To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
IEEE 2014 DOTNET DATA MINING PROJECTS A probabilistic approach to string tran...IEEEMEMTECHSTUDENTPROJECTS
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Indexing for Large DNA Database sequencesCSCJournals
Bioinformatics data consists of a huge amount of information due to the large number of sequences, the very high sequences lengths and the daily new additions. This data need to be efficiently accessed for many needs. What makes one DNA data item distinct from another is its DNA sequence. DNA sequence consists of a combination of four characters which are A, C, G, T and have different lengths. Use a suitable representation of DNA sequences, and a suitable index structure to hold this representation at main memory will lead to have efficient processing by accessing the DNA sequences through indexing, and will reduce number of disk I/O accesses. I/O operations needed at the end, to avoid false hits, we reduce the number of candidate DNA sequences that need to be checked by pruning, so no need to search the whole database. We need to have a suitable index for searching DNA sequences efficiently, with suitable index size and searching time. The suitable selection of relation fields, where index is build upon has a big effect on index size and search time. Our experiments use the n-gram wavelet transformation upon one field and multi-fields index structure under the relational DBMS environment. Results show the need to consider index size and search time while using indexing carefully. Increasing window size decreases the amount of I/O reference. The use of a single field and multiple fields indexing is highly affected by window size value. Increasing window size value lead to better searching time with special type index using single filed indexing. While the search time is almost good and the same with most index types when using multiple field indexing. Storage space needed for RDMS indexing types are almost the same or greater than the actual data.
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IEEE 2014 DOTNET DATA MINING PROJECTS A probabilistic approach to string tran...IEEEMEMTECHSTUDENTPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Indexing for Large DNA Database sequencesCSCJournals
Bioinformatics data consists of a huge amount of information due to the large number of sequences, the very high sequences lengths and the daily new additions. This data need to be efficiently accessed for many needs. What makes one DNA data item distinct from another is its DNA sequence. DNA sequence consists of a combination of four characters which are A, C, G, T and have different lengths. Use a suitable representation of DNA sequences, and a suitable index structure to hold this representation at main memory will lead to have efficient processing by accessing the DNA sequences through indexing, and will reduce number of disk I/O accesses. I/O operations needed at the end, to avoid false hits, we reduce the number of candidate DNA sequences that need to be checked by pruning, so no need to search the whole database. We need to have a suitable index for searching DNA sequences efficiently, with suitable index size and searching time. The suitable selection of relation fields, where index is build upon has a big effect on index size and search time. Our experiments use the n-gram wavelet transformation upon one field and multi-fields index structure under the relational DBMS environment. Results show the need to consider index size and search time while using indexing carefully. Increasing window size decreases the amount of I/O reference. The use of a single field and multiple fields indexing is highly affected by window size value. Increasing window size value lead to better searching time with special type index using single filed indexing. While the search time is almost good and the same with most index types when using multiple field indexing. Storage space needed for RDMS indexing types are almost the same or greater than the actual data.
Named Entity Recognition For Hindi-English code-mixed Twitter Text Amogh Kawle
Speakers often switch back and forth between languages when speaking or writing, mostly in informal settings. This language interchanging involves complex grammar and the terms “code switching” or “code mixing” are used to describe It .
USING ONTOLOGIES TO IMPROVE DOCUMENT CLASSIFICATION WITH TRANSDUCTIVE SUPPORT...IJDKP
Many applications of automatic document classification require learning accurately with little training
data. The semi-supervised classification technique uses labeled and unlabeled data for training. This
technique has shown to be effective in some cases; however, the use of unlabeled data is not always
beneficial.
On the other hand, the emergence of web technologies has originated the collaborative development of
ontologies. In this paper, we propose the use of ontologies in order to improve the accuracy and efficiency
of the semi-supervised document classification.
We used support vector machines, which is one of the most effective algorithms that have been studied for
text. Our algorithm enhances the performance of transductive support vector machines through the use of
ontologies. We report experimental results applying our algorithm to three different datasets. Our
experiments show an increment of accuracy of 4% on average and up to 20%, in comparison with the
traditional semi-supervised model.
Evaluation of models for predicting user’s next request in web usage miningIJCI JOURNAL
Prediction of web user behavior is the demand of today competitive edge of World Wide Web. Predicting the
next web page is not sufficient, evaluation of prediction models is important because every model have its own pros and cons. Prediction results will be helpful if high prediction accuracy is achieved with minimum complexity, which are depended on the prediction model. Various models and their variations are proposed for predicting the next web page accessed by the web user. Markov model and their variations are found suitable for web prediction. In this research we have evaluated and compared various models for predicting next web page accessed by the web user. Experiments are conducted on three different real datasets.
With the rapid development in Geographic Information Systems (GISs) and their applications, more and
more geo-graphical databases have been developed by different vendors. However, data integration and
accessing is still a big problem for the development of GIS applications as no interoperability exists among
different spatial databases. In this paper we propose a unified approach for spatial data query. The paper
describes a framework for integrating information from repositories containing different vector data sets
formats and repositories containing raster datasets. The presented approach converts different vector data
formats into a single unified format (File Geo-Database “GDB”). In addition, we employ “metadata” to
support a wide range of users’ queries to retrieve relevant geographic information from heterogeneous and
distributed repositories. Such an employment enhances both query processing and performance.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Using Randomized Response Techniques for Privacy-Preserving Data Mining14894
Privacy is an important issue in data mining and knowledge
discovery. In this paper, we propose to use the randomized
response techniques to conduct the data mining computation.
Specially, we present a method to build decision tree
classifiers from the disguised data. We conduct experiments
to compare the accuracy ofou r decision tree with the one
built from the original undisguised data. Our results show
that although the data are disguised, our method can still
achieve fairly high accuracy. We also show how the parameter
used in the randomized response techniques affects the
accuracy ofth e results
Keywords
Privacy, security, decision tree, data mining
Privacy preserving naive bayes classifier for horizontally partitioned data u...IJNSA Journal
In order to extract interesting patterns, data available at multiple sites has to be trained.The data available
in these sites should not be revealed while extorting patterns.Distributed Data mining enables sites to mine
patterns based on the knowledge available at different sites. In the process of sites collaborating to develop
a model, it is extremely important to protect the privacy of data or intermediate results. The features of the
data maintained at each site are often similar in nature. In this paper, we design an improved privacy- preserving distributed naive Bayesian classifier to train the horizontal data. This trained model is propagated to sitesinvolved in computation to assist classify a new tuple. We further analyze the security and complexity of the algorithm.
Performance Analysis of Hybrid Approach for Privacy Preserving in Data Miningidescitation
Now-a day’s data sharing between two organizations
is common in many application areas like business planning
or marketing. When data are to be shared between parties,
there could be some sensitive data which should not be
disclosed to the other parties. Also medical records are more
sensitive so, privacy protection is taken more seriously. As
required by the Health Insurance Portability and
Accountability Act (HIPAA), it is necessary to protect the
privacy of patients and ensure the security of the medical
data. To address this problem, released datasets must be
modified unavoidably. We propose a method called Hybrid
approach for privacy preserving and implemented it. First we
randomized the original data. Then we have applied
generalization on randomized or modified data. This
technique protect private data with better accuracy, also it can
reconstruct original data and provide data with no information
loss, makes usability of data.
Named Entity Recognition For Hindi-English code-mixed Twitter Text Amogh Kawle
Speakers often switch back and forth between languages when speaking or writing, mostly in informal settings. This language interchanging involves complex grammar and the terms “code switching” or “code mixing” are used to describe It .
USING ONTOLOGIES TO IMPROVE DOCUMENT CLASSIFICATION WITH TRANSDUCTIVE SUPPORT...IJDKP
Many applications of automatic document classification require learning accurately with little training
data. The semi-supervised classification technique uses labeled and unlabeled data for training. This
technique has shown to be effective in some cases; however, the use of unlabeled data is not always
beneficial.
On the other hand, the emergence of web technologies has originated the collaborative development of
ontologies. In this paper, we propose the use of ontologies in order to improve the accuracy and efficiency
of the semi-supervised document classification.
We used support vector machines, which is one of the most effective algorithms that have been studied for
text. Our algorithm enhances the performance of transductive support vector machines through the use of
ontologies. We report experimental results applying our algorithm to three different datasets. Our
experiments show an increment of accuracy of 4% on average and up to 20%, in comparison with the
traditional semi-supervised model.
Evaluation of models for predicting user’s next request in web usage miningIJCI JOURNAL
Prediction of web user behavior is the demand of today competitive edge of World Wide Web. Predicting the
next web page is not sufficient, evaluation of prediction models is important because every model have its own pros and cons. Prediction results will be helpful if high prediction accuracy is achieved with minimum complexity, which are depended on the prediction model. Various models and their variations are proposed for predicting the next web page accessed by the web user. Markov model and their variations are found suitable for web prediction. In this research we have evaluated and compared various models for predicting next web page accessed by the web user. Experiments are conducted on three different real datasets.
With the rapid development in Geographic Information Systems (GISs) and their applications, more and
more geo-graphical databases have been developed by different vendors. However, data integration and
accessing is still a big problem for the development of GIS applications as no interoperability exists among
different spatial databases. In this paper we propose a unified approach for spatial data query. The paper
describes a framework for integrating information from repositories containing different vector data sets
formats and repositories containing raster datasets. The presented approach converts different vector data
formats into a single unified format (File Geo-Database “GDB”). In addition, we employ “metadata” to
support a wide range of users’ queries to retrieve relevant geographic information from heterogeneous and
distributed repositories. Such an employment enhances both query processing and performance.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Using Randomized Response Techniques for Privacy-Preserving Data Mining14894
Privacy is an important issue in data mining and knowledge
discovery. In this paper, we propose to use the randomized
response techniques to conduct the data mining computation.
Specially, we present a method to build decision tree
classifiers from the disguised data. We conduct experiments
to compare the accuracy ofou r decision tree with the one
built from the original undisguised data. Our results show
that although the data are disguised, our method can still
achieve fairly high accuracy. We also show how the parameter
used in the randomized response techniques affects the
accuracy ofth e results
Keywords
Privacy, security, decision tree, data mining
Privacy preserving naive bayes classifier for horizontally partitioned data u...IJNSA Journal
In order to extract interesting patterns, data available at multiple sites has to be trained.The data available
in these sites should not be revealed while extorting patterns.Distributed Data mining enables sites to mine
patterns based on the knowledge available at different sites. In the process of sites collaborating to develop
a model, it is extremely important to protect the privacy of data or intermediate results. The features of the
data maintained at each site are often similar in nature. In this paper, we design an improved privacy- preserving distributed naive Bayesian classifier to train the horizontal data. This trained model is propagated to sitesinvolved in computation to assist classify a new tuple. We further analyze the security and complexity of the algorithm.
Performance Analysis of Hybrid Approach for Privacy Preserving in Data Miningidescitation
Now-a day’s data sharing between two organizations
is common in many application areas like business planning
or marketing. When data are to be shared between parties,
there could be some sensitive data which should not be
disclosed to the other parties. Also medical records are more
sensitive so, privacy protection is taken more seriously. As
required by the Health Insurance Portability and
Accountability Act (HIPAA), it is necessary to protect the
privacy of patients and ensure the security of the medical
data. To address this problem, released datasets must be
modified unavoidably. We propose a method called Hybrid
approach for privacy preserving and implemented it. First we
randomized the original data. Then we have applied
generalization on randomized or modified data. This
technique protect private data with better accuracy, also it can
reconstruct original data and provide data with no information
loss, makes usability of data.
A Review Study on the Privacy Preserving Data Mining Techniques and Approaches14894
In this paper we review on the
various privacy preserving data mining techniques like data
modification and secure multiparty computation based on the
different aspects.
Index Terms– Privacy and Security, Data Mining, Privacy
Preserving, Secure Multiparty Computation (SMC) and Data
Modification
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
JAVA 2013 IEEE DATAMINING PROJECT A probabilistic approach to string transfor...IEEEGLOBALSOFTTECHNOLOGIES
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Multi-modal sources for predictive modeling using deep learningSanghamitra Deb
Using Vision Language models : Is it possible to prompt them similar to LLMs? when to use out of the box and when to pre-train? General multi-modal models --- deeplearning. Machine learning metrics, feature engineering and setting up an ML problem.
The previous research has focused on quick and efficient generation of wrappers; the
development of tools for wrapper maintenance has received less attention. This is an important research
problem because Web sources often change in ways that prevent the wrappers from extracting data
correctly. Present an efficient algorithm that extract unstructured data to structural data from web. The
wrapper verification system detects when a wrapper is not extracting correct data, usually because the
Web source has changed its format. The Verification framework automatically recovers data using
Dimension Reduction Techniques from changes in the Web source by identifying data on Web pages.
After apply wrapped data to One Class Classification in Numerical features for avoid classification
problem. Finally, the result data apply in Top-K query for provide best rank based on probabilities
scores. Wrapper verification system relies on one-class classification techniques to beat previous
weaknesses to identify the problem by analysing both the signature and the classifier output. If there are
sufficient mislabelled slots, a technique to find a pattern could be explored.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
TOP 10 B TECH COLLEGES IN JAIPUR 2024.pptxnikitacareer3
Looking for the best engineering colleges in Jaipur for 2024?
Check out our list of the top 10 B.Tech colleges to help you make the right choice for your future career!
1) MNIT
2) MANIPAL UNIV
3) LNMIIT
4) NIMS UNIV
5) JECRC
6) VIVEKANANDA GLOBAL UNIV
7) BIT JAIPUR
8) APEX UNIV
9) AMITY UNIV.
10) JNU
TO KNOW MORE ABOUT COLLEGES, FEES AND PLACEMENT, WATCH THE FULL VIDEO GIVEN BELOW ON "TOP 10 B TECH COLLEGES IN JAIPUR"
https://www.youtube.com/watch?v=vSNje0MBh7g
VISIT CAREER MANTRA PORTAL TO KNOW MORE ABOUT COLLEGES/UNIVERSITITES in Jaipur:
https://careermantra.net/colleges/3378/Jaipur/b-tech
Get all the information you need to plan your next steps in your medical career with Career Mantra!
https://careermantra.net/
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
2014 IEEE JAVA DATA MINING PROJECT A probabilistic approach to string transformation
1. GLOBALSOFT TECHNOLOGIES
A Probabilistic Approach to String Transformation
Abstract:
Many problems in natural language processing, data mining, information retrieval, and
bioinformatics can be formalized as string transformation, which is a task as follows. Given an
input string, the system generates the k most likely output strings corresponding to the input
string. This paper proposes a novel and probabilistic approach to string transformation, which
is both accurate and efficient. The approach includes the use of a log linear model, a method
for training the model, and an algorithm for generating the top k candidates, whether there is or
is not a predefined dictionary. The log linear model is defined as a conditional probability
distribution of an output string and a rule set for the transformation conditioned on an input
string. The learning method employs maximum likelihood estimation for parameter estimation.
The string generation algorithm based on pruning is guaranteed to generate the optimal top k
candidates. The proposed method is applied to correction of spelling errors in queries as well
as reformulation of queries in web search. Experimental results on large scale data show that
the proposed approach is very accurate And efficient improving upon existing methods in
terms of accuracy and efficiency in different settings.
Architecture:
IEEE PROJECTS & SOFTWARE DEVELOPMENTS
IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE
BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS
CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401
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2. EXISTING SYSTEM:
Previous work on string transformation can be categorized into two groups. Some work
mainly considered efficient generation of strings. Other work tried to learn the model with
different approaches. However, efficiency is not an important factor taken into consideration
in these methods.The existing work is not focus on enhancement of both accuracy and
efficiency of string transformation.
PROPOSED SYSTEM:
String transformation has many applications in data mining, natural language processing,
information retrieval, and bioinformatics. String transformation has been studied in different
specific tasks such as database record matching, spell ing error correction, query reformulation
and synonym mining. The major difference between our work and the existing work is that we
focus on enhancement of both accuracy and efficiency of string transformation.
Modules :
1. Registration
2. Login
3. Spelling Error Correction
4. String Transformation
5. String mining
3. Modules Description
Registration:
In this module an Author(Owner) or User have to register first,then
only he/she has to access the data base.
Login:
In this module,any of the above mentioned person have to login,they
should login by giving their emailid and password .
Spelling Error Correction:
In this module if an user wants to check the spelling, He/She can check
it and correct it automatically.
String Transformation:
Here we are techniques for searching the String 1)String
Generation,2)String Transformation.
String Generation:
It means we have generated 50,000 Strings in alphabetical order.From a to z
like a,aa,…..z.
String Transformation:
4. It means we have given the user with the benefit of String Generation as well as
String alias .It will be useful for the user for example if the end user have typed “TKDE” its
equal to “Transactions
on Knowledge and Data Engineering”.
String mining:
The User has to download the string with its meanings also He/She can
download its substrings and its reverse etc.Also check the given string which is present in the
bunch of strings,if its present the result will be “String Found” otherwise ”String NotFound”.
System Configuration:-
H/W System Configuration:-
Processor - Pentium –III
Speed - 1.1 GHz
RAM - 256 MB (min)
Hard Disk - 20 GB
Floppy Drive - 1.44 MB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
5. Monitor - SVGA
S/W System Configuration:-
Operating System :Windows95/98/2000/XP
Application Server : Tomcat5.0/6.X
Front End : HTML, Java, Jsp
Scripts : JavaScript.
Server side Script : Java Server Pages.
Database : My sql
Database Connectivity : JDBC.
Conclusion:
In this paper, we have proposed a new statistical learning Approach to string transformation.
Our method is novel and unique in its model, learning algorithm, and string generation
algorithm. Two specific applications are addressed with our method, namely spelling error
correction of queries and query reformulation in web Search. Experimental results on two large
data sets and Microsoft Speller Challenge show that our method improves upon the baselines
in terms of accuracy and efficiency. Our method is particularly useful when the-problem
occurs on a large scale.