Information Sciences: an International Journal

The critical nodes (CNs) in urban transportation networks, defined as road entities (such as road segments or detectors in a road network) that present highly volatile traffic states, can significantly impact the overall traffic conditions. ...

• A novel knowledge-based spatial feature fusion (KSFF) block to explicitly consider the critical nodes in a road network.
• A KSFF-based novel spatiotemporal deep learning model for traffic state prediction considering the critical nodes.

The flexible paradigm of the Resource Description Framework (RDF) has accelerated the rate at which raw data is published on the web. Therefore, the volume of generated RDF data has increased impressively in the last decade, which promotes the ...

This paper deals with the guaranteed cost extended dissipative stabilization problem of switched IT-2 fuzzy systems (SFSs) via intermittent control. Firstly, we propose a new class of system performance coefficients, taking both the extended ...

The Belief Rule-Based (BRB) system faces the rule combination explosion issue, making it challenging to construct the rule base efficiently. The Extended Belief Rule-Based (EBRB) system offers a solution to this problem by using data-driven ...

This paper delves into the problem of fixed-time neural network adaptive prescribed performance control for a category of nonstrict-feedback systems with time-varying unknown control coefficients (UCCs). Firstly, two key technical lemmas are ...

In this study, a fixed-time consensus-tracking problem of an uncertain nonlinear multi-agent system with practical state constraints that can be fully or partially constrained is considered. A novel unified transformation is proposed to address ...

Progress in the field of Non-Intrusive Load Monitoring (NILM) has been attributed to the rise in the application of artificial intelligence. Nevertheless, the ability of energy disaggregation algorithms to disaggregate different appliance ...

Machine Unlearning is a recently proposed paradigm to make an machine learning (ML) model delete specific data. Specifically, the data owner has the right to ask the machine learning as a service (MLaaS) provider to remove the impact of specific ...

Location factor of recommender systems has been extensively studied in the past decade. However, there is no research thoroughly analyzing location’s role in news recommendation. In this paper, a comprehensive exploration on role of location in ...

To address the challenge of facilitating overall problem solving and improving the overall efficiency of intelligent systems efforts through group decision making (GDM) by experts at all stages of the systems, this paper proposes an approach that ...

In recent years, multi-label learning with missing labels (MLML) has become a popular topic. The major challenge for MLML is enhancing the performance of classifiers in the presence of missing labels. Most existing algorithms focus on recovering ...

• Different from traditional methods that recover missing labels, we learn the pure mapping between labels and features.
• Utilizing the low-rank relationship to eliminate the features caused by missing labels.
• Utilizing reverse ...

Traditional nonprofiling side-channel analysis frequently adopts divide-and-conquer strategy to recover the secret key of a cryptographic algorithm. Only a single key byte is used, whereas the remaining bytes are considered extraneous noise. If ...

In this paper, a novel dynamic event-triggered robust optimal tracking control method is proposed for multi-player nonzero-sum game problem with mismatched uncertainties and asymmetric constrained inputs. First, the original asymmetric ...

Many-objective evolutionary algorithms have gained significant achievements over the years. However, the difficulty in balancing convergence and diversity of the population remains. In this paper, we propose a cascading elimination based ...

Traffic forecasting, as the cornerstone of the development of intelligent transportation systems, plays a crucial role in facilitating accurate control and management of urban traffic. By treating sensors as nodes in a road network, recent ...

Federated Learning is a distributed machine learning method that offers inherent advantages in efficient learning and privacy protection within edge computing scenarios. However, terminal nodes often encounter challenges such as insufficient ...

Predicting 3D rotations from a single image presents a significant challenge, primarily due to the inherent uncertainty arising from factors such as high symmetry, self-obscuration, and noise in the 3D environment. In this work, we propose a ...

The 12-lead electrocardiogram (ECG) is a reliable diagnostic tool for detecting and treating severe cardiovascular conditions like arrhythmia and heart attack. Deep neural networks (DNNs) have achieved higher accuracy in recent years than ...

A widespread methodology for modeling modern day information, which consists of high-dimensional digital measurements, is to use vine copulas; they can flexibly encode the underlying dependence structure of the data. Here we introduce a new ...

This paper considers an optimal bipartite formation control problem for high-order nonlinear multiagent systems (MASs) with the consideration of obstacle/collision avoidance. Contrary to existing literature, the control strategy designed in this ...

Knowledge graph (KG) embedding methods predict missing links by computing the similarities between entities. The existing embedding methods are designed with either shallow or deep architectures. Shallow methods are scalable to large KGs but are ...

• Deep models learn expressive embeddings but require a large number of parameters.
• Shallow methods require fewer parameters but learn less expressive embeddings.
• A link predictor is proposed to balance the scalability-expressivity ...

A hybrid information system (HIS) refers to an information system (IS) including categorical attributes and numerical attributes. For an HIS, it is common to use neighborhood rough set model (NRS-model) to process different types of data. However,...

This study focuses on the event-triggered cooperative output regulation problem (CORP) of multi-agent systems with heterogeneous dynamics. Two design criteria are required be achieved: event-triggered communication between agents has strictly ...

The propagation of cyber-attacks targeting modern industrial cyber-physical systems (ICPSs) is considered a sophisticated and persistent cross-layer penetration process, posing significant cyber-to-physical (C2P) risks to critical industrial ...

Spectral clustering is a widely used method for unsupervised feature selection (UFS) to generate pseudo labels. Nonetheless, it is acknowledged that graph algorithms suffer from issues such as redundancy and the dissatisfaction of connectivity, ...

This paper aspires to explore and construct a more objective and automated large-scale group decision-making (LSGDM) model. The concept of vacillation degree based on probabilistic double hierarchy linguistic term set is proposed to describe the ...

This paper formulates two novel theoretical designs of input-to-state stabilizing control for a class of recurrent neural networks with multiple proportional delays. The analysis tool developed in this paper is based on Lyapunov function and ...

Various data hiding methods have been suggested to hide secret images within stego images. However, many of them could be easily detected by steganalytic tools due to their large hidden information. In this paper, we enhance the undetectability ...

This paper mainly investigates the nonzero-sum games of nonlinear systems with unmatched uncertainty by using actor-critic neural networks. To handle the unmatched components, an auxiliary system with a modified value function is constructed, ...