#SAL #IJSRD #National Conference #Conference in Ahmedabad

Classification of Road Accident Patterns

Author(s): Anuj Sharma,Dehradun Institute of Technology (DIT University); Dr. Sandeep Sharma, Dehradun Institute of Technology (DIT University); Dr. Santosh Kumar, Dehradun Institute of Technology (DIT University)

Keywords: Road Accident, Data Mining, Neural Network

Abstract : This paper presents the classification of vehicular road accident patterns using data mining in Uttarakhand state. In order to provide insights for the development of safety improvement strategies. At this aim, data mining techniques are used to analyze the data relative to the 4,130 crashes all including vehicular crashes. Understanding the accidents on age based classification under which drivers and passengers are more likely to be killed or more severely injured in an automobile accident can help improve the overall driving safety situation. Data mining a non-trivial extraction of novel, implicit, and actionable knowledge from large data sets is an evolving technology which is a direct result of the increasing use of computer databases in order to store and retrieve information effectively. It also enables data exploration, data analysis, and data visualization of huge databases at a high level of abstraction, without a specific hypothesis in mind. Understanding the casualties and classifying the road accidents on gender basis can help in improving traffic safety conditions. This paper presents a data mining approaches to explore various factors for vehicular collision. We have used neural network to classify various road accident patterns that can in turn help in analyzing the road accidents and casualties according to age, gender and location within the state. Feed forward neural network is used to evaluate the various results regarding the related study. With the use of data mining techniques it would be analyze and observe the large database having different records of vehicular road crashes in Uttarakhand state.

For Full Length Paper visit : http://www.ijsrd.com/C_Article.php?manuscript=NCILP027

NATIONAL CONFERENCE ON ICT Transforming ICT to IoT #IJSRD

“NATIONAL CONFERENCE ON ICT”

Transforming ICT to IoT

In association with

IJSRD – International Journal for Scientific Research & Development

SAL Institute of Technology & Engineering Research, affiliated to the Gujarat Technological University, Ahmedabad., is organizing 1-day event named “National Conference on ICT”  on 20th January 2016 in association with IJSRD.
The conference aims to provide an opportunity to teachers/mentors/ educators and students to acknowledge, celebrate and showcase research being carried out by students by enabling them to engage with the wider communities to exchange ideas and share intellectual activity through paper presentations sessions. The event shall feature research papers presentations by eminent educators and students from all over India in the field of ICT (Information and Communication Technologies) & IoT (Internet of Things). 
Researchers working on IoT and ICT can submit their papers under following themes of conference.

1. Pedagogy for effective use of ICT in Engineering Education.
2. IoT for everyday life.
3. IoT for smart house.

Chief Patron
Er. Shri Rajendra Shah
Patron
Dr. Rupesh P. Vasani Ms. Neelima Shah Ms. Pooja Shah
Convener
Dr. S. G. Desai
Coordinators	Technical Committee
Prof. Vijay Gadhavi (SALITER) Prof. Dushyant Rathod (SCE) Prof. Mayank Parekh (SCE) Prof. Tejas Mehta (SETI)	Prof. Nidhi Barot Prof. Pooja Mehta Prof. Archana Patel Prof. Ankita Gandhi

Click here to download information brochure

ORGANISED BY

SAL Institute of Technology & Engineering Research Opp, Science city, Sola Bhadaj Road, Ahmedabad, Gujarat 380060 Website : www.sal.edu.in

PUBLICATION PARTNER

International Journal  for Scientific Research & Development Website: ijsrd.com

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National Conference on "Student-driven Research for Inspired Learning" in Science and Technology : NCIL 2015 : Publication Partner www.ijsrd.com

NCIL - 2015

National Conference on "Student-driven Research for Inspired Learning" in Science and Technology

Organised by ESRC and Dept of Electronics

Publication Partner International Journal for scientific research & Development (IJSRD)

Date: 16-17 October 2015

http://ncilindia.in/

http://mac.du.ac.in/

IJSRD CALL FOR PAPER 2015 - SPECIAL ISSUE FOR DATA MINING.

Dear Researchers/Authors,

IJSRD is promoting a new field of this Digital Generation-“Data Mining”. 

In accordance to it IJSRD is inviting research Papers from you on subject of Data Mining. This is under special Issue Publication by IJSRD. In addition to this authors will have a chance to win the Best Paper Award under this category.

To submit your research paper on Data Mining Click here

What is Data Mining..?

Data mining (the analysis step of the "Knowledge Discovery in Databases" process. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.

The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records, unusual records and dependencies.The Knowledge Discovery in Databases (KDD) process is commonly defined with the stages:

(1) Selection
(2) Pre-processing
(3) Transformation
(4) Data Mining
(5) Interpretation/Evaluation.

To know more…….

Data mining involves six common classes of tasks:

Anomaly detection (Outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors that require further investigation.

Association rule learning (Dependency modelling) – Searches for relationships between variables. For example, a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.

Clustering – is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data.

Classification – is the task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as "legitimate" or as "spam".

Regression – attempts to find a function which models the data with the least error.

Summarization – providing a more compact representation of the data set, including visualization and report generation.

Application Areas….

Games

            They are used to store human strategies into databases and based on that new tactics are designed by Computer ( in association with Machine Learning, Artificial Intelligence)

Business

            Businesses employing data mining may see a return on investment. In situations where a large number of models need to be maintained, some businesses turn to more automated data mining methodologies.In business, data mining is the analysis of historical business activities, stored as static data in data warehouse databases. The goal is to reveal hidden patterns and trends. Data mining software uses advanced pattern recognition algorithms to sift through large amounts of data to assist in discovering previously unknown strategic business information. Examples of what businesses use data mining for include performing market analysis to identify new product bundles, finding the root cause of manufacturing problems, to prevent customer attrition and acquire new customers, cross-selling to existing customers, and profiling customers with more accuracy.

Science and engineering

            In recent years, data mining has been used widely in the areas of science and engineering, such as bioinformatics, genetics, medicine, education and electrical power engineering.

Human rights

            Data mining of government records – especially records of the justice system (i.e., courts, prisons) – empowers the revelation of systemic human rights infringement in association with era and publication of invalid or deceitful lawful records by different government organizations

Medical data mining

            Some machine learning algorithms can be applied in medical field as second-opinion diagnostic tools and as tools for the knowledge extraction phase in the process of knowledge discovery in databases.

Spatial data mining

            Spatial data mining is the application of data mining methods to spatial data. The end objective of spatial data mining is to find patterns in data with respect to geography. So far, data mining and Geographic Information Systems (GIS) have existed as two separate technologies, each with its own methods, traditions, and approaches to visualization and data analysis. Data mining offers great potential benefits for GIS-based applied decision-making.

Temporal data mining

            Data may contain attributes generated and recorded at different times. In this case finding meaningful relationships in the data may require considering the temporal order of the attributes.

Sensor data mining

            By measuring the spatial correlation between data sampled by different sensors, a wide class of specialized algorithms can be developed to develop more efficient spatial data mining algorithms.

Visual data mining

            During the time spent transforming from analogical into computerized, vast datasets have been created, gathered, and stored finding measurable patterns, trends and information which is covered up in real data, with a specific end goal to manufacture prescient formations(patterns).