Inadequate training poses a significant risk of injury among young firefighters. Although Human Activity Recognition (HAR) algorithms have shown potential in monitoring and evaluating performance, most existing studies focus on daily activities ...

• A novel multimodal sensor data fusion for firefighting activity recognition.
• Hybrid fusion models with inertial and sEMG data from multiple positions.
• Wearable sensors integrated into lightweight and custom-designed devices.
• ...

Federated learning (FL) facilitates a privacy-preserving neural network training paradigm through collaboration between edge clients and a central server. One significant challenge is that the distributed data is not independently and identically ...

• Federated learning preserves privacy via client–server collaborative training.
• A major challenge is data heterogeneity, divided into intra-and inter-domain.
• Cross-client local clustered feature contrast promotes knowledge-sharing.

Analyzing, manipulating, and comprehending data from multiple sources (e.g., websites, software applications, files, or databases) and of diverse modalities (e.g., video, images, audio and text) has become increasingly important in many domains. ...

• Data from multiple modalities is useful for movie and emotion classification.
• Dynamic weighting of modalities positively impacts performance.
• Strategic placing of residual connections ameliorates vanishing gradients.

The expansion of the Internet has led to the widespread proliferation of malicious URLs, becoming a primary vector for cyber threats. Detecting malicious URLs is now essential for improving network security. The technological revolution spurred ...

• We introduce a post-training program for pre-trained models.
• Character-level and subword-level URL insights are fused.
• Our method blends multi-layer Transformer encodings.
• Our method captures and integrates local and global ...

In large-scale group decision-making (LSGDM), the complexity of the decision-making environment highlights the significance of co-opetition in reaching a consensus among decision-makers (DMs) with diverse knowledge. Thus, this paper proposes an ...

• Propose an ordinal–cardinal consensus measure considering co-opetition relations.
• Construct a trust consensus network for clustering.
• Present a consensus identification mechanism based on three-way decision.
• Design a ...

• Define a new similarity measure for pairwise flexible linguistic expressions.
• Design an integrated PIS optimization model to derive the personalized semantics.
• Develop a decision-maker weight optimization model from a consensus ...

Flexible linguistic expression (FLE) is a complex linguistic representation that almost generalizes all distributed representations, posing the following challenges for the personalized individual semantics (PIS) issues in group decision-making (...

Multi-view clustering is widely applied in engineering and scientific research. It helps reveal the underlying structures and correlations behind complex multi-view data. Graph-based multi-view clustering stands as a prominent research frontier ...

• Multi-view High-order Bipartite Graph Fusion for Enhanced Clustering.
• Adaptive Fusion with Truncation Selection and Implicit Weighting
• Computational Efficiency and Superiority in Multi-View Clustering.

High-precision fault diagnosis of rotating machinery plays an important role in industrial systems. Today, rotating machinery often has multiple sensors to monitor equipment condition, so it is important to fuse data from multiple rotating ...

• A multi-layer graph data construction method is proposed.
• A multi-type feature fusion mechanism based on the attention mechanism is proposed.
• A decision fusion strategy based on information entropy is proposed.
• A multi-level ...

Rapid and accurate diagnosis of mental disorders has long been an essential challenge in clinical medicine. Due to the advantage in addressing non-Euclidean structures, graph neural networks have been increasingly used to study brain networks. ...

• This study proposes a novel approach (FC-HGNN) for mental disorder identification.
• The FC-HGNN combines the advantages of individual and population graph models.
• LGP module was used to enhance interpretability in identifying ...

Internet of Medical Things (IoMT) facilitate revolutionary development in healthcare services, recognized as smart healthcare. By collecting big healthcare data and utilizing artificial intelligence algorithms, P4-medicine can be realized in ...

• Both time-series and feature-information are extracted by the designed mechanism.
• Embedding networks embed features and enhance regularization, simultaneously.
• Heterogeneous transfer learning is able to transfer knowledge from ...

Disease assessment involves two stages of decision-making process. The first process is to infer the development trend of the disease through the existing judgment conditions, which is to make a positive estimation (prediction) decision. The ...

• Three-way decision-making and semi-supervised learning integrated model was constructed.
• The multi-modal decision information method was discussed.
• Automatic update strategy of thresholds and loss function of three-way decision ...

Recently, many works have been proposed for traffic forecasting to improve people’s daily lives. Although these works have achieved good predictive performance, they have three fundamental limitations. (i) The regional features of traffic flow ...

• Designing a hierarchical graph learning model for traffic forecasting.
• Using Neural Ordinary Differential Equations to model continuous traffic features.
• Adopting a spatio-temporal feature fusion scheme for traffic regions and ...

The brain basis of emotion has consistently received widespread attention, attracting a large number of studies to explore this cutting-edge topic. However, the methods employed in these studies typically only model the pairwise relationship ...

• Identify hierarchical emotional areas to study brain mechanisms underlying emotion.
• Conduct an in-depth theoretical analysis based on information fusion and graph theory.
• Develop a novel framework to improve emotion decoding using ...

• A new Transformer is proposed for air quality spatiotemporal prediction.
• MGSFformer full fuses multi-granularity and spatiotemporal correlations of data.
• The proposed model can outperform 11 SOTA models on 3 datasets.

Air quality spatiotemporal prediction can provide technical support for environmental governance and sustainable city development. As a classic multi-source spatiotemporal data, effective multi-source information fusion is key to achieving ...

As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for sustainable development by harnessing the power of cross-domain data fusion from diverse sources (e.g., geographical, traffic, social media, and environmental data)...

• Comprehensive survey of deep learning techniques for data fusion in urban computing.
• Innovative taxonomy framework for data sources, fusion methods, and applications.
• Detailed compilation of key datasets and fusion models for urban ...

Multi-view learning is effective in improving data classification accuracy through integrating information from multiple sources. To guarantee the reliability of multi-view classification, the trusted multi-view classification methods have been ...

• Propose a dissonance measure to evaluate data views subjected to adversarial attacks.
• Propose a dissonance-aware belief integration strategy for multi-view fusion.
• Analyze the properties of the dissonance-aware belief integration ...

Medical visual question answering (VQA) bridges the gap between visual information and clinical decision-making, enabling doctors to extract understanding from clinical images and videos. In particular, surgical VQA can enhance the interpretation ...

• We propose a Surgical-VQLA++ framework to connect answering and localization.
• We incorporate feature calibration and adversarial contrastive training techniques.
• We expand our datasets by including additional queries related to ...

Label distribution learning (LDL) has garnered increased attention in recent studies on label ambiguity. However, collecting complete annotations for LDL tasks is often time-consuming and labor-intensive compared to traditional learning ...

• Local and global label correlations are integrated into a unified framework.
• IncomLDL-LCD learns label distributions and recovers missing description degrees of labels.
• IncomLDL-LCD employs an alternating solution with accelerated ...

Federated Learning (FL) lets multiple data owners collaborate in training a global model without any violation of data privacy, which is a crucial requirement for enhancing users’ trust in Artificial Intelligence (AI) systems. Despite the ...

• A novel approach for Federated Learning of Fuzzy Regression Trees is introduced.
• The approach fulfills two requirements for trustworthy AI: privacy and transparency.
• Federated FRT outperforms FRTs learned locally on each ...

Addressing the complexity of multi-task trajectory prediction, this study introduces a novel Deep Multimodal Network (DMN), which integrates a shared feature extractor and a multi-task prediction module with translational encoders to capture both ...

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• Joint prediction of trajectory locations, travel time and travel modes.
• Dependencies and interdependencies within and across multimodal features of data.
• Incorporation of both inter- and intra-modal attention mechanisms.
• Decent ...