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Computational Intelligence and Neuroscience

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https://www.readbyqxmd.com/read/28932238/cognitive-based-eeg-bcis-and-human-brain-robot-interactions
#1
EDITORIAL
Wei Li, Jing Jin, Feng Duan
No abstract text is available yet for this article.
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28932237/a-new-hybrid-model-fpa-svm-considering-cointegration-for-particular-matter-concentration-forecasting-a-case-study-of-kunming-and-yuxi-china
#2
Weide Li, Demeng Kong, Jinran Wu
Air pollution in China is becoming more serious especially for the particular matter (PM) because of rapid economic growth and fast expansion of urbanization. To solve the growing environment problems, daily PM2.5 and PM10 concentration data form January 1, 2015, to August 23, 2016, in Kunming and Yuxi (two important cities in Yunnan Province, China) are used to present a new hybrid model CI-FPA-SVM to forecast air PM2.5 and PM10 concentration in this paper. The proposed model involves two parts. Firstly, due to its deficiency to assess the possible correlation between different variables, the cointegration theory is introduced to get the input-output relationship and then obtain the nonlinear dynamical system with support vector machine (SVM), in which the parameters c and g are optimized by flower pollination algorithm (FPA)...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28912803/optimal-parameter-selection-for-support-vector-machine-based-on-artificial-bee-colony-algorithm-a-case-study-of-grid-connected-pv-system-power-prediction
#3
Xiang-Ming Gao, Shi-Feng Yang, San-Bo Pan
Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28912802/chaotic-image-encryption-algorithm-based-on-bit-permutation-and-dynamic-dna-encoding
#4
Xuncai Zhang, Feng Han, Ying Niu
With the help of the fact that chaos is sensitive to initial conditions and pseudorandomness, combined with the spatial configurations in the DNA molecule's inherent and unique information processing ability, a novel image encryption algorithm based on bit permutation and dynamic DNA encoding is proposed here. The algorithm first uses Keccak to calculate the hash value for a given DNA sequence as the initial value of a chaotic map; second, it uses a chaotic sequence to scramble the image pixel locations, and the butterfly network is used to implement the bit permutation...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28912801/efficient-multiple-kernel-learning-algorithms-using-low-rank-representation
#5
Wenjia Niu, Kewen Xia, Baokai Zu, Jianchuan Bai
Unlike Support Vector Machine (SVM), Multiple Kernel Learning (MKL) allows datasets to be free to choose the useful kernels based on their distribution characteristics rather than a precise one. It has been shown in the literature that MKL holds superior recognition accuracy compared with SVM, however, at the expense of time consuming computations. This creates analytical and computational difficulties in solving MKL algorithms. To overcome this issue, we first develop a novel kernel approximation approach for MKL and then propose an efficient Low-Rank MKL (LR-MKL) algorithm by using the Low-Rank Representation (LRR)...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28912800/gaze-estimation-method-using-analysis-of-electrooculogram-signals-and-kinect-sensor
#6
Keiko Sakurai, Mingmin Yan, Koichi Tanno, Hiroki Tamura
A gaze estimation system is one of the communication methods for severely disabled people who cannot perform gestures and speech. We previously developed an eye tracking method using a compact and light electrooculogram (EOG) signal, but its accuracy is not very high. In the present study, we conducted experiments to investigate the EOG component strongly correlated with the change of eye movements. The experiments in this study are of two types: experiments to see objects only by eye movements and experiments to see objects by face and eye movements...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28894463/joint-extraction-of-entities-and-relations-using-reinforcement-learning-and-deep-learning
#7
Yuntian Feng, Hongjun Zhang, Wenning Hao, Gang Chen
We use both reinforcement learning and deep learning to simultaneously extract entities and relations from unstructured texts. For reinforcement learning, we model the task as a two-step decision process. Deep learning is used to automatically capture the most important information from unstructured texts, which represent the state in the decision process. By designing the reward function per step, our proposed method can pass the information of entity extraction to relation extraction and obtain feedback in order to extract entities and relations simultaneously...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28894462/a-novel-strategy-for-minimum-attribute-reduction-based-on-rough-set-theory-and-fish-swarm-algorithm
#8
Yuebin Su, Jin Guo
For data mining, reducing the unnecessary redundant attributes which was known as attribute reduction (AR), in particular, reducts with minimal cardinality, is an important preprocessing step. In the paper, by a coding method of combination subset of attributes set, a novel search strategy for minimal attribute reduction based on rough set theory (RST) and fish swarm algorithm (FSA) is proposed. The method identifies the core attributes by discernibility matrix firstly and all the subsets of noncore attribute sets with the same cardinality were encoded into integers as the individuals of FSA...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28894461/a-grey-wolf-optimizer-for-modular-granular-neural-networks-for-human-recognition
#9
Daniela Sánchez, Patricia Melin, Oscar Castillo
A grey wolf optimizer for modular neural network (MNN) with a granular approach is proposed. The proposed method performs optimal granulation of data and design of modular neural networks architectures to perform human recognition, and to prove its effectiveness benchmark databases of ear, iris, and face biometric measures are used to perform tests and comparisons against other works. The design of a modular granular neural network (MGNN) consists in finding optimal parameters of its architecture; these parameters are the number of subgranules, percentage of data for the training phase, learning algorithm, goal error, number of hidden layers, and their number of neurons...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28874909/identification-of-anisomerous-motor-imagery-eeg-signals-based-on-complex-algorithms
#10
Rensong Liu, Zhiwen Zhang, Feng Duan, Xin Zhou, Zixuan Meng
Motor imagery (MI) electroencephalograph (EEG) signals are widely applied in brain-computer interface (BCI). However, classified MI states are limited, and their classification accuracy rates are low because of the characteristics of nonlinearity and nonstationarity. This study proposes a novel MI pattern recognition system that is based on complex algorithms for classifying MI EEG signals. In electrooculogram (EOG) artifact preprocessing, band-pass filtering is performed to obtain the frequency band of MI-related signals, and then, canonical correlation analysis (CCA) combined with wavelet threshold denoising (WTD) is used for EOG artifact preprocessing...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28874908/application-of-the-intuitionistic-fuzzy-intercriteria-analysis-method-with-triples-to-a-neural-network-preprocessing-procedure
#11
Sotir Sotirov, Vassia Atanassova, Evdokia Sotirova, Lyubka Doukovska, Veselina Bureva, Deyan Mavrov, Jivko Tomov
The approach of InterCriteria Analysis (ICA) was applied for the aim of reducing the set of variables on the input of a neural network, taking into account the fact that their large number increases the number of neurons in the network, thus making them unusable for hardware implementation. Here, for the first time, with the help of the ICA method, correlations between triples of the input parameters for training of the neural networks were obtained. In this case, we use the approach of ICA for data preprocessing, which may yield reduction of the total time for training the neural networks, hence, the time for the network's processing of data and images...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28819355/box-office-forecasting-considering-competitive-environment-and-word-of-mouth-in-social-networks-a-case-study-of-korean-film-market
#12
Taegu Kim, Jungsik Hong, Pilsung Kang
Accurate box office forecasting models are developed by considering competition and word-of-mouth (WOM) effects in addition to screening-related information. Nationality, genre, ratings, and distributors of motion pictures running concurrently with the target motion picture are used to describe the competition, whereas the numbers of informative, positive, and negative mentions posted on social network services (SNS) are used to gauge the atmosphere spread by WOM. Among these candidate variables, only significant variables are selected by genetic algorithm (GA), based on which machine learning algorithms are trained to build forecasting models...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28811820/modified-mahalanobis-taguchi-system-for-imbalance-data-classification
#13
Mahmoud El-Banna
The Mahalanobis Taguchi System (MTS) is considered one of the most promising binary classification algorithms to handle imbalance data. Unfortunately, MTS lacks a method for determining an efficient threshold for the binary classification. In this paper, a nonlinear optimization model is formulated based on minimizing the distance between MTS Receiver Operating Characteristics (ROC) curve and the theoretical optimal point named Modified Mahalanobis Taguchi System (MMTS). To validate the MMTS classification efficacy, it has been benchmarked with Support Vector Machines (SVMs), Naive Bayes (NB), Probabilistic Mahalanobis Taguchi Systems (PTM), Synthetic Minority Oversampling Technique (SMOTE), Adaptive Conformal Transformation (ACT), Kernel Boundary Alignment (KBA), Hidden Naive Bayes (HNB), and other improved Naive Bayes algorithms...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28811819/adaptive-resource-utilization-prediction-system-for-infrastructure-as-a-service-cloud
#14
Qazi Zia Ullah, Shahzad Hassan, Gul Muhammad Khan
Infrastructure as a Service (IaaS) cloud provides resources as a service from a pool of compute, network, and storage resources. Cloud providers can manage their resource usage by knowing future usage demand from the current and past usage patterns of resources. Resource usage prediction is of great importance for dynamic scaling of cloud resources to achieve efficiency in terms of cost and energy consumption while keeping quality of service. The purpose of this paper is to present a real-time resource usage prediction system...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28811818/prototype-generation-using-self-organizing-maps-for-informativeness-based-classifier
#15
Leandro Juvêncio Moreira, Leandro A Silva
The k nearest neighbor is one of the most important and simple procedures for data classification task. The kNN, as it is called, requires only two parameters: the number of k and a similarity measure. However, the algorithm has some weaknesses that make it impossible to be used in real problems. Since the algorithm has no model, an exhaustive comparison of the object in classification analysis and all training dataset is necessary. Another weakness is the optimal choice of k parameter when the object analyzed is in an overlap region...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28808442/firefly-mating-algorithm-for-continuous-optimization-problems
#16
Amarita Ritthipakdee, Arit Thammano, Nol Premasathian, Duangjai Jitkongchuen
This paper proposes a swarm intelligence algorithm, called firefly mating algorithm (FMA), for solving continuous optimization problems. FMA uses genetic algorithm as the core of the algorithm. The main feature of the algorithm is a novel mating pair selection method which is inspired by the following 2 mating behaviors of fireflies in nature: (i) the mutual attraction between males and females causes them to mate and (ii) fireflies of both sexes are of the multiple-mating type, mating with multiple opposite sex partners...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28804496/shape-completion-using-deep-boltzmann-machine
#17
Zheng Wang, Qingbiao Wu
Shape completion is an important task in the field of image processing. An alternative method is to capture the shape information and finish the completion by a generative model, such as Deep Boltzmann Machine. With its powerful ability to deal with the distribution of the shapes, it is quite easy to acquire the result by sampling from the model. In this paper, we make use of the hidden activation of the DBM and incorporate it with the convolutional shape features to fit a regression model. We compare the output of the regression model with the incomplete shape feature in order to set a proper and compact mask for sampling from the DBM...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28781592/patch-based-principal-component-analysis-for-face-recognition
#18
Tai-Xiang Jiang, Ting-Zhu Huang, Xi-Le Zhao, Tian-Hui Ma
We have proposed a patch-based principal component analysis (PCA) method to deal with face recognition. Many PCA-based methods for face recognition utilize the correlation between pixels, columns, or rows. But the local spatial information is not utilized or not fully utilized in these methods. We believe that patches are more meaningful basic units for face recognition than pixels, columns, or rows, since faces are discerned by patches containing eyes and noses. To calculate the correlation between patches, face images are divided into patches and then these patches are converted to column vectors which would be combined into a new "image matrix...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28781591/feature-selection-and-parameters-optimization-of-svm-using-particle-swarm-optimization-for-fault-classification-in-power-distribution-systems
#19
Ming-Yuan Cho, Thi Thom Hoang
Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO) based support vector machine (SVM) classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR) method with a pseudorandom binary sequence (PRBS) stimulus has been used to generate a dataset for purposes of classification...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28761440/fuzzy-classification-of-high-resolution-remote-sensing-scenes-using-visual-attention-features
#20
Linyi Li, Tingbao Xu, Yun Chen
In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification...
2017: Computational Intelligence and Neuroscience
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