Showing posts with label good research journals. Show all posts
Showing posts with label good research journals. Show all posts

Tuesday, March 8, 2016

5th NATIONAL CONFERENCE on Computational and Mathematical Sciences | IJSRD


on Computational and Mathematical Sciences

(March 18th & 19th, 2016)

in association with International Journal for Scientific Research & Development (IJSRD}

The Conference proposes to bring together experts, researchers, faculties, students and the leaders of industries for sharing their knowledge and expertise in the field related to Computer and Mathematical Sciences.
We have successfully organized four National Conferences named “COMPUTATIA” since 2011. Following that this year also we are organizing COMPUTATIA-V “Two Days National Conference on Computational and Mathematical Sciences” on March 18th-19th, 2016.

Authors are requested to submit their abstracts in MS WORD file format at email-id: or on or before 9th March, 2016.
The Selected Papers will be published in IJSRD (International Journal for Scientific Research and Development) having ISSN (online): 2321-0613 and Impact Factor: 2.39

Click to Download Registration Form 

Saturday, September 5, 2015

Special Issue For Data Mining #ijsrd

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….


            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)


            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).

Thursday, August 27, 2015

Real Time Traffic Control using Image Processing #ijsrd

Real Time Traffic Control using Image Processing


Rahul Rane , Rajiv Gandhi Institute Of Technology
S.P.Khachane, Rajiv Gandhi Institute of Technology
Sayali Pathak, Rajiv Gandhi Institute of Technology
Aruta Oak, Rajiv Gandhi Institute of Technology


Traffic control, image processing, edge detection, background subtraction


The Idea behind real time traffic control using image processing is due to increase in number of vehicles on road which causes traffic congestion. There are various successful techniques to overcome this problem such as inductive loop detectors, magnetic loop detectors and video based system. We propose a system based on measurement of vehicle density on road using Real Time Image Processing. To control the congestion in traffic signal intelligently by using density information in this paper we are presenting the algorithm with the help of which the congestion in traffic can easily retrieved. The image sequences from camera are analyzed using edge detection and counting methods. Subsequently the number of vehicles at intersection is evaluated due to which traffic can easily be managed moreover determined vehicle density can easily be compared with other direction of traffic in order to control traffic signals efficiently.[1]

For More Details about this paper visit :

Monday, August 24, 2015

Understanding SQL Injection Attack Techniques and Implementation of Various Methods for Attack Detection and Prevention




SQL Injection, Website Security, SQL Injection Detection, SQL Injection Prevention, Sanitization


Security issues of different database driven web applications are continue to be an important and crucial aspect of the ongoing development of the Internet. In the last several decades, Web applications have brought new classes of computer security vulnerabilities, such as AQL injection. SQL Injection Attacks (SQLIAs) is one of the most severe threats to the security of database driven web applications as it compromises integrity and confidentiality of information in database. In this type of attack, an attacker gain control over the database of an application and consequently, he/she may be able to alter data. In this paper we present different types of SQL injection attacks and also implementation of different types of tools which can be used to detect and prevent these attacks.

Click Here to see Full Length Paper :

Good Research Journal For Engineering