The distributed permutation flow shop scheduling problem (DPFSP) is one of the hottest issues in the context of economic globalization. In this paper, a Q-learning enhanced fruit fly optimization algorithm (QFOA) is proposed to solve the DPFSP …

Malignant tumors are a common cytopathologic disease. Pathological tissue examination is a key tool for diagnosing malignant tumors. Doctors need to manually analyze the images of pathological tissue sections, which is not only time-consuming but …

The prevalence of online misinformation, termed “fake news”, has exponentially escalated in recent years. These deceptive information, often rich with multimodal content, can easily deceive individuals into spreading them via various social media …

Thanks to the success of deep learning over the past few years, the video person re-identification (ReID) algorithms have achieved high accuracy on multiple public benchmark datasets. However, the available video person ReID datasets cover a …

In recent years, the applications of digital humans have become increasingly widespread. One of the most challenging core technologies is the generation of highly realistic and automated 3D facial animation that combines facial movements and …

In recent years, there has been a significant increase in the design of neural network models for solving math word problems (MWPs). These neural solvers have been designed with various architectures and evaluated on diverse datasets, posing …

Aiming at the problem of poor autonomous adaptability of mobile robots to dynamic environments, this paper propose a YOLACT++ based semantic visual SLAM for autonomous adaptation to dynamic environments of mobile robots. First, a light-weight …

The purpose of clustering is to partition data similar with each other into a same group and partition data dissimilar with each other into different groups. However, in most existing fuzzy clustering approaches, the membership degrees of an …

To address the challenges posed by diverse pattern-background elements, intricate details, and complex textures in the semantic segmentation of ethnic clothing patterns, this research introduces a novel semantic segmentation network model called …

In complex networks, contrastive learning has emerged as a crucial technique for acquiring discriminative representations from graph data. Maximizing the similarity among relevant sample pairs while minimizing that among irrelevant pairs is …

Fires cause severe damage to the ecological environment and threaten human life and property. Although the traditional convolutional neural network method effectively detects large-area fires, it cannot capture small fires in complex areas through …

Conformer-based models have proven highly effective in Audio-visual Speech Recognition, integrating auditory and visual inputs to significantly enhance speech recognition accuracy. However, the widely utilized softmax attention mechanism within …

As potent approaches for addressing computationally expensive optimization problems, surrogate-assisted evolutionary algorithms (SAEAs) have garnered increasing attention. Prevailing endeavors in evolutionary computation predominantly concentrate …

Multi-objective optimization (MOO) endeavors to identify optimal solutions from a finite array of possibilities. In recent years, deep reinforcement learning (RL) has exhibited promise through its well-crafted heuristics in tackling NP-hard …

The human brain’s remarkable efficiency in solving puzzles through pictorial information processing serves as a valuable inspiration for computational puzzle solving. In this study, we present a nucleation algorithm for automated puzzle solving …

The onset of Web 3.0 has catalyzed the rapid advancement of social networking, transforming platforms into essential elements deeply embedded within the fabric of daily life. Researchers have proposed several methods for detecting anomalous …

To counterbalance the abilities of global exploration and local exploitation of algorithm and enhance its comprehensive performance, a multi-objective particle swarm optimization with a competitive hybrid learning strategy (CHLMOPSO) is put …

Sound event detection involves identifying sound categories in audio and determining when they start and end. However, in real-life situations, sound events are usually not isolated. When one sound event occurs, there are often other related sound …

Existing learning-based dehazing algorithms struggle to deal with real world hazy images for lack of paired clean data. Moreover, most dehazing methods require significant computation and memory. To address the above problems, we propose a joint …

X-ray diffraction (XRD) is used for characterizing the crystal structure of molecular sieves after synthetic experiments. However, for a high-throughput molecular sieve synthetic system, the huge amount of data derived from large throughput …