Neural Computing and Applications

The identity management (IM) solutions are designed for facilitating and managing in digital field that identifies to perform the authentication operation which has been widely used for the real-world applications. The blockchain-based IM ...

The metaverse network economy is built on the blockchain protocol network and occurs in the metaverse virtual space by the virtual digital avatar. This is a series of economic activities that produce, exchange, distribute and consume digital ...

A monocular vehicle speed detection method based on improved YOLOX and DeepSORT is proposed for the simple scene of fixed shooting angle without high precision but requiring control cost. For continuous video frames collected from a monocular ...

Hypergraph neural network (HGNN) for session-based recommendation (SBR) is quite rare but has been rewarded with promising performance. However, under the hypergraph framework, no works have emphasized the importance of repeat consumption, which ...

The increased volume of rumors and attention on related topics during the COVID-19 pandemic have had a significant negative social impact. To combat rumors, it is crucial to study the actors involved in their spread. In this study, we first ...

The traveling salesman problem (TSP) aims to find the shortest tour that visits each node of a given graph exactly once. TSPs have significant importance as numerous practical problems can be naturally formulated as TSPs. Various algorithms have ...

To solve the recommendation accuracy degradation problem encountered in sequential recommendation cases caused by data sparsity—such as short historical user behaviour sequences and limited information—this paper proposes a sequential ...

To improve the defense of CNN network traffic classifiers against adversarial sample attacks, the author proposes a batch adversarial training method that utilizes the characteristics of backpropagation errors during the training process, and ...

Bone age assessment is a common diagnostic method used for abnormal growth and development in children. Despite recent significant advancements in convolutional neural network (CNN)-based intelligent bone age assessment in children, there remains ...

Personalized learning has gained significant attention in education as a means to cater to the diverse needs of learners and optimize educational outcomes. However, ensuring the efficiency of personalized learning remains a challenge. It requires ...

With the rapid development of biometric technology, an increasing number of application scenarios require biometric identification systems for identity authentication, such as cell phones, gate control, and banks. The performance and operational ...

Precision agriculture has long struggled with the surveillance and control of pests. Traditional methods for estimating pest density and distribution through manual reconnaissance are often time-consuming and labor-intensive. To address these ...

The prognostic analysis for high grade serous adenocarcinoma (HGSC) holds significant clinical importance. However, current prognostic analysis primarily relies on statistical techniques like logistic regression and chi-square analysis alongside ...

The positive relationship between knowledge transfer and knowledge innovation is recognized by most scholars, while knowledge heterogeneity, as a knowledge situation of the enterprise organization, affects knowledge transfer within enterprise ...

The fierce competition in the market makes it necessary for enterprises to not only consider how to increase consumers’ purchase intention but also study to maintain high customer loyalty for continuous purchases. Taking the smartphone brands on ...

Internet medical and health services are services that require high levels of trust. We identify factors that influence user trust based on trust source credibility model and trust transitivity model and explore differences in initial and ...

According to the solution requirements, linear BP neural networks are designed which are consistent with the feature curves of the fitted equation, when the neural networks reach the equilibrium and stable state, so a optimization problem is ...

This paper studies supply chain pricing and production/ordering decisions under carbon tax policy and retailer’s stochastic demand. Firstly, a supply chain model with random demand obeying normal distribution is established. Based on this, a ...

Distracted driving is one of the leading causes of traffic accidents and has become a bottleneck for improving driver assistance technologies. It is still a challenge to detect distracted driving behavior in real-life scenarios, which have the ...

With the rapid development of data acquisition and storage technology, spatio-temporal (ST) data in various fields are growing explosively, so many ST prediction methods have emerged. The review presented in this paper mainly studies the ...

Argument visual states are helpful for detecting structured components of events in videos, and existing methods tend to use object detectors to generate their candidates. However, directly leveraging object features captured by bounding boxes ...

Graph neural networks (GNNs) are reshaping our understanding of biomedicine and diseases by revealing the deep connections among genes and cells. As both algorithmic and biomedical technologies have advanced significantly, we are entering a ...

The data-driven modular deep fuzzy model has demonstrated excellent forecasting performance due to its clear architecture and powerful fuzzy inference ability. However, the fixed structure predesigned for specific types of datasets limits the ...

Nowadays, Wireless Rechargeable Sensor Networks utilize a Mobile Charger (MC) to prevent node failure by replenishing the sensor node’s energy. Existing studies primarily focus on small-size networks using a single-node charging method, lacking ...

This article introduces a novel 2D shape data augmentation approach based on intra-class shape space exploration. The proposed method relies on a geodesic interpolation between shapes, leveraging invariant-based morphing techniques. By blending a ...

Multi-Object Tracking, also known as Multi-Target Tracking, is an important area of computer vision with various applications in different domains. The advent of deep learning has had a profound impact on this field, forcing researchers to explore ...

Over the preceding decade, there has been a discernible surge in the prominence of artificial intelligence, marked by the development of various methodologies, among which deep learning emerges as a particularly auspicious technique. The ...

In the Internet of Medical Things (IoMT)-based real-time telemedicine systems, patients can utilize a wide range of medical devices and sensors, which leads to the continuous generation of massive amounts of data. The high speed of data generation ...

Finding patches is a crucial step in a pavement performance survey. The study develops a machine learning and image processing algorithm-established automatic method for identifying asphalt pavement patches. The GrayLevel Co-Occurrence Matrix and ...

Once realized, autonomous aerial refueling will revolutionize unmanned aviation by removing current range and endurance limitations. Previous attempts at establishing vision-based solutions have come close but rely heavily on near perfect ...