Applied Intelligence

Illusions are like holes in our effortless visual mechanism through which we can peep into the internal mechanisms of the brain. Scientists attempted to explain underlying physiological, physical, and cognitive mechanisms of illusions by the ...

Macro-expression spotting is an important prior step in many dynamic facial expression analysis applications. It automatically detects the onset and offset image frames of a macro-expression in the video. The state-of-the-art methods of macro-...$_{}^{\mathrm{}}$$$$$$_{}^{\mathrm{}}$

Although the deep learning recognition model has been widely used in the condition monitoring of rotating machinery. However, it is still a challenge to understand the correspondence between the structure and function of the model and the ...

Temporal Knowledge Graph Completion (TKGC) aims to infer missing facts in Temporal Knowledge Graphs (TKGs), where facts are stored along with significant temporal information. However, existing TKGC methods only consider message passing on ...

In the field of recommendation algorithms, Knowledge Graphs are often utilized as supplementary information to enhance recommendation accuracy. However, while applying Knowledge Graphs enriches recommendation information, it also introduces ...

This paper proposes an innovative image encryption algorithm that leverages partial face recognition and DNA diffusion, building upon advancements in chaotic image encryption and face recognition technologies. The key is generated using the secure ...

Classifying information diffusion patterns is critical to many information analysis areas, e.g., misleading information detection. However, diffusion pattern classification remains challenging when multiple users are involved. To address this ...

In this paper, we focus on the automated diagnosis of physiological curvature in the cervical spine, with an emphasis on feature point localization. Cervical spine deformity is prevalent, and the Cobb angle is widely recognized as the gold ...

Text Visual Question Answering (TextVQA) task aims to enable models to read and answer questions based on images with text. Existing attention-based methods for TextVQA tasks often face challenges in effectively aligning local features between ...

As significant vehicles for applying fuzzy set theories, fuzzy inference systems (FISs) have been widely utilized in artificial intelligence. However, challenges such as computational complexity and subjective design persist in FIS implementation. ...

Electroencephalography (EEG) provides a wealth of physiological and psychological information. Decoding EEG signals enables machines to recognize brain activity, a crucial aspect in brain-computer interaction and medical rehabilitation. However, ...

In the realm of edge AI systems where deep learning is paramount, protecting the intellectual property (IP) of multimodal neural network models is crucial. Current watermarking solutions often bypass the intricacies of multimodal models and the ...

The El Niño-Southern Oscillation (ENSO) phenomenon is often accompanied by multiple extreme hazards—thus, its accurate prediction is crucial to the prevention of such crises. Recently, machine learning algorithms have exhibited excellent ENSO ...

This paper addresses the evaluation of multi-step point forecasting models. Currently, deep learning models for multi-step forecasting are evaluated on datasets by selecting one error metric that is aggregated across the time series and the ...

The popularity of Graph Neural Networks (GNNs) has grown significantly because GNNs handle relational datasets such as social networks and citation networks. However, the usual relational dataset is sparse, and GNNs are easy to overfit to the ...

Fault diagnosis plays an important role in intelligent manufacturing. Knowledge modelling is often used for intelligent fault diagnosis purposes, and link prediction is performed in knowledge graphs to locate and trace system faults. However, due ...

Sparsely annotated image segmentation has gained popularity due to its ability to significantly reduce the labeling burden on training data. However, existing methods still struggle to learn complete object structures, especially for complex ...

Answer selection requires technology that effectively captures in-depth semantic information between the question and the corresponding answer. Most existing studies focus on using linear or pooling operations to directly classify the output ...

Intelligent systems, such as driving assistance systems, can assist drivers by providing basic traffic, road blockage and possible route information to enable safe driving. The goal of scene text tracking in driver assistance systems is to locate ...

This paper introduces an algorithm to construct a bidirectional causal graph using an augmented graph. The algorithm decomposes the augmented graph, significantly reducing the size of the variable set required for conditional independence testing. ...

Deep neural networks have been used extensively for diverse visual tasks, including object detection, face recognition, and image classification. However, they face several security threats, such as adversarial attacks. To improve the resistance ...$$$$

In recent years, with the continuous development of autonomous driving, monocular 3D object detection has garnered increasing attention as a crucial research topic. However, the precision of 3D object detection is impeded by the limitations of ...

Cancer survival estimation is essential for post-cancer patient care, cancer management policy building, and the development of tailored treatment plans. Existing survival estimation methods use censored data; therefore, standard machine learning ...

Neighborhood rough set theory is an important computational model in granular computing and has been successfully applied in many areas. One of its most prominent applications is in attribute reduction. However, most current attribute reduction ...

For the semi-supervised regression task, both the similarity of paired samples and the limited label information serve as core indicators. Nevertheless, most traditional semi-supervised regression methods cannot make full use of both ...

Graph convolutional networks (GCNs) have emerged as powerful tools for handling graph-structured data. Many knowledge graph embedding models leverage GCNs as encoders to learn the relationships between central entities and their neighbors, showing ...

In recent years, crowd behavior detection has posed significant challenges in the realm of public safety and security, even with the advancements in surveillance technologies. The ability to perform real-time surveillance and accurately identify ...

Recently, boundary or edge detection has made great progress under the development of convolutional neural networks (CNNs), and some algorithms have achieved a beyond human-level performance. However, CNNs tend to generate blurred edge maps, and ...

Image captioning is a challenging task in computer vision that automatically generates a textual description of an image by integrating visual and linguistic information, as the generated captions must accurately describe the image’s content while ...

The product quality is the key index to measure the process of the industrial manufacture. Thanks to the ever-expanding scale of time-series data, the deep learning technology can be regarded as the effective approach to predict the future product ...