Applied Intelligence

Dempster-Shafer (D-S) evidence theory has extensive applications in the field of data fusion. It uses the mass function to replace the probability distribution in Bayesian Probability theory, which has the advantages of weak constraints and ...

Deep learning technology has been successfully applied in more and more fields. In this paper, the application of deep neural networks in higher-order nonparametric spatial autoregressive models is studied. For spatial model, we propose the higher-...

Deep Cross-Modal Hashing (DCMH) has garnered significant attention in the field of cross-modal retrieval due to its advantages such as high computational efficiency and small storage space. However, existing DCMH methods still face certain ...

The windspeed on the sea surface is an important factor affecting the process of detecting sea surface targets, as it affects the reflection and radiation intensity, posing challenges for precise detection of optical remote sensing targets. This ...

Human action recognition from skeleton motion sequences is widely applied in various fields such as virtual reality, human-computer interaction and kinematic rehabilitation. With the wide use of graph neural networks for extracting spatial ...

In the industrial Internet, industrial software plays a central role in enhancing the level of intelligent manufacturing. It enables the promotion of digital collaborative services. Effective anomaly detection of multivariate time series can ...$$

The rig state plays a crucial role in recognizing the operations carried out by the drilling crew and quantifying Invisible Lost Time (ILT). This lost time, often challenging to assess and report manually in daily reports, results in delays to the ...

In this paper, a new Ensemble Learning Method (ELM) is proposed to deal with multi-target regression and multi-label classification problems. In ELM, the output of each regressor or classifier unit is ensembled to the final multi-target regression ...

Variable precision fuzzy rough set (VPFRS) is widely utilized for handling various forms of uncertain information due to its fault-tolerant capability. However, a significant number of these rough sets fail to satisfy the inclusion property (lower ...

Mechanical equipment usually operates in noisy and variable load environments, which presents serious challenges for existing intelligent diagnostic models. In addition, there are few labelled fault samples in real engineering scenarios, which ...

After emergencies occur, decision-makers can reference historical cases with similar causes to take similar emergency response measures. However, information about emergencies is usually recorded and stored in textual form, and it is difficult for ...

In the realm of machine learning, feature selection emerges as a prevalent data preprocessing technique, playing a crucial role in enhancing model performance across diverse downstream tasks such as fault diagnosis, biological recognition, and ...

The article introduces a new method for applying Quantum Clustering to graph structures. Quantum Clustering (QC) is a density-based unsupervised learning method that determines cluster centers by constructing a potential function. In this method, ...$$

Deep learning-based object detection has made tremendous progress in the field of intelligent vision systems. However, one of its major complaints is the high demand for large amounts of experimental data. Few-shot object detection (FSOD) aims to ...

Facial emotion recognition has become a complicated task due to individual variations in facial characteristics, as well as racial and cultural variances. Different psychological studies show that there are complex expressions other than basic ...

This paper primarily investigates a neural-network-based safe control scheme for solving the optimal control problem of continuous-time (CT) nonlinear systems with asymmetric input constraints under non-zero-sum (NZS) differential game scenarios. ...

Maximizing the advantages of different views and mitigating their respective disadvantages in fine-grained segmentation tasks are an important challenge in the field of point cloud multi-view fusion. Traditional multi-view fusion methods ignore ...

Federated domain adaptation system aims to address the problem of domain shift in a federated learning (FL) framework, where knowledge learned from distributed source domains can be readily transferred to the target domain. However, federated ...

EpiRiskNet combines time-series data with graph and static information to enhance forecasting accuracy. This model features the SCI-Block for improved feature extraction and interaction learning, leveraging the capabilities of SCINet and Triformer ...

Medical image registration is a crucial step in computer-assisted medical diagnosis, and has seen significant progress with the adoption of deep learning methods like convolutional neural networks (CNN). Creating a deep learning network for image ...

Background: early detection of dementia and Mild Cognitive Impairment (MCI) have an utmost significance nowadays, and smart conversational agents are becoming more and more capable. DigiMoCA, an Alexa-based voice application for the screening of ...$$

Generative art has drawn increased attention in recent AI applications. Traditional approaches of robot calligraphy have faced challenges in achieving style consistency, line smoothness and high-quality structural uniformity. To address the ...

Multi-level features are commonly employed in the cascade network, which is currently the dominant framework in multi-view stereo (MVS). However, there is a potential issue that the recent popular multi-level feature extractor network overlooks ...

It has been proven that Graph Neural Networks focus more on the majority class instances and ignore minority class instances when the class distribution is imbalanced. To address the class imbalance problems on graphs, most of the existing ...

Stylized neural machine translation (NMT) aims to translate sentences of one style into sentences of another style, it is essential for the application of machine translation in a real-world scenario. Most existing methods employ an encoder-...

In this paper, our goal is to enhance the interpretability of Generalized Linear Models by identifying the most relevant interactions between categorical predictors. Searching for interaction effects can quickly become a highly combinatorial, and ...

Several studies have attempted to solve traveling salesman problems (TSPs) using various deep learning techniques. Among them, Transformer-based models show state-of-the-art performance even for large-scale Traveling Salesman Problems (TSPs). ...

Membership inference attack opens up a newly emerging and rapidly growing research to steal user privacy from text classification models, a core problem of which is shadow model construction and members distribution optimization in inadequate ...

Multi-label Short Text Classification (MSTC) is a challenging subtask of Multi-Label Text Classification (MLTC) for tagging a short text with the most relevant subset of labels from a given set of labels. Recent studies have attempted to address ...

Pan-sharpening is a technique used to generate high-resolution multi-spectral (HRMS) images by merging high-resolution panchromatic (PAN) images with low-resolution multi-spectral (LRMS) images. Many existing methods face challenges in effectively ...