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Computational Intelligence and Neuroscience

Liyong Ma, Chengkuan Ma, Yuejun Liu, Xuguang Wang
Thyroid disease has now become the second largest disease in the endocrine field; SPECT imaging is particularly important for the clinical diagnosis of thyroid diseases. However, there is little research on the application of SPECT images in the computer-aided diagnosis of thyroid diseases based on machine learning methods. A convolutional neural network with optimization-based computer-aided diagnosis of thyroid diseases using SPECT images is developed. Three categories of diseases are considered, and they are Graves' disease, Hashimoto disease, and subacute thyroiditis...
2019: Computational Intelligence and Neuroscience
Min Hao, Guangyuan Liu, Anu Gokhale, Ya Xu, Rui Chen
Hyperspectral imaging (HSI) technology can be used to detect human emotions based on the power of material discrimination from their faces. In this paper, HSI is used to remotely sense and distinguish blood chromophores in facial tissues and acquire an evaluation indicator (tissue oxygen saturation, StO2 ) using an optical absorption model. This study explored facial analysis while people were showing spontaneous expressions of happiness during social interaction. Happiness, as a psychological emotion, has been shown to be strongly linked to other activities such as physiological reaction and facial expression...
2019: Computational Intelligence and Neuroscience
Li Deng, Guohua Wang
In order to design a cultural and creative product that matched the target image, this paper proposed to use EEG, interactive genetic algorithm (IGA), and back propagation neural network (BPNN) to analyze the users' image preferences. Firstly, the pictures of cultural elements were grouped according to the pleasantness value and emotional state by PAD emotion scale, and the brain waves induced by the pictures of cultural elements with different pleasure degree were recorded by electroencephalograph. Then, the preference of cultural elements was obtained according to the theory of frontal alpha asymmetry...
2019: Computational Intelligence and Neuroscience
Jeong Woo Choi, Kwang Su Cha, Kyung Hwan Kim
The most fundamental and simplest intention for interpersonal communication may be the intentions to answer "yes" or "no" to a question, based on a binary decision. However, the neural mechanism of this type of intention has not been investigated in detail. The main purpose of this study was to investigate cortical processing of the "yes/no" intentions to answer self-referential questions. Multichannel electroencephalograms (EEGs) were recorded while covertly answering self-referential questions with either "yes" or "no"...
2019: Computational Intelligence and Neuroscience
Tze Chiang Tin, Kang Leng Chiew, Siew Chee Phang, San Nah Sze, Pei San Tan
Preventive maintenance activities require a tool to be offline for long hour in order to perform the prescribed maintenance activities. Although preventive maintenance is crucial to ensure operational reliability and efficiency of the tool, long hour of preventive maintenance activities increases the cycle time of the semiconductor fabrication foundry (Fab). Therefore, this activity is usually performed when the incoming Work-in-Progress to the equipment is forecasted to be low. The current statistical forecasting approach has low accuracy because it lacks the ability to capture the time-dependent behavior of the Work-in-Progress...
2019: Computational Intelligence and Neuroscience
Carlos Sepúlveda, Oscar Montiel, José M Cornejo Bravo, Roberto Sepúlveda
Population pharmacokinetic (PopPK) models allow researchers to predict and analyze drug behavior in a population of individuals and to quantify the different sources of variability among these individuals. In the development of PopPK models, the most frequently used method is the nonlinear mixed effect model (NLME). However, once the PopPK model has been developed, it is necessary to determine if the selected model is the best one of the developed models during the population pharmacokinetic study, and this sometimes becomes a multiple criteria decision making (MCDM) problem, and frequently, researchers use statistical evaluation criteria to choose the final PopPK model...
2018: Computational Intelligence and Neuroscience
Wenjie Wang, Xiansheng Qin, Chen Zheng, Hongbo Wang, Jing Li, Junlong Niu
As an advanced interaction mode, the gesture has been widely used for the human-computer interaction (HCI). The paper proposes a comfort evaluation model based on the mechanical energy expenditure (MEE) and the mechanical efficiency (ME) to predict the comfort of gestures. The proposed comfort evaluation model takes nineteen muscles and seven degrees of freedom into consideration based on the data of muscles and joints and is capable of simulating the MEE and the ME of both static and dynamic gestures. The comfort scores (CSs) can be therefore calculated by normalizing and assigning different decision weights to the MEE and the ME...
2018: Computational Intelligence and Neuroscience
Xueying Lv, Yitian Wang, Junyi Deng, Guanyu Zhang, Liu Zhang
In this study, an improved eliminate particle swarm optimization (IEPSO) is proposed on the basis of the last-eliminated principle to solve optimization problems in engineering design. During optimization, the IEPSO enhances information communication among populations and maintains population diversity to overcome the limitations of classical optimization algorithms in solving multiparameter, strong coupling, and nonlinear engineering optimization problems. These limitations include advanced convergence and the tendency to easily fall into local optimization...
2018: Computational Intelligence and Neuroscience
Edmanuel Cruz, Félix Escalona, Zuria Bauer, Miguel Cazorla, José García-Rodríguez, Ester Martinez-Martin, José Carlos Rangel, Francisco Gomez-Donoso
The accelerated growth of the percentage of elder people and persons with brain injury-related conditions and who are intellectually challenged are some of the main concerns of the developed countries. These persons often require special cares and even almost permanent overseers that help them to carry out diary tasks. With this issue in mind, we propose an automated schedule system which is deployed on a social robot. The robot keeps track of the tasks that the patient has to fulfill in a diary basis. When a task is triggered, the robot guides the patient through its completion...
2018: Computational Intelligence and Neuroscience
Zheng Wang, Qingbiao Wu
The neural autoregressive distribution estimator(NADE) is a competitive model for the task of density estimation in the field of machine learning. While NADE mainly focuses on the problem of estimating density, the ability for dealing with other tasks remains to be improved. In this paper, we introduce a simple and efficient reweighted scheme to modify the parameters of the learned NADE. We make use of the structure of NADE, and the weights are derived from the activations in the corresponding hidden layers...
2018: Computational Intelligence and Neuroscience
Hao Chao, Huilai Zhi, Liang Dong, Yongli Liu
Fusing multichannel neurophysiological signals to recognize human emotion states becomes increasingly attractive. The conventional methods ignore the complementarity between time domain characteristics, frequency domain characteristics, and time-frequency characteristics of electroencephalogram (EEG) signals and cannot fully capture the correlation information between different channels. In this paper, an integrated deep learning framework based on improved deep belief networks with glia chains (DBN-GCs) is proposed...
2018: Computational Intelligence and Neuroscience
Laith H Baniata, Seyoung Park, Seong-Bae Park
In this research article, we study the problem of employing a neural machine translation model to translate Arabic dialects to modern standard Arabic. The proposed solution of the neural machine translation model is prompted by the recurrent neural network-based encoder-decoder neural machine translation model that has been proposed recently, which generalizes machine translation as sequence learning problems. We propose the development of a multiytask learning (MTL) model which shares one decoder among language pairs, and every source language has a separate encoder...
2018: Computational Intelligence and Neuroscience
Chao Shan, Bin Huang, Minggao Li
A synthetic aperture radar (SAR) target recognition method is proposed in this study based on the dominant scattering area (DSA). DSA is a binary image recording the positions of the dominant scattering centers in the original SAR image. It can reflect the distribution of the scattering centers as well as the preliminary shape of the target, thus providing discriminative information for SAR target recognition. By subtracting the DSA of the test image with those of its corresponding templates from different classes, the DSA residues represent the differences between the test image and various classes...
2018: Computational Intelligence and Neuroscience
Hui Zhang
Search users rely on result captions including titles, snippets, and URLs to decide whether they should read and click a particular result or not. Snippet usually serves as a query-dependent summary of its corresponding landing page and is therefore treated as one of the most important factors in search interaction process. Although there exist many efforts in improving snippet generation algorithms and incorporating more powerful interaction functions into snippets, little attention is paid to the effect of text highlighting in user behaviors...
2018: Computational Intelligence and Neuroscience
Zhongwei Chen, Kangbo Peng, Lai Huang, Yichao Wang, Xiaozhong Wu, Zhenfeng Xiao
Texture feature extraction is a key topic in many applications of image analysis; a lot of techniques have been proposed to measure the characteristics of this field. Among them, texture energy extracted with a mask is a rotation and scale invariant texture descriptor. However, the tuning process is computationally intensive and easily trap into the local optimum. In the proposed approach, a "Tuned" mask is utilized to extract water and nonwater texture; the optimal "Tuned" mask is acquired by maximizing the texture energy value via a newly proposed cuckoo search (CS) algorithm...
2018: Computational Intelligence and Neuroscience
Muhammad AsadUllah, Muhammad Adnan Khan, Sagheer Abbas, Atifa Athar, Syed Saqib Raza, Gulzar Ahmad
Multiple-input and multiple-output (MIMO) technology is one of the latest technologies to enhance the capacity of the channel as well as the service quality of the communication system. By using the MIMO technology at the physical layer, the estimation of the data and the channel is performed based on the principle of maximum likelihood. For this purpose, the continuous and discrete fuzzy logic-empowered opposite learning-based mutant particle swarm optimization (FL-OLMPSO) algorithm is used over the Rayleigh fading channel in three levels...
2018: Computational Intelligence and Neuroscience
Victor Hugo C de Albuquerque, Plácido Rogerio Pinheiro, Roshan J Martis, João Manuel R S Tavares
No abstract text is available yet for this article.
2018: Computational Intelligence and Neuroscience
Somyot Kiatwanidvilai, Rawinun Praserttaweelap
Decision and control of SCARA robot in HGA (head gimbal assembly) inspection line is a very challenge issue in hard disk drive (HDD) manufacturing. The HGA circuit called slider FOS is a part of HDD which is used for reading and writing data inside the disk with a very small dimension, i.e., 45 × 64  µ m. Accuracy plays an important role in this inspection, and classification of defects is very crucial to assign the action of the SCARA robot. The robot can move the inspected parts into the corresponding boxes, which are divided into 5 groups and those are "Good," "Bridging," "Missing," "Burn," and "No connection...
2018: Computational Intelligence and Neuroscience
Hiram Ponce, Ernesto Moya-Albor, Jorge Brieva
Robots in assisted living (RAL) are an alternative to support families and professional caregivers with a wide range of possibilities to take care of elderly people. Navigation of mobile robots is a challenging problem due to the uncertainty and dynamics of environments found in the context of places for elderly. To accomplish this goal, the navigation system tries to replicate such a complicated process inspired on the perception and judgment of human beings. In this work, we propose a novel nature-inspired control system for mobile RAL navigation using an artificial organic controller enhanced with vision-based strategies such as Hermite optical flow (OF) and convolutional neural networks (CNNs)...
2018: Computational Intelligence and Neuroscience
Jianjun Ni, Liu Yang, Pengfei Shi, Chengming Luo
Multi-AUV cooperative target search problem in unknown 3D underwater environment is not only a research hot spot but also a challenging task. To complete this task, each autonomous underwater vehicle (AUV) needs to move quickly without collision and cooperate with other AUVs to find the target. In this paper, an improved dolphin swarm algorithm- (DSA-) based approach is proposed, and the search problem is divided into three stages, namely, random cruise, dynamic alliance, and team search. In the proposed approach, the Levy flight method is used to provide a random walk for AUV to detect the target information in the random cruise stage...
2018: Computational Intelligence and Neuroscience
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