Information Sciences: an International Journal

Next-location prediction is a special task of the next POIs recommendation. Different from general recommendation tasks, next-location prediction is highly context-dependent: (1) sequential dependency, i.e., the sequential locations ...

• A long-term and short-term preference learning model considering contextual information and sequence information is proposed.

Differential evolution (DE) is one of the most efficient evolutionary algorithms for solving numerical optimization problems; however, it still suffers from premature convergence and stagnation. To address these problems, we propose a ...

Cluster ensemble (CE) integrates multiple clustering solutions to effectively improve the accuracy and robustness of unsupervised clustering. To reduce the impacts of low-quality solutions, existing CE methods often design heuristic ...

• First meta-learning-based weighted cluster ensemble method.
• Considers data ...

Labeling rules can be leveraged to produce training data by matching the sentences in the corpus. However, the robustness of the relation extraction is reduced by noisy labels generated from incorrectly matched and missing sentences. ...

• A bootstrapping labeling rule discovery framework is proposed for robust relation extraction.

In this paper, we investigate the distributed resilient output-feedback containment problem of linear multi-agent systems (MASs) with a dynamic Luenberger observer under composite attacks, including denial of service (DoS) attacks, ...

Stochastic configuration networks (SCNs) have universal approximation capability and fast modeling properties, which have been successfully employed in large-scale data analytics. Based on SCNs, Stochastic configuration networks with ...

Feature engineering is an effective method for solving classification problems. Many existing feature engineering studies have focused on image or video data and not on structured data. This study proposes EvaGoNet, which refines the ...

This paper proposes a novel algorithm twice optimization for interval type-2 fuzzy neural networks with asymmetric membership functions (TOIT2FNN-AMF), for nonlinear system identification problems. The proposed TOIT2FNN-AMF uses an ...

Recognizing and presenting the appropriate model is of particular importance to examine the statistical models for fitting time series data. Among time series models widely used in the analysis of economic, meteorological, geographical,...

Molecular property prediction can guide molecular design and optimization in drug discovery. As molecules are inherently graph-structured data, graph learning has significantly boosted molecular property prediction tasks. However, many ...

Most knowledge graph completion methods focus on predicting existing relationships in the knowledge graph but cannot predict unseen relationships. To solve this problem, knowledge graph zero-shot relational learning (KGZSL) has gotten ...

Imbalanced data distribution is the main reason for the performance degradation of most supervised classification algorithms. When dealing with imbalanced learning problems, the prediction of traditional classifiers tends to favor the ...

Recently, a new business model called online group buying is emerging into our daily lives. For example, the online business platforms provide people group-discount coupons which will be issued for at least k buyers grouping for a ...

Variable precision (fuzzy) rough sets are interesting generalizations of Pawlak rough sets and can handle uncertain and imprecise information well due to their error tolerance capability. The comparable property (CP for short), i.e., ...

Inconsistency in classification and regression problems occurs when instances that relate in a certain way on the condition attributes, do not follow the same relation on the decision attribute. It typically appears as a result of ...

• Proposing the definition of granular approximations; a novel way of integrating machine learning, fuzzy sets, and rough sets.

This article addresses the observer-based event-triggered non-parallel distribution compensation (PDC) H ∞ control issue for networked T-S fuzzy systems (TSFSs) under actuator failures and aperiodic denial-of-service (DoS) attacks. In ...

For many classification tasks, particularly in the bioinformatics field, only experimentally validated positive samples are available, and experimentally validated negative samples are not recorded. The lack of negative samples poses a ...

• Screening reliable negative samples from unlabeled samples based on deep reinforcement learning.

k-nearest neighbors (k-NN) is a well-known classification algorithm that is widely used in different domains. Despite its simplicity, effectiveness and robustness, k-NN is limited by the use of the Euclidean distance as ...

Among the incoming challenges in monitoring systems, the aggregation, synthesis and management of knowledge through ontological structures hold an essential place. Existing knowledge extraction systems often use a supervised approach ...

• Automated validation of relations depends strongly on chosen similarity measure.

Change point detection aims to find the locations of sudden changes in the network structure, which persist with time. However, most current methods usually focus on how to accurately detect change points, without providing deeper ...

• The evolution of multiple objects and their interactions are tracked to locate change points in the dynamic networks.

In fuzzy information granule (FIG) based short-term forecasting models, the constructed FIG focuses on one of two tasks: capture data characteristic and improve semantic description at a common time concept (time interpretability). For ...

Graph classification is crucial in network analyses. Networks face potential security threats, such as adversarial attacks. Some defense methods may trade off the algorithm complexity for robustness, such as adversarial training, ...

Training of dendritic neuron model artificial neural networks is generally achieved by using nonlinear least square methods. The distribution of random error terms is ignored in training algorithms although error terms are random ...

This work proposes a strategy management technique based on hybrid peer-to-peer communication system. The main techniques used in the P2PC are: (i) Multi-objective optimization, (ii) Game theory technique, (iii) Non-linear geometric ...

• A secure IoT was introduced in smart healthcare using BIQBA-BCN.
• The proposed ...

Smart Health Care offers efficient, sustainable, along with real-time human services, and the concept of enhanced Internet of Things (IoT) lies behind the emergence of this smart Health Care (HC). Nevertheless, the association of these ...

Multiple modes are ubiquitous in current industrial processes, and the amount of historical data contained in different modes may vary considerably. Insufficient data can easily lead to cold start problems when building a fault ...

Factorization of classical, two-valued Boolean data became a widely studied topic in the past decade due to its role in analyzing relational data as well as its significance for other fields. Recently, various extensions to ...

The hash function is an efficient source of the integrity and authentication of input text and other data messages (image & audio-video) in the cryptography field. Existing hashing algorithms are time-consuming and vulnerable to ...

The continuous advancement of visual object tracking (VOT) algorithms has made significant contributions to computer vision and video processing. Correlation filter (CF)-based trackers have achieved promising tracking performance in ...