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Authorship identification is a problem of data mining and classification. There are numerous methods and algorithms have been published to understand its nature. Although, researchers still investigate best and simple solutions due to its heterogeneous and multilingual characteristics. This study introduced new authorship identification process based on artificial neural network (ANN) model using...
In this paper, we propose a nonlinear metric learning framework to boost the performance of semi-supervised learning (SSL) algorithms. Constructed on top of Laplacian SVM (LapSVM), the proposed method learns a smooth nonlinear feature space transformation that makes the input data points more linearly separable. Coherent point drifting (CPD) is utilized as the geometric model with the consideration...
Pulsar candidate selection identifies prospective observations of modern radio pulsar surveys for further inspection in search of real pulsars. Typically, human experts visually select valuable candidates and eliminate radio frequency interference or other noises. Recently, machine learning methods are adopted to automate this task, which saves human labor and makes it possible for processing millions...
Arabic language has very rich vocabulary. It is manifested in different forms. The formal, Modern Standard Arabic (MSA), and the informal, colloquial or dialects. Dialectical languages become important as a result of the proliferation of social networks which resulted in the vast unstructured dialectical text available on the web. Unique properties of modern standard Arabic and dialects present major...
Dimensional sentiment analysis approach, which represents affective states as continuous numerical values on multiple dimensions, such as valence-arousal (VA) space, allows for more fine-grained analysis than the traditional categorical approach. In recent years, it has been applied in applications such as antisocial behavior detection, mood analysis and product review ranking. In this approach, an...
This paper presents a convolutional neural network clustering approach for handwritten digits recognition. Neural networks were trained individually, using the same training set and combined into clusters, depending on the training method used. These clusters formed a layered architecture, where each layer attempted to recognize the given digit, when the previous layers were not able to do so with...
As the state-of-the-art ConvNet-based image retrieval method, spatial search has shown excellent retrieval performance and outperformed other competitors. A key component of this method is a weighted combination of distances evaluated at different regions of a query image. However, these weights are currently manually tuned, by a trial-and-error based exhaustive search. This not only incurs a lengthy...
On-line questionnaires are today widely used for various tasks, from census data collection to knowledge testing in job interviews. However, there is currently no automated system that can help us decide if the answers from the questionnaires are reliable or estimate how reliable the are. Deception is a part of everyday human behavior and deception is also present when answering on-line questionnaires...
In order to improve the reliability of the status perception, this paper defines a metric QoSTD to measure the quality of the subjectively labelled training data used with K-means clustering. On the basis of QoSTD, we propose a method that utilizes a support vector support (SVM) model to predict the specified states of an individual's status. We also present a way to determine a threshold for QoSTD...
Tongue diagnosis is one of the main components of traditional Chinese medicine (TCM). Developing an objective and quantitative recognition model is very importantly and useful in the modernization of TCM. Currently, major problems in digital diagnoses of tongue images are extracting suitable features and building a high-performance classifier. To address these two issues, we present a robust approach...
In biometric score level fusion, the scores are often assumed to be independent to simplify the fusion algorithm. In some cases, the “average” performance under this independence assumption is surprisingly successful, even competing with a fusion that incorporates dependence. We present two main contributions in score level fusion: (i) proposing a new method of measuring the performance of a fusion...
Feature selection techniques are important to select features for good classification results. In this work, Correlation based Feature Selection, Motif Discovery using Random Projection, Hybrid Feature Selection and Convex Hull Feature Selection techniques with Support Vector Machine are compared using the same dataset for network anomaly detection. The performance metrics are number of features,...
In this manuscript a novel data-centric solution, based on the use of support vector machine techniques, is proposed to solve the problem of radio planning in the 169 MHz band. Our method requires the availability of a limited set of received signal strength measurements and the knowledge of a three-dimensional map of the propagation environment of interest, and generates both an estimate of the coverage...
In this paper we present an empirical evaluation of various techniques for feature selection that are applicable for analysis of funding decisions - whether of not to award funding to a specific scientific project. Input data are a set of review forms (questionnaires), filled in by domain experts, with final decisions of the expert committee about project funding. The data was provided by the Russian...
Cylindrical Algebraic Decomposition (CAD) is a key tool in computational algebraic geometry, particularly for quantifier elimination over real-closed fields. However, it can be expensive, with worst case complexity doubly exponential in the size of the input. Hence it is important to formulate the problem in the best manner for the CAD algorithm. One possibility is to precondition the input polynomials...
In this paper, we analyze the effect of boosting in image quality assessment through multi-method fusion. On the contrary of existing studies that propose a single quality estimator, we investigate the generalizability of multi-method fusion as a framework. In addition to support vector machines that are commonly used in the multi-method fusion studies, we propose using neural networks in the boosting...
Localizing seismic structures that can form traps for hydrocarbon reservoirs within large seismic volumes is a very challenging task. Due to the lack of accurately labeled data, we propose a weakly-supervised model for labeling seismic volumes using only a few labeled exemplars. Using six manually-labeled patches, we are able to extract patches that contain instances of similar geophysical structures...
Cardiotocography (CTG) is currently the most often used technique for detection of fetal distress. Unfortunately, CTG has a poor specificity. Recent studies suggest that, in addition to CTG, information on fetal distress can be obtained from analysis of fetal heart rate variability (HRV). However, uterine contractions can strongly influence fetal HRV. The aim of this study is therefore to investigate...
HIV-1 vaccine injection has been shown less effective due to the diversity of antigens. Increasing the knowledge of the associations between immune system and virus would ultimately result in producing effective vaccines against HIV-1 virus. To increase the understanding of immunological information, computational models can be utilised to construct predictive models. The aim of this study is, therefore,...
Today iris recognition systems are extensively used for security and authentication purposes due to their simplicity and high reliability. But these systems face a major challenge of being spoofed by high quality printed iris images or pictures captured by camera. The problem is aggravated by use of varying illumination conditions in an attack access attempt. This paper investigates spoofing attempts...
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