Wednesday 23 September 2015

NCIL – 2015 #IJSRD Publication Partner

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

National Conference on "Student-driven Research for Inspired Learning" in Science and Technology-NCIL-2015:IJSRD

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://mac.du.ac.in/

NCIL - 2015

Objective

We are pleased to announce the 2nd National Conference on “Student-driven Research for Inspired Learning” (NCIL 2015) in Science and Technology on 16 - 17 October 2015 being organized by Embedded Systems and Robotics Centre (ESRC) and Department of Electronics, Maharaja Agrasen College, University of Delhi.
The primary objective of this conference is to provide a forum to share the wide and varied practices and initiatives of the student driven and institutionally-supported research at the undergraduate/ postgraduate level which leads to the combination of factors necessary for pedagogical effectiveness, enhanced learning outcomes, research productivity, promote networking and stimulate discussion.
The spot light of the conference shall vary widely from broad research to technical skills with focus on group research where students are exposed to open-ended problems, but in a more structured and less resource intensive way than one-on-one mentoring typical of research experience for undergraduate/ postgraduate programs.

Target Audience

  • Teachers / Mentors / Educators
  • Under-graduate and Post-graduate Students

from the field of Bio Technology, Chemistry, Computer Science, Electronics, Embedded Systems, Information Technology, Instrumentation, Life Sciences, Mathematics, Nanotechnology,Physics, Robotics, any other related fields.

Call for Papers

We invite Educators, Scholars and Students to contribute to the conference with papers/posters that address themes mentioned above. Faculty members / Students interested to attend the conference may register by filling registration form attached below latest by 10th September 2015. Early submissions are welcome. The papers received will be reviewed by a panel of experts and the authors of the selected papers will be informed accordingly.
All papers presented in the conference shall be published in Special edition of International Journal  for Scientific Research & Development (ISSN No (online). 2321-0613. Impact Factor: 2.39)

Organised by

Maharaja Agrasen CollegeEmbedded Systems and Robotics Center, and
Department of Electronics
Maharaja Agrasen College
University of Delhi
Vasundhara Enclave
Delhi - 110096


Publication Partner

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

Tuesday 22 September 2015

Ijsrd Call For Paper Data Mining

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

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Best 25 papers will be published online. Participate in this special issue and get a chance to win the Best Paper Award for Data Mining. Also other authors will have special prizes to be won.


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).