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index.html
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<html>
<head>
<style>
ul {
list-style-type: none;
margin: 0;
padding: 0;
overflow: hidden;
background-color: rgb(53, 198, 34);
}
li {
float: left;
border-right:1px solid #bbb;
}
li:last-child {
border-right: none;
}
li a {
display: block;
color: white;
text-align: center;
padding: 14px 16px;
text-decoration: none;
}
li a:hover:not(.active) {
background-color:rgb(0, 0, 0);
}
.active {
background-color: #dfa017;
}
</style>
</head>
<link rel="stylesheet" href="index.css">
<body>
<div style="border-style: solid; border-width: thin; border-color: black; background-color:darkblue; height: 10px;"></div>
<div style="border-style: solid; border-width: thin; border-color: black; background-color:royalblue; height: 80px;">
<section>
<div class="box" style="border-style: solid; border-width: thin; border-color:white; border-width: 3px; height: 550px; width: 900px; " >
<div class="navbox" style="border-style: solid; border-width: thin; border-color: black; background-color:royalblue; height: 60px; " >
<font color="white"><h1 align="center" style="padding: 15px;" >Detection of Fraudulent Sellers in Online Marketplaces</h1></font>
</div>
<ul>
<li><a class="active" href="#home">Home</a></li>
</ul>
<div style=" background-color:rgb(234, 234, 161); height: 380px; " >
<table width="100%" height="100%" >
<tr>
<td width="75%" >
<div style=" padding: 25px;" >
<font color="red"><h3>Detection of Fraudulent Sellers:</h3></font>
<h4>We consider the problem of building online machine -learned models
for detecting auction<br>frauds in e-commerce web sites. Since the emergence
of the world Wide Web, online shop<br>ping and online auction have gained more and
more popularity. While people are enoying<br>the benifits from online trading,criminals
are also taking advantages to condct fradulent 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 profit model framework which takes online feature selection,
coefficient bounds from human knowledge and multiple instance 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 signifcantly reduce
customer complaints compared to several baseline models and the human-tuned
rule-based system.
</h4>
</div>
</td>
<sectionlg>
<td width="20%" style="background-color: rgb(244, 235, 159); " >
<font color="red"><h2 align="center" >Registration</h2></font>
<font color="blue"><h3 align="center"><a href="http://localhost/projectwork/sellersignup.php">Seller</a></h3>
<h3 align="center" ><a href="http://localhost/projectwork/usersignup.php">User</a></h3></font>
<font color="red"><h2 align="center" >Login</h2></font>
<font color="blue"><h3 align="center" ><a href="http://localhost/projectwork/sellerlogin.php">Seller</a></h3>
<h3 align="center" ><a href="http://localhost/projectwork/userlogin.php">User</a></h3>
<h3 align="center" ><a href="http://localhost/projectwork/adminlogin.php">Admin</a></h3></font>
</td></sectionlg>
</tr>
</table>
</div>
</div>
</section>
</div>
</body>
</html>