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Botnet is a malicious software that can perform malicious activities, such as (Distributed Denial of Services) DDoS, spamming, phishing, key logging, click fraud, steal personal information and important data, etc. Botnets can replicate themselves without user consent. Several systems of botnet detection have been done by using a machine learning method with feature selection approach. Currently,...
This paper reviews the comparative performance of Support Vector Machine (SVM) using four different kernels, i.e., Linear, Polynomial, Radial Basis Function (RBF) and Sigmoid. Overall accuracy (OA), Kappa Index Analysis (KIA), Receiver Operating Characteristic (ROC) and Precision (P) have been considered as evaluation parameters in order to assess the predictive accuracy of SVM. Both high resolution...
Computers and Smartphone's becomes vital part of everyday life and hence use of internet becomes more and more. Due to internet, computers are becomes vulnerable of different kinds of security threats. Therefore it is required that we need to have efficient security method in order to avoid leakage of important data or misuse of data. This security method is called as Intrusion Detection System (IDS)...
Feature selection and learning through selected features are the two steps that are generally taken in classification applications. Commonly, each of these tasks are dealt with separately. In this paper, we introduce a method that optimally combines feature selection and learning through feature-based models. Our proposed method implicitly removes redundant and irrelevant features as it searches through...
Traditional Support Vector Regression (SVR) Machine acts as approximating a regression function. This paper, however, proposes a novel multi-class classification approach based on the SVR framework, called Support Vector Regression Machine with Consistency (SVRC). The contributions of this paper are: (1) To implement multi-class classification task, were place the margin term with its l1 norm in the...
Auroras are beautiful phenomena and attract many people. However, its physical model still remains a subject of dispute because it is caused by the interaction of diverse areas, such as solar wind, magnetosphere, and ionosphere, and it is difficult to simultaneously obtain data in such wide areas. This paper is devoted to forecasting the onset of brightening of auroras followed by poleward expansion,...
Person re-identification is an important computer vision task with many applications in areas such as surveillance or multimedia. Approaches relying on handcrafted image features struggle with many factors (e.g. lighting, camera angle) which lead to a large variety in visual appearance for the same individual. Features based on semantic attributes of a person's appearance can help with some of these...
FID is the original fuzzy decision tree, first introduced almost twenty years ago, that sparked a huge variety of hybrid algorithms merging approximate reasoning, fuzzy systems, and mainstream classification algorithms. With the continued interest, this paper describes a newly released update 3.5. One important new addition is a module that can be used to study the effect of noise and missing values...
A method for electromagnetic radiation source identification is proposed. The spatial characteristic of a radiation source is taken as the unique parameter for support vector machines (SVMs) to identify. First, the location of radiation source is determined by the triangulation method, and then its spatial characteristic is collected by a band receiver array with simulation, which removes the limit...
Traditional sleep scoring based on visual inspection of Electroencephalogram (EEG) signals is onerous for sleep scorers because of the gargantuan volume of data that have to be analyzed per examination. Computer-aided sleep staging can alleviate the onus of the sleep scorers. Again, most of the existing works on automatic sleep staging are multichannel based. Multichannel based sleep scoring is not...
We propose to address the handwritten digits recognition (HWDR) problem by using a two-dimensional (2-D) discrete cosine transform (DCT) based sparse principal component analysis (PCA) algorithm for fast classification. The gain of processing speed is achieved by utilizing the ability of 2-D DCT for energy compaction and signal decorrelation. The proposed algorithm was applied to the mixed national...
The idea of opposition-based learning was introduced 10 years ago. Since then a noteworthy group of researchers has used some notions of oppositeness to improve existing optimization and learning algorithms. Among others, evolutionary algorithms, reinforcement agents, and neural networks have been reportedly extended into their “opposition-based” version to become faster and/or more accurate. However,...
With an ever-increasing amount of information made available via the Internet, it is getting more and more difficult to find the relevant pieces of information. Recommender systems have thus become an essential part of information technology. Although a lot of research has been devoted to this area, the factors influencing the quality of recommendations are not completely understood. This paper examines...
The development of a model classification intrusion detection using Weighted Extreme Learning Machine was examined with KDD'99 data set ad 4 types of main attack : Denial of Service Attack (DoS), User to Root Attack (U2R), Remote to Local Attack (R2L), and Probing Attack, when comparing the effectiveness of working process of the method presented to SVM+GA[6] and ELM, found that weighted technique...
In low resource Automatic Speech Recognition (ASR), one usually resorts to the Statistical Machine Translation (SMT) technique to learn transform rules to refine grapheme lexicon. To do this, we face two challenges. One is to generate grapheme sequences from the training data as the targets, which is paired with the original transcripts to train SMT models; the other is to effectively prune the learned...
In developing the human-machine technology, it is essentially important to infer human mind state. A machine learning approach is promising to this need. However, the machine-learning approach essentially requires training data, ideally supervised training data, which may not be readily available. An idea is to overcome this shortcoming is to take the so-called subjective rate measure. Take the problem...
Web attacks that exploit vulnerabilities of web applications are still major problems. The number of attacks that maliciously manipulate parameters of web applications such as SQL injections and command injections is increasing nowadays. Anomaly detection is effective for detecting these attacks, particularly in the case of unknown attacks. However, existing anomaly detection methods often raise false...
In remote sensing, where training data are typically ground-based, mislabeled training data is inevitable. This work handles the mislabeling problem by exploiting the ensemble margin for identifying, then eliminating or correcting the mislabeled training data. The effectiveness of our class noise removal and correction methods is demonstrated in performing mapping of land covers. A comparative analysis...
The quality of the training data used in a supervised image classification can impact on the accuracy of the resulting thematic map obtained. Here the effects of mis-labeled training cases on the accuracy of classifications by discriminant analysis and a support vector machine were explored. The accuracy of both classifiers varied with the amount and nature of mis-labeled training cases. In particular,...
When learning a new classifier, poor quality training data can significantly degrade performance. Applying selection conditions to the training data can prevent mislabeled, noisy, or damaged data from skewing the classifier. We extend a set of action attributes and apply training case attribute selection conditions to a challenging action recognition dataset.
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