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IP Addresses are a central part of packet- and flow-based network data. However, visualization and similarity computation of IP Addresses are challenging to due the missing natural order. This paper presents a novel similarity measure IP2Vec for IP Addresses that builds on ideas from Word2Vec, a popular approach in text mining. The key idea is to learn similarities by extracting available context...
The multisensory fusion of remote sensing data has obtained a great attention in recent years. In this letter, we propose a new feature fusion framework based on deep neural networks (DNNs). The proposed framework employs deep convolutional neural networks (CNNs) to effectively extract features of multi-/hyperspectral and light detection and ranging data. Then, a fully connected DNN is designed to...
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...
Biometrie systems present some important advantages over the traditional knowledge-or possess-oriented identification systems, such as a guarantee of authenticity and convenience. However, due to their widespread usage in our society and despite the difficulty in attacking them, nowadays criminals are already developing techniques to simulate physical, physiological and behavioral traits of valid...
Inside the sets of data, hidden knowledge can be acquired by using neural network. These knowledge are described within topology, using activation function and connection weight at hidden neurons and output neurons. Is hardly to be understanding since neural networks act as a black box. The black box problem can be solved by extracting knowledge (rule) from trained neural network. Thus, the aim of...
The growing demand for smarter high-performance embedded systems leads to the integration of multiple functionalities in on-chip systems with tens (even hundreds) of cores. This trend opens a very challenging question about the optimal resource allocation in those manycore systems. Answering this question is key to meet the performance and energy requirements. This paper deals with a learning technique...
The imbalanced learning problem is becoming pervasive in today's data mining applications. This problem refers to the uneven distribution of instances among the classes which poses difficulty in the classification of rare instances. Several undersampling as well as oversampling methods were proposed to deal with such imbalance. Many undersampling techniques do not consider distribution of information...
We propose a method to extract frequent sub-sequences from sequential input. Especially, we aim to extract three or more symbols from sequential input. In our former research, we could extract only two symbols. To extract three or more symbols, we propose a learning method and structure of self-organizing spiking neural network. The learning method is based on STDP rule. An output-layer of neural...
In this paper neuro-fuzzy approach for medical data processing are considered. Architecture of multidimensional neo-fuzzy neuron and group of its adaptive learning algorithms was introduced for Medical Data Mining tasks in online-mode.
In the paper, the deep evolving neural network and its learning algorithms (in batch and on-line mode) are proposed. The deep evolving neural network's architecture is developed based on Group Method of Data Handling approach and Least Squares Support Vector Machines with fixed number of the synaptic weights. The proposed system is simple in computational implementation, characterized by high learning...
Dog breeds recognition is a typical task of fine-grained image classification, which requires both more training images to describe each dog breed and better models to automatically discriminate different dog breeds. In this paper, we use click-through logs as source data and pre-trained deep convolutional neural network (DCNN) as initial model to build our dog recognizer. To improve recognition accuracy,...
In order to extract effective audio feature using autoencoder, different from traditional bottle-neck autoencoder, bottle-body autoencoder is presented in this paper, which is constructed using restricted Boltzmann machine with the same neurons at every layer. Bottle-body feature, which is obtained by using pseudo-inverse method to initialize weights, is applied to audio signal classification. The...
Current research in radiology field is increasingly focusing on developing computer aided detection (CAD) systems able to support radiologists in the detection of suspicious regions, reducing oversight, errors and working time. Prostate cancer (PCa) is the most common cancer afflicting men in USA. Multiparametric Magnetic Resonance (mp-MR) imaging is recently emerging as a powerful tool for PCa diagnosis...
The object of the study are neural networks. The goal is to understand and develop a recognition system of mathematical formulas and symbols. The article deals with the basic provisions of neural networks, which principle of work is based on functioning neural networks of the human brain. We describe a mathematical representation of the pulse passing through the synaptic connections of neurons and...
Infertility problem is an important issue in recent decades. Semen analysis is one of the principle tasks to evaluate male partner fertility potential. It has been seen in many researches that life habits and health status affect semen quality. Data mining as a decision support system can help to recognize this effect. The artificial neural network (ANN) is a powerful data mining tool that can be...
Study of learner-oriented mobile learning (m-learning) instructions based on classification of student m-learning strategies has aroused much attention over the last decade. Due to the multivariate nature of students' learning strategies, traditional classification methods often fail to produce reliable classification results. In this paper, a new classification method based on Principal Component...
Predicting student academic performance has been an important research topic in Educational Data Mining (EDM) which uses machine learning and data mining techniques to explore data from educational settings. However measuring academic performance of students is challenging since students academic performance hinges on diverse factors. The interrelationship between variables and factors for predicting...
The automatic meteor detection solution presented in this paper uses a self-organizing map to analyze radio spectrogram data and detect the meteor samples found within. This artificial neural network is trained using data samples extracted from spectrograms of radio recordings using a rectangular sliding window. Several tests were run to find the optimal neural network topology and duration of training...
The question papers of many academic institutions contain a significant part of descriptive type questions. Generally, these question papers follow a fixed pattern. Normally, the descriptive type answers are evaluated manually with the possibilities of deviation in the evaluation of the same answer-script by different examiners with almost the same amount of experience and academic qualifications...
Software Testing is a very important phase in the cycle of software development. It is the only phase which ensures the reliability on the software. Generally 40–50% of the software development cost is spent on this phase. Though many automatic testing tools are present, but still most research is required in this field to reduce cost and time allotted for this phase. Test Oracle is a process which...
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