The aspect-category sentiment analysis can provide more and deeper information than the document-level sentiment analysis, because it aims to predict the sentiment polarities of different aspect categories in the same text. The main challenge of aspect-category sentiment analysis is that different aspect categories may present different polarities in the same text. Previous studies combine the Long

Developing the utilized intelligent systems is increasingly important to learn effective text representations, especially extract the sentence features. Numerous previous studies have been concentrated on the task of sentence representation learning based on deep learning approaches. However, the present approaches are mostly proposed with the single task or replied on the labeled corpus when learning

Air pollution is a serious environmental problem that has attracted much attention. Air quality prediction can provide useful information for urban environmental governance decision-making and residents’ daily health control. However, existing research methods have suffered from a weak ability to capture the spatial correlations and fail to model the long-term temporal dependencies of air quality.

The novel coronavirus (COVID-19) pneumonia has become a serious health challenge in countries worldwide. Many radiological findings have shown that X-ray and CT imaging scans are an effective solution to assess disease severity during the early stage of COVID-19. Many artificial intelligence (AI)-assisted diagnosis works have rapidly been proposed to focus on solving this classification problem and

Batch mode active learning (BMAL) is absorbed in training reliable classifier with deficient labeled examples by efficiently querying the most valuable unlabeled examples for supervision. In particular, BMAL always selects examples based on the decent-designed criteria, such as (un)certainty and representativeness, etc. However, existing BMAL approaches make a naive trade-off between the criteria and

All existing image inpainting methods based on neural network models are affected by structural distortions and blurred textures on visible connectivity, such that overfitting and overlearning phenomena can easily emerge in the image inpainting processing procedure. Accordingly, in an attempt to address the defects of image inpainting algorithm, such as long iteration time, poor adaptability and unsatisfactory

Multi-task learning (MTL) obtains a better classifier than single-task learning (STL) by sharing information between tasks within the multi-task models. Most existing multi-task learning models only focus on the data of the target tasks during training, and ignore the data of non-target tasks that may be contained in the target tasks. In this way, Universum data can be added to classifier training

This paper conducts a systematic study on the role of spatial and temporal attention mechanism in the video salient object detection (VSOD) task. We present a two-stage spatial-temporal attention network, named STA-Net, which makes two major contributions. In the first stage, we devise a Multi-Scale-Spatial-Attention (MSSA) module to reduce calculation cost on non-salient regions while exploiting multi-scale

The explosive growth of data has caused users to spend considerable time and effort finding the items they need. Various recommender systems have been created to provide convenience for users. This paper proposes a low-rank and sparse matrix factorization with prior relations (LSMF-PR) recommendation model, which predicts users’ ratings for items through a sum of the learned low-rank matrix and sparse

COVID-19 is a global pandemic declared by WHO. This pandemic requires the execution of planned control strategies, incorporating quarantine, self-isolation, and tracing of asymptomatic cases. Mathematical modeling is one of the prominent techniques for predicting and controlling the spread of COVID-19. The predictions of earlier proposed epidemiological models (e.g. SIR, SEIR, SIRD, SEIRD, etc.) are

Designing an intelligent and autonomous system remains a great challenge in the assembly field. Most reinforcement learning (RL) methods are applied to experiments with relatively small state spaces. However, the complicated situation and high-dimensional spaces of the assembly environment cause traditional RL methods to behave poorly in terms of their efficiency and accuracy. In this paper, a model-driven

Lung cancer is currently one of the most common causes of cancer-related death. Detecting and providing an accurate diagnosis of potentially cancerous lung nodules at an early stage of their development would increase treatment efficacy and so reduce lung cancer mortality. A key barrier to early detection is the absence of noticeable symptoms until the lung cancer has already spread. Diagnosis and

Attracted by the advantages of support vector regression and incremental learning approach, it is proposed in this work that an incremental support vector regression (ISVR) model optimized by particle swarm optimization (PSO) algorithm, and some improvements are made to be more suitable for sensor faults on-line diagnosis. To reducethe training time of ISVR model, an optimal training subset (OTS) method

Collaborative Filtering methods predict user interests and make recommendations just by using the rating matrix. However, in practice there is extensive side information about users and items, such as the age of the user, the actors in a movie, or the abstract of a journal article. In this paper, a novel model called Collaborative Poisson Factorization with Side-information (CPFS) is proposed which

Multi-label learning (MLL) trains a classification model from multiple labelled datasets, where each training instance is annotated with a set of class labels simultaneously. Following the binary relevance MLL paradigm, a recently effective spirit is to constructing specific features for each label, instead of training over the original feature space. Existing label-specific methods, however, only

In recent years there has been a significant rethinking of corporate management, which is increasingly based on customer orientation principles. As a matter of fact, customer relationship management processes and systems are ever more popular and crucial to facing today’s business challenges. However, the large number of available customer communication stimuli coming from different (direct and indirect)

Feature selection is a technique to select a subset of the most relevant features for modeling training. In this paper, a new concept of TDR is firstly proposed to improve the classification accuracy. Then, a TDR-based algorithm for text classification is advanced. Finally, the extensive experiments are made on seven datasets (K1a, K1b, WAP, R52, R8, 20NewGroups, and Cade12) for two classifiers of

Finding an optimal solution for emerging cyber physical systems (CPS) for better efficiency and robustness is one of the major issues. Meta-heuristic is emerging as a promising field of study for solving various optimization problems applicable to different CPS systems. In this paper, we propose a new meta-heuristic algorithm based on Multiverse Theory, named MVA, that can solve NP-hard optimization

This study focused on the problem of anomaly detection (AD) by means of mixture-of-experts network. Most of the existing AD methods solely based on the reconstruction errors or latent representation using a single low-dimensional manifold are often not ideal for the image objects with complex background. However, modeling the data as a mixture of low-dimensional nonlinear manifolds is natural and promising

The characteristics of randomness, running style, and unpredictability of user requirements in the cloud environment, brings great challenges to task scheduling. Meanwhile, the scheduling efficiency of cloud task allocation is an important factor affecting cloud resource systems. Therefore, this paper takes into account the characteristics of tasks, systems and users, a many-objective task scheduling

Detecting small objects is a challenging job for the single-shot multibox detector (SSD) model due to the limited information contained in features and complex background interference. Here, we increased the performance of the SSD for detecting target objects with small size by enhancing detection features with contextual information and introducing a segmentation mask to eliminate background regions

Co-training method is a branch of semi-supervised learning, which improves the performance of classifier through the complementary effect of two views. In co-training algorithm, the selection of unlabeled data often adopts the high confidence degree strategy. Obviously, the higher confidence of data signifies the higher accuracy of prediction. Unfortunately, high confidence selection strategy is not

Interval-valued fuzzy soft set is an extended model of soft set. It is a new mathematical tool that has great advantages in dealing with uncertain information and is proposed by combining soft sets and interval-valued fuzzy sets. The two existing fuzzy decision making algorithms based on interval-valued fuzzy soft sets were given. However, the two existing methods involve the high computational complexity

With the outbreak of COVID-19, medical imaging such as computed tomography (CT) based diagnosis is proved to be an effective way to fight against the rapid spread of the virus. Therefore, it is important to study computerized models for infectious detection based on CT imaging. New deep learning-based approaches are developed for CT assisted diagnosis of COVID-19. However, most of the current studies

Click-Through Rate (CTR) prediction has always been a very popular topic. In many online applications, such as online advertising and product recommendation, a small increase in CTR will bring great returns. However, CTR prediction has always faced several challenges. A large number of users and items and the different sizes of the feature space of different data types lead to high-dimensional and

In recent times, feature extraction was the focus of many researches due to its usefulness in the machine learning and pattern recognition fields. Feature extraction mainly aims to extract informative representations from the original set of features. This can be carried out using various ways. The proposed method is targeting a hybrid linear feature extraction scheme for supervised multi-class classification

Under the influence of deep learning, many trackers have emerged recently. Among them, Siamese network reaches a pleasant balance between accuracy and speed, but its tracking performance still lags behind other trackers. In this paper, we have proposed a Hierarchical Correlation Siamese Network (HC-Siam) for object tracking. The tracker uses convolutional features of each layer to compare the correlation

Multi-domain sentiment classification is a challenging topic in natural language processing, where data from multiple domains are applied to improve the performance of classification. Recently, it has been demonstrated that attention neural networks exhibit powerful performance in this task. In the present study, we propose a collaborative attention neural network (CAN). A self-attention module and

Recommender systems have played an increasingly important role in providing users with tailored suggestions based on their preferences. However, the conventional offline recommender systems cannot handle the ubiquitous data stream well. To address this issue, Streaming Recommender Systems (SRSs) have emerged in recent years, which incrementally train recommendation models on newly received data for

Traffic prediction is necessary for management departments to dispatch vehicles and for drivers to avoid congested roads. Many traffic forecasting methods based on deep learning have been proposed in recent years, and their main aim is to solve the problem of spatial dependencies and temporal dynamics. This paper proposes a useful dynamic model to predict the urban traffic volume by combining fully

In the literature, univariate uncertain data has a quantitative interval for each attribute in each transaction, which is accompanied by a probability density function indicating the probability that each value in the interval exists and appears. To the best of our knowledge, classifying univariate uncertain data has thus far seldom been addressed in the literature. Here, we propose the AssoU2Classifier

Rotatingmachinery is the main component of mechanical equipment. Nevertheless, due to variation of operating condition results in important detection performance deterioration. Therefore, fault detection and diagnosis of rotating machines is very critical for the reliable operation. In this paper, a novel classification technique is employed for fault detection of rotating machines based on kernelled

The performance of some three-dimensional mutual information-based algorithms can be affected, since only relevance and interaction are considered. Aiming at solving the problem, a feature selection algorithm based on redundancy analysis and interaction weight is proposed in this paper. The proposed algorithm adopts three-way interaction information to measure the interaction among the class label

The success of the Sigmoidal Feedforward Networks in the solution of complex learning task can be attributed to their Universal Approximation Property. These networks are trained using non-linear iterative optimization method (of first-order or second-order) to solve a learning task. The convergence rate in Sigmoidal Feedforward Network training is affected by the initial choice of weights, therefore

Node proximity estimation studies structural similarity between nodes and is the key issue of network analysis. It can exist as the node recommendation task and is a fundamental basis of other graph mining techniques. Although Discrete-time quantum walk (DTQW), a promising new technique with distinctive characters, is widely used in many graph mining problems such as graph isomorphism and graph kernel

This paper aims to offer an intuitionistic triangular fuzzy group decision making method by preference relations. For this purpose, the concept of intuitionistic triangular fuzzy preference relations (ITFPRs) is first offered. Then, an additive consistency concept for ITFPRs is introduced. Meanwhile, a programming model is built to check the consistency of ITFPRs. Considering the case where incomplete

Since December 2019, the novel COVID-19’s spread rate is exponential, and AI-driven tools are used to prevent further spreading [1]. They can help predict, screen, and diagnose COVID-19 positive cases. Within this scope, imaging with Computed Tomography (CT) scans and Chest X-rays (CXRs) are widely used in mass triage situations. In the literature, AI-driven tools are limited to one data type either

PM2.5 prediction has received much attention from researchers in recent years, as PM2.5 has been proven to have a major impact on human health. High-precision PM2.5 predictions would greatly benefit both the general public and governments. However, most of the existing studies on predicting PM2.5 or other air pollutants focus on model design and rarely discuss feature selection. Conventionally, researchers

In this study, an attempt has been made to differentiate Novel Coronavirus-2019 (COVID-19) conditions from healthy subjects in Chest radiographs using a simplified end-to-end Convolutional Neural Network (CNN) model and occlusion sensitivity maps. Early detection and faster automated screening of the COVID-19 patients is essential. For this, the images are considered from publicly available datasets

Single-person human pose estimation facilitates markerless movement analysis in sports, as well as in clinical applications. Still, state-of-the-art models for human pose estimation generally do not meet the requirements of real-life applications. The proliferation of deep learning techniques has resulted in the development of many advanced approaches. However, with the progresses in the field, more

Different modalities extracted from videos, such as RGB and optical flows, may provide complementary cues for improving video action recognition. In this paper, we introduce a new modality named video sketch, which implies the human shape information, as a complementary modality for video action representation. We show that video action recognition can be enhanced by using the proposed video sketch

As of July 17, 2020, more than thirteen million people have been diagnosed with the Novel Coronavirus (COVID-19), and half a million people have already lost their lives due to this infectious disease. The World Health Organization declared the COVID-19 outbreak as a pandemic on March 11, 2020. Since then, social media platforms have experienced an exponential rise in the content related to the pandemic

This paper proposes a mathematical model for decomposing a range face image into four basic components (named ‘complement components’) in conjunction with a simple approach for data-level fusion to generate thirty-six additional hybrid components. These forty component faces composing a new face image space called the ‘complement component face space.’ The main challenge of this work was to extract

Since their inception, convolutional neural networks (CNNs) have been shown to have powerful feature extraction and learning capabilities, and the creation of deep residual networks (DRNs) was a milestone in the development of CNNs. However, residual networks mostly use convolution structures, which are widely applied to image recognition and classification problems. Therefore, when facing a load forecasting

Among cancers, lung cancer has the highest morbidity, and mortality rate. The survival probability of lung cancer patients depends largely on an early diagnosis. For predicting lung cancer from low-dose Computed Tomography (LDCT) scans, computer-aided diagnosis (CAD) system needs to detect all pulmonary nodules, and combine their morphological features to assess the risk of cancer. An automatic lung

The competitive society in the new era calls for more research to improve the well-being of workers as well as to improve their productivity. Knowledge workers face a high mental workload in terms of planning and coordination. One solution is to predict the mental workload of knowledge workers. Some machine learning models have been implemented for mental workload prediction, but deep learning models

In this paper, we propose a method of learning representation layers with squashing activation functions within a deep artificial neural network which directly addresses the vanishing gradients problem. The proposed solution is derived from solving the maximum likelihood estimator for components of the posterior representation, which are approximately Beta-distributed, formulated in the context of

Deep Convolutional Neural Networks (DCNNs) contain a high level of complexity and nonlinearity, so it is not clear based on what features DCNN models make decisions and how they can reach such promising results. There are two types of visualization techniques to interpret and explain the deep models: Backpropagation-based and Perturbation-based algorithms. The most notable drawback of the backpropagation-based

In this study, which aims at early diagnosis of Covid-19 disease using X-ray images, the deep-learning approach, a state-of-the-art artificial intelligence method, was used, and automatic classification of images was performed using convolutional neural networks (CNN). In the first training-test data set used in the study, there were 230 X-ray images, of which 150 were Covid-19 and 80 were non-Covid-19

Meteorological parameters were crucial and effective factors in past infectious diseases, like influenza and severe acute respiratory syndrome (SARS), etc. The present study targets to explore the association between the coronavirus disease 2019 (COVID-19) transmission rates and meteorological parameters. For this purpose, the meteorological parameters and COVID-19 infection data from 28th March 2020

We study the effects of different bio-synaptic membrane potential mechanisms on the inference speed of both spiking feed-forward neural networks and spiking convolutional neural networks. These mechanisms are inspired by biological neuron phenomena include electronic conduction in neurons and chemical neurotransmitter attenuation between presynaptic and postsynaptic neurons. In the area of spiking

Dialog state tracking (DST), which estimates dialog states given a dialog context, is a core component in task-oriented dialog systems. Existing data-driven methods usually extract features automatically through deep learning. However, most of these models have limitations. First, compared with hand-crafted delexicalization features, such features in deep learning approaches are not universal. However

The lightning search algorithm (LSA) is a novel meta-heuristic optimization method, which is proposed in 2015 to solve constraint optimization problems. This paper presents a comprehensive survey of the applications, variants, and results of the so-called LSA. In LSA, the best-obtained solution is defined to improve the effectiveness of the fitness function through the optimization process by finding

Traffic speed forecasting becomes a thriving research area in modern transportation systems. The intensification of travel flow volumes due to fast urbanization, vehicle path planning, demands on efficient transport planning policies, commercial objectives, and many other factors contribute to fuel this revival dynamics. Moreover, predicting vehicle speed is of paramount importance in congestion management

Corona Virus Disease 2019 (COVID19) has emerged as a global medical emergency in the contemporary time. The spread scenario of this pandemic has shown many variations. Keeping all this in mind, this article is written after various studies and analysis on the latest data on COVID19 spread, which also includes the demographic and environmental factors. After gathering data from various resources, all

The paper proposes an approach to signal denoising based on a combination of Variational Mode Decomposition with the Split Augmented Lagrangian Shrinkage Algorithm. In our research, we found that the proposed approach gives a great improvement of denoising gyroscopic signals. In turn, the results for the synthetic signals are not straightforward. For the bumps synthetic signals, the proposed algorithm

As an important part of human-computer interactions, facial expression recognition has become a popular research topic in computer vision, pattern recognition, artificial intelligence and other fields. With the development of deep learning and convolutional neural networks, research on facial expression recognition has also made considerable progress. Because facial expressions vary in real environments

Optimal margin distribution machine (ODM) is an efficient algorithm for classification problems. ODM attempts to optimize the margin distribution by maximizing the margin mean and minimizing the margin variance simultaneously, so it can achieve a better generalization performance. However, it is relatively time-consuming for large-scale problems. In this paper, we propose a hinge loss-based optimal

Recognition of handwritten characters in two Indic scripts Bangla and Meitei Mayek is one of the challenging responsibilities due to intricate patterns and scarcity of standard datasets. Convolutional Neural Network (CNN) is one of the stablest well-known techniques for classifying objects in distinctive specialties as it has an extraordinary capability of discovering complex patterns. In this paper

The original version of this article unfortunately contained a mistake. Graphs c, d and e are missing in Figure 4. The correct and complete graphs of Figure 4 is shown here.