Article
Computer Science, Artificial Intelligence
Ivan Palomares, Carlos Porcel, Luiz Pizzato, Ido Guy, Enrique Herrera-Viedma
Summary: This paper discusses decision-making under information overload on the Internet and the role of recommender systems as personalized decision support tools. Specifically, it introduces the concept of Reciprocal Recommender System (RRS) where users act as both the recommenders and the recommendees.
INFORMATION FUSION
(2021)
Article
Computer Science, Artificial Intelligence
Kanika Narang, Yitong Song, Alexander Schwing, Hari Sundaram
Summary: Recommender systems can benefit from a variety of signals influencing user behavior, but existing methods often fail to fully utilize all available information. The 'Fusion Recommender' model is proposed, which models different factors separately and combines them in an interpretable way. This model shows promising results across multiple datasets, outperforming other techniques by over 14% while also providing insights on the importance of each factor.
DATA MINING AND KNOWLEDGE DISCOVERY
(2021)
Article
Mathematics, Applied
Yan-Li Lee, Tao Zhou, Kexin Yang, Yajun Du, Liming Pan
Summary: This paper proposes a recommendation algorithm that combines social relationships and historical behaviors, and tests its performance on real networks. The results show that the algorithm outperforms the benchmarks in terms of accuracy and diversity metrics, and has a significant improvement in the recommendation performance for cold-start users.
APPLIED MATHEMATICS AND COMPUTATION
(2023)
Article
Computer Science, Theory & Methods
Elizabeth Gomez, Carlos Shui Zhang, Ludovico Boratto, Maria Salamo, Guilherme Ramos
Summary: With the focus on educational recommender systems, this paper addresses the issue of teacher exposure and fairness. It evaluates groups of teachers based on their geographic provenience and proposes a re-ranking approach to achieve fairness. Experimental results demonstrate that the approach can provide fairness without affecting recommendation effectiveness.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Computer Science, Information Systems
Luwei Zhang, Xueting Wang, Toshihiko Yamasaki
Summary: In this study, we propose a novel method for pair matching prediction in reciprocal recommender systems. Our method learns the reciprocal information circulation between users, incorporating both side information and structural information about their behavior histories. Unlike previous RRSs, our method predicts both send and reply signals. We also introduce negative sample mining to explore the impact of different types of multiple samples on recommendation accuracy. Testing on data from an online dating service, we achieved significant improvements in AUC and AP for send prediction, reply prediction, and fusion reciprocal prediction.
Article
Computer Science, Artificial Intelligence
Yu Lei, Zhitao Wang, Wenjie Li, Hongbin Pei, Quanyu Dai
Summary: This paper proposes a method to address the issues of data sparsity and cold-start in recommender systems by leveraging social networks. Two algorithms based on this method are developed and the experimental results show their outstanding performance on real-world datasets with reasonable computation cost.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Jesus Giraldez-Cru, Manuel Chica, Oscar Cordon
Summary: Opinion dynamics study the spread and evolution of opinions among individuals, particularly important in decision-making processes and applicable to marketing and politics.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Green & Sustainable Science & Technology
Muhammad Anshari, Mohammad Nabil Almunawar, Mustafa Z. Younis, Adnan Kisa
Summary: This study explores a comprehensive approach towards patient empowerment in e-health systems, aiming to achieve best practice customer service, establish long-term customer relationships to improve customer satisfaction, and enhance individuals' health literacy.
Review
Chemistry, Multidisciplinary
Hongde Zhou, Fei Xiong, Hongshu Chen
Summary: This paper provides an overview of the research progress in recommendation systems based on deep learning and categorizes and summarizes existing methods. By introducing the definitions, challenges, and research advancements of recommendation models, the paper aims to guide novice researchers and discuss future developments in this field.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Information Systems
Can Xu, Yin Zhang, Hongyang Chen, Ligang Dong, Weigang Wang
Summary: Personalized recommendations for social tagging systems aim to provide high-quality recommendations and meaningful characteristics on items. Current tag-aware recommender systems (TRS) have no advantage compared to some state-of-the-art recommender systems in terms of performance and efficiency. We propose a fairness-aware graph contrastive learning framework named TAGCL, which utilizes two bipartite graphs and contrastive learning to encode stable and high-quality representations. Extensive experiments show that TAGCL outperforms state-of-the-art methods and TRS in accuracy of recommendations and reduces bias in heavily skewed datasets.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Hardware & Architecture
Nguyen Van Han, Phan Cong Vinh
Summary: Dynamic system is crucial for cognitive science, with cognitive map and fuzzy cognitive map being special cases. Both types of maps have complex state spaces, and linguistic dynamic system is found to be convergent.
MOBILE NETWORKS & APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Jesus Serrano-Guerrero, Mohammad Bani-Doumi, Francisco P. Romero, Jose A. Olivas
Summary: This study uses a multi-granular fuzzy linguistic model to evaluate the different features of health care systems and recommend hospitals based on user preferences. By assessing the opinions of real hospitals, the results of this approach outperform other methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Amirreza Salamat, Xiao Luo, Ali Jafari
Summary: HeteroGraphRec is a social recommender system that models the social network as a heterogeneous graph and intelligently aggregates information using GNNs with attention mechanisms. Research shows that HeteroGraphRec outperforms top social recommender systems, demonstrating strong robustness and performance superiority.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Janusz Kacprzyk, Alexander Bozhenyuk, Evgeniya Gerasimenko
Summary: Decision-making under evacuation environment is complex due to the necessity for high-speed reaction and consideration of various aspects. The proposed method uses fuzzy hesitant TOPSIS to determine the priority order of evacuation terminals based on assigned weights of separations.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Information Systems
Yanan Xu, Yanmin Zhu, Jiadi Yu
Summary: Purchase intentions have a significant impact on future purchases, but they are typically complex and subject to change. Empirical study shows that user behaviors of multiple types can indicate intentions, and users may have multiple coexisting category-level intentions that evolve over time. The proposed Intention-Aware Recommender System (TARS) effectively mines complex intentions from diverse user behaviors and outperforms state-of-the-art recommendation methods.
ACM TRANSACTIONS ON INFORMATION SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Amartya Mukherjee, Debashis De, Nilanjan Dey, Ruben Gonzalez Crespo, Enrique Herrera-Viedma
Summary: Internet of Things (IoT) application in disaster responses and management is a significant research domain. The introduction of consumer drones, flying ad-hoc networks, low latency 5G, and beyond 5G has greatly accelerated this research. This study proposes the implementation of the Consumer Internet of Drone Things (CIoDT) framework for emergency message transfer and stable network connectivity in disaster scenarios. The results show high message delivery probability, low latency, and suitability for mass production of light weight drone networks for disaster management.
IEEE TRANSACTIONS ON CONSUMER ELECTRONICS
(2023)
Article
Computer Science, Artificial Intelligence
Hossein Hassani, Roozbeh Razavi-Far, Mehrdad Saif, Enrique Herrera-Viedma
Summary: To manage consensus in opinion dynamics models (ODMs), it is crucial to remove bias from agents' interactions and consider their willingness. We propose a linguistic ODM based on Blockchain technology to build trust and consensus opinion. A Blockchain-enabled trust-building mechanism is used to improve agents' trust and guide them toward consensus opinion.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Feixia Ji, Jian Wu, Francisco Chiclana, Sha Wang, Hamido Fujita, Enrique Herrera-Viedma
Summary: This study proposes an overlapping community-driven feedback mechanism to improve consensus in social network group decision making. By guiding inconsistent subgroups to interact with each other and selecting personalized feedback parameters, this mechanism helps achieve higher levels of consensus.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Automation & Control Systems
Cristina Zuheros, Eugenio Martinez-Camara, Enrique Herrera-Viedma, Francisco Herrera
Summary: The wisdom of the crowd theory states that a nonexpert crowd makes smarter decisions than a reduced set of experts. Evaluations from social networks can enhance the quality of decision-making models. We propose a crowd decision-making model guided by sentiment analysis, which incorporates all the evaluation shades and tackles the lack of information using sparse representation. The results show that integrating the wisdom of the crowd and the different shades of the evaluations enhances the quality of the decision.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Green & Sustainable Science & Technology
Yinfeng Du, Zhen-Song Chen, Jie Yang, Juan Antonio Morente-Molinera, Lu Zhang, Enrique Herrera-Viedma
Summary: This study explores how to develop a systematic framework for doctor selection in online health communities (OHCs) based on doctor basic information and online patient reviews. The study defines quantification method for doctor basic information and uses data analysis techniques to extract core attributes and evaluations from online patient reviews. Selection rules are made according to doctor influence and patient satisfaction to choose optimal and suboptimal doctors for rational or emotional patients.
Article
Mathematics
Yi Zhou, Chonglan Guo, Guo Wei, Enrique Herrera-Viedma
Summary: In this paper, sorted negotiation is introduced into consensus decision making to improve the speed and effectiveness of consensus. The authors construct negotiation models considering efficiency and time, and use an improved genetic algorithm to solve the optimal solution in the context of China's urban demolition negotiation. Assessment criteria for the reasonableness of the sorting sequence are determined by introducing an optimum set of influential individuals.
Article
Computer Science, Artificial Intelligence
Qianlei Jia, Enrique Herrera-Viedma
Summary: In this article, a new layout coordinate system is defined to map Z-numbers to q-ROFSs. The genetic algorithm is adopted to derive the potential probability distribution. An approach for calculating the weighted information entropy of Z-numbers is proposed and proven to be rational. A linguistic Z-PFS weighted aggregation operator is presented, and a score function is defined in the coordinate system. Finally, a decision-making model is constructed based on the new solution.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jiang Deng, Jianming Zhan, Enrique Herrera-Viedma, Francisco Herrera
Summary: Behavioral decision theory modifies classic decision-making theories to make them more applicable in realistic scenarios. Regret theory, an important component of behavioral decision theories, has been widely used in theories and applications. In this study, a generalized three-way decision method is proposed based on regret theory for incomplete multiscale decision information systems. Experimental results show that the decision-making results of the proposed method maintain over 97% consistency in incomplete information systems.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Qi-Chang An, Hanfu Zhang, Kun Wang, Xinyue Liu, Jianli Wang, Chen Tao, Hong-Wen Li
Summary: In this study, a fiber-linked-wavefront-sensing system was developed to address the issues of restricted optical path spaces and decoherence encountered during the cofocusing and cophasing processes of large-space telescopes. The system achieved high accuracy in both cofocus and cophasing measurements, expanding the application range of astronomical photonics in large-space telescopes.
IEEE PHOTONICS JOURNAL
(2023)
Article
Automation & Control Systems
Jiang Deng, Jianming Zhan, Zeshui Xu, Enrique Herrera-Viedma
Summary: This article proposes a wide three-way decision model on multiscale information systems (MSIS), combining 3WD theory and regret theory, to address the two problems in existing MADM methods. The model effectively tackles misclassification and incorporates decision makers' risk attitudes and psychological behaviors. Experimental analysis confirms the effectiveness, superiority, and stability of the proposed model.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Xiaoxia Xu, Zaiwu Gong, Enrique Herrera-Viedma, Gang Kou, Francisco Javier Cabrerizo
Summary: This article extends the research on uncertain minimum cost consensus models (MCCMs) by incorporating linear uncertainty distributions (LUDs) and considering asymmetric costs. Two novel optimization-based consensus models are proposed, one for obtaining a minimum cost consensus and the other for addressing group decision making problems without presetting a specific consensus level (CL) threshold.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Mahdi Khosravy, Kazuaki Nakamura, Naoko Nitta, Nilanjan Dey, Ruben Gonzalez Crespo, Enrique Herrera-Viedma, Noboru Babaguchi
Summary: Inversion attack (MIA) poses a threat to deep-learning-based recognition systems (DLRSs). This research proposes a social IoT approach for collaborative defense against MIA-generated data clones. The proposed technique utilizes a collaborative recognition system to verify the output of the targeted recognition system, achieving a high detection rate for MIA-generated clones.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Hossein Hassani, Roozbeh Razavi-Far, Mehrdad Saif, Enrique Herrera-Viedma
Summary: This study proposes novel reinforcement learning-based adjustment mechanisms to address the tradeoff between the number of discussion rounds and the harmony degree of decision makers in group decision-making. By converting the decision environment into a Markov decision process, two independent reinforcement learning agents are trained to adjust feedback parameters and weights of decision makers, aiming to reduce discussion rounds and improve harmony degree.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Zaiwu Gong, Xiujuan Ma, Weiwei Guo, Guo Wei, Enrique Herrera-Viedma
Summary: The study proposes a consensus approach with trust relationships and adjustment cost to address the lack of attention to individual decision costs and similarity in expert decision behaviors in the social network decision process. The method consists of three stages: trust propagation, weight allocation, and consensus reaching. Uncertain theory is employed in trust propagation while comprehensive weight allocation is based on network structure and relationship strength. Consensus is considered at both individual and collective group levels using chance-constrained programming models. Comparative analysis is conducted to evaluate the effectiveness and advancement of the proposed method.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Shubo Wang
Summary: This article proposes a novel nonlinear uncertainty estimator-based time-varying sliding mode control (SMC) scheme for servo systems with prescribed performance. The scheme uses a nonlinear uncertainty estimator to handle unknown nonlinearities and a robust integral of the sign of the error (RISE) feedback to handle estimation errors and uncertainties. A modified prescribed performance function (PPF) is incorporated into the control design to restrict tracking errors within predefined boundaries, and a time-varying sliding mode (TVSM) controller is developed to improve control performance. The validity and feasibility of the proposed scheme are verified through simulations and experiments based on a motor driving system.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2023)
Article
Automation & Control Systems
Carmen Bisogni, Lucia Cimmino, Michele Nappi, Toni Pannese, Chiara Pero
Summary: This paper presents a gait-based emotion recognition method that does not rely on facial cues, achieving competitive performance on small and unbalanced datasets. The proposed approach utilizes advanced deep learning architecture and achieves high recognition and accuracy rates.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Soung Sub Lee
Summary: This study proposed a satellite constellation method that utilizes machine learning and customized repeating ground track orbits to optimize satellite revisit performance for each target.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jian Wang, Xiuying Zhan, Yuping Yan, Guosheng Zhao
Summary: This paper proposes a method of user recruitment and adaptation degree improvement via community collaboration to solve the task allocation problem in sparse mobile crowdsensing. By matching social relationships and perception task characteristics, the entire perceptual map can be accurately inferred.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yuhang Gai, Bing Wang, Jiwen Zhang, Dan Wu, Ken Chen
Summary: This paper investigates how to reconfigure existing compliance controllers for new assembly objects with different geometric features. By using the proposed Equivalent Theory of Compliance Law (ETCL) and Weighted Dimensional Policy Distillation (WDPD) method, the learning cost can be reduced and better control performance can be achieved.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zhihao Xu, Zhiqiang Lv, Benjia Chu, Zhaoyu Sheng, Jianbo Li
Summary: Predicting future urban health status is crucial for identifying urban diseases and planning cities. By applying an improved meta-analysis approach and considering the complexity of cities as systems, this study selects eight urban factors and explores suitable prediction methods for these factors.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yulong Ye, Qiuzhen Lin, Ka-Chun Wong, Jianqiang Li, Zhong Ming, Carlos A. Coello Coello
Summary: This paper proposes a localized decomposition evolutionary algorithm (LDEA) to tackle imbalanced multi-objective optimization problems (MOPs). LDEA assigns a local region for each subproblem using a localized decomposition method and restricts the solution update within the region to maintain diversity. It also speeds up convergence by evolving only the best-associated solution in each subproblem while balancing the population's diversity.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Longxin Zhang, Jingsheng Chen, Jianguo Chen, Zhicheng Wen, Xusheng Zhou
Summary: This study proposes a lightweight PCB image defect detection network (LDD-Net) that achieves high accuracy by designing a novel lightweight feature extraction network, multi-scale aggregation network, and lightweight decoupling head. Experimental results show that LDD-Net outperforms state-of-the-art models in terms of accuracy, computation, and detection speed, making it suitable for edge systems or resource-constrained embedded devices.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Kemal Ucak, Gulay Oke Gunel
Summary: This paper introduces a novel adaptive stable backstepping controller based on support vector regression for nonlinear dynamical systems. The controller utilizes SVR to identify the dynamics of the nonlinear system and integrates stable BSC behavior. The experimental results demonstrate successful control performance for both nonlinear systems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Dexuan Zou, Mengdi Li, Haibin Ouyang
Summary: In this study, a photovoltaic thermal collector is integrated into a combined cooling, heating, and power system to reduce primary energy consumption, operation cost, and carbon dioxide emission. By applying a novel genetic algorithm and constraint handling approach, it is found that the CCHP scenarios with PV/T are more efficient and achieve the lowest energy consumption.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Abhinav Pandey, Litton Bhandari, Vidit Gaur
Summary: This research proposes a novel model-agnostic framework based on genetic algorithms to identify and optimize the set of coefficients of the constitutive equations of engineering materials. The framework demonstrates solution convergence, scalability, and high explainability for a wide range of engineering materials. The experimental validation shows that the proposed framework outperforms commercially available software in terms of optimization efficiency.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zahra Ramezanpoor, Adel Ghazikhani, Ghasem Sadeghi Bajestani
Summary: Time series analysis is a method used to analyze phenomena with temporal measurements. Visibility graphs are a technique for representing and analyzing time series, particularly when dealing with rotations in the polar plane. This research proposes a visibility graph algorithm that efficiently handles biological time series with rotation in the polar plane. Experimental results demonstrate the effectiveness of the proposed algorithm in both synthetic and real world time series.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
ChunLi Li, Qintai Hu, Shuping Zhao, Jigang Wu, Jianbin Xiong
Summary: Efficient and accurate diagnosis of rotating machinery in the petrochemical industry is crucial. However, the nonlinear and non-stationary vibration signals generated in harsh environments pose challenges in distinguishing fault signals from normal ones. This paper proposes a BP-Incremental Broad Learning System (BP-INBLS) model to address these challenges. The effectiveness of the proposed method in fault diagnosis is demonstrated through validation and comparative analysis with a published method.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Fatemeh Chahkoutahi, Mehdi Khashei
Summary: The classification rate is the most important factor in selecting an appropriate classification approach. In this paper, the influence of different cost/loss functions on the classification rate of different classifiers is compared, and empirical results show that cost/loss functions significantly affect the classification rate.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jicong Duan, Xibei Yang, Shang Gao, Hualong Yu
Summary: The study proposes a novel partition-based imbalanced multi-label learning algorithm, MLHC, which divides the original label space into disconnected subspaces using hierarchical clustering. It successfully tackles the class imbalance problem in multi-label data and outperforms other class imbalance multi-label learning algorithms.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Review
Automation & Control Systems
Qing Qin, Yuanyuan Chen
Summary: This paper offers a comprehensive review of retinal vessel automatic segmentation research, including both traditional methods and deep learning methods. In particular, supervised learning methods are summarized and analyzed based on CNN, GAN, and UNet. The advantages and disadvantages of existing segmentation methods are also outlined.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
