Applied Intelligence

Facial expression recognition (FER) on real world databases is an active and challenging research topic. Existing CNN-based facial expression classifiers usually have good performance on common expressions, including happy and surprise, but have ...

The severe spread of the COVID-19 pandemic has created a situation of public health emergency and global awareness. In our research, we analyzed the demographical factors affecting the global pandemic spread along with the features that lead to ...

The trisecting-acting-outcome (TAO) model of three-way decisions includes trisecting a universal set into three separate and closely connected regions, devising, and applying efficient strategies on the three regions, furthermore evaluating the ...

The development of efficient algorithms that perform qualitative operations in the processing and segmentation of graphical images requires good knowledge on both technical and biological aspects of the vision. Due to the subjective nature of the ...

Mining of colossal patterns is used to mine patterns in databases with many attributes and values, but the number of instances in each database is small. Although many efficient approaches for extracting colossal patterns have been proposed, they ...

The Quality of Service (QoS) of virtual machines (VMs) are ensured through the Service Level Agreements (SLAs) signed between the consumers and the cloud providers. A main way to avoid the SLAs violation is to analyze the relationships among the ...

The Pigeon-Inspired Optimization (PIO) algorithm is an intelligent algorithm inspired by the behavior of pigeons returned to the nest. The binary pigeon-inspired optimization (BPIO) algorithm is a binary version of the PIO algorithm, it can be ...

To address the lack of convergence speed and diversity of Ant Colony Optimization (ACO), a dynamic reproductive ant colony algorithm based on piecewise clustering (RCACS) is proposed to optimize the problems. First, the data is segmented by the ...

This article develops a group decision support scheme that extends the widely accepted ELECTRE-II model to the Pythagorean fuzzy (PF) context. ELECTRE-II takes into consideration subjective human opinions, and establishes two types of embedded ...

In the multimodal multi-objective optimization problems (MMOPs), at least two equivalent Pareto optimal solutions in decision space with an identical objective value are desired. The challenge for solving MMOPs is locating equivalent Pareto ...

Given an undirected graph G(V,E), the minimum total dominating set (MTDS) problem consists of finding a subset $D\subseteq V$ with the minimum vertices such that every vertex v ∈ V is adjacent to at least one vertex in D. That is, even for the vertices in D ...

A scene graph can describe images concisely and structurally. However, existing methods of scene graph generation have low capabilities of inferring certain relationships, because of the lack of semantic information and their heavy dependence on ...

This paper focus on multiple CNN-based (Convolutional Neural Network) models for COVID-19 forecast developed by our research team during the first French lockdown. In an effort to understand and predict both the epidemic evolution and the impacts ...

Breast cancer is currently the second most fatal cancer in women, but timely diagnosis and treatment can reduce its mortality. Breast masses are the most obvious means of cancer identification, and thus, accurate segmentation of masses is ...

The analysis of teacher behavior of massive teaching videos has become a surge of research interest recently. Traditional methods rely on accurate manual analysis, which is extremely complex and time-consuming for analyzing massive teaching ...

Positive and unlabeled learning (PU learning) is designed to solve the problem that we only utilize the labeled positive examples and the unlabeled examples to train a classifier. A variety of methods have been proposed to solve this problem by ...

Writer identification (WI) is a typical pattern recognition problem with the goal of recognizing the writer of a text from images of his or her handwriting. For handwriting-based applications, a new approach is required that can confirm the writer ...

The automatic segmentation of coronary artery in coronary computed tomography angiography (CCTA) image is of great significance for clinicians to evaluate patients with coronary heart disease. When a 3D image is limited by the amount of available ...

Entity alignment refers to discovering two entities in different knowledge bases that represent the same thing in reality. Existing methods generally only adopt TransE or TransE-like knowledge graph representation learning models, which usually ...

Crowd counting has received increasing attention in the field of video surveillance and urban security system. However, many previous models are prone to poor generalization capability to unknown samples when limited labeled samples are available. ...

It is crucial and challenging for the question-answering robot (Qabot) to match the customer-input questions with the priori identification questions due to highly diversified expressions, especially in the case of Chinese. This article proposes a ...

An effective software fault prediction (SFP) model could help developers in the quick and prompt detection of faults and thus help enhance the overall reliability and quality of the software project. Variations in the prediction performance of ...

Multi-target regression (MTR) is an exciting area of machine learning where the challenge is to predict the values of more than one target variables which can take on continuous values. These variables may or may not be correlated. Such problems ...

The rapid spread of coronavirus disease has become an example of the worst disruptive disasters of the century around the globe. To fight against the spread of this virus, clinical image analysis of chest CT (computed tomography) images can play ...

Gaussian kernel support vector machine recursive feature elimination (GKSVM-RFE) is a method for feature ranking in a nonlinear way. However, GKSVM-RFE suffers from the issue of high computational complexity, which hinders its applications. This ...

Path planning of intelligent driving vehicles in emergencies is a hot research issue, this paper proposes a new method of the best path selection for the intelligent driving vehicles to solve this problem. Based on the prior knowledge applied ...

In the paper, we propose a variant of Variational Autoencoder (VAE) for sequence generation task, called SeqVAE, which is a combination of recurrent VAE and policy gradient in reinforcement learning. The goal of SeqVAE is to reduce the deviation ...

Most malware employs packing technology to escape detection; thus, packer identification has become increasingly important in malware detection. To improve the accuracy of packer identification, this article analyses the differences in the ...

Previous research for synchronization of neural networks have obtained some good results, but there are still some shortcomings in the research of uncertain delayed neural networks (DNN) with packet dropout. In this paper, we investigate the ...

Despite the impressive performance of some recent state-of-the-art detectors, small target detection, scale variation, and label ambiguities remain challenges. To tackle these issues, we present a coordinate-based anchor-free (CBAF) module for ...