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Visual-Electrotactile Stimulation Feedback to Improve Immersive Brain-Computer Interface Based on Hand Motor Imagery Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2021-02-25 David Achanccaray; Shin-Ichi Izumi; Mitsuhiro Hayashibe
In the aging society, the number of people suffering from vascular disorders is rapidly increasing and has become a social problem. The death rate due to stroke, which is the second leading cause of global mortality, has increased by 40% in the last two decades. Stroke can also cause paralysis. Of late, brain-computer interfaces (BCIs) have been garnering attention in the rehabilitation field as assistive
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Deep Residual Network in Network Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2021-02-23 Hmidi Alaeddine; Malek Jihene
Deep network in network (DNIN) model is an efficient instance and an important extension of the convolutional neural network (CNN) consisting of alternating convolutional layers and pooling layers. In this model, a multilayer perceptron (MLP), a nonlinear function, is exploited to replace the linear filter for convolution. Increasing the depth of DNIN can also help improve classification accuracy while
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Future Trends for Human-AI Collaboration: A Comprehensive Taxonomy of AI/AGI Using Multiple Intelligences and Learning Styles Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2021-02-23 Andrzej Cichocki; Alexander P. Kuleshov
This article discusses some trends and concepts in developing a new generation of future Artificial General Intelligence (AGI) systems which relate to complex facets and different types of human intelligence, especially social, emotional, attentional, and ethical intelligence. We describe various aspects of multiple human intelligences and learning styles, which may affect a variety of AI problem domains
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Decoding of Walking Imagery and Idle State Using Sparse Representation Based on fNIRS Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2021-02-22 Hongquan Li; Anmin Gong; Lei Zhao; Wei Zhang; Fawang Wang; Yunfa Fu
Objectives. Brain-computer interface (BCI) based on functional near-infrared spectroscopy (fNIRS) is expected to provide an optional active rehabilitation training method for patients with walking dysfunction, which will affect their quality of life seriously. Sparse representation classification (SRC) oxyhemoglobin (HbO) concentration was used to decode walking imagery and idle state to construct
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Taking a Closed-Book Examination: Decoupling KB-Based Inference by Virtual Hypothesis for Answering Real-World Questions Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2021-02-22 Xiao Zhang; Guorui Zhao
Complex question answering in real world is a comprehensive and challenging task due to its demand for deeper question understanding and deeper inference. Information retrieval is a common solution and easy to implement, but it cannot answer questions which need long-distance dependencies across multiple documents. Knowledge base (KB) organizes information as a graph, and KB-based inference can employ
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Predicting Slurry Pressure Balance with a Long Short-Term Memory Recurrent Neural Network in Difficult Ground Condition Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2021-02-22 Qiang Wang; Xiongyao Xie; Hongjie Yu; Michael A Mooney
The safety of tunneling with shield tunnel boring machines largely depends on the tunnel face pressure, which is currently decided by human operators empirically. Face pressure control is vulnerable to human misjudgment and human errors can cause severe consequences, especially in difficult ground conditions. From a practical perspective, it is therefore beneficial to have a model capable of predicting
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MTQA: Text-Based Multitype Question and Answer Reading Comprehension Model Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2021-02-19 Deguang Chen; Ziping Ma; Lin Wei; Jinlin Ma; Yanbin Zhu
Text-based multitype question answering is one of the research hotspots in the field of reading comprehension models. Multitype reading comprehension models have the characteristics of shorter time to propose, complex components of relevant corpus, and greater difficulty in model construction. There are relatively few research works in this field. Therefore, it is urgent to improve the model performance
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A Novel Time-Incremental End-to-End Shared Neural Network with Attention-Based Feature Fusion for Multiclass Motor Imagery Recognition Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2021-02-18 Shidong Lian; Jialin Xu; Guokun Zuo; Xia Wei; Huilin Zhou
In the research of motor imagery brain-computer interface (MI-BCI), traditional electroencephalogram (EEG) signal recognition algorithms appear to be inefficient in extracting EEG signal features and improving classification accuracy. In this paper, we discuss a solution to this problem based on a novel step-by-step method of feature extraction and pattern classification for multiclass MI-EEG signals
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An Efficient Automatic Gait Anomaly Detection Method Based on Semisupervised Clustering Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2021-02-16 Zhenlun Yang
The aim of this work is to develop a common automatic computer method to distinguish human individuals with abnormal gait patterns from those with normal gait patterns. As long as the silhouette gait images of the subjects are obtainable, the proposed method is capable of providing online anomaly gait detection result without additional work on analyzing the gait features of the target subjects before
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Automatic Impervious Surface Area Detection Using Image Texture Analysis and Neural Computing Models with Advanced Optimizers Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2021-02-16 Nhat-Duc Hoang
Up-to-date information regarding impervious surface is valuable for urban planning and management. The objective of this study is to develop neural computing models used for automatic impervious surface area detection at a regional scale. To achieve this task, advanced optimizers of adaptive moment estimation (Adam), a variation of Adam called Adamax, Nesterov-accelerated adaptive moment estimation
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Transfer Extreme Learning Machine with Output Weight Alignment Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2021-02-12 Shaofei Zang; Yuhu Cheng; Xuesong Wang; Yongyi Yan
Extreme Learning Machine (ELM) as a fast and efficient neural network model in pattern recognition and machine learning will decline when the labeled training sample is insufficient. Transfer learning helps the target task to learn a reliable model by using plentiful labeled samples from the different but relevant domain. In this paper, we propose a supervised Extreme Learning Machine with knowledge
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A New Recognition Method for the Auditory Evoked Magnetic Fields Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2021-02-11 Yulong Feng; Wei Xiao; Teng Wu; Jianwei Zhang; Jing Xiang; Hong Guo
Magnetoencephalography (MEG) is a persuasive tool to study the human brain in physiology and psychology. It can be employed to obtain the inference of change between the external environment and the internal psychology, which requires us to recognize different single trial event-related magnetic fields (ERFs) originated from different functional areas of the brain. Current recognition methods for the
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Intelligent Design of Product Forms Based on Design Cognitive Dynamics and a Cobweb Structure Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2021-02-10 Wenjin Yang; Jian-Ning Su; Shutao Zhang; Kai Qiu; Xinxin Zhang
Design is a complex, iterative, and innovative process. By traditional methods, it is difficult for designers to have an integral priori design experience to fully explore a wide range of design solutions. Therefore, refined intelligent design has become an important trend in design research. More powerful design thinking is needed in intelligent design process. Combining cognitive dynamics and a cobweb
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Intelligent Neutrosophic Diagnostic System for Cardiotocography Data Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2021-02-10 Belal Amin; A. A. Salama; I. M. El-Henawy; Khaled Mahfouz; Mona G. Gafar
Cardiotocography data uncertainty is a critical task for the classification in biomedical field. Constructing good and efficient classifier via machine learning algorithms is necessary to help doctors in diagnosing the state of fetus heart rate. The proposed neutrosophic diagnostic system is an Interval Neutrosophic Rough Neural Network framework based on the backpropagation algorithm. It benefits
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Preshooting Electroencephalographic Activity of Professional Shooters in a Competitive State Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2021-02-10 Jie Zhang; Yunxu Shi; Chienkai Wang; Chunmei Cao; Changshui Zhang; Linhong Ji; Jia Cheng; Fangfang Wu
This study investigated the influence of competitive state on cerebral cortex activity of professional shooters with 10 m air rifle before shooting. Generally, professional athletes have higher neural efficiency compared with ordinary people. We recruited 11 national shooters to complete 60 shots under both noncompetitive and competitive shooting conditions, and simultaneously collected their electroencephalogram
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Effects of Skin Friction on Tactile P300 Brain-Computer Interface Performance Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2021-02-09 Ying Mao; Jing Jin; Shurui Li; Yangyang Miao; Andrzej Cichocki
Tactile perception, the primary sensing channel of the tactile brain-computer interface (BCI), is a complicated process. Skin friction plays a vital role in tactile perception. This study aimed to examine the effects of skin friction on tactile P300 BCI performance. Two kinds of oddball paradigms were designed, silk-stim paradigm (SSP) and linen-stim paradigm (LSP), in which silk and linen were wrapped
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Enhanced Comprehensive Learning Particle Swarm Optimization with Dimensional Independent and Adaptive Parameters Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2021-02-05 Xiang Yu; Yu Qiao
Comprehensive learning particle swarm optimization (CLPSO) and enhanced CLPSO (ECLPSO) are two literature metaheuristics for global optimization. ECLPSO significantly improves the exploitation and convergence performance of CLPSO by perturbation-based exploitation and adaptive learning probabilities. However, ECLPSO still cannot locate the global optimum or find a near-optimum solution for a number
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A Data-Driven and Biologically Inspired Preprocessing Scheme to Improve Visual Object Recognition Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2021-01-29 Zahra Sadat Shariatmadar; Karim Faez
Autonomous object recognition in images is one of the most critical topics in security and commercial applications. Due to recent advances in visual neuroscience, the researchers tend to extend biologically plausible schemes to improve the accuracy of object recognition. Preprocessing is one part of the visual recognition system that has received much less attention. In this paper, we propose a new
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A Hybrid EMD-Wavelet EEG Feature Extraction Method for the Classification of Students’ Interest in the Mathematics Classroom Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2021-01-25 Areej Babiker; Ibrahima Faye
Situational interest (SI) is one of the promising states that can improve student’s learning and increase the acquired knowledge. Electroencephalogram- (EEG-) based detection of SI could assist in understanding SI neuroscientific causes that, as a result, could explain the SI role in student’s learning. In this study, 26 participants were selected based on questionnaires to participate in the mathematics
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Enhancing Hyperheuristics for the Knapsack Problem through Fuzzy Logic Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2021-01-25 Frumen Olivas; Ivan Amaya; José Carlos Ortiz-Bayliss; Santiago E. Conant-Pablos; Hugo Terashima-Marín
Hyperheuristics rise as powerful techniques that get good results in less computational time than exact methods like dynamic programming or branch and bound. These exact methods promise the global best solution, but with a high computational time. In this matter, hyperheuristics do not promise the global best solution, but they promise a good solution in a lot less computational time. On the contrary
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Improved Loss Function for Image Classification Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2021-01-23 Chenrui Wen; Xinhao Yang; Ke Zhang; Jiahui Zhang
An improved loss function free of sampling procedures is proposed to improve the ill-performed classification by sample shortage. Adjustable parameters are used to expand the loss scope, minimize the weight of easily classified samples, and further substitute the sampling function, which are added to the cross-entropy loss and the SoftMax loss. Experiment results indicate that improvements in all classification
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Sonar Objective Detection Based on Dilated Separable Densely Connected CNNs and Quantum-Behaved PSO Algorithm Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2021-01-19 Zhen Wang; Buhong Wang; Jianxin Guo; Shanwen Zhang
Underwater sonar objective detection plays an important role in the field of ocean exploration. In order to solve the problem of sonar objective detection under the complex environment, a sonar objective detection method is proposed based on dilated separable densely connected convolutional neural networks (DS-CNNs) and quantum-behaved particle swarm optimization (QPSO) algorithm. Firstly, the dilated
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A Parallel Bioinspired Algorithm for Chinese Postman Problem Based on Molecular Computing Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2021-01-16 Zhaocai Wang; Xiaoguang Bao; Tunhua Wu
The Chinese postman problem is a classic resource allocation and scheduling problem, which has been widely used in practice. As a classical nondeterministic polynomial problem, finding its efficient algorithm has always been the research direction of scholars. In this paper, a new bioinspired algorithm is proposed to solve the Chinese postman problem based on molecular computation, which has the advantages
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Application of Rough Ant Colony Algorithm in Adolescent Psychology Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2021-01-15 Tao Cong; Lin Jiang; Qihang Sun; Yang Li
With the rapid development of big data, big data research in the security protection industry has been increasingly regarded as a hot spot. This article mainly aims at solving the problem of predicting the tendency of juvenile delinquency based on the experimental data of juvenile blindly following psychological crime. To solve this problem, this paper proposes a rough ant colony classification algorithm
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Study on Evaluation Model of Emergency Rescue Capability of Chemical Accidents Based on PCA-BP Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2021-01-15 Jianghong Liu; Junfeng Wu; Weisi Liu
The emergency management of chemical accidents plays an important role in preventing the expansion of chemical accidents. In recent years, the evaluation and research of emergency management of chemical accidents has attracted the attention of many scholars. However, as an important part of emergency management, the professional rescue team of chemicals has few evaluation models for their capabilities
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An Image Enhancement Algorithm Based on Fractional-Order Phase Stretch Transform and Relative Total Variation Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2021-01-15 Wei Wang; Ying Jia; Qiming Wang; Pengfei Xu
The main purpose of image enhancement technology is to improve the quality of the image to better assist those activities of daily life that are widely dependent on it like healthcare, industries, education, and surveillance. Due to the influence of complex environments, there are risks of insufficient detail and low contrast in some images. Existing enhancement algorithms are prone to overexposure
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Deep Ensemble Model for Classification of Novel Coronavirus in Chest X-Ray Images Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2021-01-12 Fareed Ahmad; Amjad Farooq; Muhammad Usman Ghani
The novel coronavirus, SARS-CoV-2, can be deadly to people, causing COVID-19. The ease of its propagation, coupled with its high capacity for illness and death in infected individuals, makes it a hazard to the community. Chest X-rays are one of the most common but most difficult to interpret radiographic examination for early diagnosis of coronavirus-related infections. They carry a considerable amount
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Facial Expression Recognition with LBP and ORB Features Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2021-01-12 Ben Niu; Zhenxing Gao; Bingbing Guo
Emotion plays an important role in communication. For human–computer interaction, facial expression recognition has become an indispensable part. Recently, deep neural networks (DNNs) are widely used in this field and they overcome the limitations of conventional approaches. However, application of DNNs is very limited due to excessive hardware specifications requirement. Considering low hardware specifications
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Text Semantic Classification of Long Discourses Based on Neural Networks with Improved Focal Loss Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2021-01-08 Dan Jiang; Jin He
Semantic classification of Chinese long discourses is an important and challenging task. Discourse text is high-dimensional and sparse. Furthermore, when the number of classes of dataset is large, the data distribution will be seriously imbalanced. In solving these problems, we propose a novel end-to-end model called CRAFL, which is based on the convolutional layer with attention mechanism, recurrent
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Multiparameter Space Decision Voting and Fusion Features for Facial Expression Recognition Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2020-12-29 Yan Wang; Ming Li; Xing Wan; Congxuan Zhang; Yue Wang
Obtaining a valid facial expression recognition (FER) method is still a research hotspot in the artificial intelligence field. In this paper, we propose a multiparameter fusion feature space and decision voting-based classification for facial expression recognition. First, the parameter of the fusion feature space is determined according to the cross-validation recognition accuracy of the Multiscale
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Application of a Modified Generative Adversarial Network in the Superresolution Reconstruction of Ancient Murals Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2020-12-29 Jianfang Cao; Zibang Zhang; Aidi Zhao
Considering the problems of low resolution and rough details in existing mural images, this paper proposes a superresolution reconstruction algorithm for enhancing artistic mural images, thereby optimizing mural images. The algorithm takes a generative adversarial network (GAN) as the framework. First, a convolutional neural network (CNN) is used to extract image feature information, and then, the
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Image Localized Style Transfer to Design Clothes Based on CNN and Interactive Segmentation Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2020-12-29 Hanying Wang; Haitao Xiong; Yuanyuan Cai
In recent years, image style transfer has been greatly improved by using deep learning technology. However, when directly applied to clothing style transfer, the current methods cannot allow the users to self-control the local transfer position of an image, such as separating specific T-shirt or trousers from a figure, and cannot achieve the perfect preservation of clothing shape. Therefore, this paper
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Lw-CNN-Based Myoelectric Signal Recognition and Real-Time Control of Robotic Arm for Upper-Limb Rehabilitation Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2020-12-29 Benzhen Guo; Yanli Ma; Jingjing Yang; Zhihui Wang; Xiao Zhang
Deep-learning models can realize the feature extraction and advanced abstraction of raw myoelectric signals without necessitating manual selection. Raw surface myoelectric signals are processed with a deep model in this study to investigate the feasibility of recognizing upper-limb motion intents and real-time control of auxiliary equipment for upper-limb rehabilitation training. Surface myoelectric
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Intelligence Is beyond Learning: A Context-Aware Artificial Intelligent System for Video Understanding Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2020-12-24 Ahmed Ghozia; Gamal Attiya; Emad Adly; Nawal El-Fishawy
Understanding video files is a challenging task. While the current video understanding techniques rely on deep learning, the obtained results suffer from a lack of real trustful meaning. Deep learning recognizes patterns from big data, leading to deep feature abstraction, not deep understanding. Deep learning tries to understand multimedia production by analyzing its content. We cannot understand the
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A Knowledge-Fusion Ranking System with an Attention Network for Making Assignment Recommendations Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2020-12-23 Canghong Jin; Yuli Zhou; Shengyu Ying; Chi Zhang; Weisong Wang; Minghui Wu
In recent decades, more teachers are using question generators to provide students with online homework. Learning-to-rank (LTR) methods can partially rank questions to address the needs of individual students and reduce their study burden. Unfortunately, ranking questions for students is not trivial because of three main challenges: (1) discovering students’ latent knowledge and cognitive level is
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Inherent Importance of Early Visual Features in Attraction of Human Attention Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2020-12-22 Reza Eghdam; Reza Ebrahimpour; Iman Zabbah; Sajjad Zabbah
Local contrasts attract human attention to different areas of an image. Studies have shown that orientation, color, and intensity are some basic visual features which their contrasts attract our attention. Since these features are in different modalities, their contribution in the attraction of human attention is not easily comparable. In this study, we investigated the importance of these three features
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Tracking Objects Based on Multiple Particle Filters for Multipart Combined Moving Directions Information Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2020-12-17 Ngo Duong Ha; Ikuko Shimizu; Pham The Bao
Object tracking is an important procedure in the computer vision field as it estimates the position, size, and state of an object along the video’s timeline. Although many algorithms were proposed with high accuracy, object tracking in diverse contexts is still a challenging problem. The paper presents some methods to track the movement of two types of objects: arbitrary objects and humans. Both problems
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An Empirical Investigation of Transfer Effects for Reinforcement Learning Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2020-12-16 Jung-Sing Jwo; Ching-Sheng Lin; Cheng-Hsiung Lee; Ya-Ching Lo
Previous studies have shown that training a reinforcement model for the sorting problem takes very long time, even for small sets of data. To study whether transfer learning could improve the training process of reinforcement learning, we employ Q-learning as the base of the reinforcement learning algorithm, apply the sorting problem as a case study, and assess the performance from two aspects, the
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Ensemble Framework of Deep CNNs for Diabetic Retinopathy Detection Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2020-12-15 Gao Jinfeng; Sehrish Qummar; Zhang Junming; Yao Ruxian; Fiaz Gul Khan
Diabetic retinopathy (DR) is an eye disease that damages the blood vessels of the eye. DR causes blurred vision or it may lead to blindness if it is not detected in early stages. DR has five stages, i.e., 0 normal, 1 mild, 2 moderate, 3 severe, and 4 PDR. Conventionally, many hand-on projects of computer vision have been applied to detect DR but cannot code the intricate underlying features. Therefore
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A Lightweight Object Detection Network for Real-Time Detection of Driver Handheld Call on Embedded Devices Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2020-12-15 Zuopeng Zhao; Zhongxin Zhang; Xinzheng Xu; Yi Xu; Hualin Yan; Lan Zhang
It is necessary to improve the performance of the object detection algorithm in resource-constrained embedded devices by lightweight improvement. In order to further improve the recognition accuracy of the algorithm for small target objects, this paper integrates 5 × 5 deep detachable convolution kernel on the basis of MobileNetV2-SSDLite model, extracts features of two special convolutional layers
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Continuous Similarity Learning with Shared Neural Semantic Representation for Joint Event Detection and Evolution Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2020-12-14 Pengpeng Zhou; Yao Luo; Nianwen Ning; Zhen Cao; Bingjing Jia; Bin Wu
In the era of the rapid development of today’s Internet, people often feel overwhelmed by vast official news streams or unofficial self-media tweets. To help people obtain the news topics they care about, there is a growing need for systems that can extract important events from this amount of data and construct the evolution procedure of events logically into a story. Most existing methods treat event
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HDEC: A Heterogeneous Dynamic Ensemble Classifier for Binary Datasets Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2020-12-14 Nasrin Ostvar; Amir Masoud Eftekhari Moghadam
In recent years, ensemble classification methods have been widely investigated in both industry and literature in the field of machine learning and artificial intelligence. The main advantage of this approach is to benefit from a set of classifiers instead of using a single classifier with the aim of improving the prediction performance, such as accuracy. Selecting the base classifiers and the method
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Discriminative Label Relaxed Regression with Adaptive Graph Learning Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2020-12-14 Jingjing Wang; Zhonghua Liu; Wenpeng Lu; Kaibing Zhang
The traditional label relaxation regression (LRR) algorithm directly fits the original data without considering the local structure information of the data. While the label relaxation regression algorithm of graph regularization takes into account the local geometric information, the performance of the algorithm depends largely on the construction of graph. However, the traditional graph structures
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A Semisupervised Learning Scheme with Self-Paced Learning for Classifying Breast Cancer Histopathological Images Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2020-12-08 Sarpong Kwadwo Asare; Fei You; Obed Tettey Nartey
The unavailability of large amounts of well-labeled data poses a significant challenge in many medical imaging tasks. Even in the likelihood of having access to sufficient data, the process of accurately labeling the data is an arduous and time-consuming one, requiring expertise skills. Again, the issue of unbalanced data further compounds the abovementioned problems and presents a considerable challenge
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EEG Assessment in a 2-Year-Old Child with Prolonged Disorders of Consciousness: 3 Years’ Follow-up Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2020-11-27 Gang Xu; Qianqian Sheng; Qinggang Xin; Yanxin Song; Gaoyan Zhang; Lin Yuan; Peng Zhao; Jun Liang
A 2-year-old girl, diagnosed with traumatic brain injury and epilepsy following car trauma, was followed up for 3 years (a total of 15 recordings taken at 0, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 14, 19, 26, and 35 months). There is still no clear guidance on the diagnosis, treatment, and prognosis of children with disorders of consciousness. At each appointment, recordings included the child’s height,
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Binary Political Optimizer for Feature Selection Using Gene Expression Data Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2020-11-29 Ghaith Manita; Ouajdi Korbaa
DNA Microarray technology is an emergent field, which offers the possibility of obtaining simultaneous estimates of the expression levels of several thousand genes in an organism in a single experiment. One of the most significant challenges in this research field is to select high relevant genes from gene expression data. To address this problem, feature selection is a well-known technique to eliminate
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A Study on Differences between Simplified and Traditional Chinese Based on Complex Network Analysis of the Word Co-Occurrence Networks Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2020-12-03 Zhongqiang Jiang; Dongmei Zhao; Jiangbin Zheng; Yidong Chen
Currently, most work on comparing differences between simplified and traditional Chinese only focuses on the character or lexical level, without taking the global differences into consideration. In order to solve this problem, this paper proposes to use complex network analysis of word co-occurrence networks, which have been successfully applied to the language analysis research and can tackle global
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Disruption-Based Multiobjective Equilibrium Optimization Algorithm Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2020-12-01 Hao Chen; Weikun Li; Weicheng Cui
Nature-inspired computing has attracted huge attention since its origin, especially in the field of multiobjective optimization. This paper proposes a disruption-based multiobjective equilibrium optimization algorithm (DMOEOA). A novel mutation operator named layered disruption method is integrated into the proposed algorithm with the aim of enhancing the exploration and exploitation abilities of DMOEOA
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Graph Neural Network and Context-Aware Based User Behavior Prediction and Recommendation System Research Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2020-11-30 Qian Gao; Pengcheng Ma
Due to the influence of context information on user behavior, context-aware recommendation system (CARS) has attracted extensive attention in recent years. The most advanced context-aware recommendation system maps the original multi-field features into a shared hidden space and then simply connects it to a deep neural network (DNN) or other specially designed networks. However, for different areas
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Schizophrenia EEG Signal Classification Based on Swarm Intelligence Computing Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2020-11-30 Sunil Kumar Prabhakar; Harikumar Rajaguru; Sun-Hee Kim
One of the serious mental disorders where people interpret reality in an abnormal state is schizophrenia. A combination of extremely disordered thinking, delusion, and hallucination is caused due to schizophrenia, and the daily functions of a person are severely disturbed because of this disorder. A wide range of problems are caused due to schizophrenia such as disturbed thinking and behaviour. In
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Mixed-Level Neural Machine Translation Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2020-11-29 Thien Nguyen; Huu Nguyen; Phuoc Tran
Building the first Russian-Vietnamese neural machine translation system, we faced the problem of choosing a translation unit system on which source and target embeddings are based. Available homogeneous translation unit systems with the same translation unit on the source and target sides do not perfectly suit the investigated language pair. To solve the problem, in this paper, we propose a novel heterogeneous
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Driver Distraction Detection Method Based on Continuous Head Pose Estimation Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2020-11-29 Zuopeng Zhao; Sili Xia; Xinzheng Xu; Lan Zhang; Hualin Yan; Yi Xu; Zhongxin Zhang
In view of the fact that the detection of driver’s distraction is a burning issue, this study chooses the driver’s head pose as the evaluation parameter for driving distraction and proposes a driver distraction method based on the head pose. The effects of single regression and classification combined with regression are compared in terms of accuracy, and four kinds of classical networks are improved
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Image Retrieval Using the Fused Perceptual Color Histogram Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2020-11-25 Guang-Hai Liu; Zhao Wei
Extracting visual features for image retrieval by mimicking human cognition remains a challenge. Opponent color and HSV color spaces can mimic human visual perception well. In this paper, we improve and extend the CDH method using a multi-stage model to extract and represent an image in a way that mimics human perception. Our main contributions are as follows: (1) a visual feature descriptor is proposed
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A User-Oriented Intelligent Access Selection Algorithm in Heterogeneous Wireless Networks Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2020-11-24 Gen Liang; Xiaoxue Guo; Guoxi Sun; Jingcheng Fang
A heterogeneous wireless network (HWN) contains many kinds of wireless networks with overlapping areas of signal coverage. One of the research topics on HWNs is how to make users choose the most suitable network. This paper designs a user-oriented intelligent access selection algorithm in HWNs with five modules (input, user preference calculation, candidate network score calculation, output, and learning)
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Improved Distance Functions for Instance-Based Text Classification Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2020-11-23 Khalil El Hindi; Bayan Abu Shawar; Reem Aljulaidan; Hussien Alsalamn
Text classification has many applications in text processing and information retrieval. Instance-based learning (IBL) is among the top-performing text classification methods. However, its effectiveness depends on the distance function it uses to determine similar documents. In this study, we evaluate some popular distance measures’ performance and propose new ones that exploit word frequencies and
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Stability Analysis for Nonlinear Impulsive Control System with Uncertainty Factors Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2020-11-21 Zemin Ren; Shiping Wen; Qingyu Li; Yuming Feng; Ning Tang
Considering the limitation of machine and technology, we study the stability for nonlinear impulsive control system with some uncertainty factors, such as the bounded gain error and the parameter uncertainty. A new sufficient condition for this system is established based on the generalized Cauchy–Schwarz inequality in this paper. Compared with some existing results, the proposed method is more practically
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Hierarchical Multimodal Adaptive Fusion (HMAF) Network for Prediction of RGB-D Saliency Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2020-11-21 Ying Lv; Wujie Zhou
Visual saliency prediction for RGB-D images is more challenging than that for their RGB counterparts. Additionally, very few investigations have been undertaken concerning RGB-D-saliency prediction. The proposed study presents a method based on a hierarchical multimodal adaptive fusion (HMAF) network to facilitate end-to-end prediction of RGB-D saliency. In the proposed method, hierarchical (multilevel)
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Driver Fatigue Detection Based on Convolutional Neural Networks Using EM-CNN Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2020-11-18 Zuopeng Zhao; Nana Zhou; Lan Zhang; Hualin Yan; Yi Xu; Zhongxin Zhang
With a focus on fatigue driving detection research, a fully automated driver fatigue status detection algorithm using driving images is proposed. In the proposed algorithm, the multitask cascaded convolutional network (MTCNN) architecture is employed in face detection and feature point location, and the region of interest (ROI) is extracted using feature points. A convolutional neural network, named
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Sea Clutter Suppression Method of HFSWR Based on RBF Neural Network Model Optimized by Improved GWO Algorithm Comput. Intell. Neurosci. (IF 2.284) Pub Date : 2020-11-16 Shang Shang; Kang-Ning He; Zhao-Bin Wang; Tong Yang; Ming Liu; Xiang Li
The detection performance of high-frequency surface-wave radar (HFSWR) is closely related to the suppression effect of sea clutter. To effectively suppress sea clutter, a sea clutter suppression method based on radial basis function neural network (RBFNN) optimized by improved gray wolf optimization (IGWO) algorithm is proposed. Firstly, according to shortcomings of the standard gray wolf optimization