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This paper discusses the similarities and differences in both ideology expressed and practices employed by two terrorist groups that operated in Greece between the years of 1975 and 2017: Revolutionary Organization 17 November and Conspiracy of Fire Nuclei. Within this line of thought, we will briefly provide an outline of the political and ideological framework of the groups on focus in an effort...
There is no previous research that compares the results of k-means, CLOPE clustering and Latent Dirichlet Allocation (LDA) topic modeling algorithms for detecting trending topics on tweets. Since not all tweets contain hashtags, we considered three training data feature sets: hashtags, keywords and keywords + hashtags in this study. Our proposed methodology proved that CLOPE can also be used in a...
In this digital world, we are facing the flood of data, but depriving for knowledge. The eminent need of mining is useful to extract the hidden pattern from the wide availability of vast amount of data. Clustering is one such useful mining tool to handle this unfavorable situation by carrying out crucial steps refers as cluster analysis. It is the process of a grouping of patterns into clusters based...
Classification is a central problem in the fields of data mining and machine learning. Using a training set of labeled instances, the task is to build a model (classifier) that can be used to predict the class of new unlabelled instances. Data preparation is crucial to the data mining process, and its focus is to improve the fitness of the training data for the learning algorithms to produce more...
Text summarization, a field of data mining, is very important for developing various real-life applications. Many techniques have been developed for summarizing English text(s). But, a few attempts have been made for Bengali text because of its some multifaceted structure. This paper presents a method for text summarization which extracts important sentences from a single or multiple Bengali documents...
Analysis of lace texture images is a challenging problem because the lace is a soft and extensible material and can be easily deformed. This paper investigates a whole system for lace classification. A first step, based on Otsu's segmentation method, allows to remove the background. Then the lace texture is characterized using local binary patterns (LBP). In order to be robust against rotation the...
In this paper, we propose a scene clustering algorithm which uses straight line features. Scenes are represented as nodes in the graph, and each connectivity between nodes is calculated by a pre-trained vocabulary tree. By applying a spectral clustering algorithm to the constructed graph, the scenes are partitioned into k groups where k is determined by the proposed estimation method. Instead of using...
Clustering, or unsupervised classification, is animportant issue in Bioinformatics. It serves to automaticallygroup protein sequences into families. Most researchers treatthe biclass clustering problem. In this paper we present ourapproach for the multiclass clustering of protein sequences. It isa difficult problem, because we are based on primary structure. This approach consists of four steps. In...
Graphs are ubiquitous and are the best data structure for representing linked data because of their flexibility, scalability, and power to deal with complexity. Storing big graphs in graph databases leads to difficult computation and increased time complexity. The best alternative is to use inmemory representations such as compact data structures. They compress the graph sufficiently such that it...
Malware is one of the most terrible and major security threats facing the Internet today. In practice, the most widely used malware detection method is static detection. Static detection is effective for many types of malware. Operation code (opcode) sequences is one of the most important malware features for static analysis. In this paper, our goal is to optimize the accuracy and performance based...
This paper introduces two novel algorithms for detecting groups of people standing or freely moving in a crowded environment. The proposed algorithms exploit low-level features extracted from videos. The first algorithm, the Link Method, uses a learning and forgetting strategy for modeling dynamics of proxemics between individuals. Two versions of this algorithm are proposed: they differ in the analysis...
Data mining techniques have been found useful in understanding and enhancing student performance as well as decision making related to teaching and learning in HEIs. Literature review enabled the choice of time to degree and cumulative grade point average (CGPA) as examples of student performance factors for investigation. Student features that could be extracted using SQL query from student dataset...
Indoor localization using fingerprinting techniques became more attracting to researchers in recent years because of their high accuracy. However, unpredictable Received Signal Strength (RSS) is one of the challenges. In our proposed system, it can be reduced by using strong Access Points (APs) selection method to select a subset of reliable APs and decrease the input of feature dimension. In addition,...
Trajectories of moving objects provide crucial clues for video event analysis especially in surveillance applications. In this paper, we proposed a novel approach for detecting abnormal events in video surveillance. Our approach is based on trajectory analysis involving two phases. In the first phase, we extracted clusters of normal events through an agglomerative hierarchical clustering of saved...
In this present paper, a Radial Basis Function Network (RBFN) based on Modified Optimal Clustering Algorithm (MOCA) have been developed for clear and occluded fingerprint identification. Unlike conventional OCA technique which only considers intra cluster similarity for performing the desired number of clusters, MOCA combines both intra and inter cluster similarity while grouping such that not only...
Humans can view an image and immediately determine what the image is trying to convey. While this may be an easy event for humans, it is still considerably difficult for a computer to understand of its own accord. The challenge broadly lies in developing an automatic process to complement and supplant human visual and neural systems. In this paper, we address the core issue of imparting an image the...
To protect computer networks from attacks and hackers, an intrusion detection system (IDS) should be integrated in the security architecture. Although the detection of intrusions and attacks is the ultimate goal, IDSs generate a huge amount of false alerts which cannot be properly managed by the administrator, along with many noisy alerts or outliers. Many research works were conducted to improve...
In this paper, we present a Gaussian test-based hierarchical clustering method for high-resolution TerraSAR-X images. The purpose is to obtain homogeneous clusters. k-means is used to split image features to create a hierarchical structure. As image feature vectors usually fall into high dimensional feature space, we test different distance metrics, in order to try to tackle the curse of dimensionality...
In analytical optical character recognition, effects of noise and overlapping character blocks constitute a major problem to feature extraction algorithms. This problem degrades the performance of the recognition stage. In this approach, an adaptive clustering algorithm and Hamming Distance computation are proposed to aid the extraction and recognition processes. Initially a line is picked from the...
In this model, we attack the common problem of varying comprehending and perception capacities which differ with every individual. For understanding any concept, different individuals might require different levels of difficulty. Thus, we propose a model that performs clustering of text based on difficulty. Initially, with different feature extraction techniques, the scores of various textual characteristics...
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