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Image annotation is always an easy task for humans but a tough task for machines. Inspired by human's thinking mode, there is an assumption that the computer has double systems. Each of the systems can handle the task individually and in parallel. In this paper, we introduce a new hierarchical model for image annotation, based on constructing a novel, hierarchical tree, which consists of exploring...
Bagging is a popular method used to increase the accuracy of classification, by training a set of classifiers on slightly different datasets and aggregating their output by voting. Usually, the majority voting is used for this purpose, or the plurality voting, when the problem has multiple class values. In this study, we analyze the influence of several voting methods on the performance of two classification...
The aim of the paper was to apply MapReduce paradigm to the algorithm SplitBal which classifies imbalanced datasets and perform the evaluation of results for different parameters. Parallelization of time consuming operations allows to classify larger datasets, in perspective Big Data.
Prolonged and sustained warming of the sea, acidification of surface water and rising of sea levels, creates significant habitat losses, resulting in the proliferation and spread of invasive species which immigrate to foreign regions seeking colder climate conditions. This is happening either because their natural habitat does not satisfy the temperature range in which they can survive, or because...
Image classification mainly uses the classifier to classify the extracted image features. In the traditional image feature extraction, it is difficult to set the appropriate feature patterns for the complex images. Simultaneously, the training algorithm of the classifier also affects the accuracy of image classification. In order to solve these problems, the combination of deep belief networks and...
This paper is an approach for pedestrian detection and tracking with infrared imagery. The detection phase is performed by AdaBoost algorithm based on Haar-like features. AdaBoost classifier is trained with datasets generated from infrared images. The number of negative images used for training with AdaBoost algorithm is 3000. For positive training, 1000 samples are used After detecting the pedestrian...
In the last decade, numerous fake websites have been developed on the World Wide Web to mimic trusted websites, with the aim of stealing financial assets from users and organizations. This form of online attack is called phishing, and it has cost the online community and the various stakeholders hundreds of million Dollars. Therefore, effective counter measures that can accurately detect phishing...
The purpose of data mining is to explore, find and hence analyze relevant data from a massive data source using various technical means. This paper introduces the development of data mining to date, its functions, tasks and algorithms, as well as the process of data mining. The application and problems of data mining are also presented and finally the potential future development of data mining technology...
The basic idea behind the classifier ensembles is to use more than one classifier by expecting to improve the overall accuracy. It is known that the classifier ensembles boost the overall classification performance by depending on two factors namely, individual success of the base learners and diversity. One way of providing diversity is to use the same or different type of base learners. When the...
There is a demand for a new neurorehabilitation modality with a brain-computer interface for stroke patients with insufficient or no remaining hand motor function. We previously developed a robotic hand rehabilitation system triggered by multichannel near-infrared spectroscopy (NIRS) to address this demand. In a preliminary prototype system, a robotic hand orthosis, providing one degree-of-freedom...
With the advancement of data processing technology, it is a significance task for machine learning to handle massive amounts of data. The traditional classification method is a supervised learning method, which requires a large number of labeled samples. But it is difficult to achieve. In this paper, a semi-supervised learning algorithm combining co-training with support vector machine (SVM) classification...
Imbalanced data is an important problem for medical data analysis. Medical datasets are often not balanced in their class labels. The traditional classifiers can be seriously affected by the imbalanced class distribution in the data. This is because they aim to optimize the overall accuracy without considering the relative distribution of each class. This study searches the effect of class imbalance...
Tuberculosis is one of the top ten causes of death worldwide. Although this disease is curable and preventable, yet many new tuberculosis cases still occur especially in developing countries. Many low-income families cannot afford the medical diagnosis for tuberculosis. Therefore, this paper proposes an initial screening for tuberculosis infection using a data mining approach. In this paper, the initial...
Web attacks are increasing and the scale of malicious URL continues to expand with the rapid development of the Internet, so that the network security situation is increasingly grim. In this case, this paper studies the URL multi-classification problem, which is a continuation of the reference [1] and follows the data sets and most of feature selection methods in it. Firstly, different types of URL...
An ultra-low power neural spike sorting technique for implantable, multi-channel neural implant is proposed. It involves spiking neural network (SNN) with binary weights as an energy and area efficient classifier, along with a suitable frontend for spike encoding of the recorded neuro-potential. The proposed scheme employs two step training to implement supervised learning for the classifier, in order...
This paper introduces a novel open access resource, the machine-readable phonetic dictionary for Romanian — MaRePhoR. It contains over 70,000 word entries, and their manually performed phonetic transcription. The paper describes the dictionary format and statistics, as well as an initial use of the phonetic transcription entries by building a grapheme to phoneme converter based on decision trees....
Activity recognition systems are widely used in monitoring physical and physiological conditions as well as observing the short/long term behavioral patterns for the purpose of improving the health and wellbeing of the users. The major obstacle in widespread use of these systems is the need for collecting labeled data to train the activity recognition model. While a personalized model outperforms...
In this paper, we ask whether accurate recognition of activity can be obtained by using a network of smart objects. The approach consists in the classification of certain activities of the subjects: walking, standing, sitting and lying down. The study uses a network of commonly connected objects: a smart watch, a smartphone and a remote control and transported by the participants during an uncontrolled...
The recent advances of Brain Computer Interfaces (BCI) systems, can provide effective assistance for real time prognosis systems for patients who suffered from epileptic seizures. This paper presents an EEG classification strategy for short-term epilepsy prognosis, using software for Brain-Computer Interface (BCI) systems. A training scenario is presented, where significant features are extracted...
Diagnosis and staging of liver diseases are essential for the therapeutic efficacy of medication and treatment strategies. Measuring the Collagen Proportional Area (CPA) in liver biopsies recently becomes an effective tool for the assessment of fibrosis in liver tissues. State of the art image processing techniques are employed to analyze biopsy images, providing objective assessment of diseases severity...
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