24 research outputs found
Customers Behavior Modeling by Semi-Supervised Learning in Customer Relationship Management
Leveraging the power of increasing amounts of data to analyze customer base
for attracting and retaining the most valuable customers is a major problem
facing companies in this information age. Data mining technologies extract
hidden information and knowledge from large data stored in databases or data
warehouses, thereby supporting the corporate decision making process. CRM uses
data mining (one of the elements of CRM) techniques to interact with customers.
This study investigates the use of a technique, semi-supervised learning, for
the management and analysis of customer-related data warehouse and information.
The idea of semi-supervised learning is to learn not only from the labeled
training data, but to exploit also the structural information in additionally
available unlabeled data. The proposed semi-supervised method is a model by
means of a feed-forward neural network trained by a back propagation algorithm
(multi-layer perceptron) in order to predict the category of an unknown
customer (potential customers). In addition, this technique can be used with
Rapid Miner tools for both labeled and unlabeled data
A Machine Learning Based Analytical Framework for Semantic Annotation Requirements
The Semantic Web is an extension of the current web in which information is
given well-defined meaning. The perspective of Semantic Web is to promote the
quality and intelligence of the current web by changing its contents into
machine understandable form. Therefore, semantic level information is one of
the cornerstones of the Semantic Web. The process of adding semantic metadata
to web resources is called Semantic Annotation. There are many obstacles
against the Semantic Annotation, such as multilinguality, scalability, and
issues which are related to diversity and inconsistency in content of different
web pages. Due to the wide range of domains and the dynamic environments that
the Semantic Annotation systems must be performed on, the problem of automating
annotation process is one of the significant challenges in this domain. To
overcome this problem, different machine learning approaches such as supervised
learning, unsupervised learning and more recent ones like, semi-supervised
learning and active learning have been utilized. In this paper we present an
inclusive layered classification of Semantic Annotation challenges and discuss
the most important issues in this field. Also, we review and analyze machine
learning applications for solving semantic annotation problems. For this goal,
the article tries to closely study and categorize related researches for better
understanding and to reach a framework that can map machine learning techniques
into the Semantic Annotation challenges and requirements
XML and Semantics
Since the early days of introducing eXtensible Markup Language (XML), owing to its expressive capabilities and flexibilities, it became the defacto standard for representing, storing, and interchanging data on the Web. Such features have made XML one of the building blocks of the Semantic Web. From another viewpoint, since XML documents could be considered from content, structural, and semantic aspects, leveraging their semantics is very useful and applicable in different domains. However, XML does not by itself introduce any built-in mechanisms for governing semantics. For this reason, many studies have been conducted on the representation of semantics within/from XML documents. This paper studies and discusses different aspects of the mentioned topic in the form of an overview with an emphasis on the state of semantics in XML and its presentation methods
An Effective chaos-based image watermarking scheme using fractal coding
AbstractThe image watermarking technology is a technique of embedding hidden data in an original image. In this paper, a new watermarking method for embedding watermark bits based on Chaos-Fractal Coding is given. A chaotic signal is defined as being deterministic, pseudo periodic and presenting sensitivity to initial conditions. Combining a chaos system with Fractal Coding plays an important role in the security, invisibility and capacity of the proposed scheme. The main idea of the new proposed algorithm for coding is to determine a set of selective blocks for steady embedding. Simulation results show that the CFC algorithm (Chaos-Fractal Coding) has a confident capacity. The embedding technique that proposed in this paper is quite general, and can be applied to the extracting scheme with demanded changes