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This paper argues that, the third generation of neural networks—the spiking neural networks (SNNs), can be used to model dynamic, spatio-temporal, cognitive brain processes measured as functional magnetic resonance imaging (fMRI) data. This paper proposes a novel method based on the NeuCube SNN architecture for which the following new algorithms are introduced: fMRI data encoding into spike sequences;...
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...
Selecting an efficient classifier for medical data is considered as one of the most important part of today's computer aided diagnosis. The performance of single classifiers such as decision tree classifier can be increased by ensemble method. However, this approach relies on the data quality and missing values. In this paper, we propose a new ensemble classifier to overcome overfitting and biasness...
Slowing cystic fibrosis (CF) lung disease progression is crucial to survival, but point-of-care technologies aimed at early detection — and possibly prevention — of rapid lung function decline are limited. This proof-of-principle study leverages a rich national patient registry and follow-up data on a local CF cohort to build an algorithm and prototype prognostic tool aimed at early detection of rapid...
The biological signals collected by the multi-electrode array are contaminated by heavy noise signals. How to quickly classify the original action potential from the measured noisy signals accurately is the basis of researches in the field of neuroscience. In this paper, we analyze the characteristics and shortcomings of Wave-clus sorting algorithm, and present a novel sorting algorithm to solve the...
According to the noise and overlapping characteristics of agricultural irrigation water quality monitoring data for the comprehensive evaluation may bring about the boundary fuzzy problem. This paper proposes an improved Genetic Algorithm (GA) to avoid premature convergence, the global optimal solution of the function of the Projection Pursuit (PP) function is used as the comprehensive evaluation...
Digital libraries can provide information services for users with diverse needs. Due to a large amount of data that exists in digital library systems, including text and multimedia resources, with different cohorts of users, and the challenges with existing digital library systems in terms of maintaining privacy and confidentiality, it is very difficult to provide personalised library services and...
The fuzzy restricted Boltzmann machine (FRBM) is demonstrated to have better generative and discriminative capabilities than traditional RBM. We now further investigate and compare the generative ability of DBN with FRBM on image reconstruction. The DBN is pre-trained by stacking RBMs layer by layer and then fine-tuned by the wake-sleep algorithm. Then the FRBM, RBM and DBN are compared in detail...
Research on activity recognition provides a wide range of ubiquitous computing applications. Once activities are recognized, computers can use this information to provide people with suitable services. In the past decade, many classification algorithms have been applied to activity recognition. However, most of them were based on the use of inertial measurement sensors, such as tri-axial accelerometers...
To improve the accuracy of fault diagnosis for multimodal process, an ensemble fault diagnosis approach based on IJITL-WEMD-RLSSVM (short words of, improved just-intime-learning (IJITL), empirical mode decomposition with window (WEMD), recursive least squares support vector machine (RLSSVM)) is presented. Firstly, the corresponding data are found in historical data through IJITL method and the small...
This paper presents a design for a High Performance Machine Learning (HPML) framework to support DDDAS decision processes. The HPML framework can provide a high performance computing environment to implement large scale machine learning algorithms that leverages Big Data tools (e.g., SPARK, Hadoop), parallel algorithms, and MapReduce programming paradigm. The framework provides the following capabilities:...
In view of textual remote sensing image classification, a classification approach based on Extreme Learning Machine (ELM) in introduced. As the performance of ELM is mainly affected by the value of input weights and hidden biases genetic algorithm (GA) and particle swarm optimization algorithm (PSO) have been used to learn these parameters for ELM in order to improve the stability of extreme learning...
The article describes typical problems solved by means of inductive modeling, provides information on the development of this scientific direction in Ukraine and abroad, characterizes the basic fundamental, applied and technological achievements, and formulates the most promising ways of further research.
In this work, we present a performance comparison of the Multi Layer Perceptron (MLP), Support Vector Machines (SVM) and Voted Perceptron (VP) when applied to a social signal processing task. The signal processing task is in the field of computational politics where the aim is to predict the political parties of American congress members based on their response to certain questions. Using this dataset...
Recent meta-learning approaches are oriented towards algorithm selection, optimization or recommendation of existing algorithms. In this paper we show how Inductive algorithms constructed from building blocks on small data sub-sample can be scaled up to model large data sets. We demonstrate how one particular template (simple ensemble of fast sigmoidal regression models) outperforms state-of-the-art...
Flight parameters record the flight state and performance of the each flight phase. The precise division of the aircraft flight process using flight parameters can not only perform the stage quality evaluation of the whole flight process, but also can detect the aircraft faults. In this paper, the decision tree classifier is used to divide the flight parameters. The parameter reduction is carried...
With advances in technology, high volumes of a wide variety of valuable data of different veracity can be easily collected or generated at a high velocity in the current era of big data. Embedded in these big data are implicit, previously unknown and potentially useful information. Hence, fast and scalable big data science and engineering solutions that mine and discover knowledge from these big data...
Automatic data classification is often performed by supervised learning algorithms, producing a model to classify new instances. Reflecting that labeled instances are expensive, semisupervised learning (SSL) methods prove to be an alternative to performing data classification, once the learning demands only a few labeled instances. There are many SSL algorithms, and graph-based ones have significant...
Data mining technology is the key technology and core content of big data age. The undergraduate data mining course introduces the basic concepts, basic principles and application techniques of data mining, as well as the characteristics and new technologies of data mining under the background of big data. According to the characteristics of undergraduate students, the curriculum should weaken the...
A large number of text data are regularly published in social networks and the media. Processing and analysis of such information is an highly required direction. This paper focuses on the way to use the entropy measure when dealing with big volumes of text data in classification. The used entropy measure stands for algorithm quality criteria when defining a class in a set of data. The work also features...
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