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Showing posts with label how to submit manuscript online. Show all posts
Showing posts with label how to submit manuscript online. Show all posts

Saturday, December 19, 2015

Conference On Mechanical Engineering & Technology | #COMET2016

IIT(BHU), Varanasi 
Mechanical Engineering Society
presents 
COMET 2016
(Conference On Mechanical Engineering & Technology)

in association with
IJSRD – International Journal for Scientific Research & Development


Conference On Mechanical Engineering and Technology (COMET) was commenced with the aim of promoting excellence in the field of mechanical engineering and to provide a platform for bringing out the best among the department’s students. What started as a simple initiative soon spiralled into a conclave. Needless to say it has experienced exponential growth in the sheer number of participants, both internal and external alike, augmented by the quality alongside. The events in its fold have enthused engineers from colleges nationwide thus increasing its domain of influence over the years. Attracting almost 1000 odd external participants in its previous edition, we are confident that these will be more than double positively for the upcoming edition.

COLLOQUIUM

Colloquium,the paper presentation contest, offers a platform to the participants to present & exchange their ideas, innovations and solutions to engineering problems in front of a very scholarly audience. The conference will bring together leading researchers, engineers and scientists in the domain of interest from around the world. COLLOQUIUM is the premier forum for the presentation of new advances and research results in the fields of theoretical and experimental mechanical engineering. Students (B.E/B.Tech./M.Tech./Ph.D.), researchers and qualified personnel interested in the field of mechanical engineering are invited to present papers.
We hope that conference results will lead to significant contributions to the knowledge in these up-to-date engineering and scientific fields. 

  1. Topics of interest for submission include, but are not limited to:
  2. Advanced Manufacturing System.
  3. Applications of Aerospace Technology.
  4. Automation and Robotics.
  5. Automobile engineering.
  6. Biomechanics and Bioinstrumentation.
  7. Concurrent Design of Vibration System.
  8. Fluid Mechanics.
  9. Human Values and Professional ethics.
  10. Industrial Management.
  11. Material Science.
  12. Nanotechnology.
  13. Non-Conventional Sources of Energy.
  14. Thermal Engineering



Organized by

Mechanical Engineering Society,
Mechanical Engineering Department, 
IIT(BHU)-Varanasi 



Publication Partner

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

Monday, September 21, 2015

#IJSRD FOUND USEFUL INFORMATION FOR DATA MINING (WWW.IJSRD.COM)

Nine Laws of Data Mining

Data mining is the creation of new knowledge in natural or artificial form, by using business knowledge to discover and interpret patterns in data.
In its current form, data mining as a field of practise came into existence in the 1990s, aided by the emergence of data mining algorithms packaged within workbenches so as to be suitable for business analysts.  Perhaps because of its origins in practice rather than in theory, relatively little attention has been paid to understanding the nature of the data mining process.  The development of the CRISP-DM(#ijsrd) methodology in the late 1990s was a substantial step towards a standardised description of the process that had already been found successful and was (and is) followed by most practising data miners.
 Although CRISP-DM(#ijsrd) describes how data mining is performed, it does not explain what data mining is or why the process has the properties that it does.  In this paper I propose nine maxims or “laws” of data mining (most of which are well-known to practitioners), together with explanations where known.  This provides the start of a theory to explain (and not merely describe) the data mining process.
 It is not my purpose to criticise CRISP-DM(#ijsrd); many of the concepts introduced by CRISP-DM(#ijsrd) are crucial to the understanding of data mining outlined here, and I also depend on CRISP-DM’s(#ijsrd) common terminology.  This is merely the next step in the process that started with CRISP-DM(#ijsrd).
1st Law of Data Mining – “Business Goals Law”:
Business objectives are the origin of every data mining solution
2nd Law of Data Mining – “Business Knowledge Law”:
Business knowledge is central to every step of the data mining process
3rd Law of Data Mining – “Data Preparation Law”:
Data preparation is more than half of every data mining process
4th Law of Data Mining – “NFL-DM”:
The right model for a given application can only be discovered by experiment or“There is No Free Lunch for the Data Miner”
5th Law of Data Mining – “Watkins’ Law”:
There are always patterns
6th Law of Data Mining – “Insight Law”:
Data mining amplifies perception in the business domain
7th Law of Data Mining – “Prediction Law”: 
Prediction increases information locally by generalization
8th Law of Data Mining – “Value Law”:
The value of data mining results is not determined by the accuracy or stability of predictive models
9th Law of Data Mining – “Law of Change”:
All patterns are subject to change
For more such useful information about data mining and other latest research topics please visit www.ijsrd.com

Saturday, September 5, 2015

CALL FOR PAPER : 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


 IJSRD

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).