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Computational Intelligence and Neuroscience

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https://www.readbyqxmd.com/read/29123546/on-the-accuracy-and-parallelism-of-gpgpu-powered-incremental-clustering-algorithms
#1
Chunlei Chen, Li He, Huixiang Zhang, Hao Zheng, Lei Wang
Incremental clustering algorithms play a vital role in various applications such as massive data analysis and real-time data processing. Typical application scenarios of incremental clustering raise high demand on computing power of the hardware platform. Parallel computing is a common solution to meet this demand. Moreover, General Purpose Graphic Processing Unit (GPGPU) is a promising parallel computing device. Nevertheless, the incremental clustering algorithm is facing a dilemma between clustering accuracy and parallelism when they are powered by GPGPU...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/29118809/nonintrusive-load-monitoring-based-on-advanced-deep-learning-and-novel-signature
#2
Jihyun Kim, Thi-Thu-Huong Le, Howon Kim
Monitoring electricity consumption in the home is an important way to help reduce energy usage. Nonintrusive Load Monitoring (NILM) is existing technique which helps us monitor electricity consumption effectively and costly. NILM is a promising approach to obtain estimates of the electrical power consumption of individual appliances from aggregate measurements of voltage and/or current in the distribution system. Among the previous studies, Hidden Markov Model (HMM) based models have been studied very much...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/29118808/pathological-brain-detection-using-weiner-filtering-2d-discrete-wavelet-transform-probabilistic-pca-and-random-subspace-ensemble-classifier
#3
Debesh Jha, Ji-In Kim, Moo-Rak Choi, Goo-Rak Kwon
Accurate diagnosis of pathological brain images is important for patient care, particularly in the early phase of the disease. Although numerous studies have used machine-learning techniques for the computer-aided diagnosis (CAD) of pathological brain, previous methods encountered challenges in terms of the diagnostic efficiency owing to deficiencies in the choice of proper filtering techniques, neuroimaging biomarkers, and limited learning models. Magnetic resonance imaging (MRI) is capable of providing enhanced information regarding the soft tissues, and therefore MR images are included in the proposed approach...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/29118807/a-novel-active-semisupervised-convolutional-neural-network-algorithm-for-sar-image-recognition
#4
Fei Gao, Zhenyu Yue, Jun Wang, Jinping Sun, Erfu Yang, Huiyu Zhou
Convolutional neural network (CNN) can be applied in synthetic aperture radar (SAR) object recognition for achieving good performance. However, it requires a large number of the labelled samples in its training phase, and therefore its performance could decrease dramatically when the labelled samples are insufficient. To solve this problem, in this paper, we present a novel active semisupervised CNN algorithm. First, the active learning is used to query the most informative and reliable samples in the unlabelled samples to extend the initial training dataset...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/29085426/thai-finger-spelling-recognition-using-a-cascaded-classifier-based-on-histogram-of-orientation-gradient-features
#5
Kittasil Silanon
Hand posture recognition is an essential module in applications such as human-computer interaction (HCI), games, and sign language systems, in which performance and robustness are the primary requirements. In this paper, we proposed automatic classification to recognize 21 hand postures that represent letters in Thai finger-spelling based on Histogram of Orientation Gradient (HOG) feature (which is applied with more focus on the information within certain region of the image rather than each single pixel) and Adaptive Boost (i...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/29085425/new-dandelion-algorithm-optimizes-extreme-learning-machine-for-biomedical-classification-problems
#6
Xiguang Li, Shoufei Han, Liang Zhao, Changqing Gong, Xiaojing Liu
Inspired by the behavior of dandelion sowing, a new novel swarm intelligence algorithm, namely, dandelion algorithm (DA), is proposed for global optimization of complex functions in this paper. In DA, the dandelion population will be divided into two subpopulations, and different subpopulations will undergo different sowing behaviors. Moreover, another sowing method is designed to jump out of local optimum. In order to demonstrate the validation of DA, we compare the proposed algorithm with other existing algorithms, including bat algorithm, particle swarm optimization, and enhanced fireworks algorithm...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/29056964/hyperbolic-modeling-of-subthalamic-nucleus-cells-to-investigate-the-effect-of-dopamine-depletion
#7
Mohammad Daneshzand, Miad Faezipour, Buket D Barkana
To investigate how different types of neurons can produce well-known spiking patterns, a new computationally efficient model is proposed in this paper. This model can help realize the neuronal interconnection issues. The model can demonstrate various neuronal behaviors observed in vivo through simple parameter modification. The behaviors include tonic and phasic spiking, tonic and phasic bursting, class 1 and class 2 excitability, rebound spike, rebound burst, subthreshold oscillation, and accommodated spiking along with inhibition neuron responses...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/29056963/fusion-of-facial-expressions-and-eeg-for-multimodal-emotion-recognition
#8
Yongrui Huang, Jianhao Yang, Pengkai Liao, Jiahui Pan
This paper proposes two multimodal fusion methods between brain and peripheral signals for emotion recognition. The input signals are electroencephalogram and facial expression. The stimuli are based on a subset of movie clips that correspond to four specific areas of valance-arousal emotional space (happiness, neutral, sadness, and fear). For facial expression detection, four basic emotion states (happiness, neutral, sadness, and fear) are detected by a neural network classifier. For EEG detection, four basic emotion states and three emotion intensity levels (strong, ordinary, and weak) are detected by two support vector machines (SVM) classifiers, respectively...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28951736/classification-of-hand-grasp-kinetics-and-types-using-movement-related-cortical-potentials-and-eeg-rhythms
#9
Mads Jochumsen, Cecilie Rovsing, Helene Rovsing, Imran Khan Niazi, Kim Dremstrup, Ernest Nlandu Kamavuako
Detection of single-trial movement intentions from EEG is paramount for brain-computer interfacing in neurorehabilitation. These movement intentions contain task-related information and if this is decoded, the neurorehabilitation could potentially be optimized. The aim of this study was to classify single-trial movement intentions associated with two levels of force and speed and three different grasp types using EEG rhythms and components of the movement-related cortical potential (MRCP) as features. The feature importance was used to estimate encoding of discriminative information...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28932238/cognitive-based-eeg-bcis-and-human-brain-robot-interactions
#10
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
#11
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
#12
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
#13
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
#14
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
#15
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
#16
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
#17
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
#18
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
#19
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
#20
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
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