A high speed roller dung beetles clustering algorithm and its architecture for real-time image segmentation
Several practical applications like disaster detection, remote surveillance, object recognition using remote sensing satellite images, object monitoring and tracking using radar images etc. essentially require real-time image segmentation. In ...
Inception single shot multi-box detector with affinity propagation clustering and their application in multi-class vehicle counting
Multi-class vehicle detection and counting in video-based traffic surveillance systems with real-time performance and acceptable precision are challenging. This paper proposes a modified single shot multi-box convolutional neural network named ...
GRU-based capsule network with an improved loss for personnel performance prediction
Personnel performance is a key factor to maintain core competitive advantages. Thus, predicting personnel future performance is a significant research domain in human resource management (HRM). In this paper, to improve the performance, we propose ...
AdaDT: An adaptive decision tree for addressing local class imbalance based on multiple split criteria
As it is well known, decision tree is a kind of data-driven classification model, and its primary core is the split criterion. Although a great deal of split criteria have been proposed so far, almost all of them focus on the global class ...
Machine learning-based consensus decision-making support for crowd-scale deliberation
With the rapid development of Internet, the online discussion system or social democratic system has become an important and effective vehicle for group decision-making support since it can continue collecting the opinions from the public at ...
Detection of rumor conversations in Twitter using graph convolutional networks
With the increasing popularity of the social network Twitter and its use to propagate information, it is of vital importance to detect rumors prior to their dissemination on Twitter. In the present paper, a model to detect rumor conversations is ...
Novel social network community discovery method combined local distance with node rank optimization function
In view of that most of the current community discovery methods in social network do not consider node self-transfer and node bias, so that it is not possible to extract the graph features effectively, which leads to the ineffective problem by the ...
Linguistic frequent pattern mining using a compressed structure
Traditional association-rule mining (ARM) considers only the frequency of items in a binary database, which provides insufficient knowledge for making efficient decisions and strategies. The mining of useful information from quantitative databases ...
A novel binary farmland fertility algorithm for feature selection in analysis of the text psychology
Feature selection plays a key role in data mining and machine learning algorithms to reduce the processing time and increase the accuracy of classification of high dimensional datasets. One of the most common feature selection methods is the ...
A weighted intrusion detection model of dynamic selection
In view of the difficulty of existing intrusion detection methods in dealing with new forms, large scale, and high concealment of network intrusion behaviors, this paper presents a weighted intrusion detection model of the dynamic selection (...
Anomaly detection via a combination model in time series data
Since the time series data have the characteristics of a large amount of data and non-stationarity, we usually cannot obtain a satisfactory result by a single-model-based method to detect anomalies in time series data. To overcome this problem, in ...
Adaptive diagnosis of DC motors using R-WDCNN classifiers based on VMD-SVD
Traditional fault diagnosis methods of DC (direct current) motors require high expertise and human labor. However, the other disadvantages of these methods are low efficiency and poor accuracy. To address these problems, a new adaptive and ...
Cost-sensitive probability for weighted voting in an ensemble model for multi-class classification problems
Ensemble learning is an algorithm that utilizes various types of classification models. This algorithm can enhance the prediction efficiency of component models. However, the efficiency of combining models typically depends on the diversity and ...
TBTF: an effective time-varying bias tensor factorization algorithm for recommender system
Context-aware processing is a research hotspot in the recommendation area, which achieves better recommendation accuracy by considering more context information such as time, location and etc. besides the information of the users, items and ...
Automatic fabric defect detection using a wide-and-light network
Automatic fabric defect detection systems improve the quality of textile production across the industry. To make these automatic systems accessible to smaller businesses, one potential solution is to use limited memory capacity chips that can be ...
An elite-guided hierarchical differential evolution algorithm
Population structure has an impact on the performance of metaheuristic algorithms. To better improve the performance of differential evolution (DE), an elite-guided hierarchical differential evolution algorithm (EHDE) is proposed. First, an elite-...
Enhancing the competitive swarm optimizer with covariance matrix adaptation for large scale optimization
Competitive swarm optimizer (CSO) has been shown to be an effective optimization algorithm for large scale optimization. However, the learning strategy of a loser particle used in CSO is axis-parallel. Then, it may not be able to solve the high ...
E-GCN: graph convolution with estimated labels
G raph C onvolutional N etwork (GCN) has been commonly applied for semi-supervised learning tasks. However, the established GCN frequently only considers the given labels in the topology optimization, which may not deliver the best performance for ...
Sentiment analysis of Chinese stock reviews based on BERT model
A large number of stock reviews are available on the Internet. Sentiment analysis of stock reviews has strong significance in research on the financial market. Due to the lack of a large amount of labeled data, it is difficult to improve the ...
DC-EDN: densely connected encoder-decoder network with reinforced depthwise convolution for face alignment
High accuracy and fast face alignment algorithms play an important role in many face-related applications. Generally, the model speed is inversely related to the number of parameters. We construct our network based on densely connected encoder-...
Teaching-learning-based pathfinder algorithm for function and engineering optimization problems
Pathfinder algorithm (PFA) for finding the best food area or prey based on the leadership of collective action in animal groups is a new metaheuristic algorithm for solving optimization problems with different structures. PFA is divided into two ...
Residual learning of the dynamics model for feeding system modelling based on dynamic nonlinear correlate factor analysis
Feeding system modelling is the foundation for control strategy optimization, contour error compensation, etc., to improve the productivity and quality of a part. This paper proposes a novel residual learning approach for fitting the simulation ...
X-ray image super-resolution reconstruction based on a multiple distillation feedback network
The super-resolution reconstruction of X-ray images is one of the hot issues in the field of medical imaging. Due to the limitations of X-ray machines, the acquired images often have some problems, such as blurred details, unclear edges and low ...
Feature relevance term variation for multi-label feature selection
Multi-label feature selection is a critical dimension reduction technique in multi-label learning. In conventional multi-label feature selection methods based on information theory, feature relevance is evaluated by mutual information between ...
Low-light image enhancement based on multi-illumination estimation
Images captured by cameras in low-light conditions have low quality and appear dark due to insufficient light exposure, which critically affects the view. Most of the traditional enhancement methods are based on the entire image for exposure ...
Variational autoencoder Bayesian matrix factorization (VABMF) for collaborative filtering
Probabilistic matrix factorization (PMF) is the most popular method among low-rank matrix approximation approaches that address the sparsity problem in collaborative filtering for recommender systems. PMF depends on the classical maximum a ...
SAT-Net: a side attention network for retinal image segmentation
Retinal vessel segmentation plays an important role in the automatic assessment of eye health. Deep learning technology has been extensively employed in medical image segmentation. Specifically, U-net based methods achieve great success in medical ...
A spiderweb model for community detection in dynamic networks
Community detection in dynamic networks is one of the most challenging tasks in the field of network analysis. In general, networks often evolve smoothly between successive snapshots. Therefore, the community structure detected in each snapshot ...
Effect of random walk methods on searching efficiency in swarm robots for area exploration
The objective of area exploration is to traverse the area effectively and random walk methods are the most commonly used searching strategy for swarm robots. Existing research mainly compares the effectiveness of various random walk methods ...
Feature weighting to tackle label dependencies in multi-label stacking nearest neighbor
In multi-label learning, each instance is associated with a subset of predefined labels. One common approach for multi-label classification has been proposed in Godbole and Sarawagi (2004) based on stacking which is called as Meta Binary Relevance ...
