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Background Dengue fever is a re-emerging viral disease commonly occurring in tropical and subtropical areas. The clinical features and abnormal laboratory test results of dengue infection are similar to those of other febrile illnesses; hence, its accurate and timely diagnosis for providing appropriate treatment is difficult. Delayed diagnosis may be associated with inappropriate treatment and higher...
Session Initiation Protocol (SIP), which is an IP based telephony protocol, is used mainly for the purpose of starting, sustaining and ending sessions related to multimedia communications on the Internet. The SIP protocol, which works on the top of TCP or UDP, is basically an open text-based protocol. Hence, to ensure security is of utmost importance. The original SIP used HTTP-digest based challenge-response...
The telecommunication social network analysis (TSNA) recognizes the relationships among customers in multi-relational and heterogeneous network represent as graph, where vertices are the customers of the telecom network and edges representing some relationships. The major task in TSNA is detection of the communities, which is clustering the telecom customers into subgroups. The structure of the subgroup...
Background: Automated disease classification using machine learning often relies on features derived from segmenting individual objects, which can be difficult to automate. Proposed model is a classification based an efficient approach in which machine learning concepts are used for the detection of Lung cancer diseases. The algorithm obtained encouraging results but requires considerable computational...
Dermatological diseases are the most prevalent diseases worldwide. Despite being common, its diagnosis is extremely difficult and requires extensive experience in the domain. In this research paper, we provide an approach to detect various kinds of these diseases. We use a dual stage approach which effectively combines Computer Vision and Machine Learning on clinically evaluated histopathological...
Detection of hotspots (also known as dense subgraphs) in network data is an important data analysis problem due to it's significance in many contemporary applications. Clique-based formulation of this problem employing maximum flow implementation turns out to be an optimization task limiting the solution to be an approximate one. On the other hand, an iterative method building the hotspots (dense...
The explosive growth of social networks has led to prolific availability in customer tastes and preferences. This data can be exploited to serve the customers better and offer them the advertisements they would be delighted to see. To provide relevant advertisements to consumers, one has to consider the location of the consumer as well. The consumers will be highly contented if the offers shown to...
Summary The chapter deals with use of different soft computing tools to achieve web intelligence. It summarizes different characteristics of web data, the basic components of web mining and its different types, and their current states of the art. The reason for considering web mining, a separate field from data mining, is explained. The limitations of some of the existing web mining methods and tools...
We describe our project that marries data mining together with Grid computing. Specifically, we focus on one data mining application - the Minnesota Intrusion Detection System (MINDS), which uses a suite of data mining based algorithms to address different aspects of cyber security including malicious activities such as denial-of-service (DoS) traffic, worms, policy violations and inside abuse. MINDS...
Extracting the customers who share similar interests that are connected via set of relationships in Telecom social network is a challenging scenario. This paper addresses an efficient method of building multi-relational and heterogeneous social network for telecom customers and identifying social structures present in the telecommunication network. The telecom social network is constructed by considering...
Visual data analysis has been emerging as an attractive technique in addressing the challenges of the current computing research. It relies upon the human ability in grasping the intrinsic characteristics of the data through visual exploration. This human visual perception combined with the domain specific data semantics can lead to effective extraction of knowledge that is of immense use in real...
Clustering is an exploratory data analysis technique, which categorizes the dataset into some groups. These groups are formed in a way so that items which have similar features live in same group and those have dissimilar features remain in other. There are many clustering algorithm available. Different kinds of algorithms are best used for different kinds of data. K-means is most used clustering...
Augmented by rule based reasoning and data mining method, case-based reasoning (CBR) has emerged as a major research area because CBR is not only a psychological theory for human knowledge, but also will be a new comer stone of the intelligent computer system technology. This paper focuses on the coalition between the research areas of Data Mining, CBR System and decision support systems (DSSs). A...
Anomaly detection for symbolic sequence data is a highly important area of research and is relevant in many application domains. While several techniques have been proposed within different domains, understanding of their relative strengths and weaknesses is limited. The key factor for this is that the nature of sequence data varies significantly across domains, and hence while a technique might perform...
With the advent of cost effective storage systems and high speed network connectivity, the amount of data gathered by various transactional systems has increased manifold and processing the same has become a real challenge. A number of data mining systems have been developed for processing such huge data in a meaningful way. Most of these systems are stand-alone in nature except a few of them that...
Insider threats are veritable needles within the haystack. Their occurrence is rare and when they do occur, are usually masked well within normal operation. The detection of these threats requires identifying these rare anomalous needles in a contextualized setting where behaviors are constantly evolving over time. To this refined search, this paper proposes and tests an unsupervised, ensemble based...
Graph mining has been a widely studied domain over the years. Graph representation of real world problems has enabled the development of simple solutions bringing in better clarity. Graph mining has various sub domains among which graph matching is a prominent one having a number of algorithms. With the rise of new applications involving large sets of networked data, the performance of these algorithms...
Most web search engines use only the search keywords for searching. Due to the ambiguity of semantics and usages of the search keywords, the results are noisy and many of them do not match the user's search goals. This paper presents the design of an intelligent Search Bot, which operates as an agent for a user by simulating the user activity of filtering only the relevant search results. It learns...
To make decisions about the long-term preservation and access of large digital collections, archivists gather information such as the collections' contents, their organizational structure, and their file format composition. To date, the process of analyzing a collection — from data gathering to exploratory analysis and final conclusions — has largely been conducted using pen and paper methods. To...
Land cover change, especially deforestation, is a priority issue for policymakers at the local, national and international scale. Deforestation's contribution of up to 20% of global green-house gas emissions is already well known; the loss of biodiversity from land conversion is also well established [7, 13]. Policymakers at the UN Framework Convention on Climate Change negotiations are addressing...
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