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Cardiovascular risk prediction is a vital aspect of personalized health care. In this study, retinal vascular function is assessed in asymptomatic participants who are classified into risk groups based on Framingham Risk Score. Feature selection, oversampling and state-of-the-art classification methods are applied to provide a sound individual risk prediction based on Retinal Vessel Analysis (RVA)...
Anomalies in computer networks has increased in the last decades and raised concern to create techniques to identify these unusual traffic patterns. This research aims to use data mining techniques in order to correctly identify these anomalies, particularly in spam detection, for it was applied an collection of machine learning algorithms for data mining tasks and an dataset called SPAMBASE to identify...
Earthquakes are what happens when immediate vibrations which shake earth surface, spread as waves as a result of earth crust cracks. Earthquakes depend on variables such as the way of spreading of these waves, calculation of these waves and calculating methods, evaluations of these recorded data sets. Predicting probable earthquakes and minimizing the damages are the important factors. Decision systems...
With the huge growth in the volume of data today, there is an enhanced need to extract meaningful information from the data. Data mining contributes towards this and finds its application across various diverse domains such as in information technology, retail, stock markets, banking, and healthcare among others. The increase in population coupled with the growth in diseases has necessitated the inclusion...
This paper presents a comparison between DMPML and three data mining applications (Weka, RapidMiner, and KN-IME) that implement the directed graph approach, concerning the time spent to create and execute the data preparation tasks for two data mining algorithms. The tests were executed using different types of data sets: numerical, categorical, and mixed. We observed that the scheme used by the DMPML...
Reversible watermarking aims to restore the original data after watermark extraction, which is more suitable for copyright protection of 2D-vector maps. In this paper, we present a reversible watermarking strategy for 2D-vector maps based on iterative embedding. It begins with vertex grouping of each polyline. Then only the highly correlated data sets are selected as the cover data for iterative embedding...
Learning in a non-stationary environment and in the presence of class imbalance has been receiving more recognition from the computational intelligence community, but little work has been done to create an algorithm or a framework that can handle both issues simultaneously. We have recently introduced a new member to the Learn++ family of algorithms, Learn++.NSE, which is designed to track non-stationary...
Non-negative matrix factorization (NMF) is useful in finding basis information of non-negative data. It is a new dimension reduction method. Currently, the multiplicative update method proposed by Lee and Seung is a simple and popular way to find the factorization. In this paper, an adaptive NMF method is proposed. The primary ideas of methods as follows: when columns are added to the data matrix,...
Routing table size and route length are two key metrics for evaluating a routing scheme, and there is an obvious tradeoff between them, i.e. the space-stretch tradeoff. The generic shortest-path routing takes an extreme position by only optimizing the route length, hence the routing table size grows linearly with the network size. Compact routing refers to design of routing schemes with optimized...
This paper considers when a discrete-time periodic non-homogeneous system can be transformed to a time-invariant one by using regular linear mappings of state variables, inputs and outputs, respectively. The problem on a homogeneous system has been already solved as discrete-time Floquet transformation, and also the similarity classes of Floquet transformations have been characterized. Those previous...
This paper considers existence and uniqueness of solution for cellular neural networks with impulsive effects. We present that the iterative analysis method dealing with the existence and uniqueness of solution for impulsive cellular neural networks is still valid. What's more, we get some new results which extend and improve the earlier publications.
Signature verification is the process used to recognize an individual's handwritten signature to prevent fraud. In this paper pressure at the pen-tip together with the x, and y coordinates of the signature are measured and features extracted from these are used to verify the signature. A pressure pad was used to obtain signature samples. A signature verification system using SOM neural network was...
This paper shows text document dimension reduction and clustering technique which is called the bigradient learning algorithm. This algorithm is based on the two learning parameters. The results show, that bigradient learning algorithm, used with proper selected values, does almost the same clustering as the other arbitrary PCA learning method by neural network. At the end, the three linear PCA methods...
Feature Subset Selection has become the focus of much research in areas of application for Multivariate Time Series (MTS). MTS data sets are common in many multimedia and medical applications such as gesture recognition, video sequence matching and EEG/ECG data analysis. MTS data sets are high dimensional as they consist of a series of observations of many variables at a time. The objective of feature...
Because the optimized grey models fits the approximate nonhomogeneous index sequences, this paper puts forward the concepts of the broad class ratio approach degree and the broad smooth degree which can judge the approach degree of the raw data and the nonhomogeneous index sequences, and deduces some remarks, then introduces the prior check of modeling the nonhomogeneous exponential sequences by these...
The likelihood of reaching agreement was increased and quality of an agreement was promoted by exchanging arguments which influence each others’ states, and trust mechanism was introduced into the process of negotiation by combining the agent’s honesty and agent’s capability. This paper proposed a new reward-based negotiation’s model by introducing the concept and algorithm of agent’s honesty and...
This paper discusses the design and use of a digital community noticeboard (called Nnub) located at a suburban general store. The intention is to design information and communication technology (ICT) such as situated displays and Internet technologies to support local communications. We use a reflective, agile, and iterative design (RAID) framework to evolve the technology, aiming to engage local...
In this study radial basis function neural network (RBFNN) was trained by different methods to study performance of each method on classification of ECG beats. To train the neural networks six types of beats including, normal beat (N), premature ventricular contraction (PVC), fusion of ventricular and normal beat (F), atrial premature beat (A), right bundle branch blok beat (R), and fusion of paced...
Recent years HOG algorithm has been used to recognize objects in images, with complex content, with a very high success rate. Hardware implementation of this algorithm is very important because of the fact that it can be used in many object recognition applications. In this work HOG algorithm is implemented on FPGA to recognize different geometrical figures with a very high success rate. Objects vertical...
A multilayer perceptron is a feedforward artificial neural network model that maps sets of input data onto a set of appropriate output. It is a modification of the standard linear perceptron in that it uses three or more layers of neurons (nodes) with nonlinear activation functions, and is more powerful than the perceptron in that it can distinguish data that is not linearly separable, or separable...
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