Article
Computer Science, Artificial Intelligence
Xiaojian Xu, Xiaobin Xu, Pengfei Shi, Zifa Ye, Yu Bai, Shuo Zhang, Schahram Dustdar, Guodong Wang
Summary: This paper proposes a classification model based on attribute vectorization and evidential reasoning (AV-ER), which can effectively handle the uncertainty in the mapping relationship between input attributes and output classes and improve classification performance without increasing the number of model parameters.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Yating Liu, Yao Li, Zhen Zhang, Yi Xu, Yucheng Dong
Summary: This paper investigates the classification-based multiple attribute decision making (MADM) problem and the strategic weight manipulation. By constructing mixed linear programming models, the classification range of alternatives and the strategic attribute weights are analyzed. Moreover, some existence conditions for the strategic attribute weights are provided. Finally, numerical example and simulation experiments are conducted to verify the effectiveness and defending performance of the proposed models.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Parfait Atchade Adelomou, Daniel Casado Fauli, Elisabet Golobardes Ribe, Xavier Vilasis-Cardona
Summary: “Case-Based Reasoning (CBR) is an artificial intelligence approach that has achieved success. This article proposes using Quantum Computing to improve CBR and presents a comparative study between quantum and classical CBR.”
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Automation & Control Systems
Elena Hernandez-Nieves, Guillermo Hernandez, Ana B. Gil-Gonzalez, Sara Rodriguez-Gonzalez, Juan M. Corchado
Summary: By adhering to data ethics and respecting clients' privacy, the banking sector can use available data to provide personalized services. Intelligent recommender systems and specialized technological architectures support this initiative. Through a case study, it has been demonstrated that recommendations based on user profiles, previous ratings, and additional knowledge can enhance user satisfaction.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Gabriele Civitarese, Timo Sztyler, Daniele Riboni, Claudio Bettini, Heiner Stuckenschmidt
Summary: In this paper, a framework called POLARIS is proposed for unsupervised activity recognition, utilizing semantics, context data, and sensors for complex ADL recognition. The system leverages ontological reasoning to establish correlations between activities and sensor events, improving recognition accuracy through statistical reasoning and probabilistic reasoning. Experimental results show that the unsupervised method achieves comparable accuracy to supervised approaches, with the online version performing essentially the same as the offline version.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Computer Science, Information Systems
Xin Wen, Deyu Li, Chao Zhang, Yanhui Zhai
Summary: This paper proposes a hybrid framework combining rough sets with ML-KNN for multi-label learning, aiming to improve classification performance by depicting misclassified samples and evaluating attribute discernibility. Experimental results demonstrate the significant improvement in effectiveness compared to other state-of-the-art multi-label classification methods.
INFORMATION PROCESSING & MANAGEMENT
(2022)
Article
Computer Science, Hardware & Architecture
Shanshan Tu, Muhammad Waqas, Fengming Huang, Ghulam Abbas, Ziaul Haq Abbas
Summary: Fog computing is a revolutionary technology aiming to bridge the gap between cloud data centers and end-users, but its features bring security challenges. The attribute-based encryption technology in traditional cloud computing is not suitable for end users due to restricted computing resources and high end-to-end delay. Therefore, this paper recommends a multi-authority attribute-based encryption (MA-ABE) technique to support revocation and outsourcing attributes to fog computation.
Article
Computer Science, Information Systems
Weiping Ding, Tingzhen Qin, Xinjie Shen, Hengrong Ju, Haipeng Wang, Jiashuang Huang, Ming Li
Summary: Research on efficient attribute reduction for massive dynamic datasets is important. Traditional incremental methods are inefficient when applied to large datasets. This study proposes an incremental acceleration strategy based on attribute trees, clustering attributes into multiple trees to improve efficiency, and introducing a branch coefficient in the stop criterion to avoid redundant calculations.
INFORMATION SCIENCES
(2022)
Article
Environmental Sciences
Ju Wu, Yi Liu, Fang Liu, Hao Gong
Summary: This study evaluates land reclamation schemes in mining areas using a multi-attribute group decision-making method. The proposed method provides a simple and effective evaluation by determining expert weights and attribute weights. The practicability of this method is verified through a comparative analysis of land reclamation schemes for four mining areas in Sichuan Province, China.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Chemistry, Analytical
Sunghun Kang, Gwangsu Kim, Chang D. Yoo
Summary: Recent studies have shown concerns about racial and gender disparities in facial attribute classification. Simple disparate treatment is ineffective in reducing performance disparities due to the complex correlation between attributes and sensitive attributes. This paper proposes a method for achieving counterfactual fairness in facial attribute classification by generating synthetic images under factual and counterfactual assumptions. The method utilizes a causal graph-based attribute translation approach to generate realistic counterfactual images that consider the complex causal relationship among attributes.
Article
Mathematics
Tingting Zhao, Jie Lin, Zhenyu Zhang
Summary: This paper explores a new method for predicting posting popularity in online communities using a case-based reasoning approach combined with attribute feature mining, with the concept of intrinsically interpretable attribute features proposed. The study shows that this method is suitable for the complex social network environment and can effectively support decision makers in finding excellent solutions for popularity prediction in the network community.
Article
Geography, Physical
Rui Li, Jingqi Wang, Shunli Wang, Huayi Wu
Summary: This paper proposes a similarity calculation method of urban planning cases based on the geographical case-based reasoning (CBR) framework. By integrating case attributes and considering similarity weights, the proposed method can predict network public opinion features and provide decision support for urban planning. The experimental results demonstrate the effectiveness of the method with an average MIC-F1 score of over 74%.
INTERNATIONAL JOURNAL OF DIGITAL EARTH
(2022)
Review
Computer Science, Information Systems
Alejandro Penuelas-Angulo, Claudia Feregrino-Uribe, Miguel Morales-Sandoval
Summary: This article presents a systematic literature review of attribute-based encryption (ABE) schemes that provide revocation mechanisms in the fog-enabled internet of things (IoT) application domain. The study surveys and discusses existing revocation approaches, explores how the fog is exploited in the reviewed schemes, presents a qualitative comparison, and provides a quantitative comparison of the associated costs. It also discusses opportunities for improving revocable ABE schemes for fog-enabled IoT and the challenges faced by these systems.
INTERNET OF THINGS
(2023)
Article
Chemistry, Multidisciplinary
Shui Ming Li, Carman Ka Man Lee
Summary: Enterprises are facing challenges in their new-product development processes due to the changing business environment, which calls for the revamping of existing processes. New-product portfolio management is an active research area, and this study explores the standardization of its configuration mechanism in a systematic way. By applying case-based reasoning and transfer-learning-based text classification model, the values of enterprises and customers can be balanced, facilitating new-product portfolio management.
APPLIED SCIENCES-BASEL
(2022)
Article
Chemistry, Multidisciplinary
Guiliang Ou, Yulin He, Philippe Fournier-Viger, Joshua Zhexue Huang
Summary: This paper proposes an improved approach for constructing NBC called MAF-NBC, which overcomes the limitations of the NBC through a mixed-attribute fusion mechanism and an improved autoencoder neural network. Experimental results demonstrate that MAF-NBC outperforms eight state-of-the-art Bayesian algorithms in classification performance.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)