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Computational Intelligence and Neuroscience

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https://www.readbyqxmd.com/read/28740505/object-extraction-in-cluttered-environments-via-a-p300-based-ifce
#1
Xiaoqian Mao, Wei Li, Huidong He, Bin Xian, Ming Zeng, Huihui Zhou, Linwei Niu, Genshe Chen
One of the fundamental issues for robot navigation is to extract an object of interest from an image. The biggest challenges for extracting objects of interest are how to use a machine to model the objects in which a human is interested and extract them quickly and reliably under varying illumination conditions. This article develops a novel method for segmenting an object of interest in a cluttered environment by combining a P300-based brain computer interface (BCI) and an improved fuzzy color extractor (IFCE)...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28717361/research-of-hubs-location-method-for-weighted-brain-network-based-on-nos-fa
#2
Zhengkui Weng, Bin Wang, Jie Xue, Baojie Yang, Hui Liu, Xin Xiong
As a complex network of many interlinked brain regions, there are some central hub regions which play key roles in the structural human brain network based on T1 and diffusion tensor imaging (DTI) technology. Since most studies about hubs location method in the whole human brain network are mainly concerned with the local properties of each single node but not the global properties of all the directly connected nodes, a novel hubs location method based on global importance contribution evaluation index is proposed in this study...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28706534/bag-of-visual-words-model-with-deep-spatial-features-for-geographical-scene-classification
#3
Jiangfan Feng, Yuanyuan Liu, Lin Wu
With the popular use of geotagging images, more and more research efforts have been placed on geographical scene classification. In geographical scene classification, valid spatial feature selection can significantly boost the final performance. Bag of visual words (BoVW) can do well in selecting feature in geographical scene classification; nevertheless, it works effectively only if the provided feature extractor is well-matched. In this paper, we use convolutional neural networks (CNNs) for optimizing proposed feature extractor, so that it can learn more suitable visual vocabularies from the geotagging images...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28702051/random-forest-based-approach-for-maximum-power-point-tracking-of-photovoltaic-systems-operating-under-actual-environmental-conditions
#4
Hussain Shareef, Ammar Hussein Mutlag, Azah Mohamed
Many maximum power point tracking (MPPT) algorithms have been developed in recent years to maximize the produced PV energy. These algorithms are not sufficiently robust because of fast-changing environmental conditions, efficiency, accuracy at steady-state value, and dynamics of the tracking algorithm. Thus, this paper proposes a new random forest (RF) model to improve MPPT performance. The RF model has the ability to capture the nonlinear association of patterns between predictors, such as irradiance and temperature, to determine accurate maximum power point...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28659973/forest-pruning-based-on-branch-importance
#5
Xiangkui Jiang, Chang-An Wu, Huaping Guo
A forest is an ensemble with decision trees as members. This paper proposes a novel strategy to pruning forest to enhance ensemble generalization ability and reduce ensemble size. Unlike conventional ensemble pruning approaches, the proposed method tries to evaluate the importance of branches of trees with respect to the whole ensemble using a novel proposed metric called importance gain. The importance of a branch is designed by considering ensemble accuracy and the diversity of ensemble members, and thus the metric reasonably evaluates how much improvement of the ensemble accuracy can be achieved when a branch is pruned...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28638405/a-combined-one-class-svm-and-template-matching-approach-for-user-aided-human-fall-detection-by-means-of-floor-acoustic-features
#6
Diego Droghini, Daniele Ferretti, Emanuele Principi, Stefano Squartini, Francesco Piazza
The primary cause of injury-related death for the elders is represented by falls. The scientific community devoted them particular attention, since injuries can be limited by an early detection of the event. The solution proposed in this paper is based on a combined One-Class SVM (OCSVM) and template-matching classifier that discriminate human falls from nonfalls in a semisupervised framework. Acoustic signals are captured by means of a Floor Acoustic Sensor; then Mel-Frequency Cepstral Coefficients and Gaussian Mean Supervectors (GMSs) are extracted for the fall/nonfall discrimination...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28634488/antiherding-in-financial-decision-increases-valuation-of-return-on-investment-an-event-related-potential-study
#7
Cuicui Wang, Jia Jin, João Paulo Vieito, Qingguo Ma
Using event-related potentials, this study investigated how financial herding or antiherding affected the valuation of subsequent outcomes. For each trial, subjects decided whether to buy the stock according to its net money flow information which could be used to reflect the strength of buying power or selling power of the stock. The return on investment (ROI) as feedback included the increase or decrease percentage after subjects' responses. Results showed that, compared with herding, antiherding induced larger discrepancies of FRN and P300 amplitude between positive ROI and negative ROI, indicating that individuals under antiherding condition had stronger motivation and paid more attention in the evaluation process of ROI...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28634487/dynamic-inertia-weight-binary-bat-algorithm-with-neighborhood-search
#8
Xingwang Huang, Xuewen Zeng, Rui Han
Binary bat algorithm (BBA) is a binary version of the bat algorithm (BA). It has been proven that BBA is competitive compared to other binary heuristic algorithms. Since the update processes of velocity in the algorithm are consistent with BA, in some cases, this algorithm also faces the premature convergence problem. This paper proposes an improved binary bat algorithm (IBBA) to solve this problem. To evaluate the performance of IBBA, standard benchmark functions and zero-one knapsack problems have been employed...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28630620/cloud-model-based-artificial-immune-network-for-complex-optimization-problem
#9
Mingan Wang, Shuo Feng, Jianming Li, Zhonghua Li, Yu Xue, Dongliang Guo
This paper proposes an artificial immune network based on cloud model (AINet-CM) for complex function optimization problems. Three key immune operators-cloning, mutation, and suppression-are redesigned with the help of the cloud model. To be specific, an increasing half cloud-based cloning operator is used to adjust the dynamic clone multipliers of antibodies, an asymmetrical cloud-based mutation operator is used to control the adaptive evolution of antibodies, and a normal similarity cloud-based suppressor is used to keep the diversity of the antibody population...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28630619/a-swarm-optimization-genetic-algorithm-based-on-quantum-behaved-particle-swarm-optimization
#10
Tao Sun, Ming-Hai Xu
Quantum-behaved particle swarm optimization (QPSO) algorithm is a variant of the traditional particle swarm optimization (PSO). The QPSO that was originally developed for continuous search spaces outperforms the traditional PSO in search ability. This paper analyzes the main factors that impact the search ability of QPSO and converts the particle movement formula to the mutation condition by introducing the rejection region, thus proposing a new binary algorithm, named swarm optimization genetic algorithm (SOGA), because it is more like genetic algorithm (GA) than PSO in form...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28611841/applications-of-computational-intelligence-in-time-series
#11
EDITORIAL
Francisco Martínez-Álvarez, Alicia Troncoso, Jorge Reyes, María Martínez-Ballesteros, José C Riquelme
No abstract text is available yet for this article.
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28611840/deep-learning-for-plant-identification-in-natural-environment
#12
Yu Sun, Yuan Liu, Guan Wang, Haiyan Zhang
Plant image identification has become an interdisciplinary focus in both botanical taxonomy and computer vision. The first plant image dataset collected by mobile phone in natural scene is presented, which contains 10,000 images of 100 ornamental plant species in Beijing Forestry University campus. A 26-layer deep learning model consisting of 8 residual building blocks is designed for large-scale plant classification in natural environment. The proposed model achieves a recognition rate of 91.78% on the BJFU100 dataset, demonstrating that deep learning is a promising technology for smart forestry...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28607551/robust-grape-detector-based-on-svms-and-hog-features
#13
Pavel Škrabánek, Petr Doležel
Detection of grapes in real-life images is a serious task solved by researchers dealing with precision viticulture. In the case of white wine varieties, grape detectors based on SVMs classifiers, in combination with a HOG descriptor, have proven to be very efficient. Simplified versions of the detectors seem to be the best solution for practical applications. They offer the best known performance versus time-complexity ratio. As our research showed, a conversion of RGB images to grayscale format, which is implemented at an image preprocessing level, is ideal means for further improvement of performance of the detectors...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28588611/refining-automatically-extracted-knowledge-bases-using-crowdsourcing
#14
Chunhua Li, Pengpeng Zhao, Victor S Sheng, Xuefeng Xian, Jian Wu, Zhiming Cui
Machine-constructed knowledge bases often contain noisy and inaccurate facts. There exists significant work in developing automated algorithms for knowledge base refinement. Automated approaches improve the quality of knowledge bases but are far from perfect. In this paper, we leverage crowdsourcing to improve the quality of automatically extracted knowledge bases. As human labelling is costly, an important research challenge is how we can use limited human resources to maximize the quality improvement for a knowledge base...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28572817/development-of-a-novel-motor-imagery-control-technique-and-application-in-a-gaming-environment
#15
Ting Li, Jinhua Zhang, Tao Xue, Baozeng Wang
We present a methodology for a hybrid brain-computer interface (BCI) system, with the recognition of motor imagery (MI) based on EEG and blink EOG signals. We tested the BCI system in a 3D Tetris and an analogous 2D game playing environment. To enhance player's BCI control ability, the study focused on feature extraction from EEG and control strategy supporting Game-BCI system operation. We compared the numerical differences between spatial features extracted with common spatial pattern (CSP) and the proposed multifeature extraction...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28553351/fast-recall-for-complex-valued-hopfield-neural-networks-with-projection-rules
#16
Masaki Kobayashi
Many models of neural networks have been extended to complex-valued neural networks. A complex-valued Hopfield neural network (CHNN) is a complex-valued version of a Hopfield neural network. Complex-valued neurons can represent multistates, and CHNNs are available for the storage of multilevel data, such as gray-scale images. The CHNNs are often trapped into the local minima, and their noise tolerance is low. Lee improved the noise tolerance of the CHNNs by detecting and exiting the local minima. In the present work, we propose a new recall algorithm that eliminates the local minima...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28546809/comparison-of-brain-activation-during-motor-imagery-and-motor-movement-using-fnirs
#17
Alyssa M Batula, Jesse A Mark, Youngmoo E Kim, Hasan Ayaz
Motor-activity-related mental tasks are widely adopted for brain-computer interfaces (BCIs) as they are a natural extension of movement intention, requiring no training to evoke brain activity. The ideal BCI aims to eliminate neuromuscular movement, making motor imagery tasks, or imagined actions with no muscle movement, good candidates. This study explores cortical activation differences between motor imagery and motor execution for both upper and lower limbs using functional near-infrared spectroscopy (fNIRS)...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28546808/improving-classification-performance-through-an-advanced-ensemble-based-heterogeneous-extreme-learning-machines
#18
Adnan O M Abuassba, Dezheng Zhang, Xiong Luo, Ahmad Shaheryar, Hazrat Ali
Extreme Learning Machine (ELM) is a fast-learning algorithm for a single-hidden layer feedforward neural network (SLFN). It often has good generalization performance. However, there are chances that it might overfit the training data due to having more hidden nodes than needed. To address the generalization performance, we use a heterogeneous ensemble approach. We propose an Advanced ELM Ensemble (AELME) for classification, which includes Regularized-ELM, L2-norm-optimized ELM (ELML2), and Kernel-ELM. The ensemble is constructed by training a randomly chosen ELM classifier on a subset of training data selected through random resampling...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28536602/a-decision-based-modified-total-variation-diffusion-method-for-impulse-noise-removal
#19
Hongyao Deng, Qingxin Zhu, Xiuli Song, Jinsong Tao
Impulsive noise removal usually employs median filtering, switching median filtering, the total variation L1 method, and variants. These approaches however often introduce excessive smoothing and can result in extensive visual feature blurring and thus are suitable only for images with low density noise. A new method to remove noise is proposed in this paper to overcome this limitation, which divides pixels into different categories based on different noise characteristics. If an image is corrupted by salt-and-pepper noise, the pixels are divided into corrupted and noise-free; if the image is corrupted by random valued impulses, the pixels are divided into corrupted, noise-free, and possibly corrupted...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28512467/extra-facial-landmark-localization-via-global-shape-reconstruction
#20
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
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