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In this study, malicious users who cause to resource exhausting are tried to detect in a telecommunication company network. Non-Legitimate users could cause lack of information availability and need countermeasures to prevent threat or limit permissions on the system. For this purpose, ANN based intelligent system is proposed and compared to SVM which is well known classification technique. According...
Advances in high frequency trading in financial markets have exceeded the ability of regulators to monitor market stability, creating the need for tools that go beyond market microstructure theory and examine markets in real time, driven by algorithms, as employed in practice. This paper investigates the design, performance and stability of high frequency trading rules using a hybrid evolutionary...
The self-organizing map (SOM) is a useful tool for creating abstractions of high-dimensional distributions of inputs. It computes the ideal mapping of the domain of observations, using either discrete or continuous distributions of values [1]. The SOM benefits from the coupling with a genetic algorithm (GA). GAs are optimization algorithms that allow the user to "evolve" a solution from...
In this paper, we consider the adaptation of two Partial Differential Equations (PDEs) on weighted graphs, p-Laplacian and eikonal equations, for semi-supervised classification tasks. These equations are a discrete analogue of well known geometric PDEs, which are widely used in image processing. While the p-Laplacian on graphs was intensively used in data classification, few works relate to the eikonal...
Feature selection is still a vital area for research in the machine learning field. After the emergence of big data, the need for mining large data sizes has increased to provide faster and more accurate predictions. Feature selection is concerned with selecting the most important features from a set of input features since some datasets may contain irrelevant and/or redundant features. In this paper,...
A common approach to sentiment classification is to identify a set of sentiment-carrying words and then to use machine learning to build a classifier that can classify sentiment based on the presence/absence of those words. In this paper, we propose a Fourier-based extension of this approach. Specifically, we introduce a spectral learning algorithm that implicitly identifies sentiment-carrying words...
In the past few years, instant messaging (IM) has been widely used in daily communication. However, due to the dispersion of topics and meaningless chatting, online IM groups are filled with useless messages. In order to help IM users capture what the IM group is talking about without reading all the messages, topic discovery in instant messages becomes a significant but challenging research task...
Learning in non-stationary environments is not an easy task and requires a distinctive approach. The learning model must not only have the ability to continuously learn, but also the ability to acquired new concepts and forget the old ones. Additionally, given the significant importance that social networks gained as information networks, there is an ever-growing interest in the extraction of complex...
In the last decade we have witnessed a huge increase of interest in data stream learning algorithms. A stream is an ordered sequence of data records. It is characterized by properties such as the potentially infinite and rapid flow of instances. However, a property that is common to various application domains and is frequently disregarded is the very high fluctuating data rates. In domains with fluctuating...
A more effective vision of machine learning systems entails tools that are able to improve task after task and to reuse the patterns and knowledge that are acquired previously for future tasks. This incremental, long-life view of machine learning goes beyond most of state-of-the-art machine learning techniques that learn throw-away models. In this paper we present a long-life knowledge acquisition,...
Dealing with multiple labels is a supervised learning problem of increasing importance. Multi-label classifiers face the challenge of exploiting correlations between labels. While in existing work these correlations are often modelled globally, in this paper we use the divide-and-conquer approach of decision trees which enables taking local decisions about how best to model label dependency. The resulting...
The problem of predicting outcome of patients in intensive care units (ICUs) is of great importance in critical care medicine, and has wide implications for quality control in ICUs. A dominant approach to this problem has been to use an ICU score system such as, for example, the Acute Physiology and Chronic Health Evaluation (APACHE) system, and the Simplified Acute Physiology Score (SAPS) system,...
CAPTCHAs are challenge-response tests that are widely used in the Internet to distinguish human users from machines. In addition to the well-known visual CAPTCHAs, most Internet services also provide an audio-based scheme, e.g., To enable access for visually impaired users. Recent research has shown that most CAPTCHAs are vulnerable as they can be broken by machine learning techniques. However, such...
The goal of this paper is to present a critical comparison of existing classical techniques on recognition of human faces. This paper describes the four major classical face recognition techniques i.e., i) Principal Component Analysis (PCA), ii) Linear Discriminant Analysis (LDA), iii) Discrete Cosine Transform (DCT), and iv) Independent Component Analysis (ICA). Strong and weak features of these...
This paper presents a novel diagnosis approach for crisis management using fuzzy discrete event system (model). The method exploits the output events and membership value of each active state as input events of the diagnoser (diagnosis module). In our work the choice of fuzzy system representation is justified by the assumption that, during crisis management, the stress and/or impact emotion of the...
Colleges and universities are increasingly interested in tracking student progress as they monitor and work to improve their retention and graduation rates. Ideally, early indicators of student progress, or lack thereof, can be used to provide appropriate interventions that increase the likelihood of student success. In this paper we present a framework that uses data mining and machine learning techniques,...
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