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Road pixel segmentation in airborne data is an important and challenging task. Recently, a sophisticated and robust approach based on superpixels and minimum cost paths has been published. In order to find out which of the numerous features are most essential, we propose a forward-search wrapper approach for feature selection which was tested with two different classifiers and with both generic and...
In the intelligent transportation system, the geometry for the street is an important factor in vehicle monitoring. It helps to point out areas of interest, reduce computing costs, increased accuracy in detecting and identifying objects and facilitate data collection. In this paper, a new robust method of extracting the geometric model of the road is presented. The method is based on vehicle motion...
This paper presents a rapidly and lower neural networks to treat those waste water index that is difficult to be measured. Model called soft sensor is composited two parts: one is used to estimate the principal linear output, the other one is used to adjust estimated error to obtain better accuracy. Selection of features that effects greatly computation scale and predict accuracy is discussed also...
The purpose of this study is to clarify the applicability of data-driven approach in accounting area. As the first stage, focusing on the model comparison, this paper shows the effectiveness of model selection with data mining technique for the development of earnings prediction model based on financial statement data. In accounting area, researchers have not considered the characteristic of financial...
Evaluating motion quality has many applications in health promotion and exercise coaching. This study aimed to develop an approach for automatic and cost-efficient evaluation on motion quality using the Nintendo Wii Balance Board (WBB) and machine learning techniques. We conducted a pilot study with twelve participants to collect data of chest rotation and hip joint rotation. We used support vector...
Together with the technology advancement, Computer Vision plays an important role in enhancing smart computing systems to help people overcome obstacles in their daily lives. One of the common troublesome problems is human memorization ability, especially memorizing things such as personal items. It is annoying for people to waste their time finding lost items manually by recall or notes. This motivates...
Less than optimal choice of the university department is one of the serious problems Turkish high school students have been suffering. There are a number of potential factors affecting the student's choice of her future profession. Some of these have received attention in the literature, but such studies do not always involve an investigation of the relationship between the factors analyzed and subsequent...
Intelligent Tutoring Systems (ITS) are typically designed to offer one-on-one tutoring on a subject to students in an adaptive way so that students can learn the subject at their own pace. The ability to predict student performance enables an ITS to make informed decisions towards meeting the individual needs of students. It is also useful for ITS designers to validate if students are actually able...
The open nature of Android allows application developers to take full advantage of the system. While the flexibility is brought to developers and users, it may raise significant issues related to malicious applications. Traditional malware detection approaches based on signatures or abnormal behaviors are invalid when dealing with novel malware. To solve the problem, machine learning algorithms are...
Personalization of movie recommendations is a widely researched topic. Personalization is usually carried out using local resources that are available at one's disposal. This local resource presents a snapshot of user preference at a particular moment. It doesn't address the long term user preferences. These concerns can be addressed using resources available with the user. This paper proposes a model...
Stack Overflow is one of the most popular question-and-answer sites for programmers. However, there are a great number of duplicate questions that are expected to be detected automatically in a short time. In this paper, we introduce two approaches to improve the detection accuracy: splitting body into different types of data and using word-embedding to treat word ambiguities that are not contained...
Opinion mining is the field of extracting and studying people's' opinions, sentiments, attitudes, and emotions expressed in a different digital form in the form of reviews on e-commerce and other social networking sites. It has a broad range of applications which varies from understanding the personal likes and dislikes of the particular user to predicting his shopping habits. The prime objective...
The scale of big data is increasing in every minute, and it becomes important to handle massive data. The familiar problem of Big data is not only huge volume but also planned in many places to provide high dimensionality in feature selection. In numerous big data application, feature selection is significant to select the essential features from the known data set and it removes unrelated and disused...
Sparse subspace learning has been demonstrated to be effective in data mining and machine learning. In this paper, we cast the unsupervised feature selection scenario as a matrix factorization problem from the view of sparse subspace learning. By minimizing the reconstruction residual, the learned feature weight matrix with the l2,1-norm and the non-negative constraints not only removes the irrelevant...
Currently, existing continuous identity verification methods mostly need to analyze a lot of keystroke data to ensure the authentication credibility. To achieve certification results with less data, in the paper, a new continuous verification method is proposed. This method, based on free-text keystroke dynamics, excavates the nearest character sequences of the users from their typing patterns, then...
This paper proposes a novel method for offline text-independent writer identification by using convolutional neural network (CNN) and joint Bayesian, which consists of two stages, i.e. feature extraction and writer identification. In the stage of feature extraction, since a large number of data is essential to train an effective CNN model with high generalizability and the amount of handwriting is...
Dermatological diseases are the most prevalent diseases worldwide. Despite being common, its diagnosis is extremely difficult and requires extensive experience in the domain. In this research paper, we provide an approach to detect various kinds of these diseases. We use a dual stage approach which effectively combines Computer Vision and Machine Learning on clinically evaluated histopathological...
Traditional convolutional layers extract features from patches of data by applying a non-linearity on an affine function of the input. We propose a model that enhances this feature extraction process for the case of sequential data, by feeding patches of the data into a recurrent neural network and using the outputs or hidden states of the recurrent units to compute the extracted features. By doing...
In this paper, the unsupervised approach recently proposed by the authors for automatic leakage detection in smart water grids is extended. First of all, the EPANET tool is adopted in order to simulate more realistic leakages. Also, with respect to the original work, an additional time resolution, of 30 minutes, is included, based on the water dataset of the Almanac of Minutely Power Dataset (AMPds)...
Online reviews nowadays are an important source of information for consumers to evaluate online services and products before deciding which product and which provider to choose. Therefore, online reviews have significant power to influence consumers' purchase decisions. Being aware of this, an increasing number of companies have organized spammer review campaigns, in order to promote their products...
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