Group competition-cooperation optimization algorithm
In order to solve complex practical problems, the model of deep learning can not be limited to models such as deep neural networks. To deepen the learning model, we must actively explore various depth models. Based on this, we propose a deep ...
Neural attention model for recommendation based on factorization machines
In recommendation systems, it is of vital importance to comprehensively consider various aspects of information to make accurate recommendations for users. When the low-order feature interactions between items are insufficient, it is necessary to ...
Multi-scale fractal residual network for image super-resolution
Recent studies have shown that the use of deep convolutional neural networks (CNNs) can improve the performance of single image super-resolution reconstruction (SISR) methods. However, the existing CNN-based SISR model ignores the multi-scale ...
FB-GSA: A fuzzy bi-level programming based gravitational search algorithm for unconstrained optimization
The Gravitational Search Algorithm (GSA) which is a prominent nature-inspired computing technique outperforms in the exploration stage, but its performance degrades in the exploitation stage. A fuzzy bi-level programming based gravitational search ...
Cluster-based information retrieval using pattern mining
This paper addresses the problem of responding to user queries by fetching the most relevant object from a clustered set of objects. It addresses the common drawbacks of cluster-based approaches and targets fast, high-quality information ...
Exploiting multi-attention network with contextual influence for point-of-interest recommendation
Point-of-Interest (POI) recommendation has become an important service on Location-Based Social Networks (LBSNs). In order to improve the performance of recommendation, besides the check-in data generated in LBSNs, researchers are striving to ...
An effective method using clustering-based adaptive decomposition and editing-based diversified oversamping for multi-class imbalanced datasets
For multi-class imbalanced classification tasks that occur in many real-world applications, the class imbalance, which is caused by the case that some classes are not as frequent as other classes, and class overlap, which is caused by the case ...
Forecasting and simulation of cutting force in virtual surgery based on particle filtering
An accurate and realistic force feedback is very important in determining the realism of virtual surgery. In order to improve the accuracy of force simulation in cutting procedures, we proposed a novel method for forecasting and simulating the ...
Local-CycleGAN: a general end-to-end network for visual enhancement in complex deep-water environment
Underwater image analysis is crucial for many applications such as seafloor survey, biological and environment monitoring, underwater vehicle navigation, inspection and maintenance of underwater infrastructure etc. However, due to light absorption ...
An automatic framework for endoscopic image restoration and enhancement
Despite its success in the field of minimally invasive surgery, endoscopy image analysis remains challenging due to limited image settings and control conditions. The low resolution and existence of large number of reflections in endoscopy images ...
Reconstruction of gene regulatory networks with multi-objective particle swarm optimisers
The computational reconstruction of Gene Regulatory Networks (GRNs) from gene expression data has been modelled as a complex optimisation problem, which enables the use of sophisticated search methods to address it. Among these techniques, ...
A novel bat algorithm with dynamic membrane structure for optimization problems
To improve the optimization efficiency for different optimization problems and take advantage of the dynamic membrane computing framework, this paper proposes an improved bat algorithm, namely, Dynamic Membrane-driven Bat Algorithm (DMBA). The ...
Infrared image super-resolution reconstruction by using generative adversarial network with an attention mechanism
Due to the limitations of infrared imaging principles and imaging systems, many problems are typically encountered with collected infrared images, such as low resolution, insufficient detail information, and blurred edges. In response to these ...
Chameleon algorithm based on mutual k-nearest neighbors
Clustering is a typical unsupervised data analysis method, which divides a given data set without label information into multiple clusters. The data on each cluster has a great deal of association, which can be used as the preprocessing stage of ...
Evolutionary many-objective optimization algorithm based on angle and clustering
In evolutionary multi-objective optimization, maintaining a well balance of convergence and diversity is particularly important for the performance of evolutionary algorithms. Considering the convergence and diversity at the same time, a many-...
PointFusionNet: Point feature fusion network for 3D point clouds analysis
The 3D point clouds is an important type of geometric data structure, and the analysis of 3D point clouds based on deep learning is a very challenging task due to the disorder and irregularity. In existing research, RS-CNN provides an effective ...
Fast Top-K association rule mining using rule generation property pruning
Traditional association rule mining algorithms can have a long runtime, high memory consumption, and generate a huge number of rules. Browsing through numerous rules and adjusting parameters to find just enough rules is a tedious task for users, ...
A novel agent-based, evolutionary model for expressing the dynamics of creative open-problem solving in small groups
Understanding the process of producing creative responses to open-ended problems solved in small groups is important for many modern domains, like health care, manufacturing, education, banking, and investment. Some of the main theoretical ...
Vehicle theft recognition from surveillance video based on spatiotemporal attention
Frequent vehicle thefts have a highly detrimental impact on public safety. Thanks to surveillance equipment distributed throughout a city, a large number of videos that can be used to recognize vehicle theft are available. However, vehicle theft ...
Intelligent fault diagnosis of rolling bearings using a semi-supervised convolutional neural network
The success of convolutional neural networks (CNNs) in intelligent fault diagnosis is largely dependent on massive amounts of labelled data. In a real-world case, however, massive amounts of labelled data are difficult or costly to collect, ...
A deep learning approach for person identification using ear biometrics
Automatic person identification from ear images is an active field of research within the biometric community. Similar to other biometrics such as face, iris and fingerprints, ear also has a large amount of specific and unique features that allow ...
WOLIF: An efficiently tuned classifier that learns to classify non-linear temporal patterns without hidden layers
We present in this paper a computationally efficient and biologically plausible classifier WOLIF, using Grey Wolf Optimizer (GWO) tuned error function obtained from Leaky-Integrate-and-Fire (LIF) spiking neuron. Unlike traditional artificial ...
Dual-Y network: infrared-visible image patches matching via semi-supervised transfer learning
Infrared-visible image patches matching has many applications, such as target recognition, vision-based navigation, and others. At present, deep learning has achieved excellent performance in visible image patches matching. Due to imaging ...
Detecting unusual input to neural networks
Evaluating a neural network on an input that differs markedly from the training data might cause erratic and flawed predictions. We study a method that judges the unusualness of an input by evaluating its informative content compared to the ...
Cost-sensitive feature selection on multi-label data via neighborhood granularity and label enhancement
Multi-label feature selection, which is an efficient and effective pre-processing step in machine learning and data mining, can select a feature subset that contains more contributions for multi-label classification while improving the performance ...
TenLa: an approach based on controllable tensor decomposition and optimized lasso regression for judgement prediction of legal cases
With the development of big data and artificial intelligence technology, the computer-assisted judgment of legal cases has become an inevitable trend in the intersection of computer science and law. Judgment prediction methods of legal cases ...
SL2E-AFRE : Personalized 3D face reconstruction using autoencoder with simultaneous subspace learning and landmark estimation
3D face reconstruction from single face image has received much attention in the past decade, as it has been used widely in many applications in the field of computer vision. Despite more accurate solutions by 3D scanners and several commercial ...
Robustness comparison between the capsule network and the convolutional network for facial expression recognition
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 ...
Multi-parameter safe screening rule for hinge-optimal margin distribution machine
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 ...
Bangla-Meitei Mayek scripts handwritten character recognition using Convolutional Neural Network
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 ...
