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It is important to develop defense mechanisms to bolster the cyber-physical security of critical infocomm infrastructure (CII) systems. A basic method of defense for CII systems is a firewall. Since SCADA / ICS systems may be negatively impacted by latencies and delays introduced by firewalls, which will translate to real world impacts, any implemented firewall in the network should attempt to minimize...
Dictionary Learning is a method used in signal and image processing. In this study, classification of mammogram images were realized by using dictionary learning and sparse representation algorithms. The attributes of the images were detected with Wavelet Transform and PCA, and the new dataset which was created by the obtained attributes were classified by Dictionary Learning. Moreover, the classification...
There are vigorous developments of social network which affect out life greatly. User influence is an important reason to promote the interaction in social network. When we analyze user influence, single value can’t indicate the user influence in different domains. This paper puts forward the design of User Classification PageRank (UCPR) to solve this problem. Firstly, we classify users according...
Crime analysis is a methodical approach for identifying and analyzing patterns and trends in crime. With the increasing origin of computerized systems, crime data analysts can help the Law enforcement officers to speed up the process of solving crimes. Using the concept of data mining, we can analyze previously unknown, useful information from an unstructured data. Predictive policing means, using...
Class imbalance of instances is a common problem in the field of data mining and machine learning. A dataset is considered to be imbalanced if one of the classes (further called a minority class or positive class) contains much smaller number of instances than the remaining classes (majority classes or negative class). We describe a new approach to balance data with improved classification. Resampling...
This paper focuses on a method to obtain sentiments of people from twitter and using it to analyze whether they can become threat to particular person or society. Machine learning is one of the technologies which is used for decision making and also make predictions by learning more from dataset. Since huge amount of data is streaming very fast, it needs to be classified in short duration. In this...
Research focus increases rapidly in recent years in mining imbalanced data sets, because of its challenge and its extensive application in the real world. A dataset is said to be imbalanced, if the representation of attribute categories are not approximately even. All the existing classifiers are inclined to perform poorly on imbalanced datasets. Hence it is very essential to go for well balanced...
Finding of frequent sub-graphs is an important operation on graphs and it is defined as detection of all sub-graphs that appear frequently in a set of graphs. This paper proposes detection of frequent sub-community graph from n-set of community graph of villages; are useful for characterizing community graph sets, finding difference among groups of community graphs, classifying and clustering of community...
The opinion mining is very much essential in e-commerce websites, furthermore advantageous with individual. An ever increasing amount of results are stored in the web as well as the amount of people would acquiring items from web are increasing. As a result, the users' reviews or posts are increasing day by day. The reviews toward shipper sites express their feeling. Any organization for example,...
In this paper, we try to make an author identification of two ancient Arabic religious books dating from the 6th century: The holy Quran and the Hadith. The authorship identification process is achieved through four phases which are: documents collection, text preprocessing, features extraction and classification model building. Thus, two series of experiments are undergone and commented. The first...
Web usage mining, is the method of mining for user browsing and access patterns. Usage data captures the identity or origin of Web users along with their surfing behavior at a Web site. This paper aims to classify user behavior in identifying the patterns of the browsing and navigation data of web users and also measure the performance of the Frequent Pattern (FP) Growth algorithm and Apriori algorithm...
In this study, we propose a hybrid knowledge-based framework for author name disambiguation. The developed approach helps incrementally identify authors of documents in data acquired from various sources. The nature of the problem calls for an orchestrated use of several methods; thus, the framework is composed of two levels. The first level contains a rule-based disambiguation algorithm. The second...
Eye gaze patterns or scanpaths of subjects looking at art while answering questions related to the art have been used to decode those tasks with the use of certain classifiers and machine learning techniques. Some of these techniques require the artwork to be divided into several Areas or Regions of Interest. In this paper, two ways of clustering the static visual stimuli - k-means and the density...
In this paper, a progressive learning algorithm for multi-label classification to learn new labels while retaining the knowledge of previous labels is designed. New output neurons corresponding to new labels are added and the neural network connections and parameters are automatically restructured as if the label has been introduced from the beginning. This work is the first of the kind in multi-label...
When traditional sample selection methods are used to compress large data sets, the computational complexity turns out to be very high and it is really time consuming. To avoid these shortcomings, we propose a new method to select samples based on non-stable cut points. With the basic characteristic of convex function that its extreme values occur at the endpoints of intervals, the method measures...
Uncertain data clustering is an essential task in the research of data mining. Lots of traditional clustering methods are extended with new similarity measurements to tackle this issue. Different from certain data clustering, uncertain data clustering focus more on the evaluation of distribution similarity between uncertain data objects. In this paper, based on the KL-divergence and the JS-divergence,...
An attack which can be performed with a steganographic approach is detecting the presence of information in a file. There are three challenges involved in steganalysis. First, there is no guarantee for the presence of hidden data in a file. Second, there is a possibility for a data encrypted in the file. Third, for efficient transmission of message sender may add a noise in a cover medium. For overcoming...
Rapid Evaluation-Based Feature Diminution algorithm involves finding a reduced set with the essential Feature which produces as the original set of Feature. The Feature diminution is performed by removing the irrelevant and redundant Features. The Rapid Evaluation-Based Feature Diminution algorithm (REFD) removes the irrelevancy by identifying relevant Feature to the target, and removing the rest...
In this paper, we propose two novel active learning algorithms: 1) k-mode for classifying the certain and uncertain dataset in a whole dataset, 2) Priority R-Tree clustering the certain and uncertain data for each domain. They handle both supervised and unsupervised dataset. These techniques improve the robustness and accuracy of the clustering outcome to a great extent. By minimizing the expected...
Sentiment analysis is the process of identifying people's point of view and its impact on them. It determines whether apiece of writing is positive, negative, or neutral. Now-a-days one of the most common tool for sharing opinion is the Internet. This opinion may be different moods of people or reviews about products and so on. People share their views on social-networking websites which proves to...
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