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Feature selection is selecting a subset of optimal features. Feature selection is being used in high dimensional data reduction and it is being used in several applications like medical, image processing, text mining, etc. Several methods were introduced for unsupervised feature selection. Among those methods some are based on filter approach and some are based on wrapper approach. In the existing...
In the Smart Grid context, one of the most broadly investigated areas is the matter of flexibility. This term is still not defined in a unified way. As such, it is frequently encountered in both transmission and distribution system studies, nonetheless, with various perspectives. To some extent, the reason for this is that flexibility is a multidimensional commodity. It can be associated with a unit...
Thanks to the advent of clock synchronization via the global positioning system (GPS), phasor measurement units (PMUs) collecting both the magnitude and phase angle with higher precision have implications for monitoring performance across the wide area of smart grids. Owing to high PMUs installation costs, developing optimal strategy of PMUs placement is important to monitor the transmission system...
We proposed an approach to predict the availability of volunteer sensor networks (VSN) node. It is based on the Stronger Intelligent selection (SIS). First, the availability of VSN node is analyzed and predicted based on its location. The stronger model is defined and Studied on the Optimization Rules and Solution Tactics of availability. A simple and efficient stronger searching mechanism is presented...
Normally, statistical methods are used to generate rankings for genes in terms of their ability to distinguish between normal and malignant tumors from a gene expression dataset. However, different statistical methods yield different ranks for same gene and there is no universally accepted method for ranking. Therefore rank aggregation is required to find the overall ranking of the set of genes. There...
Learning rate and momentum coefficient are critical parameters on back propagation algorithm because of their effect on learning speed and deviation ratio from global minimum. Hidden neuron number has an effect on classification accuracy, and excessive number of hidden neuron causes to increase the operation load. Because these parameters are selected randomly, finding the accurate values requires...
Wireless Sensor Network (WSN) is fixed in many sensing environments to capture and monitor events. The sensing values come from sensor device that may contain noise, missing values, and redundant features. Noise, missing values and redundant features should be removed from the streamed data using an efficient preprocessing mechanism and then preprocessed data can be provided for further processing...
Asynchronous or event-driven programming is now being used to develop a wide range of systems, including mobile and Web 2.0 applications, Internet-of-Things, and even distributed servers. We observe that these programs perform poorly on conventional processor architectures that are heavily optimized for the characteristics of synchronous programs. Execution characteristics of asynchronous programs...
Analyzing the system of several Chinese word segmentation algorithms for the current existing problems, we design and construct a new dictionary mechanism. We present a segmentation algorithm, forward maximum matching method that adding 1 word each time and self-define the IDC function as disambiguation algorithm to achieve a relatively complete system of experimental Chinese word segmentation. By...
In this paper, we present multiple parallelized support vector machines (MPSVMs), which aims to deal with the situation when multiple SVMs are required to be performed concurrently. The proposed MPSVM is based on an optimization procedure for nonnegative quadratic programming (NQP), called multiplicative updates. By using graphical processing units (GPUs) to parallelize the numerical procedure of...
Data sets ordinarily includes a huge number of attributes, with irrelevant and redundant attributes. Redundant and irrelevant attributes might minimize the classification accuracy because of the huge search space. The main goal of attribute reduction is choose a subset of relevant attributes from a huge number of available attributes to obtain comparable or even better classification accuracy than...
In general, the Cooperative Coevolutionary Algorithms based on separability have shown good performance when solving high dimensional optimization problems. However, the number of function evaluations required for the decomposition stage of these algorithms can growth very fast, and depends on the dimensionality of the problem. In cases where a single function evaluation is computationally expensive...
The choice of hyper-parameters in Support Vector Machines (SVM)-based learning is a crucial task, since different values may degrade its performance, as well as can increase the computational burden. In this paper, we introduce a recently developed nature-inspired optimization algorithm to find out suitable values for SVM kernel mapping named Social-Spider Optimization (SSO). We compare the results...
A brain-computer interface (BCI) based on steady-state visual evoked potentials (SSVEP) is one of the most practical BCI, because of high recognition accuracies and short time training. Phase of SSVEPs can be potentially applicable for generating device commands. However, the effective method of estimating the phase of SSVEPs has not yet been established, especially, in the case of using multi-channel...
This paper presents an essential algorithm for optimization-based image processing using the bilateral filter (BLF), called constant-time transposed BLF (O(1) TBLF). Some iterative solvers for optimization problems require a pair of filters defined as multiplying a filter matrix or its transpose to vectorized images. Since the BLF can be described as a matrix form, its paired filter also exists, called...
Issues related to Wireless Sensor Networks (WSNs) are inseparable part of concerns in Big Data, due to they can provide a large amount of real-time data to the processing units. A service-oriented wireless sensor network aims to manage the procedure for data-provision by considering the term "service" as a user-specified requirement. In another words, it provides a user-friendly interface...
Feature extraction is an essential step in pattern classification, which is normally divided into two tasks: transforming the input vector into a feature vector and/or reducing its dimensionality. A well-defined feature extraction algorithm makes the subsequent classification process more effective and efficient. One of the most important feature extraction algorithms is linear discriminant analysis...
Most of the real world networks we encounter today are complex networks and one of the important characteristics of these networks is the community structure. Identifying communities in a complex network is classified as computably hard and thus many metaheuristic approaches have been proposed in the past. In this paper we propose an improved differential evolution based algorithm which exploits the...
Train Traffic Optimization System is a research targeting to seek a solution to minimize train delays and maximize train productivity in Srilanka Railways. Main purpose of the research is to find out feasible heuristic algorithm for train traffic optimization. Time table generation and application of evolutionary computing to solve above problem is another consideration of the research. This research...
In this paper we introduced a new method to optimally select the time window for a single-trial classification problem in BCI system. As a hybrid-BCI, we combine EEG and NIRS signals to improve the performance of BCI system. Since there's a coupled relationship between EEG and NIRS, we try to define the activation state of subject's brain according to the changes of hemoglobin. We therefore defined...
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