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In this paper, we discuss the importance of feature subset selection methods in machine learning techniques. An analysis of microarray expression was used to check whether global biological differences underlie common pathological features for different types of cancer datasets and to identify genes that might anticipate the clinical behavior of this disease. One way of finding relevant gene selection...
Financial fraud is an ever growing menace with far consequences in the financial industry. Data mining had played an imperative role in the detection of credit card fraud in online transactions. Credit card fraud detection, which is a data mining problem, becomes challenging due to two major reasons — first, the profiles of normal and fraudulent behaviours change constantly and secondly, credit card...
The inherent dependencies between visual elements and aural elements are crucial for affective video content analyses, yet have not been successfully exploited. Therefore, we propose a multimodal deep regression Bayesian network (MMDRBN) to capture the dependencies between visual elements and aural elements for affective video content analyses. The regression Bayesian network (RBN) is a directed graphical...
The framework consisting of a pixel-wise classification followed by a Markov random field has been very successful for spatial-spectral hyperspectral classification. While training such frameworks, the classifier and the Markov random field are generally tuned greedily one after another. However, better results could be obtained by tuning both of the components simultaneously with the objective of...
In the complex pattern classification problem, the reliability of classifier output for the patterns located at different regions of the data set may be different. In order to efficiently improve the classification accuracy, we propose a new method to correct the original classifier output using the local knowledge of the classifier performance in different regions. The training data set can be divided...
Many real-time tasks, such as human-computer interaction, require fast and efficient facial gender classification. Although deep CNN nets have been very effective for a multitude of classification tasks, their high space and time demands make them impractical for personal computers and mobile devices without a powerful GPU. In this paper, we develop a 16-layer, yet lightweight, neural network which...
Motor dexterity assessment is regularly performed in rehabilitation wards to establish patient status and automatization for such routinary task is sought. A system for automatizing the assessment of motor dexterity based on the Fugl-Meyer scale and with loose restrictions on sensing technologies is presented. The system consists of two main elements: 1) A data representation that abstracts the low...
The remaining useful life (RUL) prediction of bearings has emerged as a critical technique for providing failure warnings in advance, reducing costly unscheduled maintenance and enhancing the reliability of bearings. Recently, a fusion prognostics method combining exponential model and relevance vector machine (RVM) has been proposed and applied to the RUL prediction of bearings. This fusion prognostics...
The financial market is very fickle and investors have the difficult task of following and trying to predict the swings of the market so that their strategies result in better financial returns. With the use of Big Data and Bayesian mathematical statistics based on prior knowledge and examples of training to determine the likelihood of a hypothesis, financial news can be tracked continuously and affecting...
During the past years various fraud detection techniques were proposed and used to reduce fraudulent activities. Selection an optimal fraud detection model becomes keen area of interest for researchers in the field of anomaly detection. Methods and tools for fraud detection model selection which were used in the literature used a limited no of model selection criteria for finding the detection of...
With the appearance and development of the technology of malicious codes and other unknown threats, information security has drawn people's attention. In this paper, we investigate on behavior-based detection which is different from traditional static detection technology. Firstly, we discuss the procedure in detail, especially feature extraction and classification. Several machine learning methods...
In fingerprint verification systems impressive improvements have been achieved through multi sample fusion methods. Among fusion methods, score level fusion with its simplicity and high performance is the most common and useful fusion method. But the quality of fingerprints has direct effect on performance and accuracy of these systems. In this paper, we present a combination approach in Receiver...
This article aims at finding the risk factors for hepatitis B virus (HBV) reactivation after the precise radiotherapy in patients with primary liver cancer (PLC). We use sequential forward selection and sequential backward selection to extract features which would be combined into an optimal feature subset, and then establish Bayesian and support vector machine (SVM) classification model. We use sequential...
Many operators are working in jobs that require stressful mental tasks such as transportation supervision, vehicle driving, banking and others. Prevention of fatigued-based human error, that has been a standing challenge in such work areas, can be detected and quantified using human performance level. This paper proposes an enhanced method for operator fatigue detection based on computer-keyboard...
Sentimental Analysis is reference to the task of Natural Language Processing to determine whether a text contains subjective information and what information it expresses i.e., whether the attitude behind the text is positive, negative or neutral. This paper focuses on the several machine learning techniques which are used in analyzing the sentiments and in opinion mining. Sentimental analysis with...
In this paper, we propose an application of non-parametric Bayesian (NPB) models to classification of fetal heart rate recordings. More specifically, the models are used to discriminate between fetal heart rate recordings that belong to fetuses that may have adverse asphyxia outcomes and those that are considered normal. In our work we rely on models based on hierarchical Dirichlet processes. Two...
As there is a exponential growth of social networks and due to large usage of social media, there is a increasing demand for data in the web for the users which leads to recent trends and ideas in the field of research. The users will be eagerly using these data for the future purpose and get information about the opinions of others thus there is a need of automatic summarization of opinion of the...
Predicting the survival status of patients who will undergo breast cancer surgery is highly important, where it indicates whether conducting a surgery is the best solution for the presented medical case or not. Since this is a case of life or death, the need to explore better prediction techniques to ensure accurate survival status prediction cannot be overemphasized. In this paper we evaluate the...
Efficiently allocating resources and predicting cell handovers is essential in modern wireless networks; however, this is only possible if there is an efficient way to estimate the future state of the network. In order to accomplish this, we investigate two learning techniques to predict the long-term channel gains in a wireless network. Previous works in the literature found efficient methods to...
There is a tremendous growth in the number of Internet users every day. These users are spread all over the globe belonging to different community speaking different languages. India being a multilingual country has more than six crores people speaking Kannada (south Indian regional) language. There is demand for many applications to be effective to solve problems related to native languages. In this...
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