Information Sciences: an International Journal

Data Envelopment Analysis (DEA) as a data-oriented benchmarking tool is considered a powerful and promising instrument for performance evaluation in various application areas. In DEA, the set of all decision-making units (DMUs) is divided into ...

The main goal of this paper is to develop a new less conservative linear matrix inequality (LMI) condition for the asymptotic stability of continuous-time Takagi-Sugeno fuzzy systems. A key advantage of this new condition is its independence from ...

Network traffic anomaly detection is a crucial task for today's network monitoring and maintenance. However, with the rapid growth of network data volume, the data structure has become more and more complex, showing multi-modal characteristics, ...

Accurately forecasting carbon dioxide emissions is crucial for policymakers and researchers aiming to combat climate change and develop effective emission reduction strategies. This study introduces an innovative method that leverages multi-...

• Unstructured data can compensate for the lack of information in structured data.
• Media information can effectively improve the accuracy of carbon emission forecasts.
• Tensor decomposition can extract and model complex spaces among ...

Semi-supervised clustering is an effective method, which improves the clustering performance based on pairwise constraints. However, state-of-the-art methods suffer from two issues: 1) due to the high dimensionality and multiple variables of ...

In this research, a structure modification based PID neural network (PIDNN) decoupling strategy is proposed to solve the difficulty in controller caused by the strong coupling in nonlinear multivariable systems. Incomplete differential neurons ...

• Introduction of Incomplete Differential Neurons: Mitigates abrupt changes and oscillations.
• Improved Integration Strategy: Enables faster convergence and better accuracy.
• Intelligent Optimization Algorithms: Optimize initial ...

• We propose a novel multi-step forecasting framework named MFTM-Informer.
• Pattern matching is extended to multivariate scenarios for extracting systemic similarity information.
• Multivariate Fuzzy Trend Matching (MFTM) is developed ...

Multi-step forecasting is a critical process in various fields, such as disaster warning and financial analysis. Nevertheless, achieving precise multi-step forecasting is challenging due to the intricate nature of the factors influencing the time ...

Analytical studies of the class of the fuzzy Langevin fractional delay differential equations (FLFDDEs) are frequently complex and challenging. It is necessary to construct an effective technique for the solution of FLFDDEs. This article presents ...

Some social network group decision making (SNGDM) researches may overlook two issues: (1) the impact of consistency on decision reliability and decision makers’ (DMs’) status in social network, and (2) DMs’ personalization during consensus ...

Class incremental learning (CIL) can learn new classes continuously by updating the model rather than retraining a model from scratch with all seen classes like traditional offline learning, therefore, CIL is more suitable for classification in ...

• Our CIL-KLMES imposes a KL divergence term to alleviate the CF problem at the parameter level.
• A multi-strategy exemplar selection method is developed to further alleviate CF problem at the data level.
• The parameters are inferred ...

Attributed graph clustering is a fundamental task in complex network analysis. Many existing graph clustering methods utilize graph representation learning techniques to learn node representations, subsequently applying K-means for clustering. ...

• An attributed graph clustering method under the contrastive mechanism is proposed.
• A cluster-aware contrasting view based on EBC and kNN graph is built.
• Multilevel contrast and self-supervised clustering are jointly optimized.
• ...

Multivariate time series (MTS) forecasting plays an essential role in the automation and optimization process of intelligent applications. However, capturing correlations and dependencies among variables in MTS data remains a major challenge for ...

Open intent classification aims to identify the unknown (open) intents and simultaneously classify the known ones under the open-world assumption. However, the existing studies still face two challenges, i.e., coarse-grained representation ...

Fair clustering aims to prevent sensitive attributes (e.g., race or gender) from dominating the clustering process. However, real-world datasets, often characterized by low quality and high dimensionality, restrict existing fair clustering ...

• Blockchain adoption enhances emission reduction in EV closed-loop supply chains.
• Adoption of blockchain increases market demand for power batteries in EVs.
• Echelon utilization and blockchain adoption increase recycling quantity in ...

The rapid increase in ownership of new energy vehicles has resulted in a surge in retired power batteries, necessitating the development of an efficient recycling system. Given the application of blockchain in recycling, we analyze the blockchain ...

In recent years, multi-task optimisation, aimed at handling multiple optimisation problems simultaneously, has received great attention in the field of evolutionary algorithms. Research on multi-task evolutionary algorithms mostly focuses on ...

This paper addresses an adaptive fuzzy prescribed performance synchronization for a class of uncertain non-strict feedback chaotic systems subject to input quantization and time-varying actuator faults. The proposed approach utilizes fuzzy logic ...

Multiview clustering has gained attention for its ability to incorporate complementary information from multiple sources of data, leading to better clustering results. However, these methods don't sufficiently mine high-dimensional information of ...

Neural networks have dramatically increased our capacity to learn from large, high-dimensional datasets across innumerable disciplines. However, their decisions are not easily interpretable, their computational costs are high, and building and ...

Graph anomaly detection is essential for identifying irregular patterns and outliers within complex network structures in domains like social networks, cybersecurity, finance, and transportation systems. It helps detect security breaches, fraud, ...

As an effective tool for data mining, formal concept analysis can yield interpretable decision rules using attribute reduction. Currently, existing reduction methods within the framework of formal concept analysis result in all samples sharing ...

In the context of recommender systems, collaborative filtering is the method of predicting the ratings of a set of items given by a set of users based on partial knowledge of the ratings. Commonly, items and users are represented via vectors, and ...

In this work, for obtaining the inverses of time-variant complex matrices, four new kinds of recurrent neural network models [named modified finite-time convergent complex-valued zeroing neural network (MFTCVZNN) models] are put forward by ...

Large language model (LLM) platform vendors have begun to make their models available for developers to build for different use cases. However, the emergence of LLM-based applications may raise security and privacy issues, and even LLM-based ...

In social network group decision-making (SNGDM) problems, decision-makers (DMs) often express their opinions or preferences using probabilistic linguistic term sets (PLTSs). In this paper, a novel SNGDM method for probabilistic linguistic ...

Using the relational coarsest partition (RCP) method, this article aims to study the observability problem of probabilistic Boolean networks (PBNs). The key step to solving this problem is to find the parallel cycle and the coarsest refinement ...

Semi-supervised graph clustering with constraints has received considerable attention in the last decade. They use pre-given constraints to guide the clustering process and improve the performance. Nonetheless, most of related research works ...

As a consequence of the ability to incorporate information from different perspectives, multi-view clustering has gained significant attention. Nevertheless, 1) its high computational cost, particularly when processing large-scale and high-...