Information Sciences最新文献

Zilean: A modularized framework for large-scale temporal concept drift type classification

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-28 DOI: 10.1016/j.ins.2025.122134

Zhao Deng , QuanXi Feng , Bin Lin , Gary G. Yen

{"title":"Zilean: A modularized framework for large-scale temporal concept drift type classification","authors":"Zhao Deng , QuanXi Feng , Bin Lin , Gary G. Yen","doi":"10.1016/j.ins.2025.122134","DOIUrl":"10.1016/j.ins.2025.122134","url":null,"abstract":"<div><div>In the analysis of time series data, particularly in real-world applications, concept drift classification is crucial for enabling models to adapt in a differentiated manner to future data. To address the challenge of identifying diverse types of drift, we propose Zilean, a novel framework that integrates feature-based and predictor-based techniques while accounting for drift residues and fragmentation during repeated drift detection. The framework incorporates the pre-trained BERT-Base language model into its classifier design, leveraging deep learning for automatic drift classification and eliminating the need for judgment curve analysis. To evaluate its performance, experiments were conducted on a variety of real-world and synthetic datasets, each exhibiting different types of concept drift. The results show that on real-world datasets, our framework achieves a classification accuracy of 91.03%, outperforming XGBoost by 7.94% and surpassing TCN-CNN by 4.28%. Additionally, experiments exploring a frozen parameter strategy and the use of a more lightweight language model, DistilBERT, further enhance accuracy to 96.93% and 97.17%, respectively. These findings underscore the framework's effectiveness in large-scale temporal concept drift classification.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"712 ","pages":"Article 122134"},"PeriodicalIF":8.1,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel missing value imputation for multi-criteria recommender systems

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-28 DOI: 10.1016/j.ins.2025.122139

Gökhan Göksel, Ahmet Aydın, Zeynep Batmaz, Cihan Kaleli

{"title":"A novel missing value imputation for multi-criteria recommender systems","authors":"Gökhan Göksel, Ahmet Aydın, Zeynep Batmaz, Cihan Kaleli","doi":"10.1016/j.ins.2025.122139","DOIUrl":"10.1016/j.ins.2025.122139","url":null,"abstract":"<div><div>As the internet continues to expand, users increasingly rely on digital platforms for activities such as movie streaming and travel planning, leading to an overwhelming amount of information. This information overload complicates the process of making efficient and informed decisions. To address this challenge, personalized recommender systems, particularly multi-criteria collaborative filtering (MCCF) models, have been developed to tailor recommendations based on detailed user evaluations across various sub-criteria. However, as the number of criteria in MCCF systems increases, the issue of data sparsity becomes more prominent, with more criteria resulting in more missing evaluations. In this study, we introduce an innovative application of the Bidirectional Encoder Representations from Transformers (BERT) model—well-known for its advances in natural language processing—to impute missing values within MCCF systems. By leveraging contextual insights from user reviews, we hypothesize that BERT can enhance the imputation process, thereby improving coverage and recommendation accuracy in MCCF models. Our experimental results indicate that BERT-based imputation significantly reduces data sparsity and enhances the accuracy and coverage of recommendations. This study underscores BERT's potential in processing linguistic data and highlights its utility in multi-criteria recommender systems. Integrating BERT with MCCF offers a promising advancement in addressing the inherent challenges of personalized recommendation systems.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"712 ","pages":"Article 122139"},"PeriodicalIF":8.1,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing time-dependent multi-agile Earth observation satellite scheduling problem using deep Q-learning and ensemble heuristics

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-28 DOI: 10.1016/j.ins.2025.122140

Bohua Li , Ming Chen , Lining Xing , Yingguo Chen , Yingwu Chen

{"title":"Optimizing time-dependent multi-agile Earth observation satellite scheduling problem using deep Q-learning and ensemble heuristics","authors":"Bohua Li , Ming Chen , Lining Xing , Yingguo Chen , Yingwu Chen","doi":"10.1016/j.ins.2025.122140","DOIUrl":"10.1016/j.ins.2025.122140","url":null,"abstract":"<div><div>The Multi-Agile Earth Observation Satellite Scheduling Problem (MAEOSSP) aims to maximize the total observation profits of a multi-satellite system while satisfying temporal and resource constraints. This problem has gained prominence owing to the increasing complexity of task scheduling scenarios and challenges in satellite management and control. MAEOSSP typically consists of two decision-making stages: task allocation and single-satellite scheduling. However, its NP-hard characteristic renders traditional methods susceptible to problem instance variations and computationally intensive. To address these issues, we propose the TAM-ECH, which integrates a Task Allocation Model (TAM) and an Ensemble Construction Heuristic (ECH) to solve MAEOSSP. TAM utilizes a Deep Q-learning Network (DQN) to efficiently allocate tasks to each satellite, while ECH employs ensemble heuristics for fast and stable single-satellite scheduling. Experimental results demonstrate that TAM-ECH outperforms state-of-the-art algorithms in both optimization speed and quality. On average, it achieves a profit rate 6% higher than GRILS and 30% higher than A-ALNS, with reduced solving time. Further experiments validate TAM's efficiency in task allocation and ECH's ability to provide stable, high-quality foundational assistance. Therefore, TAM-ECH provides an efficient solution to MAEOSSP, demonstrating its potential for application in upcoming large constellations and new management modes.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"712 ","pages":"Article 122140"},"PeriodicalIF":8.1,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

General decay projection synchronization of proportional delay memristive competitive neural networks with uncertain parameters for image encryption

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-27 DOI: 10.1016/j.ins.2025.122136

Yaru Wang , Liqun Zhou

引用次数: 0

Distributed neurodynamic optimization for optimal multicluster resource allocation with cardinality constraints

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-27 DOI: 10.1016/j.ins.2025.122138

Meng Lin , Zicong Xia , Shuting Sun , Yang Liu

引用次数: 0

Polytomous knowledge structures constructed by L-fuzzy approximation operators

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-27 DOI: 10.1016/j.ins.2025.122137

Bochi Xu , Jinjin Li , Fugui Shi

{"title":"Polytomous knowledge structures constructed by L-fuzzy approximation operators","authors":"Bochi Xu , Jinjin Li , Fugui Shi","doi":"10.1016/j.ins.2025.122137","DOIUrl":"10.1016/j.ins.2025.122137","url":null,"abstract":"<div><div>Rough set theory is more concerned with the character of the upper and lower approximations of a particular set than with the overall structure. Knowledge space theory can provide another new perspective on rough sets. In this paper, we establish a theoretical linkage between polytomous knowledge structures and <em>L</em>-fuzzy approximation operators. We generate polytomous knowledge structures constructed by <em>L</em>-fuzzy approximation operators and give the corresponding properties, and find that a polytomous knowledge space (polytomous closure space, respectively) and can be completely characterized by an upper (lower, respectively) <em>L</em>-fuzzy approximation. In particular, we discuss the dichotomous knowledge structure by the means of fuzzy approximation operators, which corresponds to the method of fuzzy skill maps. Finally, by <em>L</em>-fuzzy relation constructing two particular dichotomous knowledge structures, which are called backward-graded and forward-graded, is also discussed. This study proposes a framework to analyze <em>L</em>-fuzzy rough sets through knowledge space theory, bridging these mathematical disciplines.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"712 ","pages":"Article 122137"},"PeriodicalIF":8.1,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Knowledge-aware differential equation discovery with automated background knowledge extraction

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-26 DOI: 10.1016/j.ins.2025.122131

Elizaveta Ivanchik, Alexander Hvatov

引用次数: 0

A stable framework-based modeling of the complex dynamical system using a double context layered with self-weighted output feedback loop Elman recurrent neural network

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-26 DOI: 10.1016/j.ins.2025.122132

Rajesh Kumar

{"title":"A stable framework-based modeling of the complex dynamical system using a double context layered with self-weighted output feedback loop Elman recurrent neural network","authors":"Rajesh Kumar","doi":"10.1016/j.ins.2025.122132","DOIUrl":"10.1016/j.ins.2025.122132","url":null,"abstract":"<div><div>In this paper, a modified structure of the classical Elman recurrent neural network (ERNN) named Double context layered with output self-weighted feedback loop Elman recurrent neural network (DCLOSWFLERNN) is proposed. It consists of two additional components (as compared to the ERNN model): one extra context layer and an adjustable weighted feedback loop in the output layer. This has resulted in the model's ability to approximate the underlying unknown mathematical relationship relating to the input-output data (obtained from any complex dynamical plant). The second emphasis of this paper pertains to the stability component, wherein the Lyapunov stability is utilized to develop a stable Back-propagation (BP) based weight update rule. Lastly, an adjustable learning rate is also suggested, which contributes to improving the learning algorithm's overall performance. The simulation results reveal that the proposed model has given better modeling accuracy as compared to the other considered neural models. This can be observed from the values obtained of the error-based indicators such as Root Mean Square Error (RMSE) and Mean Average Error (MSE). The values of RMSE and MAE obtained from the proposed model during the modeling procedure are 0.0028 and 0.0035 which are the least among the obtained with other neural models.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"712 ","pages":"Article 122132"},"PeriodicalIF":8.1,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive fuzzy funnel control of nonlinear multi-agent systems via dual-channel event-triggered strategy

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-26 DOI: 10.1016/j.ins.2025.122129

Xiaoyang Liu , Minghao Hui , Lu Fan , Wenwu Yu , Jinde Cao

引用次数: 0

Semantic enhanced bi-syntactic graph convolutional network for aspect-based sentiment analysis

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-25 DOI: 10.1016/j.ins.2025.122130

Junyang Xiao , Yun Xue , Fenghuan Li

{"title":"Semantic enhanced bi-syntactic graph convolutional network for aspect-based sentiment analysis","authors":"Junyang Xiao , Yun Xue , Fenghuan Li","doi":"10.1016/j.ins.2025.122130","DOIUrl":"10.1016/j.ins.2025.122130","url":null,"abstract":"<div><div>Previous work on fine-grained sentiment analysis focuses on establishing the semantic correlations between words by means of attention mechanisms. More recently, effects of syntax-based models, applying graph convolution operation over dependency trees, are highlighted due to their superiority. However, these methods still have deficiencies. For one thing, little aspect-specific information is considered during semantic modeling of contextual words, which introduces irrelevant noise toward the aspect. For another, current syntax-based approaches either ignore the syntactic constituent knowledge, or fail to maintain the syntactic information from the reconstructed constituent tree. As such, no relation among words, phrases and clauses is built. In this work, a Semantic Enhanced Bi-Syntax Graph Convolutional Network (SEBS-GCN) is proposed to enhance semantics of context to the aspect, and capture the sentiment relevance among words, phrases and clauses. Specifically, we devise an aspect-aware gated mechanism to obtain the aspect-aware feature, based on the semantic correlations between the specific aspect and its contexts. Furthermore, the syntax information of the constituent tree is sufficiently exploited to analyze the hierarchical structure and the logical relation among words, phrases and clauses, based on which to capture the sentiment clues of the aspect.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"712 ","pages":"Article 122130"},"PeriodicalIF":8.1,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0