Sequence-based prediction of protein-protein interaction sites by simplified long short-term memory network
In this study, we propose to use a deep learning method for improving the imbalanced prediction of protein interaction sites. We develop a new simplified long short-term memory (SLSTM) network to implement a deep learning architecture (named DLPred). To deal with the imbalanced classification in the deep learning model, we explore three new ideas. First, our collection of the training data is to construct a set of protein sequences, instead of a set of just single residues, to retain the entire sequential completeness of each protein. Second, a new penalization factor is appended to the loss function such that the penaliza...
Source: Neurocomputing - May 23, 2019 Category: Neuroscience Source Type: research

Large-Scale Offline Signature Recognition via Deep Neural Networks and Feature Embedding
In this study we propose a new convolutional neural network (CNN) structure named Large-Scale Signature Network (LS2Net) with batch normalization to deal with the large-scale training problem. Moreover, we present, Class Center based Classifier (C3) algorithm, which relies on 1-Nearest Neighbor (1-NN) classification task by using the class-centers of the feature embeddings obtained from fully-connected layers. In addition to these, by replacing the activation function rectifier linear unit (ReLU) with leaky ReLU, we create a new network structure called LS2Net_v2. 96k signatures obtained from 4k signers of GPDS-4000 datase...
Source: Neurocomputing - May 23, 2019 Category: Neuroscience Source Type: research

A Spatial-aware Joint Optic Disc and Cup Segmentation Method
Publication date: Available online 22 May 2019Source: NeurocomputingAuthor(s): Qing Liu, Xiaopeng Hong, Shuo Li, Zailiang Chen, Guoying Zhao, Beiji ZouAbstractWhen dealing with the optic disc and cup in the optical nerve head images, their joint segmentation confronts two critical problems. One is that the spatial layout of the vessels in the optic nerve head images is variant. The other is that the landmarks for the optic cup boundaries are spatially sparse and at small spatial scale. To solve these two problems, we propose a spatial-aware joint segmentation method by explicitly considering the spatial locations of the pi...
Source: Neurocomputing - May 23, 2019 Category: Neuroscience Source Type: research

Stopping rules for mutual information-based feature selection
Publication date: Available online 22 May 2019Source: NeurocomputingAuthor(s): Jan Mielniczuk, PaweĊ‚ TeisseyreAbstractIn recent years feature selection methods based on mutual information have attracted a significant attention. Most of the proposed methods are based on sequential forward search which add at each step a feature which is most relevant in explaining a class variable when considered together with the already chosen features. Such procedures produce ranking of features ordered according to their relevance. However significant limitation of all existing methods is lack of stopping rules which separate relevant ...
Source: Neurocomputing - May 23, 2019 Category: Neuroscience Source Type: research

Manifold Regularized Stacked Denoising Autoencoders with Feature Selection
This study concentrates on using PSO to simultaneously optimize structure and parameters of SDAEs through a specific particle representation and learning method. MRSDAE aims to generate discriminant features from the data based on the integration of these effective techniques, i.e., structure and parameter optimization, manifold regularization and feature selection. The experimental results on a number of benchmark classification datasets demonstrate that MRSDAE can construct compact SDAEs with high generalization performance. Finding from this study can be used as effective guideline in learning both the structure and par...
Source: Neurocomputing - May 23, 2019 Category: Neuroscience Source Type: research

Complementary Coded Aperture Set for Compressive High-Resolution Imaging
Publication date: Available online 22 May 2019Source: NeurocomputingAuthor(s): Wei Sun, Jinqiu Sun, Yu Zhu, Yaoqi Hu, Chen Ding, Haisen Li, Yanning ZhangAbstractThe traditional imaging approach with circular aperture lenses lose the high frequency part of the scene because of limited cut-off frequency of the aperture, which could not be recovered only with the post-processing method. Our analysis of the frequency shows that different apertures have different frequency retention, and a single aperture can not preserve more high frequency information, which brings on unsuccessful reconstruction of High-Resolution(HR) images....
Source: Neurocomputing - May 23, 2019 Category: Neuroscience Source Type: research

Multi-label Classification using a Cascade of Stacked Autoencoder and Extreme Learning Machines
This article introduces a cascade of neural networks for classification of multi-label data. Two types of networks, namely, stacked autoencoder (SAE) and extreme learning machine (ELM) have been incorporated in the proposed system. ELM is a compact and efficient single-label classifier which seems to lose its efficiency while dealing with multi-label data. This happens due to the complex nature of the multi-label data, which makes it difficult for the smaller networks to interpret it accurately. In our proposed work, we attempt to deal with few of the bottlenecks faced while handling multi-label data. Thus, we aim to enhan...
Source: Neurocomputing - May 23, 2019 Category: Neuroscience Source Type: research

Grassroots VS Elites: Which ones are better candidates for influence maximization in social networks?
Publication date: Available online 23 May 2019Source: NeurocomputingAuthor(s): Dong Li, Wei Wang, Jiming LiuAbstractHow to select a set of seed users under a limited budget from the social networks to maximize information/influence diffusion is a critical task in the social computing filed, called as influence maximization (IM) problem. Existing studies usually seek users with high influence (“elites”) as seeds. Each selected elite user can take a large influence increment, however, s/he also consumes a high cost (e.g. money). In the time of Web 2.0, ordinary users (“grassroots”) become the main bod...
Source: Neurocomputing - May 23, 2019 Category: Neuroscience Source Type: research

Editorial Board
Publication date: 3 September 2019Source: Neurocomputing, Volume 356Author(s): (Source: Neurocomputing)
Source: Neurocomputing - May 23, 2019 Category: Neuroscience Source Type: research

Low-resolution Palmprint Image Denoising by Generative Adversarial Networks
Publication date: Available online 21 May 2019Source: NeurocomputingAuthor(s): Shengjie Chen, Shuo Chen, Zhenhua Guo, Yushen ZuoAbstractPalmprint recognition is a reliable biometric identification method because palmprints contain rich and discriminative features. Low-resolution palmprints have attracted much attention due to their simple acquisition and low computational cost. Many previous works have achieved impressive results. However, we noticed that the performances of these methods declined significantly when there was noise in the palmprint images. Traditional denoising algorithms cannot address multiple types of n...
Source: Neurocomputing - May 23, 2019 Category: Neuroscience Source Type: research

Editorial Board
Publication date: 25 August 2019Source: Neurocomputing, Volume 355Author(s): (Source: Neurocomputing)
Source: Neurocomputing - May 23, 2019 Category: Neuroscience Source Type: research

Deterministic Learning from Sampling Data
Publication date: Available online 21 May 2019Source: NeurocomputingAuthor(s): Weiming Wu, Cong Wang, Chengzhi YuanAbstractIn this paper, based on the deterministic learning mechanism, we present an alternative systematic scheme for dynamics identification from sampling data sequences. The proposed scheme belongs to a dynamical machine learning framework based on Lyapunov stability theory rather than optimization estimation approaches. Given a sampling data sequence collected from an unknown deterministic nonlinear dynamical system, we show that the inherent dynamics of the sampling data sequence can be locally accurately ...
Source: Neurocomputing - May 22, 2019 Category: Neuroscience Source Type: research

Editorial Board
Publication date: 11 August 2019Source: Neurocomputing, Volume 353Author(s): (Source: Neurocomputing)
Source: Neurocomputing - May 18, 2019 Category: Neuroscience Source Type: research

Multimodal Multiclass Boosting and Its Application to Cross-modal Retrieval
Publication date: Available online 16 May 2019Source: NeurocomputingAuthor(s): Shixun Wang, Zhi Dou, Deng Chen, Hairong Yu, Yuan Li, Peng PanAbstractAlthough Boosting approach has been proved to be a very successful ensemble learning technology, the conventional ones are limited to two classes or single modality. In this paper, to deal with multiclass setting and heterogeneous modalities, we propose a multimodal multiclass boosting framework called MMBoost, in which the intra-modal semantic information and inter-modal semantic correlation can be captured at the same time. By utilizing the multiclass exponential and logisti...
Source: Neurocomputing - May 17, 2019 Category: Neuroscience Source Type: research

Correlation Filter Tracker with Siamese: A Robust and Real-time Object Tracking Framework
Publication date: Available online 16 May 2019Source: NeurocomputingAuthor(s): Gengzheng Pan, Guochun Chen, Wenxiong Kang, Junhui HouAbstractCorrelation filter (CF) based trackers have shown promising performance in object tracking. However, both the accuracy and efficiency of existing CF based trackers are limited. In this paper, we propose a robust and real-time object tracking framework, based on a canonical CF tracker. Specifically, we first propose an adaptive model update strategy for preventing the tracker from being contaminated when the target is occluded or disappears in sight. Then, we propose a multimodal valid...
Source: Neurocomputing - May 17, 2019 Category: Neuroscience Source Type: research

Global synchronization in finite time for fractional-order coupling complex dynamical networks with discontinuous dynamic nodes
Publication date: Available online 16 May 2019Source: NeurocomputingAuthor(s): You Jia, Huaiqin WuAbstractThe global Mittag-Leffler synchronization and global synchronization problem in finite time for fractional-order complex networks which with discontinuous nodes is studied in this paper. When the coupling matrix is time-varying and unknown, under the designed adaptive law with respect to the coupling matrix, by utilizing nonsmooth analysis method and Lyapunov functional approach as well as Laplace transform technique, the conditions of global Mittag-Leffler synchronization are achieved in terms of linear matrix inequal...
Source: Neurocomputing - May 17, 2019 Category: Neuroscience Source Type: research

Breast Cancer Diagnosis through Active Learning in Content-based Image Retrieval
Publication date: Available online 17 May 2019Source: NeurocomputingAuthor(s): Rafael S. Bressan, Pedro H. Bugatti, Priscila T.M. SaitoAbstractOne of the cornerstones of content-based image retrieval (CBIR) for medical image diagnosis is to select the images that present higher similarity with a given query image. Different from previous literature efforts, the present work aims to seamlessly fuse a powerful machine learning strategy based on the active learning paradigm, in order to obtain greater efficacy regarding similarity queries in medical CBIR systems. To do so, we propose a new approach, named as Medical Active le...
Source: Neurocomputing - May 17, 2019 Category: Neuroscience Source Type: research

A Hybrid-Backward Refinement Model for Salient Object Detection
Publication date: Available online 17 May 2019Source: NeurocomputingAuthor(s): Dakhia Abdelhafid, Tiantian Wang, Huchuan LuAbstractThe deep Convolutional Neural Networks (CNNs) have been investigated in many salient object detection works and have achieved state-of-the-art performance compared to the classic methods. However, most of the existing CNN-based methods still struggle in addressing the problem of incomplete contours of salient objects. To overcome this problem, this paper focuses on accurately capturing the fine details of salient objects by proposing a novel Hybrid-Backward Refinement Network (HBRNet), which co...
Source: Neurocomputing - May 17, 2019 Category: Neuroscience Source Type: research

Status detection from spatial-temporal data in pipeline network using data transformation convolutional neural network
Publication date: Available online 17 May 2019Source: NeurocomputingAuthor(s): Xuguang Hu, Huaguang Zhang, Dazhong Ma, Rui WangAbstractWith the scale expansion and structural upgrading of pipeline network, the detection methods based on both ends of the pipeline pressure have appeared the limitations of judging pipeline status in the multi-mode and complex network system. To overcome the limitation of early methods, a pipeline network status detection method based on data transformation convolutional neural network (DT-CNN) is proposed in this paper. Firstly, the difference among the eigenvalue distribution of data covaria...
Source: Neurocomputing - May 17, 2019 Category: Neuroscience Source Type: research

Detail-Preserving Image Super-Resolution via Recursively Dilated Residual Network
Publication date: Available online 17 May 2019Source: NeurocomputingAuthor(s): Feng Li, Huihui Bai, Yao ZhaoAbstractConvolutional neural network (CNN) methods have been successfully applied in single image super-resolution (SR). However, existing very deep CNN based SR methods face with the challenge of memory footprint and computational complexity for real-world applications. Besides, many previous methods lack flexible ability to emphasize local spatial informative areas, which is limited to recover the high-frequency detail of LR input. In this paper, to address these problems, we implement a spatial modulated residual ...
Source: Neurocomputing - May 17, 2019 Category: Neuroscience Source Type: research

Nonlinear control scheme for general decay projective synchronization of delayed memristor-based BAM neural networks
Publication date: Available online 15 May 2019Source: NeurocomputingAuthor(s): Abdujelil Abdurahman, Haijun JiangAbstractIn this paper, we have made an effort to investigate the general decay projective synchronization (GDPS) problem of a type of delayed memristor-based BAM neural networks. First, we introduced a type of novel nonlinear controller. Then, we derived some sufficient conditions ensuring the GDPS of considered networks by employing differential inclusion theory and using well-known Lyapunov functional method. Lastly, an example is given to show the correctness of obtained results. To the authors’ knowled...
Source: Neurocomputing - May 17, 2019 Category: Neuroscience Source Type: research

Optimizing Simple Deterministically Constructed Cycle Reservoir Network with a Redundant Unit Pruning Auto-Encoder Algorithm
Publication date: Available online 16 May 2019Source: NeurocomputingAuthor(s): Heshan Wang, Q. M. Jonathan Wu, Jie Wang, Wei Wu, Kunjie YuAbstractEcho State Network (ESN) is a specific form of recurrent neural network, which displays very rich dynamics owing to its reservoir based hidden neurons. In the issue, ESN is viewed as a powerful approach to model real-valued time series processes. Nevertheless, ESN has been criticized for its manually experienced or brute-force searching parameters, such as initial input weights and reservoir layer weights, i.e., the conventional randomly generated ESN is unlikely to be optimal be...
Source: Neurocomputing - May 17, 2019 Category: Neuroscience Source Type: research

Reweighted Sparse Representation with Residual Compensation for 3D Human Pose Estimation from a Single RGB Image
Publication date: Available online 16 May 2019Source: NeurocomputingAuthor(s): Mengxi Jiang, Zhuliang Yu, Yan Zhang, Qicong Wang, Cuihua Li, Yunqi LeiAbstract3D human pose estimation from 2D joints of an image is a worthwhile and challenging research topic. Since a specific 2D pose could be projected from various 3D poses, the ambiguity becomes a difficult obstacle when recovering 3D pose from 2D. Many supervised learning solutions have been proposed in recent years, however, most of them require an abundant of well-annotated training samples to get satisfied estimation performance. In this paper, an unsupervised approach ...
Source: Neurocomputing - May 17, 2019 Category: Neuroscience Source Type: research

Ensemble Clustering based on Dense Representation
Publication date: Available online 16 May 2019Source: NeurocomputingAuthor(s): Jie Zhou, Hongchan Zheng, Lulu PanAbstractEnsemble clustering has emerged as a powerful tool for improving the stability and accuracy of the clustering task. Although various approaches have been proposed for improving the performance of algorithms, most of them ignored two crucial messages provided by base clusterings. First, some samples of input data may be outliers that locate the boundary of the clusters and can be easily partitioned into different clusters. Second, must-link information exists amongst some instances. In this paper, we deve...
Source: Neurocomputing - May 17, 2019 Category: Neuroscience Source Type: research

Multiple Convolutional Neural Networks for Multivariate Time Series Prediction
Publication date: Available online 16 May 2019Source: NeurocomputingAuthor(s): Kang Wang, Kenli Li, Liqian Zhou, Yikun Hu, Zhongyao Cheng, Jing Liu, Cen ChenAbstractMultivariate time series prediction, with a profound impact on human social life, has been attracting growing interest in machine learning research. However, the task of time series forecasting is very challenging because it is affected by many complex factors. For example, in predicting traffic and solar power generation, weather can bring great trouble. In particular, for strictly periodic time series, if the periodic information can be extracted from the his...
Source: Neurocomputing - May 17, 2019 Category: Neuroscience Source Type: research

Hybrid Attention for Chinese Character-Level Neural Machine Translation
Publication date: Available online 16 May 2019Source: NeurocomputingAuthor(s): Feng Wang, Wei Chen, Zhen Yang, Shuang Xu, Bo XuAbstractThis paper proposes a novel character-level neural machine translation model which can effectively improve the Neural Machine Translation (NMT) by fusing word and character attention information. In our work, the bidirectional Gated Recurrent Unit (GRU) network is utilized to compose word-level information from the input sequence of characters automatically. Contrary to traditional NMT models, two kinds of different attentions are incorporated into our proposed model: One is the character-l...
Source: Neurocomputing - May 17, 2019 Category: Neuroscience Source Type: research

Lifelong Representation Learning in Dynamic Attributed Networks
Publication date: Available online 16 May 2019Source: NeurocomputingAuthor(s): Hao Wei, Guyu Hu, Wei Bai, Shiming Xia, Zhisong PanAbstractNetwork embedding or network representation learning aims at learning a low-dimensional vector for each node in a network. The learned embeddings could advance various learning tasks in the network analysis area. Most existing embedding methods focus on plain and static networks while ignoring network dynamics. However, in real world networks, structure often evolves over time. In addition, many networks contain rich attributes and their attributes are changing over time. Naively applyin...
Source: Neurocomputing - May 17, 2019 Category: Neuroscience Source Type: research

Editorial Board
Publication date: 4 August 2019Source: Neurocomputing, Volume 352Author(s): (Source: Neurocomputing)
Source: Neurocomputing - May 17, 2019 Category: Neuroscience Source Type: research

Editorial Board
Publication date: 18 August 2019Source: Neurocomputing, Volume 354Author(s): (Source: Neurocomputing)
Source: Neurocomputing - May 17, 2019 Category: Neuroscience Source Type: research

Hand-raising Gesture Detection in Real Classrooms Using Improved R-FCN
Publication date: Available online 14 May 2019Source: NeurocomputingAuthor(s): Jiaxin Si, Jiaojiao Lin, Fei Jiang, Ruimin ShenAbstractThis paper proposes a novel method for hand-raising detection in real classroom environments. Different from traditional motion detection, the hand-raising detection is quite challenging due to complex backgrounds, various gestures and low resolutions. To solve these challenges, we build up a large-scale dataset from videos of real classrooms, and propose a novel neural network architecture based on region-based, fully convolutional networks (R-FCN). Specifically, we first design an adaptive...
Source: Neurocomputing - May 15, 2019 Category: Neuroscience Source Type: research

Exponential synchronization of inertial reaction-diffusion coupled neural networks with proportional delay via periodically intermittent control
Publication date: Available online 14 May 2019Source: NeurocomputingAuthor(s): Peng Wan, Dihua Sun, Dong Chen, Min Zhao, Linjiang ZhengAbstractThis paper focuses on the global exponential synchronization problem for a class of inertial reaction-diffusion coupled neural networks with proportional delay. Through a variable transformation, the inertial reaction-diffusion neural networks are transformed into neural networks with first-order time and space derivative of the states. By taking new Lyapunov-Krasovskii functional, utilizing Wirtinger inequality, a sufficient criterion is obtained to make the addressed networks glob...
Source: Neurocomputing - May 15, 2019 Category: Neuroscience Source Type: research

Semantic-filtered Soft-Split-Aware Video Captioning with Audio-Augmented Feature
Publication date: Available online 14 May 2019Source: NeurocomputingAuthor(s): Yuecong Xu, Jianfei Yang, Kezhi MaoAbstractAutomatic video description, or video captioning, is a challenging yet much attractive task. It aims to combine video with text. Multiple methods have been proposed based on neural networks, utilizing Convolutional Neural Networks (CNN) to extract features, and Recurrent Neural Networks (RNN) to encode and decode videos to generate descriptions. Previously, a number of methods used in video captioning task are motivated by image captioning approaches. However, videos carry much more information than ima...
Source: Neurocomputing - May 15, 2019 Category: Neuroscience Source Type: research

Recognition Oriented Facial Image Quality Assessment via Deep Convolutional Neural Network
Publication date: Available online 15 May 2019Source: NeurocomputingAuthor(s): Ning Zhuang, Qiang Zhang, Cenhui Pan, Bingbing Ni, Yi Xu, Xiaokang Yang, Wenjun ZhangAbstractQuality of facial images significantly impacts the performance of face recognition algorithms. Being able to predict “which facial image is good for recognition” is of great importance for real application scenarios, where a sequence of facial images are always presented and one should select the image frame with “best quality” for the subsequent matching and recognition task. To this end, we introduce a novel facial image quality...
Source: Neurocomputing - May 15, 2019 Category: Neuroscience Source Type: research

Hyperspectral Imagery Classification with Deep Metric Learning
Publication date: Available online 15 May 2019Source: NeurocomputingAuthor(s): Xianghai Cao, Yiming Ge, Renjie Li, Jing Zhao, Licheng JiaoAbstractThe high dimensionality of hyperspectral imagery often introduces challenge for the conventional data analysis techniques. In order to improve the classification performance of hyperspectral imagery, metric learning is often introduced to assign small distances between samples from the same class and large distances from different class. However, most of the traditional metric learning methods only adopt linear transformations, which cannot capture the complex nonlinear relations...
Source: Neurocomputing - May 15, 2019 Category: Neuroscience Source Type: research

Triple-Translation GAN with Multi-layer Sparse Representation for Face Image Synthesis
Publication date: Available online 15 May 2019Source: NeurocomputingAuthor(s): Linbin Ye, Bob Zhang, Meng Yang, Wei LianAbstractFace image synthesis with facial feature and identity preserving is one of the key challenges in computer vision. Recently, outstanding performances in image translation and synthesis have been reported in CycleGAN. However, for the task of face image synthesis, there are still several remaining issues (e.g., poor-visual-quality facial feature, changed face identity, unstable model optimization). In order to solve the above issues, in this paper we propose a novel model of triple translation GAN (...
Source: Neurocomputing - May 15, 2019 Category: Neuroscience Source Type: research

Graph Convolutional Network for Multi-label VHR Remote Sensing Scene Recognition
Publication date: Available online 15 May 2019Source: NeurocomputingAuthor(s): Nagma Khan, Ushasi Chaudhuri, Biplab Banerjee, Subhasis ChaudhuriAbstractWe address the problem of multi-label scene classification from Very High Resolution (VHR) satellite remote sensing (RS) images in this paper by exploring the deep graph convolutional network (GCN). Since a given VHR RS scene contains several local features, the traditional single-label classification frameworks do not convey the true semantics of the scene. The multi-label classification approaches, on the other hand, is expected to aid in better characterization of the ar...
Source: Neurocomputing - May 15, 2019 Category: Neuroscience Source Type: research

Dynamics of anti-periodic solutions on shunting inhibitory cellular neural networks with multi-proportional delays
Publication date: Available online 15 May 2019Source: NeurocomputingAuthor(s): Chuangxia Huang, Shigang Wen, Lihong HuangAbstractSince proportional delay is monotonically increasing, a neural network involving multi-proportional delays is obviously not anti-periodic, yet a very interesting fact in this paper shows that it is possible there is an anti-periodic solution for such systems. This paper aims to deal with the issue of anti-periodic solutions for SICNNs (Shunting Inhibitory Cellular Neural Networks) involving multi-proportional delays. With the help of Lyapunov method, inequality techniques and a concise mathematic...
Source: Neurocomputing - May 15, 2019 Category: Neuroscience Source Type: research

VCG: Exploiting Visual Contents and Geographical Influence for Point-of-Interest Recommendation
Publication date: Available online 15 May 2019Source: NeurocomputingAuthor(s): Zhibin Zhang, Cong Zou, Ruifeng Ding, Zhenzhong ChenAbstractThe rapid development of location-based social networks (LBSNs) provides a substantial amount of image data which not only reveals visual contents of POIs but also users’ visual preferences. We argue that the combination of visual content and other side information (e.g., geographical influence) can lead to a more accurate and personalized recommendation performance. In this paper, we enhance POI recommendation by proposing a unified framework named VCG, which incorporates visual ...
Source: Neurocomputing - May 15, 2019 Category: Neuroscience Source Type: research

Editorial Board
Publication date: 25 July 2019Source: Neurocomputing, Volume 351Author(s): (Source: Neurocomputing)
Source: Neurocomputing - May 15, 2019 Category: Neuroscience Source Type: research

Adaptive neural practically finite-time congestion control for TCP/AQM network
Publication date: 25 July 2019Source: Neurocomputing, Volume 351Author(s): Yang Liu, Yuanwei Jing, Xiangyong ChenAbstractInspired by the prescribed performance control (PPC), a new performance function, called finite-time performance function (FTPF), is first defined in this paper, by which a novel finite-time control design process is introduced. This work is also the first to solve the finite-time control issue for a class of transmission control protocol/active queue management (TCP/AQM) networks. Meanwhile, with the aid of FTPF, PPC and neural networks, a new adaptive practically pre-assigned finite-time controller is ...
Source: Neurocomputing - May 15, 2019 Category: Neuroscience Source Type: research

Optimization of Neural Network with Wavelet Transform and Improved Data Selection using Bat Algorithm for Short-Term Load Forecasting
Publication date: Available online 13 May 2019Source: NeurocomputingAuthor(s): P.M.R Bento, J.A.N. Pombo, M.R.A. Calado, S.J.P.S. MarianoAbstractShort-term load forecasting is very important for reliable power system operation, even more so under electricity market deregulation and integration of renewable resources framework. This paper presents a new enhanced method for one day ahead load forecast, combing improved data selection and features extraction techniques (similar/recent day-based selection, correlation and wavelet analysis), which brings more “regularity” to the load time-series, an important precon...
Source: Neurocomputing - May 15, 2019 Category: Neuroscience Source Type: research

MMAN: Multi-Modality Aggregation Network for Brain Segmentation from MR Images
Publication date: Available online 14 May 2019Source: NeurocomputingAuthor(s): Jingcong Li, Zhu Liang Yu, Zhenghui Gu, Yuanqing LiAbstractBrain tissue segmentation from Magnetic resonance (MR) image is significant for assessing both neurologic conditions and brain disease. Manual brain tissue segmentation is time-consuming, tedious and subjective which indicates a need for more efficiently automated approaches. However, due to ambiguous boundaries, anatomically complex structure and individual differences, conventional automated segmentation methods performed poorly. Therefore, more effective feature extraction techniques ...
Source: Neurocomputing - May 15, 2019 Category: Neuroscience Source Type: research

Automated hepatobiliary toxicity prediction after liver stereotactic body radiation therapy with deep learning-based portal vein segmentation
ConclusionThe proposed framework automates the HB toxicity prediction with the accuracy similar to manual analysis-based HB toxicity prediction. The strategy is quite general and extendable to the automated prediction of toxicities of other organs. (Source: Neurocomputing)
Source: Neurocomputing - May 15, 2019 Category: Neuroscience Source Type: research

Distributed event-triggered circle formation control for multi-agent systems with limited communication bandwidth
Publication date: Available online 14 May 2019Source: NeurocomputingAuthor(s): Jiayan Wen, Peng Xu, Chen Wang, Guangming Xie, Yuan GaoAbstractThis paper investigates event-triggered circle formation problem of multi-agent systems (MASs) with limited communication bandwidth over such network setup among agents, in which each agent can only perceive the angular distance from itself to the nearest neighbor in counterclockwise direction as well as the counterpart in the clockwise direction be acquired through communication. To solve the concerned problem, a distributed algorithm relied on the combination between the quantized ...
Source: Neurocomputing - May 15, 2019 Category: Neuroscience Source Type: research

Data-Driven Finite-Horizon Optimal Tracking Control Scheme for Completely Unknown Discrete-Time Nonlinear Systems
Publication date: Available online 14 May 2019Source: NeurocomputingAuthor(s): Ruizhuo Song, Yulong Xie, Zenglian ZhangAbstractThis paper proposes finite-horizon optimal tracking control approach based on data for completely unknown discrete-time nonlinear affine systems. First, the identifier is designed by input and output data, which is used to identify system function and system model. And based on tracking error, the system function is transformed to the augmentation system with finite-time optimal performance. In finite time, by minimizing the performance index function, the iterative approximate dynamic programming ...
Source: Neurocomputing - May 15, 2019 Category: Neuroscience Source Type: research

Convolutional Neural Network Based Diagnosis of Bone Pathologies of Proximal Humerus
In this study automatically segmented PD weighted shoulder images were evaluated by the proposed convolutional neural network (CNN) to extract features and classify humeral head in three groups as normal, edematous and Hill -Sachs lesion with a success rate of %98.43. Compared to the state of art methods, our proposed CNN based diagnosis system is very promising to assist radiologists and orthopedists in decision making. (Source: Neurocomputing)
Source: Neurocomputing - May 15, 2019 Category: Neuroscience Source Type: research

Diversified Textual Features based Image Retrieval
Publication date: Available online 14 May 2019Source: NeurocomputingAuthor(s): Bo Yuan, Xinbo GaoAbstractHow to target the users’ demands faster and more accurately has become a hot issue in the domain of image retrieval. Most of the existing techniques focus on retrieving the most relevant images to the query, which will reduce the search efficiency and make the retrieval process boring for users. For these reasons, diversity-induced image retrieval is proposed to guarantee that the retrieval results are not only relevant to the query, but also cover the aspects of the query as many as possible. Most of the traditio...
Source: Neurocomputing - May 15, 2019 Category: Neuroscience Source Type: research

Recognizing Road From Satellite Images by Structured Neural Network
Publication date: Available online 11 May 2019Source: NeurocomputingAuthor(s): Guangliang Cheng, Chongruo Wu, Qingqing Huang, Yu Meng, Jianping Shi, Jiansheng Chen, Dongmei YanAbstractRecognizing and extracting roads accurately are significant for auto-driving cars and map providers. Thanks to the power of deep learning, it is possible to achieve high accuracy with a large amount of labeled data. However, as far as we know, there is not enough public data for road recognition from satellite images, especially for the urban scene. To provide sufficient data for training a neural network, we collect a large dataset for road ...
Source: Neurocomputing - May 12, 2019 Category: Neuroscience Source Type: research

Sentiment Analysis through Critic Learning for Optimizing Convolutional Neural Networks with Rules
Publication date: Available online 11 May 2019Source: NeurocomputingAuthor(s): Bowen Zhang, Xiaofei Xu, Xutao Li, Xiaojun Chen, Yunming Ye, Zhongjie WangAbstractSentiment analysis is an important task in natural language processing. Previous studies have shown that integrating the knowledge rules into conventional classifiers can effectively improve the sentiment analysis accuracy. However, they suffer from two key deficiencies: (1) the given knowledge rules often contain mistakes or violations, which may hurt the performance if they cannot be adaptively utilized; (2) most of the studies leverage only the simple knowledge ...
Source: Neurocomputing - May 12, 2019 Category: Neuroscience Source Type: research

Rademacher Dropout: An Adaptive Dropout For Deep Neural Network Via Optimizing Generalization Gap
Publication date: Available online 11 May 2019Source: NeurocomputingAuthor(s): Haotian Wang, Wenjing Yang, Zhenyu Zhao, Tingjin Luo, Ji Wang, Yuhua TangAbstractDropout plays an important role in improving the generalization ability in deep learning. However, the empirical and fixed choice of dropout rates in traditional dropout strategies may increase the generalization gap, which is counter to one of the principle aims of dropout. To handle this problem, in this paper, we propose a novel dropout method. By the theoretical analysis of Dropout Rademacher Complexity, we first prove that the generalization gap of a deep model...
Source: Neurocomputing - May 12, 2019 Category: Neuroscience Source Type: research