Information Sciences: an International Journal

Different from traditional analytical and numerical simulation methods, data-driven methods can more effectively describe the effects of nonlinear and coupling factors. Although the fluctuations of deformation resistance and thickness for hot-...

This paper pays attention to the output tracking control problem of nonlinear systems where the output is uniquely measurable and subject to an intermittent constraint. A multiplicative transformation based on the shifting-replaying function is ...

Graph contrastive clustering (GCC) has achieved numerous advantageous results due to the information mining capability of self-supervised learning. Multi-view attribute graph clustering, as a means of addressing complex attribute graph data with ...

Today, more and more data is collected and stored by different organizations. When the data is distributed among multiple users who wish to perform data mining on the joint data, outsourcing the task to cloud servers becomes an attractive ...

Crowdsourcing provides an inexpensive solution to employ crowd workers labeling instances, and hence each instance is labeled with a multiple noisy label set instead of its true label. Label integration aims to infer a true label from its ...

• Crowdsourcing provides an effective way to collect labels from crowd workers.
• Label integration aims to infer each instance's true label.
• This paper proposes an instance redistribution-based label integration method.
• The ...

In addressing complex and dynamic meteorological disaster decision-making environment, the traditional multi-attribute group decision-making domain model is often unable to effectively deal with the correlation between attributes and the mutual ...

• Propose an innovative multi-attribute quantum group decision-making model, reflecting the mutual influence of group opinions.
• Adopt a 2-additive Choquet integral based on Mobius transformation to handle the correlation between ...

In real life, multiple issues may have different weights in leading to conflicts. Some of the existing conflict analysis literature related to weight does not involve an explicit weight method, and some use common methods such as entropy weight ...

Graph Neural Networks (GNNs) have been extensively used for mining graph-structured data with impressive performance. However, because these traditional GNNs do not distinguish among various downstream tasks, embeddings embedded by them are not ...

• We propose a novel graph neural network architecture named CustomGNN.
• CustomGNN incorporates task-oriented semantic features with generic graph features.
• Two unsupervised loss functions are proposed to regularize the learning ...

Commonsense knowledge bases play an essential role in a wide range of natural language processing tasks. This paper studies the problem of representation learning for commonsense knowledge bases to effectively incorporate their knowledge into ...

In current opinion dynamics models for predicting public opinion, the spread of events within social media has been inadequately considered, resulting in suboptimal prediction performance and inefficient strategies for public opinion management. ...

Modern techniques of pre-training and fine-tuning have significantly improved the performance of models on downstream tasks. However, this improvement faces challenges when pre-trained models encounter the necessity to adapt sequentially to ...

Information exchange among individuals is one of the key factors leading to formation of modular structures in social networks, often referred as communities. In this paper, we devise a novel information diffusion based approach to leverage the ...

• A new community detection algorithm based on cascading is proposed.
• Seed communities generated subsequently with cascading.
• Novel properties proposed to address monster community formation and over-partitioning.
• Experimental ...

This article is aimed at studying the finite-time reliable asynchronous control issue for continuous semi-Markov jump systems (S-MJSs) with actuator failures and randomly occurring uncertainties (ROUs). To characterize the asynchronous phenomenon ...

• The HSM approach, whose TP matrix is polytope structured, is adopted to describe the asynchronous mode phenomenon.
• A novel framework of reliable asynchronous control is first established for S-MJSs with actuator failures and ROUs.
• ...

This article presents a novel fuzzy event-triggered optimized strategy for slowly switched nonlinear system using reinforcement learning technique. For addressing intermittent state constraints problem, some improved shifting functions and ...

Software defect detection focuses on the automatic identification of flaws in software modules. Given the great importance of the problem, numerous researchers have introduced a rich collection of deep learning approaches to confront it. However, ...

• A technique for mitigating class imbalance in software defect detection tasks.
• Applies local resampling at clusters created hierarchically.
• Discarding the majority class outliers benefits classification performance.
• Conditional ...

Three-way decision (3WD) has been widely applied in diverse fields in tackling uncertainty, particularly in classification domain. As a discriminative learning algorithm, Large Margin Distribution Machine (LDM) aims to maximize the inter-class ...

• A CS3W-IFLMC model based on cost-sensitive 3WD and IF is proposed.
• CS3W-IFLMC provides a new mechanism to address uncertainty and noise sensitivity.
• The novel IF strategy is proposed based on dual-category centroids.
• ...

Distance-based Join Queries (DJQs) have multiple applications in spatial databases, Geographic Information Systems, and other areas. The K Closest Pairs Query (KCPQ) and the ε Distance Join Query (εDJQ) are well-known DJQs that have been widely ...

Large-scale multiobjective optimization problems (LSMOPs) have exponential growth in the search space as the decision variables increase, and the vast search space poses a challenge to the performance of multiobjective evolutionary algorithms (...

• Criteria system construction for selecting GRID suppliers.
• Introduction of novel fuzzy sets (SVZs, SVZPRs) combining Z-numbers and CST.
• Improved efficiency of S-BWM through SVZPR extension.
• Development of TOPSIS with SVZs ...

Firms need a reliable decision-making framework to select suppliers who are Green, Resilient, and embrace Inclusive Development (GRID). However, GRID supplier selection is challenged by events that are uncertain and often dependent. Such events ...

Density-Based Spatial Clustering of Applications with Noise (DBSCAN) identifies high-density connected areas as clusters, so that it has advantages in discovering arbitrary-shaped clusters. However, it has difficulty in adjusting parameters and ...

Time series forecasting holds significant value across various application scenarios. However, practical implementations often encounter challenges related to low-quality time series data resulting from system failures or external interference. ...

Granular Computing (GrC)-based feature selection can remove redundant features from a massive amount of data and improve the efficiency of information processing. However, the existing method of neighborhood-based information granule only ...

This work focuses on fuzzy orthopartitions and credal partitions, which are distinct mathematical models representing partitions where the membership of elements to classes is only partially known. Firstly, we show that fuzzy orthopartitions and ...

In group decision-making, trust relationships are the basis of interactions among decision makers (DMs) and play an important role in maintaining cooperation. However, DMs from different backgrounds may use the trust relationship to influence the ...