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The recent computing trend is producing tons of data every minutes where the amount of imbalanced data is quite high as far as real life data sets are concerned. In practical aspects of data mining, the imbalanced data set is prone to misguide a data mining model. However, data set needs pre-processing before mining. This work focuses on some practical data mining techniques and produces a valid evaluation...
Web applications hosted on the Internet are naturally exposed to a variety of attacks and constantly probed by hackers for vulnerabilities. SQL Injection Attack (SQLIA) has been a major security threat on web applications since over 15 years. Detecting SQLIA at runtime is a challenging problem because of extreme heterogeneity of the attack vectors. This paper explores application of node centrality...
Load balancers and firewalls are entry points to many key websites in any network infrastructure. Any downtime due to these devices, even for a few minutes, may result in major business impact. There are many proactive event management systems/products which can monitor their health and alert on device failure. However, predicting failure with sufficient lead time still remains a challenging problem...
Extreme learning machine (ELM) and support vector machine (SVM) classifiers are developed to detect rales (a gurgling sound that is a symptom of respiratory diseases in poultry). These classifiers operate on Mel-scaled spectral features calculated from recordings of healthy and sick chickens during a vaccine trial. Twenty minutes of labeled data were used to train and test the classifiers, then they...
Recognition of fruits automatically using machine vision is considered as challenging task as fruits exist in various colors, sizes, shapes and textures. Additionally, when images are acquired of them, variation is introduced due to imaging conditions also. In this paper we have recognized nine different classes of fruits. Fruit image dataset are obtained from web as well as certain images are acquired...
Domain adaption tends to transfer knowledge across domains following dissimilar distribution and where target domain has inadequate labelled samples. When knowledge is transferred from abundantly irrelevant sources negative transfer may occur resulting in poor classification of test samples. Deep learning research illustrates the semantic clustering as well as transferability of deep convolutional...
Pattern classification in domains that follow dissimilar distribution and where target domain has insufficient labelled samples, requires transfer of knowledge across domains through a process called domain adaption. Deep learning research demonstrates the transferability of deep convolutional features that are activations of intermediate layers of convolutional neural networks for domain adaption...
Face recognition has been receiving continuous academic and commercial attention for the last decades. In this paper, we construct two face recognition systems adopting SVM and Adaboost as the classifiers with fast PCA for facial feature representation. The detailed discussions about algorithm realization are given. Comparison between the two systems and analysis of them are provided through several...
Polarity detection is a research topic of major interest, with many applications including detecting the polarity of product reviews. However, in some cases, the polarity of the product reviews might not be available while the polarity of the product itself might be, prohibiting the use of any form of fully supervised learning technique. This scenario, while different, is close to that of multiple...
As one of the machine learning methods that has been widely used in recent years, SVM can be applied to pattern classification and nonlinear regression. This paper proposes the basic modeling process by using SVM, and introduces the processing technique of dimension reduction by using MATLAB and principal component analysis method, and provides the process of classification forecasting by using SVM...
Physical measurement have been becoming increasingly helpful in monitoring the humans health status. Manual measurement of physical status is time consuming and may result in misdiagnosing, so an automatic method for identification the status of physical is urgently needed. This paper presents a novel feature extraction method based on using constrained high dispersal network for depth images and...
In this paper, we propose a mutual framework that combines two state-of-the-art visual object tracking algorithms. Both trackers benefit from each other's advantage leading to an efficient visual tracking approach. Many state-of-the-art trackers have poor performance due to rain, fog or occlusion in real-world scenarios. Often, after several frames, objects are getting lost, only leading to a short-term...
Speech impaired people are detached from the mainstream society due to the lacking of proper communication aid. Sign language is the primary means of communication for them which normal people do not understand. In order to facilitate the conversation conversion of sign language to audio is very necessary. This paper aims at conversion of sign language to speech so that disabled people have their...
Motor is a kind of imperative driving device, whether a motor can monitor its state precisely and diagnose fault timely have a profound impact. This paper mainly investigates the improvement of the general method of motor defect diagnosis to achieve higher accuracy. Unfortunately, every classifier has their own respective advantages and disadvantages, using the typical machine learning methods separately...
The availability of rich datasets is a pre-requisite for proposing robust sentiment analysis systems. A variety of such datasets exists in English language. However, it is rare or nonexistent for the Arabic language except for a recent LABR dataset, which consists of a little bit over 63,000 book reviews extracted from. Goodreads. com. We introduce BRAD 1.0, the largest Book Reviews in Arabic Dataset...
This paper presents our work on developing Vietnamese fundamental tools and a resource for analysis. These tools are for word segmentation and part-of-speech tagging, diacritics restoration, and orthographical variants dictionary. All of them have been either not publicly available so far or not attaining sufficient performance. We have developed the tools and released the tools to the public, in...
In this paper, we investigate a range of strategies for combining multiple machine learning techniques for recognizing Arabic characters, where we are faced with imperfect and dimensionally variable input characters. Experimental results show that combined confidence-based backoff strategies can produce more accurate results than each technique produces by itself and even the ones exhibited by the...
This letter is the first attempt to conflate a machine learning technique with wireless communications. Through interpreting the antenna selection (AS) in wireless communications (i.e., an optimization-driven decision) to multiclass-classification learning (i.e., data-driven prediction), and through comparing the learning-based AS using $k$ -nearest neighbors and support vector machine algorithms...
We study in this paper an authorship attribution in Arabic poetry using text mining classification. Several features such as Characters, Poetry Sentence length; Word length, Rhyme, Meter and First word in the sentence are used as input data for text mining classification algorithms Naïve Bayes NB, Support Vector Machine SVM, and Sequential Minimal Optimization SMO. The data set of experiment was divided...
Feature extraction addresses the problem of finding the most compact and informative set of features. To maximize the effectiveness of each single feature extraction algorithm and to develop an efficient intrusion detection system, an ensemble of Linear Discriminant Analysis (LDA) and Principle Component Analysis (PCA) feature extraction algorithms is implemented. This ensemble PCA-LDA method has...
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