Multipose face recognition system is one of the recent challenges faced by the researchers interested in security applications. Different researches have been introduced discussing the accuracy improvement of multipose face recognition through enhancing the face detector as Viola-Jones, Real Adaboost, and Cascade Object Detector while others concentrated on the recognition systems as support vector

The effect of emotion on prospective memory on those of different age groups and its neural mechanism in Chinese adults are still unclear. The present study investigated the effect of emotion on prospective memory during the encoding and retrieval phases in younger and older adults by using event-related potentials (ERPs). In the behavioral results, a shorter response time was found for positive prospective

Heavy-tailed distributions play an important role in modeling data in actuarial and financial sciences. In this article, a new method is suggested to define new distributions suitable for modeling data with a heavy right tail. The proposed method may be named as the Z-family of distributions. For illustrative purposes, a special submodel of the proposed family, called the Z-Weibull distribution, is

As a respiratory infection, pneumonia has gained great attention from countries all over the world for its strong spreading and relatively high mortality. For pneumonia, early detection and treatment will reduce its mortality rate significantly. Currently, X-ray diagnosis is recognized as a relatively effective method. The visual analysis of a patient’s X-ray chest radiograph by an experienced doctor

Education competitiveness is a key feature of national competitiveness. It is crucial for nations to develop and enhance student and teacher potential to increase national competitiveness. The decreasing population of children has caused a series of social problems in many developed countries, directly affecting education and com.petitiveness in an international environment. In Taiwan, a low birthrate

Weighted spiking neural P systems with anti-spikes (AWSN P systems) are proposed by adding anti-spikes to spiking neural P systems with weighted synapses. Anti-spikes behave like spikes of inhibition of communication between neurons. Both spikes and anti-spikes are used in the rule expressions. An illustrative example is given to show the working process of the proposed AWSN P systems. The Turing universality

Emotions are fundamental for human beings and play an important role in human cognition. Emotion is commonly associated with logical decision making, perception, human interaction, and to a certain extent, human intelligence itself. With the growing interest of the research community towards establishing some meaningful “emotional” interactions between humans and computers, the need for reliable and

Human action recognition is a trending topic in the field of computer vision and its allied fields. The goal of human action recognition is to identify any human action that takes place in an image or a video dataset. For instance, the actions include walking, running, jumping, throwing, and much more. Existing human action recognition techniques have their own set of limitations when it concerns model

In this paper, a fusion method based on multiple features and hidden Markov model (HMM) is proposed for recognizing dynamic hand gestures corresponding to an operator’s instructions in robot teleoperation. In the first place, a valid dynamic hand gesture from continuously obtained data according to the velocity of the moving hand needs to be separated. Secondly, a feature set is introduced for dynamic

As the usage of social media has increased, the size of shared data has instantly surged and this has been an important source of research for environmental issues as it has been with popular topics. Sentiment analysis has been used to determine people's sensitivity and behavior in environmental issues. However, the analysis of Turkish texts has not been investigated much in literature. In this article

Collecting parallel sentences from nonparallel data is a long-standing natural language processing research problem. In particular, parallel training sentences are very important for the quality of machine translation systems. While many existing methods have shown encouraging results, they cannot learn various alignment weights in parallel sentences. To address this issue, we propose a novel parallel

This article reports the results of the study related to emotion recognition by using eye-tracking. Emotions were evoked by presenting a dynamic movie material in the form of 21 video fragments. Eye-tracking signals recorded from 30 participants were used to calculate 18 features associated with eye movements (fixations and saccades) and pupil diameter. To ensure that the features were related to emotions

To overcome the difficulty of automating and intelligently classifying the ground features in remote-sensing hyperspectral images, machine learning methods are gradually introduced into the process of remote-sensing imaging. First, the PaviaU, Botswana, and Cuprite hyperspectral datasets are selected as research subjects in this study, and the objective is to process remote-sensing hyperspectral images

The aim of the reported experiment was to investigate the effects of inhibition of return (IOR) and level-priming on the global precedence effect (GPE). The classical hierarchical stimuli combined with IOR and the level-priming paradigm were used. The participants selectively attended to the global or local features of compound numerals. The results showed that IOR inhibited the response to the global

In the increasingly complex electromagnetic environment of modern battlefields, how to quickly and accurately identify radar signals is a hotspot in the field of electronic countermeasures. In this paper, USRP N210, USRP-LW N210, and other general software radio peripherals are used to simulate the transmitting and receiving process of radar signals, and a total of 8 radar signals, namely, Barker,

This study proposes a home care system (HCS) based on a brain-computer interface (BCI) with a smartphone. The HCS provides daily help to motor-disabled people when a caregiver is not present. The aim of the study is two-fold: (1) to develop a BCI-based home care system to help end-users control their household appliances, and (2) to assess whether the architecture of the HCS is easy for motor-disabled

The neurocognitive characteristics of mathematically gifted adolescents are characterized by highly developed functional interactions between the right hemisphere and excellent cognitive control of the prefrontal cortex, enhanced frontoparietal cortex, and posterior parietal cortex. However, it is still unclear when and how these cortical interactions occur. In this paper, we used directional coherence

This study introduces a supercapacitor hybrid energy storage system in a wind-solar hybrid power generation system, which can remarkably increase the energy storage capacity and output power of the system. In the specific solution, this study combines the distributed power generation system and the hybrid energy storage system, while using the static reactive power compensation system and the conductance-fuzzy

Symptoms of nutrient deficiencies in rice plants often appear on the leaves. The leaf color and shape, therefore, can be used to diagnose nutrient deficiencies in rice. Image classification is an efficient and fast approach for this diagnosis task. Deep convolutional neural networks (DCNNs) have been proven to be effective in image classification, but their use to identify nutrient deficiencies in

In the field of asset allocation, how to balance the returns of an investment portfolio and its fluctuations is the core issue. Capital asset pricing model, arbitrage pricing theory, and Fama–French three-factor model were used to quantify the price of individual stocks and portfolios. Based on the second-order stochastic dominance rule, the higher moments of return series, the Shannon entropy, and

In this paper, we propose to embed a content-rich network for the purpose of similarity searching for a query node. In this network, besides the information of the nodes and edges, we also have the content of each node. We use the convolutional neural network (CNN) to represent the content of each node and then use the graph convolutional network (GCN) to further represent the node by merging the representations

Extreme learning machine is a fast learning algorithm for single hidden layer feedforward neural network. However, an improper number of hidden neurons and random parameters have a great effect on the performance of the extreme learning machine. In order to select a suitable number of hidden neurons, this paper proposes a novel hybrid learning based on a two-step process. First, the parameters of hidden

In recent years, image processing methods based on convolutional neural networks (CNNs) have achieved very good results. At the same time, many branch techniques have been proposed to improve accuracy. Aiming at the change detection task of remote sensing images, we propose a new network based on U-Net in this paper. The attention mechanism is cleverly applied in the change detection task, and the

Emergency response to hazardous gases in the environment is an important research field in environmental monitoring. In recent years, with the rapid development of sensor technology and mobile device technology, more autonomous search algorithms for hazardous gas emission sources are proposed in uncertain environment, which can avoid emergency personnel from contacting hazardous gas in a short distance

This paper proposes a clustering ensemble method that introduces cascade structure into the self-organizing map (SOM) to solve the problem of the poor performance of a single clusterer. Cascaded SOM is an extension of classical SOM combined with the cascaded structure. The method combines the outputs of multiple SOM networks in a cascaded manner using them as an input to another SOM network. It also

Grid cells and place cells are important neurons in the animal brain. The information transmission between them provides the basis for the spatial representation and navigation of animals and also provides reference for the research on the autonomous navigation mechanism of intelligent agents. Grid cells are important information source of place cells. The supervised learning and unsupervised learning

Otsu’s algorithm is one of the most well-known methods for automatic image thresholding. 2D Otsu’s method is more robust compared to 1D Otsu’s method. However, it still has limitations on salt-and-pepper noise corrupted images and uneven illumination images. To alleviate these limitations and improve the overall performance, here we propose an improved 2D Otsu’s algorithm to increase the robustness

In this paper, a time-delayed fractional order adaptive sliding mode control algorithm is proposed for a two-wheel self-balancing vehicle system. The closed-loop system is proved based on the Lyapunov-Razumikhin function. The switching function is designed to make the system robust when facing uncertainties and external disturbances. It is designed to avoid monotonically increasing gains and can handle

An image target recognition approach based on mixed features and adaptive weighted joint sparse representation is proposed in this paper. This method is robust to the illumination variation, deformation, and rotation of the target image. It is a data-lightweight classification framework, which can recognize targets well with few training samples. First, Gabor wavelet transform and convolutional neural

With the development of society, more and more attention has been paid to cultural festivals. In addition to the government’s emphasis, the increasing consumption in festivals also proves that cultural festivals are playing increasingly important role in public life. Therefore, it is very vital to grasp the public festival sentiment. Text sentiment analysis is an important research content in the field

Text classification plays an important role in various applications of big data by automatically classifying massive text documents. However, high dimensionality and sparsity of text features have presented a challenge to efficient classification. In this paper, we propose a compressive sensing- (CS-) based model to speed up text classification. Using CS to reduce the size of feature space, our model

Bird swarm algorithm is one of the swarm intelligence algorithms proposed recently. However, the original bird swarm algorithm has some drawbacks, such as easy to fall into local optimum and slow convergence speed. To overcome these short-comings, a dynamic multi-swarm differential learning quantum bird swarm algorithm which combines three hybrid strategies was established. First, establishing a dynamic

Renewable energy (RE) systems play a key role in producing electricity worldwide. The integration of RE systems is carried out in a distributed aspect via an autonomous hybrid microgrid (A-HMG) system. The A-HMG concept provides a series of technological solutions that must be managed optimally. As a solution, this paper focuses on the application of a recent nature-inspired metaheuristic optimization

In image denoising (IDN) processing, the low-rank property is usually considered as an important image prior. As a convex relaxation approximation of low rank, nuclear norm-based algorithms and their variants have attracted a significant attention. These algorithms can be collectively called image domain-based methods whose common drawback is the requirement of great number of iterations for some acceptable

A new clustering membrane system using a complex chained P system (CCP) based on evolutionary mechanism is designed, developed, implemented, and tested. The purpose of CCP is to solve clustering problems. In CCP, two kinds of evolution rules in different chained membranes are used to enhance the global search ability. The first kind of evolution rules using traditional and modified particle swarm optimization

In the research work of the brain-computer interface and the function of human brain work, the state classification of multitask state fMRI data is a problem. The fMRI signal of the human brain is a nonstationary signal with many noise effects and interference. Based on the commonly used nonstationary signal analysis method, Hilbert–Huang transform (HHT), we propose an improved circle-EMD algorithm

Computer vision is one of the hottest research fields in deep learning. The emergence of generative adversarial networks (GANs) provides a new method and model for computer vision. The idea of GANs using the game training method is superior to traditional machine learning algorithms in terms of feature learning and image generation. GANs are widely used not only in image generation and style transfer

Because deep neural networks (DNNs) are both memory-intensive and computation-intensive, they are difficult to apply to embedded systems with limited hardware resources. Therefore, DNN models need to be compressed and accelerated. By applying depthwise separable convolutions, MobileNet can decrease the number of parameters and computational complexity with less loss of classification precision. Based

In the optimization of problems in dynamic environments, algorithms need to not only find the global optimal solutions in a specific environment but also to continuously track the moving optimal solutions over dynamic environments. To address this requirement, a species conservation-based particle swarm optimization (PSO), combined with a spatial neighbourhood best searching technique, is proposed

Brain-computer interface (BCI) is a communication and control system linking the human brain and computers or other electronic devices. However, irrelevant channels and misleading features unrelated to tasks limit classification performance. To address these problems, we propose an efficient signal processing framework based on particle swarm optimization (PSO) for channel and feature selection, channel

Ultrasonography is widely used in the clinical diagnosis of thyroid nodules. Ultrasound images of thyroid nodules have different appearances, interior features, and blurred borders that are difficult for a physician to diagnose into malignant or benign types merely through visual recognition. The development of artificial intelligence, especially deep learning, has led to great advances in the field

Accurate predictions of remaining useful life (RUL) of important components play a crucial role in system reliability, which is the basis of prognostics and health management (PHM). This paper proposed an integrated deep learning approach for RUL prediction of a turbofan engine by integrating an autoencoder (AE) with a deep convolutional generative adversarial network (DCGAN). In the pretraining stage

Due to the complexity of the underwater environment, underwater acoustic target recognition (UATR) has always been challenging. Although deep neural networks (DNN) have been used in UATR and some achievements have been made, the performance is not satisfactory when recognizing underwater targets with different Doppler shifts, signal-to-noise ratios (SNR), and interferences. In the paper, a one-dimensional

Stress is categorized as a condition of mental strain or pressure approaches because of upsetting or requesting conditions. There are various sources of stress initiation. Researchers consider human cerebrum as the primary wellspring of stress. To study how each individual encounters stress in different forms, researchers conduct surveys and monitor it. The paper presents the fusion of 5 algorithms

This paper proposes an optimization system for solving an NP-hard problem by using several new algorithms and application programs. This study aims to identify a suitable distribution of investment projects across several developed industrial regions. It is assumed that all industrial regions involved have the same economic and strategic characteristics. The problem involves a set of projects that

Multiobjective evolutionary algorithms (MOEAs) with higher population diversity have been extensively presented in literature studies and shown great potential in the approximate Pareto front (PF). Especially, in the recent development of MOEAs, the reference line method is increasingly favored due to its diversity enhancement nature and auxiliary selection mechanism based on the uniformly distributed

Density peaks clustering algorithm (DPC) has attracted the attention of many scholars because of its multiple advantages, including efficiently determining cluster centers, a lower number of parameters, no iterations, and no border noise. However, DPC does not provide a reliable and specific selection method of threshold (cutoff distance) and an automatic selection strategy of cluster centers. In this

Aiming at high-resolution radar target recognition, new convolutional neural networks, namely, Inception-based VGG (IVGG) networks, are proposed to classify and recognize different targets in high range resolution profile (HRRP) and synthetic aperture radar (SAR) signals. The IVGG networks have been improved in two aspects. One is to adjust the connection mode of the full connection layer. The other

Cross-modal search has become a research hotspot in the recent years. In contrast to traditional cross-modal search, social network cross-modal information search is restricted by data quality for arbitrary text and low-resolution visual features. In addition, the semantic sparseness of cross-modal data from social networks results in the text and visual modalities misleading each other. In this paper

The global fishmeal production is used for animal feed, and protein is the main component that provides nutrition to animals. In order to monitor and control the nutrition supply to animal husbandry, near-infrared (NIR) technology was utilized for rapid detection of protein contents in fishmeal samples. The aim of the NIR quantitative calibration is to enhance the model prediction ability, where the

In this paper, we want to find out whether gender bias will affect the success and whether there are some common laws driving the success in show business. We design an experiment, set the gender and productivity of an actor or actress in a certain period as the independent variables, and introduce deep learning techniques to do the prediction of success, extract the latent features, and understand

An electroencephalogram (EEG) is a test that records electrical activity of the brain using electrodes attached to the scalp, and it has recently been used in conjunction with BMI (Brain-Machine Interface). Currently, the analysis of the EEG is visual, using graphic tools such as topographic maps. However, this analysis can be very difficult, so in this work, we apply a methodology of EEG analysis

With the wide application of high-resolution radar, the application of Radar Automatic Target Recognition (RATR) is increasingly focused on how to quickly and accurately distinguish high-resolution radar targets. Therefore, Synthetic Aperture Radar (SAR) image recognition technology has become one of the research hotspots in this field. Based on the characteristics of SAR images, a Sparse Data Feature

White blood cells (leukocytes) are a very important component of the blood that forms the immune system, which is responsible for fighting foreign elements. The five types of white blood cells include neutrophils, eosinophils, lymphocytes, monocytes, and basophils, where each type constitutes a different proportion and performs specific functions. Being able to classify and, therefore, count these

The present work visualizes the evolution of primitive digital circuits as a development problem. The development of the digital circuit is implemented similar to the development of a human embryo from a single cell to the complete organism. The constituent parts making up a primitive digital circuit are encoded into binary strings. Each binary string is viewed as a cell, and several such cells are

[This corrects the article DOI: 10.1155/2019/1353601.].

With the higher-order neighborhood information of a graph network, the accuracy of graph representation learning classification can be significantly improved. However, the current higher-order graph convolutional networks have a large number of parameters and high computational complexity. Therefore, we propose a hybrid lower-order and higher-order graph convolutional network (HLHG) learning model

Miryoku engineering is a design concept based on customer preferences, with the goal of creating attractive products or spaces. However, traditional Miryoku engineering faces two main issues: (1) the upper Kansei factor ranks the weights by the number of mentions, but it does not represent the importance of customers; (2) the mapping connection between the upper Kansei factor and the lower specific

Chemical event evolutionary graph (CEEG) is an effective tool to perform safety analysis, early warning, and emergency disposal for chemical accidents. However, it is a complicated work to find causality among events in a CEEG. This paper presents a method to accurately extract event causality by using a neural network and structural analysis. First, we identify the events and their component elements

When some attributes of a formal context can be decomposed into some subattributes a model of layered concept lattice to improve the efficiency of building concept lattice with complex structure attribute data is studied, the relationship between concept lattice and layered concept is discussed. Two algorithms are proposed: one is the roll-up building algorithm in which the upper concepts are built