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Computational Intelligence and Neuroscience

Atcharin Klomsae, Sansanee Auephanwiriyakul, Nipon Theera-Umpon
Neurodegenerative diseases that affect serious gait abnormalities include Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and Huntington disease (HD). These diseases lead to gait rhythm distortion that can be determined by stride time interval of footfall contact times. In this paper, we present a new method for gait classification of neurodegenerative diseases. In particular, we utilize a symbolic aggregate approximation algorithm to convert left-foot stride-stride interval into a sequence of symbols using a symbolic aggregate approximation...
2018: Computational Intelligence and Neuroscience
Bing Han, Huayou Chen, Jiaming Zhu, Jinpei Liu
This paper proposes an approach to linguistic multiple attribute decision-making problems with interactive unbalanced linguistic assessment information by unbalanced linguistic generalized Heronian mean aggregation operators. First, some generalized Heronian mean aggregation operators with unbalanced linguistic information are proposed, involving the unbalanced linguistic generalized arithmetic Heronian mean operator and the unbalanced linguistic generalized geometric Heronian mean operator. For the situation that the input arguments have different degrees of importance, the unbalanced linguistic generalized weighted arithmetic Heronian mean operator and the unbalanced linguistic generalized weighted geometric Heronian mean operator are developed...
2018: Computational Intelligence and Neuroscience
Roberto Munoz, Rodrigo Olivares, Carla Taramasco, Rodolfo Villarroel, Ricardo Soto, Thiago S Barcelos, Erick Merino, María Francisca Alonso-Sánchez
Emotions are a critical aspect of human behavior. One widely used technique for research in emotion measurement is based on the use of EEG signals. In general terms, the first step of signal processing is the elimination of noise, which can be done in manual or automatic terms. The next step is determining the feature vector using, for example, entropy calculation and its variations to generate a classification model. It is possible to use this approach to classify theoretical models such as the Circumplex model...
2018: Computational Intelligence and Neuroscience
Abdu Gumaei, Rachid Sammouda, Abdul Malik S Al-Salman, Ahmed Alsanad
Multispectral palmprint recognition system (MPRS) is an essential technology for effective human identification and verification tasks. To improve the accuracy and performance of MPRS, a novel approach based on autoencoder (AE) and regularized extreme learning machine (RELM) is proposed in this paper. The proposed approach is intended to make the recognition faster by reducing the number of palmprint features without degrading the accuracy of classifier. To achieve this objective, first, the region of interest (ROI) from palmprint images is extracted by David Zhang's method...
2018: Computational Intelligence and Neuroscience
Victor Hugo C de Albuquerque, Robertas Damaševičius, João Manuel R S Tavares, Plácido R Pinheiro
No abstract text is available yet for this article.
2018: Computational Intelligence and Neuroscience
Yange Sun, Zhihai Wang, Yang Bai, Honghua Dai, Saeid Nahavandi
It is common in real-world data streams that previously seen concepts will reappear, which suggests a unique kind of concept drift, known as recurring concepts. Unfortunately, most of existing algorithms do not take full account of this case. Motivated by this challenge, a novel paradigm was proposed for capturing and exploiting recurring concepts in data streams. It not only incorporates a distribution-based change detector for handling concept drift but also captures recurring concept by storing recurring concepts in a classifier graph...
2018: Computational Intelligence and Neuroscience
Fangzhao Li, Changjian Wang, Xiaohui Liu, Yuxing Peng, Shiyao Jin
Wound segmentation plays an important supporting role in the wound observation and wound healing. Current methods of image segmentation include those based on traditional process of image and those based on deep neural networks. The traditional methods use the artificial image features to complete the task without large amounts of labeled data. Meanwhile, the methods based on deep neural networks can extract the image features effectively without the artificial design, but lots of training data are required...
2018: Computational Intelligence and Neuroscience
Zhenquan Shi, Shiping Chen
The knowledge characteristics weighting plays an extremely important role in effectively and accurately classifying knowledge. Most of the existing characteristics weighting methods always rely heavily on the experts' a priori knowledge, while rough set weighting method does not rely on experts' a priori knowledge and can meet the need of objectivity. However, the current rough set weighting methods could not obtain a balanced redundant characteristic set. Too much redundancy might cause inaccuracy, and less redundancy might cause ineffectiveness...
2018: Computational Intelligence and Neuroscience
Robertas Damaševičius, Rytis Maskeliūnas, Egidijus Kazanavičius, Marcin Woźniak
Cryptographic frameworks depend on key sharing for ensuring security of data. While the keys in cryptographic frameworks must be correctly reproducible and not unequivocally connected to the identity of a user, in biometric frameworks this is different. Joining cryptography techniques with biometrics can solve these issues. We present a biometric authentication method based on the discrete logarithm problem and Bose-Chaudhuri-Hocquenghem (BCH) codes, perform its security analysis, and demonstrate its security characteristics...
2018: Computational Intelligence and Neuroscience
Youngjoo Kim, Jiwoo You, Heejun Lee, Seung Min Lee, Cheolsoo Park
The Strong Uncorrelating Transform Complex Common Spatial Patterns (SUTCCSP) algorithm, designed for multichannel data analysis, has a limitation on keeping the correlation information among channels during the simultaneous diagonalization process of the covariance and pseudocovariance matrices. This paper focuses on the importance of preserving the correlation information among multichannel data and proposes the correlation assisted SUTCCSP (CASUT) algorithm to address this issue. The performance of the proposed algorithm was demonstrated by classifying the motor imagery electroencephalogram (EEG) dataset...
2018: Computational Intelligence and Neuroscience
Peng Wang, Kun Cheng, Yan Huang, Bo Li, Xinggui Ye, Xiuhong Chen
This paper presents a variant of multiscale quantum harmonic oscillator algorithm for multimodal optimization named MQHOA-MMO. MQHOA-MMO has only two main iterative processes: quantum harmonic oscillator process and multiscale process. In the two iterations, MQHOA-MMO only does one thing: sampling according to the wave function at different scales. A set of benchmark test functions including some challenging functions are used to test the performance of MQHOA-MMO. Experimental results demonstrate good performance of MQHOA-MMO in solving multimodal function optimization problems...
2018: Computational Intelligence and Neuroscience
Athanasios Voulodimos, Nikolaos Doulamis, George Bebis, Tania Stathaki
No abstract text is available yet for this article.
2018: Computational Intelligence and Neuroscience
Jaehong Yoon, Jungnyun Lee, Mincheol Whang
Feature of event-related potential (ERP) has not been completely understood and illiteracy problem remains unsolved. To this end, P300 peak has been used as the feature of ERP in most brain-computer interface applications, but subjects who do not show such peak are common. Recent development of convolutional neural network provides a way to analyze spatial and temporal features of ERP. Here, we train the convolutional neural network with 2 convolutional layers whose feature maps represented spatial and temporal features of event-related potential...
2018: Computational Intelligence and Neuroscience
Jianfang Cao, Lichao Chen, Min Wang, Yun Tian
The Canny operator is widely used to detect edges in images. However, as the size of the image dataset increases, the edge detection performance of the Canny operator decreases and its runtime becomes excessive. To improve the runtime and edge detection performance of the Canny operator, in this paper, we propose a parallel design and implementation for an Otsu-optimized Canny operator using a MapReduce parallel programming model that runs on the Hadoop platform. The Otsu algorithm is used to optimize the Canny operator's dual threshold and improve the edge detection performance, while the MapReduce parallel programming model facilitates parallel processing for the Canny operator to solve the processing speed and communication cost problems that occur when the Canny edge detection algorithm is applied to big data...
2018: Computational Intelligence and Neuroscience
Jihyun Kim, Thi-Thu-Huong Le, Howon Kim
[This corrects the article DOI: 10.1155/2017/4216281.].
2018: Computational Intelligence and Neuroscience
João W M de Souza, Shara S A Alves, Elizângela de S Rebouças, Jefferson S Almeida, Pedro P Rebouças Filho
Parkinson's disease affects millions of people around the world and consequently various approaches have emerged to help diagnose this disease, among which we can highlight handwriting exams. Extracting features from handwriting exams is an important contribution of the computational field for the diagnosis of this disease. In this paper, we propose an approach that measures the similarity between the exam template and the handwritten trace of the patient following the exam template. This similarity was measured using the Structural Cooccurrence Matrix to calculate how close the handwritten trace of the patient is to the exam template...
2018: Computational Intelligence and Neuroscience
Hamed Atyia Soodi, Ahmet Mete Vural
Different optimization techniques are used for the training and fine-tuning of feed forward neural networks, for the estimation of STATCOM voltages and reactive powers. In the first part, the paper presents the voltage regulation in IEEE buses using the Static Compensator (STATIC) and discusses efficient ways to solve the power systems featuring STATCOM by load flow equations. The load flow equations are solved using iterative algorithms such as Newton-Raphson method. In the second part, the paper focuses on the use of estimation techniques based on Artificial Neural Networks as an alternative to the iterative methods...
2018: Computational Intelligence and Neuroscience
N Firat Ozkan, Emin Kahya
Brain-Computer Interfaces (BCI) are systems originally developed to assist paralyzed patients allowing for commands to the computer with brain activities. This study aims to examine cognitive state with an objective, easy-to-use, and easy-to-interpret method utilizing Brain-Computer Interface systems. Seventy healthy participants completed six tasks using a Brain-Computer Interface system and participants' pupil dilation, blink rate, and Galvanic Skin Response (GSR) data were collected simultaneously. Participants filled Nasa-TLX forms following each task and task performances of participants were also measured...
2018: Computational Intelligence and Neuroscience
Yuan-Yuan Wang, Huan Zhang, Chen-Hui Qiu, Shun-Ren Xia
The paper presents a novel approach for feature selection based on extreme learning machine (ELM) and Fractional-order Darwinian particle swarm optimization (FODPSO) for regression problems. The proposed method constructs a fitness function by calculating mean square error (MSE) acquired from ELM. And the optimal solution of the fitness function is searched by an improved particle swarm optimization, FODPSO. In order to evaluate the performance of the proposed method, comparative experiments with other relative methods are conducted in seven public datasets...
2018: Computational Intelligence and Neuroscience
Computational Intelligence And Neuroscience
[This retracts the article DOI: 10.1155/2014/571632.].
2018: Computational Intelligence and Neuroscience
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