The Journal of Supercomputing

The threat alert fatigue or alert overload problem has become critical in recent years. In practice, the volume of threat alerts is higher than the volume of alerts that SOC analysts can investigate. In this paper, we propose “Threat Inspection ...$$

k-Nearest Neighbors (k-NN) is one of the most widely used classification algorithms in real-world machine learning applications such as computer vision, speech recognition, and data mining. Massive high-dimensional datasets, reasonable accuracy of ...

A three party authenticated key agreement protocol (3PAKA) enables two entities to establish a session key with the assistance of a dedicated server via an insecure channel. Recently, Islam et al. (J Inf Secur Appl 6363:103026, 2021) proposed a ...

This study examines the potential of BIP-152’s Compact Block Relay (CBR) to enhance the Bitcoin network. This work explores the block propagation efficiency through dynamic prefilling of transactions. In addition, an enhanced CBR model is proposed ...

Planning and sequencing for hot strip mills in the steel industry is a challenging, complex problem that has fascinated optimization researchers and practitioners alike. This paper applies a combinatory heuristic search and a multi-objective ...

Since the advent of the registration method based on deep learning, it has demonstrated a time efficiency advantage several orders of magnitude higher than traditional methods. However, the current deep networks have not fully explored the ...

With the advancement of Internet of Things (IoT) technology, IoT systems have been widely infiltrating and deployed on a large scale globally. Consequently, network attacks on IoT devices and the intermediary communication media have increased ...

Multimodal aspect-based sentiment analysis aims to predict the sentiment polarity of all aspect targets from text-image pairs. Most existing methods fail to extract fine-grained visual sentiment information, leading to alignment issues between the ...

One of the most serious security threats faced by the Internet today is multi-stage attacks. In response to this challenge, anomaly detection-based methods have been widely used to identify different stages of such attacks. However, current ...

Wireless sensor network consists of many tiny sensors that can be a powerful tool for data collection in various environments. The optimal scenario for sensor networks is for all nodes to reach the end of their energy, together or through regular ...

The Internet of Things (IoT) technology has led to the development of intelligent hardware devices that can interact with each other through the internet, enabling the interconnection of everything. However, this interconnectivity also increases ...

In the current healthcare landscape, accurately diagnosing patients with respiratory conditions while preserving data privacy has become a critical global concern. To address this issue, federated learning (FL) presents an innovative solution that ...

Due to the rapid development of the Internet of Vehicles (IoV), the combination of IoV and edge computing, known as vehicle edge computing (VEC), has received considerable attention from both academia and industry. However, task offloading in ...

The integer factorization problem, concerning the product of two primes N = pq, has been a focal point for researchers since its rise to prominence with the RSA algorithm, a renowned public key scheme. It is well known that the integer ...

The manuscript focuses on establishing an optimal accurate parameter-uniform scheme for singularly perturbed reaction-diffusion type problems in two dimensions. Along with the higher dimensions, these model problems have other complexities like ...

The dynamic multi-objective optimization evolutionary algorithm (DMOEA) has garnered widespread attention due to its superiority in solving dynamic multi-objective optimization problems (DMOPs). Existing DMOEAs do not judge the intensity of ...

The present investigation studies performance modeling and analysis of an M/M/1 queue with a hybrid vacation policy and Bernoulli service interruption. When the system becomes empty, the server has the choice to switch over to a complete vacation ...

Cross-domain few-shot learning, which aims to solve the problem of domain gap in few-shot learning, has recently received more and more attention. Specifically, when there are great differences between the source domain and the target domain ...

In the era of digitization and informatization, 3D models are used in a variety of fields, notably in medicine, engineering and design, seamlessly integrating into people's daily lives. Particularly within the medical field, 3D models enjoy ...

In the medical field, the analysis and understanding of human emotions is a key approach to the study of mental diseases. Many psychological or psychiatric disorders exhibit inconsistent and often subtle symptoms, which complicates the prediction ...

Currently, deep learning methods have become prevalent in the field of electrical load forecasting. These approaches have shown a great potential to map complex nonlinear feature interactions. However, many existing electrical load forecasting ...

This paper proposes an online medical pre-diagnosis scheme based on blockchain. It mainly focuses on the problems faced in the medical diagnosis scheme nowadays, such as the huge scale of medical treatment, the lack of data sharing, the ...

In addressing the challenges of wind turbine defect detection, such as different defect scales in UAV aerial photography, interference from different lighting conditions, and small-sized target defects leading to low detection accuracy and ...

Loop closure detection (LCD) is crucial for simultaneous localization and mapping (SLAM). Current LiDAR-based methods focus on global scenes and often overlook the rich geometric features of crossroads. These scenes, with their irregular contours, ...

Deformable image registration minimizes the discrepancy between moving and fixed images by establishing linear and nonlinear spatial correspondences. It plays a crucial role in surgical navigation, image fusion and disease analysis. Its challenge ...

Digital therapeutics (DTx) can be used in conjunction with wearable devices to continuously collect, transmit, and analyze patients’ physiological data, achieving personalized precision medicine. In practice, security and privacy problems may ...

This research study involves modeling Newton–Leipnik attractors within the domain of fractional variable-order (FVO) dynamics using a nonlinear and adaptable radial basis function neural network (RBFNN). The numerical solution for the FVO Newton–...

Time series forecasting plays a crucial role in various fields such as financial market analysis, weather prediction, and traffic flow forecasting. Although long short-term memory (LSTM) performs well in traditional time series forecasting tasks, ...

Agriculture is one of the fundamental components of human civilization, contributing not only to food production but also to economic growth. Early identification of diseases in plants presents a significant challenge, as timely identification is ...

Recently, deep learning models have achieved notable success in long-term time series forecasting. However, real-world time series data typically exhibit complex temporal patterns, characterized by both short-term and long-term variations across ...