetherness in the information retrieval with privacy and data quality is crucial. A detailed survey of the present methodologies for the association rule data mining and a review of the state of art method for privacy preserving association rule mining is presented in this paper. An analysis is provided based on the association rule mining algorithm techniques, objective measures, performance metrics and results achieved. The metrics and the short comings of the various existing technologies are also analysed. Finally, the authors present various research issues which can be useful for the researchers to accomplish further research on the privacy preserving association rule data mining.

Survey on Privacy Preserving Association Rule Data Mining

2019 ◽  
pp. 624-643
Author(s):  
Geeta S. Navale ◽  
Suresh N. Mali
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Performance Metrics ◽  
Relevant Information ◽  
Privacy Preserving ◽  
Sensitive Information ◽  
Rule Mining ◽  
Research Issues ◽  
Data Mining Techniques

The progress in the development of data mining techniques achieved in the recent years is gigantic. The collative data mining techniques makes the privacy preserving an important issue. The ultimate aim of the privacy preserving data mining is to extract relevant information from large amount of data base while protecting the sensitive information. The togetherness in the information retrieval with privacy and data quality is crucial. A detailed survey of the present methodologies for the association rule data mining and a review of the state of art method for privacy preserving association rule mining is presented in this paper. An analysis is provided based on the association rule mining algorithm techniques, objective measures, performance metrics and results achieved. The metrics and the short comings of the various existing technologies are also analysed. Finally, the authors present various research issues which can be useful for the researchers to accomplish further research on the privacy preserving association rule data mining.

Optimal Inventory Classification using Data Mining Techniques

2016 ◽  
pp. 236-255 ◽  
Author(s):  
Reshu Agarwal ◽  
Mandeep Mittal ◽  
Sarla Pareek
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Pattern Mining ◽  
Rule Mining ◽  
Data Mining Techniques ◽  
Consumer Behavior Analysis ◽  
Relationship Extraction ◽  
Using Data ◽  
Research Studies

Data mining has long been used in relationship extraction from large amount of data for a wide range of applications such as consumer behavior analysis in marketing. Data mining techniques, such as classification, association rule mining, temporal association rule mining, sequential pattern mining, decision trees, and clustering, have attracted attention of several researchers. Some research studies have also extended the usage of this concept in inventory management to determine the optimal economic order quantity. Yet, not many research studies have considered the application of the data mining approach on inventory classification to predict the most profitable items which is also a significant factor to the manager for optimal inventory control. In this chapter, three different cases for inventory classification based on loss rule is presented. An example is illustrated to validate the results.

Association Rule Mining

2011 ◽  
pp. 172-178
Author(s):  
Vasudha Bhatnagar ◽  
Sarabjeet Kochhar
Keyword(s):  
Data Mining ◽  
Portfolio Management ◽  
Association Rule ◽  
Association Rule Mining ◽  
Process Management ◽  
Rule Mining ◽  
Data Mining Techniques ◽  
Future Data ◽  
Manufacturing Process Management ◽  
Care Improvement

Data mining is a field encompassing study of the tools and techniques to assist humans in intelligently analyzing (mining) mountains of data. Data mining has found successful applications in many fields including sales and marketing, financial crime identification, portfolio management, medical diagnosis, manufacturing process management and health care improvement etc.. Data mining techniques can be classified as either descriptive or predictive techniques. Descriptive techniques summarize / characterize general properties of data, while predictive techniques construct a model from the historical data and use it to predict some characteristics of the future data. Association rule mining, sequence analysis and clustering are key descriptive data mining techniques, while classification and regression are predictive techniques. The objective of this article is to introduce the problem of association rule mining and describe some approaches to solve the problem.

scholarly journals Road Transport Accidents in Nigeria and the Role of Automotive Active Safety System

2016 ◽  
Vol 11 (1) ◽  
pp. 49-54
Author(s):  
Olasunkanmi Oriola Akinyemi ◽  
Hezekiah O Adeyemi ◽  
Olusegun Jinadu
Keyword(s):  
Traffic Safety ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Transportation Network ◽  
Road Transport ◽  
Road Accident ◽  
Active Safety ◽  
Road Accidents ◽  
Road Transportation ◽  
Road Traffic Safety

Abstract Analysis of road traffic accidents revealed that most accidents are as a result of drivers’ errors. Over the years, active safety systems (ASS) were devised in vehicle to reduce the high level of road accidents, caused by human errors, leading to death and injuries. This study however evaluated the impacts of ASS inclusions into vehicles in Nigeria road transportation network. The objectives was to measure how ASS contributed to making driving safer and enhanced transport safety. Road accident data were collected, for a period of eleven years, from Lagos State Ministry of Economic Planning and Budget, Central Office of Statistics. Quantitative analysis of the retrospective accident was conducted by computing the proportion of yearly number of vehicles involved in road accident to the total number of vehicles for each year. Results of the analysis showed that the proportion of vehicles involved in road accidents decreased from 16 in 1996 to 0.89 in 2006, the injured persons reduced from 15.58 in 1998 to 0.3 in 2006 and the death rate diminished from 4.45 in 1998 to 0.1 in 2006. These represented 94.4 %, 95 % and 95 % improvement respectively on road traffic safety. It can therefore be concluded that the inclusions of ASS into design of modern vehicles had improved road safety in Nigeria automotive industry.

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