Online Crime File Management System

Introduction About The Project    

            The project titled as “Online Crimefile Management “is a web based application. This software provides facility for reporting online crimes, complaints, missing persons, show mostwanted person details mailing as well as chatting. Any Number of clients can connect to the server. Each user first makes their login to sever to show their availability. The server can be any Web Server. An SMTP Server must be maintained for temporary storage of emails and chat jar files for enable the chatting facilities.

Drawbacks Of Existing System

1) More man power.

2) Time consuming.

3) Consumes large volume of pare work.

4) Needs manual calculations.

5) No direct role for the higher officials.

6) Damage of machines due to lack of attention.

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Java Project - Discovery of Name and Aliases from the Web

Abstract :- :- Automatic Discovery of Association Orders between Name and Aliases from the Web :- :- 

Many celebrities and experts from various fields may have been referred by not only their personal names but also by their aliases on web. Aliases are very important in information retrieval to retrieve complete information about a personal name from the web, as some of the web pages of the person may also be referred by his aliases. The aliases for a personal name are extracted by previously proposed alias extraction method. In information retrieval, the web search engine automatically expands the search query on a person name by tagging his aliases for complete information retrieval thereby improving recall in relation detection task and achieving a significant mean reciprocal rank (MRR) of search engine. 

For the further substantial improvement on recall and MRR from the previously proposed methods, our proposed method will order the aliases based on their associations with the name using the definition of anchor texts-based co-occurrences between name and aliases in order to help the search engine tag the aliases according to the order of associations. The association orders will automatically be discovered by creating an anchor texts-based co-occurrence graph between name and aliases.

Ranking support vector machine (SVM) will be used to create connections between name and aliases in the graph by performing ranking on anchor texts-based co-occurrence measures. The hop distances between nodes in the graph will lead to have the associations between name and aliases. The hop distances will be found by mining the graph. The proposed method will outperform previously proposed methods, achieving substantial growth on recall and MRR.

Existing System

The existing namesake disambiguation algorithm assumes the real name of a person to be given and does not attempt to disambiguate people who are referred only by aliases.

Disadvantage

1)     To low MRR and AP scores on all data sets.

2)     To complex hub discounting measure.

Proposed System

The proposed method will work on the aliases and get the association orders between name and aliases to help search engine tag those aliases according to the orders such as first order associations, second order associations etc so as to substantially increase the recall and MRR of the search engine while searching made on person names. The term recall is defined as the percentage of relevant documents that were in fact retrieved for a search query on search engine. 

The mean reciprocal rank of the search engine for a given sample of queries is that the average of the reciprocal ranks for each query. The term word co-occurrence refers to the temporal property of the two words occurring at the same web page or same document on the web. The anchor text is the clickable text on web pages, which points to a particular web document. Moreover the anchor texts are used by search engine algorithms to provide relevant documents for search results because they point to the web pages that are relevant to the user queries. 

So the anchor texts will be helpful to find the strength of association between two words on the web. The anchor texts-based co-occurrence means that the two anchor texts from the different web pages point to the same the URL on the web. The anchor texts which point to the same URL are called as inbound anchor texts. 

The proposed method will find the anchor texts-based co-occurrences between name and aliases using co-occurrence statistics and will rank the name and aliases by support vector machine according to the co-occurrence measures in order to get connections among name and aliases for drawing the word co-occurrence graph.

Then a word co-occurrence graph will be created and mined by graph mining algorithm so as to get the hop distance between name and aliases that will lead to the association orders of aliases with the name. The search engine can now expand the search query on a name by tagging the aliases according to their association orders to retrieve all relevant pages which in turn will increase the recall and achieve a substantial MRR.

Modules Description

1. Co-occurrences in Anchor Texts

The proposed method will first retrieve all corresponding URLs from search engine for all anchor texts in which name and aliases appear. Most of the search engines provide search operators to search in anchor texts on the web. For example, Google provides In anchor or Allinanchor search operator to retrieve URLs that are pointed by the anchor text given as a query. For example, query on “Allinanchor:Hideki Matsui” to the Google will provide all URLs pointed by Hideki Matsui anchor text on the web.

2. Role of Anchor Texts

The main objective of search engine is to provide the most relevant documents for a user’s query. Anchor texts play a vital role in search engine algorithm because it is clickable text which points to a particular relevant page on the web. Hence search engine considers anchor text as a main factor to retrieve relevant documents to the user’s query. Anchor texts are used in synonym extraction, ranking and classification of web pages and query translation in cross language information retrieval system.

3. Anchor Texts Co-Occurrence Frequency

The two anchor texts appearing in different web pages are called as inbound anchor texts if they point to the same URL. Anchor texts co-occurrence frequency  between anchor texts refers to the number of different URLs on which they co-occur. For example, if p and x that are two anchor texts are co-occurring, then p and x point to the same URL. If the co-occurrence frequency between p and x is that say an example k, and then p and x co-occur in k number of different URLs. For example, the picture of Arnold Schwarzenegger is shown in Fig 2 which is being liked by four different anchor texts. 

5. Ranking Anchor Texts

Ranking SVM will be used for ranking the aliases. The ranking SVM will be trained by training samples of name and aliases. All the co-occurrence measures for the anchor texts of the training samples will be found and will be normalized into the range of [0-1]. The normalized values termed as feature vectors will be used to train the SVM to get the ranking function to test the given anchor texts of name and aliases. Then for each anchor text, the trained SVM using the ranking function will rank the other anchor texts with respect to their co-occurrence measures with it. The highest ranking anchor text will be elected to make a first–order association with its corresponding anchor text for which ranking was performed. Next the word co-occurrence graph will be drawn for name and aliases according to the first order associations between them.

6. Discovery Of Association Orders

Using the graph mining algorithm, the word co-occurrence graph will be mined to find the hop distances between nodes in graph. The hop distances between two nodes will be measured by counting the number of edges in-between the corresponding two nodes. The number of edges will yield the association orders between two nodes. According to the definition, a node that lies n hops away from p has an n-order co-occurrence with p. Hence the first, second and higher order associations between name and aliases will be identified by finding the hop distances between them. The search engine can now expand the query on person names by tagging the aliases according to the association orders with the name. Thereby the recall will be substantially improved by 40% in relation detection task. Moreover the search engine will get a substantial MRR for a sample of queries by giving relevant search results.

For More Details About This Project Please Contact

Logic Systems

Hyderabad - 500038

Email id: logicsystems24x7@gmail.com

Website: - www.logicsystems.org.in

System Configuration

Hardware Configuration

   1.  Processor :-  Pentium –III

   2.  Speed :- 1.1 GHz

   3.  RAM :- 256 MB (min)

   4.  Hard Disk :- 20 GB

   5.  Floppy Drive :- 1.44 MB

   6.  Key Board :- Standard Windows Keyboard

   7.  Mouse :- Two or Three Button Mouse

   8.  Monitor :- SVGA

Software Configuration

   1.  Operating System :- Windows95/98/2000/XP

   2.  Application  Server  :- Tomcat5.0/6.X 

   3.  Front End        :- HTML, Java, Jsp

   4.  Scripts :- JavaScript.

   5.  Server side Script :- Java Server Pages.

   6.  Database :- Mysql

   7.  Database Connectivity :- JDBC.

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Online Modeling IEEE Java Project

Abstract: - :- Online Modeling: - :-   

                    

We consider the problem of building online machine-learned models for detecting auction frauds in e-commerce web sites. Since the emergence of the World Wide Web, online shopping and online auction have gained more and more popularity. While people are enjoying the benefits from online trading, criminals are also taking advantages to conduct fraudulent activities against honest parties to obtain illegal profit. 

Hence proactive fraud-detection moderation systems are commonly applied in practice to detect and prevent such illegal and fraud activities. Machine-learned models, especially those that are learned online, are able to catch frauds more efficiently and quickly than human-tuned rule-based systems. In this paper, we propose an online probity model framework which takes online feature selection, coefficient bounds from human knowledge and multiple instances learning into account simultaneously. 

By empirical experiments on a real-world online auction fraud detection data we show that this model can potentially detect more frauds and significantly reduce customer complaints compared to several baseline models and the human-tuned rule-based system.

 Existing System

The traditional online shopping business model allows sellers to sell a product or service at a preset price, where buyers can choose to purchase if they find it to be a good deal. Online auction however is a different business model by which items are sold through price bidding. There is often a starting price and   expiration time specified by the sellers. Once the auction starts, potential buyers bid against each other, and the winner gets the item with their highest winning bid.

Proposed System

We propose an online probity model framework which takes online feature selection, coefficient bounds from human knowledge and multiple instances learning into account simultaneously. By empirical experiments on a real-world online auction fraud detection data we show that this model can potentially detect more frauds and significantly reduce customer complaints compared to several baseline models and the human-tuned rule-based system. 

Human experts with years of experience created many rules to detect whether a user is fraud or not. If the fraud score is above a certain threshold, the case will enter a queue for further investigation by human experts. Once it is reviewed, the final result will be labeled as Boolean, i.e. fraud or clean. Cases with higher scores have higher priorities in the queue to be reviewed. The cases whose fraud score are below the threshold are determined as clean by the system without any human judgment.

Module Description

Rule-Based Features

Human experts with years of experience created many rules to detect whether a user is fraud or not. An example of such rules is “blacklist”, i.e. whether the user has been detected or complained as fraud before. Each rule can be regarded as a binary feature that indicates the fraud likeliness. 

Selective Labeling

If the fraud score is above a certain threshold, the case will enter a queue for further investigation by human experts. Once it is reviewed, the final result will be labeled as Boolean, i.e. fraud or clean. Cases with higher scores have higher priorities in the queue to be reviewed. The cases whose fraud Score are below the threshold are determined as clean by the system without any human judgment.

Fraud Churn

Once one case is labeled as fraud by human experts, it is very likely that the seller is not trustable and may be also selling other frauds; hence all the items submitted by the same seller are labeled as fraud too. The   fraudulent seller along with his/her cases will be removed from the website immediately once detected.

User Complaint

Buyers can file complaints to claim loss if they are recently deceived by fraudulent sellers. The Administrator views the various type of complaints and the percentage of various type complaints. The complaints values of a products increase some threshold value the administrator set the trust ability of the product as Untrusted or banded. If the products set as banaded, the user cannot view the products in the website.

System Configuration

Hardware 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

·        Monitor          -    SVGA

 Software 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    :   Mysql

·        Database Connectivity  :   JDBC.

Conclusion

In this paper we build online models for the auction fraud moderation and detection system designed for a major Asian online auction website. By empirical experiments on a real world online auction fraud detection data, we show that our proposed online probity model framework, which combines online feature selection, bounding coefficients from expert knowledge and multiple instance learning, can significantly improve over baselines and the human-tuned model. 

Note that this online modeling framework can be easily extended to many other applications, such as web spam detection, content optimization and so forth. Regarding to future work, one direction is to include the adjustment of the selection bias in the online model training process. It has been proven to be very effective for offline models in [38]. The main idea there is to assume all the unlabeled samples have response equal to 0 with a very small weight. Since the unlabeled samples are obtained from an effective moderation system, it is reasonable to assume that with high probabilities they are non-fraud. Another future work is to deploy the online models described in this paper to the real production system, and also other applications.

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