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We introduce a visual analysis method for multiple causal graphs with different outcome variables, namely, multi-outcome causal graphs. Multi-outcome causal graphs are important in healthcare for understanding multimorbidity and comorbidity. To support ...

Pixel prediction is an important issue in the field of reversible data hiding. Neural networks are gradually used to improve the accuracy of pixel prediction owing to their excellent performance. However, current neural network-based pixel ...

• A novel neural network-based PEE-RDH scheme for medical images is proposed.
• We design an efficient neural network-based pixel predictor to obtain sharper PEH.
• A class-based embedding strategy is exploited to reduce ROI distortion.

To ensure the users’ immersive experience in cross-modal communication, overcoming the end-to-end (E2E) latency through prediction has attracted attention and shown its superiority. However, existing prediction schemes encounter formidable ...

Phishing detection in Semantic Web systems is crucial to safeguarding users from malicious attacks. In this context, this work presents a deep learning-based phishing attack detection model using MobileBERT for feature extraction and hyperparameter ...

As a contemporary mode of transportation for medium-to-long distances, the widespread adoption of free-floating shared electric vehicles has the potential to reduce urban carbon emissions and alleviate traffic congestion. However, this mode of ...

Federated Learning has received much attention due to its data privacy benefits, but most existing approaches assume that client classes are fixed. Clients may remove old classes and add new ones, leading to catastrophic forgetting of the model. ...

Reflection Ultrasound Computed Tomography (RUCT) is gaining prominence as an essential instrument for breast cancer screening. However, image quality is often compromised by the variability in sound speed across breast tissue. Traditionally, RUCT ...

Computing-in-memory (CIM) architecture has become a promising solution to improve the parallelism of the multiply-and-accumulation (MAC) operation for artificial intelligence (AI) processors. Recently, revived CIM engine partly relieves the memory wall ...

Unmanned aerial vehicle (UAV) swarm networking and collaboration have significant prospects in both civilian and military applications, due to its remarkable properties in cooperative efficiency, reduced risks, and operational cost. Traditional algorithms ...

As the digital transformation of industries accelerates, the security and stability of web systems, which are key interfaces for user interaction and data transmission, become critical. Vulnerabilities in these systems can lead to severe disruptions and ...

Sepsis patients in the ICU face heightened mortality risks. There still exist challenges that hinder the development of mortality risk prediction models for sepsis patients. In the ensemble model, the differences between base classifier ...

In massive open online course (MOOC) discussion board, students' learning experience, reflecting implicit cognitive and affective states, is related to their learning outcomes and course's completion rates. The majority of researches about ...

• Patterns of learning experiences were automatically identified by AI technique.
• Learning experiences corresponding to interactive roles were thoroughly explored.
• Effects of learning experience on achievements about interactive ...

Digital twins are essential visualization applications in the manufacturing field. However, their development requires specialized 3D engineers, which often complicates modifications during the operation stage. To address this complexity, we introduce ...

The goal of this study is to reconstruct a high-quality computed tomography (CT) image from low-dose acquisition using an unrolling deep learning-based reconstruction network with less computational complexity and a more generalized model. We ...

Remaining useful life (RUL) prediction and health status (HS) assessment are two key tasks in aero-engine prognostics and health management (PHM) system. However, existing deep learning-based prognostic models perform RUL prediction and HS ...

• A M2STCNN is proposed that can achieve joint learning of RUL prediction and HS assessment.
• MSF can extract multi-scale degradation features, and MLC can integrate important information from different levels.
• MTL is constructed and ...

In order to achieve the secure, efficient storage and transmission of medical images, we propose a joint lossless compression and encryption (JLCE) scheme. First, according to the intra-block correlation degree, the original medical image is divided into ...

In recent years, BIM (Building Information Modeling) because of its unified and efficient digitalization, visualization, synergistic and other engineering advantages is being widely concerned and researched and applied in various fields related to ...

High-accuracy parameter learning in Bayesian Networks (BNs) is a key challenge in real-time decision support applications, particularly when the available data are limited. Prior/Expert knowledge was introduced to eliminate the drawbacks of ...

Fire poses a huge safety risk to heritage or historic buildings. Traditional approaches are unsuitable because of their high false alarm rates, delayed features, and vulnerability in heritage building environments. To reduce the serious ...

Since the multi-modal services, which elaborately integrate audio, video, and haptic streams, are able to substantially improve the users' immersive experience, they are gradually becoming the main-stream applications. However, the existing ...