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Computational Intelligence and Neuroscience

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https://www.readbyqxmd.com/read/28408923/novel-methods-for-measuring-depth-of-anesthesia-by-quantifying-dominant-information-flow-in-multichannel-eegs
#1
Kab-Mun Cha, Byung-Moon Choi, Gyu-Jeong Noh, Hyun-Chool Shin
In this paper, we propose novel methods for measuring depth of anesthesia (DOA) by quantifying dominant information flow in multichannel EEGs. Conventional methods mainly use few EEG channels independently and most of multichannel EEG based studies are limited to specific regions of the brain. Therefore the function of the cerebral cortex over wide brain regions is hardly reflected in DOA measurement. Here, DOA is measured by the quantification of dominant information flow obtained from principle bipartition...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28392799/image-encryption-algorithm-based-on-hyperchaotic-maps-and-nucleotide-sequences-database
#2
Ying Niu, Xuncai Zhang, Feng Han
Image encryption technology is one of the main means to ensure the safety of image information. Using the characteristics of chaos, such as randomness, regularity, ergodicity, and initial value sensitiveness, combined with the unique space conformation of DNA molecules and their unique information storage and processing ability, an efficient method for image encryption based on the chaos theory and a DNA sequence database is proposed. In this paper, digital image encryption employs a process of transforming the image pixel gray value by using chaotic sequence scrambling image pixel location and establishing superchaotic mapping, which maps quaternary sequences and DNA sequences, and by combining with the logic of the transformation between DNA sequences...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28356908/random-deep-belief-networks-for-recognizing-emotions-from-speech-signals
#3
REVIEW
Guihua Wen, Huihui Li, Jubing Huang, Danyang Li, Eryang Xun
Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN) can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN) method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28337216/smart-data-where-the-big-data-meets-the-semantics
#4
EDITORIAL
Trong H Duong, Hong Q Nguyen, Geun S Jo
No abstract text is available yet for this article.
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28331490/ranking-of-sites-for-installation-of-hydropower-plant-using-mlp-neural-network-trained-with-ga-a-madm-approach
#5
Benjamin A Shimray, Kh Manglem Singh, Thongam Khelchandra, R K Mehta
Every energy system which we consider is an entity by itself, defined by parameters which are interrelated according to some physical laws. In recent year tremendous importance is given in research on site selection in an imprecise environment. In this context, decision making for the suitable location of power plant installation site is an issue of relevance. Environmental impact assessment is often used as a legislative requirement in site selection for decades. The purpose of this current work is to develop a model for decision makers to rank or classify various power plant projects according to multiple criteria attributes such as air quality, water quality, cost of energy delivery, ecological impact, natural hazard, and project duration...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28321249/mexican-hat-wavelet-kernel-elm-for-multiclass-classification
#6
Jie Wang, Yi-Fan Song, Tian-Lei Ma
Kernel extreme learning machine (KELM) is a novel feedforward neural network, which is widely used in classification problems. To some extent, it solves the existing problems of the invalid nodes and the large computational complexity in ELM. However, the traditional KELM classifier usually has a low test accuracy when it faces multiclass classification problems. In order to solve the above problem, a new classifier, Mexican Hat wavelet KELM classifier, is proposed in this paper. The proposed classifier successfully improves the training accuracy and reduces the training time in the multiclass classification problems...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28321248/patch-based-multiple-instance-learning-algorithm-for-object-tracking
#7
Zhenjie Wang, Lijia Wang, Hua Zhang
To deal with the problems of illumination changes or pose variations and serious partial occlusion, patch based multiple instance learning (P-MIL) algorithm is proposed. The algorithm divides an object into many blocks. Then, the online MIL algorithm is applied on each block for obtaining strong classifier. The algorithm takes account of both the average classification score and classification scores of all the blocks for detecting the object. In particular, compared with the whole object based MIL algorithm, the P-MIL algorithm detects the object according to the unoccluded patches when partial occlusion occurs...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28316619/a-method-for-consensus-reaching-in-product-kansei-evaluation-using-advanced-particle-swarm-optimization
#8
Yan-Pu Yang
Consumers' opinions toward product design alternatives are often subjective and perceptual, which reflect their perception about a product and can be described using Kansei adjectives. Therefore, Kansei evaluation is often employed to determine consumers' preference. However, how to identify and improve the reliability of consumers' Kansei evaluation opinions toward design alternatives has an important role in adding additional insurance and reducing uncertainty to successful product design. To solve this problem, this study employs a consensus model to measure consistence among consumers' opinions, and an advanced particle swarm optimization (PSO) algorithm combined with Linearly Decreasing Inertia Weight (LDW) method is proposed for consensus reaching by minimizing adjustment of consumers' opinions...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28316618/an-evolutionary-method-for-financial-forecasting-in-microscopic-high-speed-trading-environment
#9
Chien-Feng Huang, Hsu-Chih Li
The advancement of information technology in financial applications nowadays have led to fast market-driven events that prompt flash decision-making and actions issued by computer algorithms. As a result, today's markets experience intense activity in the highly dynamic environment where trading systems respond to others at a much faster pace than before. This new breed of technology involves the implementation of high-speed trading strategies which generate significant portion of activity in the financial markets and present researchers with a wealth of information not available in traditional low-speed trading environments...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28316617/an-efficient-framework-for-eeg-analysis-with-application-to-hybrid-brain-computer-interfaces-based-on-motor-imagery-and-p300
#10
Jinyi Long, Jue Wang, Tianyou Yu
The hybrid brain computer interface (BCI) based on motor imagery (MI) and P300 has been a preferred strategy aiming to improve the detection performance through combining the features of each. However, current methods used for combining these two modalities optimize them separately, which does not result in optimal performance. Here, we present an efficient framework to optimize them together by concatenating the features of MI and P300 in a block diagonal form. Then a linear classifier under a dual spectral norm regularizer is applied to the combined features...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28316616/a-novel-graph-constructor-for-semisupervised-discriminant-analysis-combined-low-rank-and-k-nearest-neighbor-graph
#11
Baokai Zu, Kewen Xia, Yongke Pan, Wenjia Niu
Semisupervised Discriminant Analysis (SDA) is a semisupervised dimensionality reduction algorithm, which can easily resolve the out-of-sample problem. Relative works usually focus on the geometric relationships of data points, which are not obvious, to enhance the performance of SDA. Different from these relative works, the regularized graph construction is researched here, which is important in the graph-based semisupervised learning methods. In this paper, we propose a novel graph for Semisupervised Discriminant Analysis, which is called combined low-rank and k-nearest neighbor (LRKNN) graph...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28316615/a-robust-shape-reconstruction-method-for-facial-feature-point-detection
#12
Shuqiu Tan, Dongyi Chen, Chenggang Guo, Zhiqi Huang
Facial feature point detection has been receiving great research advances in recent years. Numerous methods have been developed and applied in practical face analysis systems. However, it is still a quite challenging task because of the large variability in expression and gestures and the existence of occlusions in real-world photo shoot. In this paper, we present a robust sparse reconstruction method for the face alignment problems. Instead of a direct regression between the feature space and the shape space, the concept of shape increment reconstruction is introduced...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28316614/image-classification-using-biomimetic-pattern-recognition-with-convolutional-neural-networks-features
#13
Liangji Zhou, Qingwu Li, Guanying Huo, Yan Zhou
As a typical deep-learning model, Convolutional Neural Networks (CNNs) can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR) with CNNs is proposed for image classification...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28293256/ranking-support-vector-machine-with-kernel-approximation
#14
Kai Chen, Rongchun Li, Yong Dou, Zhengfa Liang, Qi Lv
Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM) is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels) can give higher accuracy than linear RankSVM (RankSVM with a linear kernel) for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28280505/advanced-interval-type-2-fuzzy-sliding-mode-control-for-robot-manipulator
#15
Ji-Hwan Hwang, Young-Chang Kang, Jong-Wook Park, Dong W Kim
In this paper, advanced interval type-2 fuzzy sliding mode control (AIT2FSMC) for robot manipulator is proposed. The proposed AIT2FSMC is a combination of interval type-2 fuzzy system and sliding mode control. For resembling a feedback linearization (FL) control law, interval type-2 fuzzy system is designed. For compensating the approximation error between the FL control law and interval type-2 fuzzy system, sliding mode controller is designed, respectively. The tuning algorithms are derived in the sense of Lyapunov stability theorem...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28280504/reversible-data-hiding-based-on-dna-computing
#16
Bin Wang, Yingjie Xie, Shihua Zhou, Changjun Zhou, Xuedong Zheng
Biocomputing, especially DNA, computing has got great development. It is widely used in information security. In this paper, a novel algorithm of reversible data hiding based on DNA computing is proposed. Inspired by the algorithm of histogram modification, which is a classical algorithm for reversible data hiding, we combine it with DNA computing to realize this algorithm based on biological technology. Compared with previous results, our experimental results have significantly improved the ER (Embedding Rate)...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28261267/a-novel-multilevel-svd-method-to-improve-multistep-ahead-forecasting-in-traffic-accidents-domain
#17
Lida Barba, Nibaldo Rodríguez
Here is proposed a novel method for decomposing a nonstationary time series in components of low and high frequency. The method is based on Multilevel Singular Value Decomposition (MSVD) of a Hankel matrix. The decomposition is used to improve the forecasting accuracy of Multiple Input Multiple Output (MIMO) linear and nonlinear models. Three time series coming from traffic accidents domain are used. They represent the number of persons with injuries in traffic accidents of Santiago, Chile. The data were continuously collected by the Chilean Police and were weekly sampled from 2000:1 to 2014:12...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28255297/a-dynamic-bioinspired-neural-network-based-real-time-path-planning-method-for-autonomous-underwater-vehicles
#18
Jianjun Ni, Liuying Wu, Pengfei Shi, Simon X Yang
Real-time path planning for autonomous underwater vehicle (AUV) is a very difficult and challenging task. Bioinspired neural network (BINN) has been used to deal with this problem for its many distinct advantages: that is, no learning process is needed and realization is also easy. However, there are some shortcomings when BINN is applied to AUV path planning in a three-dimensional (3D) unknown environment, including complex computing problem when the environment is very large and repeated path problem when the size of obstacles is bigger than the detection range of sensors...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28255296/a-theoretical-analysis-of-why-hybrid-ensembles-work
#19
Kuo-Wei Hsu
Inspired by the group decision making process, ensembles or combinations of classifiers have been found favorable in a wide variety of application domains. Some researchers propose to use the mixture of two different types of classification algorithms to create a hybrid ensemble. Why does such an ensemble work? The question remains. Following the concept of diversity, which is one of the fundamental elements of the success of ensembles, we conduct a theoretical analysis of why hybrid ensembles work, connecting using different algorithms to accuracy gain...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28250765/a-novel-ensemble-method-for-imbalanced-data-learning-bagging-of-extrapolation-smote-svm
#20
Qi Wang, ZhiHao Luo, JinCai Huang, YangHe Feng, Zhong Liu
Class imbalance ubiquitously exists in real life, which has attracted much interest from various domains. Direct learning from imbalanced dataset may pose unsatisfying results overfocusing on the accuracy of identification and deriving a suboptimal model. Various methodologies have been developed in tackling this problem including sampling, cost-sensitive, and other hybrid ones. However, the samples near the decision boundary which contain more discriminative information should be valued and the skew of the boundary would be corrected by constructing synthetic samples...
2017: Computational Intelligence and Neuroscience
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