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Computational Intelligence and Neuroscience

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https://www.readbyqxmd.com/read/28512467/extra-facial-landmark-localization-via-global-shape-reconstruction
#1
Shuqiu Tan, Dongyi Chen, Chenggang Guo, Zhiqi Huang
Localizing facial landmarks is a popular topic in the field of face analysis. However, problems arose in practical applications such as handling pose variations and partial occlusions while maintaining moderate training model size and computational efficiency still challenges current solutions. In this paper, we present a global shape reconstruction method for locating extra facial landmarks comparing to facial landmarks used in the training phase. In the proposed method, the reduced configuration of facial landmarks is first decomposed into corresponding sparse coefficients...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28491091/identification-of-functionally-interconnected-neurons-using-factor-analysis
#2
Jorge H Soletta, Fernando D Farfán, Ana L Albarracín, Alvaro G Pizá, Facundo A Lucianna, Carmelo J Felice
The advances in electrophysiological methods have allowed registering the joint activity of single neurons. Thus, studies on functional dynamics of complex-valued neural networks and its information processing mechanism have been conducted. Particularly, the methods for identifying neuronal interconnections are in increasing demand in the area of neurosciences. Here, we proposed a factor analysis to identify functional interconnections among neurons via spike trains. This method was evaluated using simulations of neural discharges from different interconnections schemes...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28487725/comparison-of-the-bci-performance-between-the-semitransparent-face-pattern-and-the-traditional-face-pattern
#3
Jiao Cheng, Jing Jin, Xingyu Wang
Brain-computer interface (BCI) systems allow users to communicate with the external world by recognizing the brain activity without the assistance of the peripheral motor nervous system. P300-based BCI is one of the most common used BCI systems that can obtain high classification accuracy and information transfer rate (ITR). Face stimuli can result in large event-related potentials and improve the performance of P300-based BCI. However, previous studies on face stimuli focused mainly on the effect of various face types (i...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28484488/progress-in-eeg-based-brain-robot-interaction-systems
#4
REVIEW
Xiaoqian Mao, Mengfan Li, Wei Li, Linwei Niu, Bin Xian, Ming Zeng, Genshe Chen
The most popular noninvasive Brain Robot Interaction (BRI) technology uses the electroencephalogram- (EEG-) based Brain Computer Interface (BCI), to serve as an additional communication channel, for robot control via brainwaves. This technology is promising for elderly or disabled patient assistance with daily life. The key issue of a BRI system is to identify human mental activities, by decoding brainwaves, acquired with an EEG device. Compared with other BCI applications, such as word speller, the development of these applications may be more challenging since control of robot systems via brainwaves must consider surrounding environment feedback in real-time, robot mechanical kinematics, and dynamics, as well as robot control architecture and behavior...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28479916/corrigendum-to-time-shift-correlation-algorithm-for-p300-event-related-potential-brain-computer-interface-implementation
#5
Ju-Chi Liu, Hung-Chyun Chou, Chien-Hsiu Chen, Yi-Tseng Lin, Chung-Hsien Kuo
[This corrects the article DOI: 10.1155/2016/3039454.].
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28473849/directed-bee-colony-optimization-algorithm-to-solve-the-nurse-rostering-problem
#6
M Rajeswari, J Amudhavel, Sujatha Pothula, P Dhavachelvan
The Nurse Rostering Problem is an NP-hard combinatorial optimization, scheduling problem for assigning a set of nurses to shifts per day by considering both hard and soft constraints. A novel metaheuristic technique is required for solving Nurse Rostering Problem (NRP). This work proposes a metaheuristic technique called Directed Bee Colony Optimization Algorithm using the Modified Nelder-Mead Method for solving the NRP. To solve the NRP, the authors used a multiobjective mathematical programming model and proposed a methodology for the adaptation of a Multiobjective Directed Bee Colony Optimization (MODBCO)...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28473848/memetic-differential-evolution-with-an-improved-contraction-criterion
#7
Lei Peng, Yanyun Zhang, Guangming Dai, Maocai Wang
Memetic algorithms with an appropriate trade-off between the exploration and exploitation can obtain very good results in continuous optimization. In this paper, we present an improved memetic differential evolution algorithm for solving global optimization problems. The proposed approach, called memetic DE (MDE), hybridizes differential evolution (DE) with a local search (LS) operator and periodic reinitialization to balance the exploration and exploitation. A new contraction criterion, which is based on the improved maximum distance in objective space, is proposed to decide when the local search starts...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28469669/pairs-prediction-of-activation-inhibition-regulation-signaling-pathway
#8
Tengjiao Wang, Yanghe Feng, Qi Wang
Uncovering the signaling architecture in protein-protein interaction (PPI) can certainly benefit the understanding of disease mechanisms and promise to facilitate the therapeutic interventions. Therefore, it is important to reveal the signaling relationship from one protein to another in terms of activation and inhibition. In this study, we propose a new measurement to characterize the regulation relationship of a PPI pair. By utilizing both Gene Ontology (GO) functional annotation and protein domain information, we developed a tool called Prediction of Activation/Inhibition Regulation Signaling Pathway (PAIRS) that takes protein interaction pairs as input and gives both known and predicted result of the human protein regulation relationship in terms of activation and inhibition...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28458687/a-bee-evolutionary-guiding-nondominated-sorting-genetic-algorithm-ii-for-multiobjective-flexible-job-shop-scheduling
#9
Qianwang Deng, Guiliang Gong, Xuran Gong, Like Zhang, Wei Liu, Qinghua Ren
Flexible job-shop scheduling problem (FJSP) is an NP-hard puzzle which inherits the job-shop scheduling problem (JSP) characteristics. This paper presents a bee evolutionary guiding nondominated sorting genetic algorithm II (BEG-NSGA-II) for multiobjective FJSP (MO-FJSP) with the objectives to minimize the maximal completion time, the workload of the most loaded machine, and the total workload of all machines. It adopts a two-stage optimization mechanism during the optimizing process. In the first stage, the NSGA-II algorithm with T iteration times is first used to obtain the initial population N, in which a bee evolutionary guiding scheme is presented to exploit the solution space extensively...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28458686/a-global-relationship-dissimilarity-measure-for-the-k-modes-clustering-algorithm
#10
Hongfang Zhou, Yihui Zhang, Yibin Liu
The k-modes clustering algorithm has been widely used to cluster categorical data. In this paper, we firstly analyzed the k-modes algorithm and its dissimilarity measure. Based on this, we then proposed a novel dissimilarity measure, which is named as GRD. GRD considers not only the relationships between the object and all cluster modes but also the differences of different attributes. Finally the experiments were made on four real data sets from UCI. And the corresponding results show that GRD achieves better performance than two existing dissimilarity measures used in k-modes and Cao's algorithms...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28408923/novel-methods-for-measuring-depth-of-anesthesia-by-quantifying-dominant-information-flow-in-multichannel-eegs
#11
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
#12
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
#13
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
#14
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
#15
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
#16
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
#17
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
#18
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
#19
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
#20
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
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