Article
Computer Science, Artificial Intelligence
David Valle-Cruz, Vanessa Fernandez-Cortez, Asdrubal Lopez-Chau, Rodrigo Sandoval-Almazan
Summary: Investors are constantly monitoring the behavior of stock markets which is influenced by social media reactions and emotions, especially during pandemics. Through financial sentiment analysis of Twitter data and financial indices, it was found that the markets reacted 0 to 10 days after information was shared on Twitter during the COVID-19 pandemic and 0 to 15 days after during the H1N1 pandemic. A lexicon-based approach and correlation analysis using SenticNet were effective in detecting highly shifted correlations. The most influential Twitter accounts during the pandemic were found to have a high correlation between sentiments on Twitter and stock market behavior.
COGNITIVE COMPUTATION
(2022)
Article
Computer Science, Information Systems
Huyen Trang Phan, Ngoc Thanh Nguyen, Van Cuong Tran, Dosam Hwang
Summary: The study proposed a method to utilize users' opinions on social networks to support decision-making by analyzing sentiments and mining fuzzy decision trees to overcome previous methods' disadvantages. Experimental results showed that the method achieved promising results in terms of accuracy and information retrieval.
INFORMATION SCIENCES
(2021)
Article
Automation & Control Systems
Enrique Herrera-Viedma, Ivan Palomares, Cong-Cong Li, Francisco Javier Cabrerizo, Yucheng Dong, Francisco Chiclana, Francisco Herrera
Summary: The article provides an overview of fuzzy and linguistic decision-making trends, studies, methodologies, and models developed in the last 50 years. It discusses core decision-making frameworks and new complex decision-making frameworks that have emerged in recent years. The challenges associated with these frameworks and key guidelines for future research in the field are highlighted.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Walayat Hussain, Muhammad Raheel Raza, Mian Ahmad Jan, Jose M. Merigo, Honghao Gao
Summary: This article proposes an SLA violation risk mitigation model that uses OWA and LSTM for complex QoS prediction. The approach considers all possible attitudinal behavior of the service provider and provides intelligent recommendations for mitigating actions.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Automation & Control Systems
Yuanhang Zheng, Zeshui Xu, Witold Pedrycz
Summary: This article proposes a new hesitant fuzzy linguistic method to handle hesitant and uncertain preference information provided by decision makers, improving the consistency of preference matrices by characterizing them with granular linguistic preference matrices. By designing a multiplicative consistency index, calculating thresholds, constructing models, and developing algorithms, the method integrates assessment information to derive final decision-making results, demonstrating its reasonability and validity through comparative studies and simulation experiments.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Review
Mathematics
Simona Dzitac, Sorin Nadaban
Summary: This paper pays tribute to Professor Ioan Dzitac and his significant contributions in the field of soft computing methods in a fuzzy environment. It also highlights his achievements and gratitude towards his mentor, Lotfi A. Zadeh, and discusses future trends in the field.
Article
Mathematics
Yaya Liu, Rosa M. Rodriguez, Luis Martinez
Summary: Large-scale group decision-making (LS-GDM) problems are common in daily life and face challenges in information fusion and computing with words (CWW) technologies. This research applies proportional hesitant fuzzy linguistic term set (PHFLTS) to capture sub-group preferences in LS-GDM, reducing information lost in fusion processes. Novel fuzzy semantic representation models of PHFLTS and fuzzy entropies facilitate the CWW process. A new LS-GDM method, considering sub-group size, cohesion, and reliability, is proposed. The proposed decision method and CWW tools are applied to an urban renewal plan selection process.
Article
Computer Science, Artificial Intelligence
Biswajit Sarkar, Animesh Biswas
Summary: This article introduces a new family of linguistic Pythagorean fuzzy aggregation operations and discusses their necessary properties. A methodology for addressing multi-criteria group decision-making problems is proposed using weighted distance measures and entropy measures. Aggregation is done at the final stage to obtain the final ranking of alternatives.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Dan Peng, Jie Wang, Donghai Liu, Yu Cheng
Summary: The paper introduces an interactive fuzzy linguistic term set for multi-attribute decision making problems to describe interactive information and discusses its advantages and application through numerical examples.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2022)
Article
Computer Science, Interdisciplinary Applications
Huimin Zhang, Yiyi Dai
Summary: This paper introduces new distance and entropy measures for hesitant fuzzy linguistic term sets (HFLTSs) and hesitant fuzzy linguistic preference relations (HFLPRs) and proposes an information aggregation method and two consensus improvement models for group decision making (GDM). The first model is a four-stage optimization model, based on which the revised individual and collective opinions can be obtained. The results demonstrate that the proposed models can better deal with the issues in existing consensus models.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Abid Hussain, Jin Chun, Maria Khan
Summary: This study introduces a new multicriteria decision making method, FTBWA, to address consistency, complexity, and reliability issues in existing methods. The researchers propose a consistency ratio to check the reliability of FTBWA results and conduct two case studies to verify practicality. Additionally, a comprehensive analysis including comparative analysis, rank reversal analysis, and support for group decision making shows that FTBWA outperforms existing fuzzy/crisp MCDM methods.
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, Interdisciplinary Applications
Wenting Xue, Zeshui Xu, Xiaomei Mi
Summary: In this paper, a matrix game method is proposed from the perspective of decision makers and Nature, considering uncertainty and fuzziness to investigate decision makers' risk tendencies in gains and losses. By establishing hesitant fuzzy nonlinear programming models, optimal solutions are obtained and applied in economic strategy development. The comparative analysis verifies the availability and reasonability of the proposed method.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Mingwei Lin, Zheyu Chen, Riqing Chen, Hamido Fujita
Summary: The study introduces a novel hesitant fuzzy linguistic decision-making method to evaluate startup companies. By using the proposed method, all alternatives can be ranked and nonoptimal alternatives can be improved.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Hanane Grissette, El Habib Nfaoui
Summary: The research introduces a novel encoding method for affective biomedical concepts using sentic computing and neural networks, which enables sentiment analysis based on patient narrative data. Tested on COVID-19 related self-reports, the approach demonstrated effectiveness in inferring emotional information related to medication subjects, showing a positive impact on public health.
COGNITIVE COMPUTATION
(2022)
Article
Automation & Control Systems
Danilo Cavaliere, Vincenzo Loia, Alessia Saggese, Sabrina Senatore, Mario Vento
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2019)
Article
Computer Science, Information Systems
Danilo Cavaliere, Sabrina Senatore, Vincenzo Loia
IEEE SYSTEMS JOURNAL
(2019)
Article
Computer Science, Artificial Intelligence
Danilo Cavaliere, Juan Antonio Morente-Molinera, Sabrina Senatore, Enrique Herrera-Viedma
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
(2020)
Article
Computer Science, Artificial Intelligence
Danilo Cavaliere, Juan Antonio Morente-Molinera, Vincenzo Loia, Sabrina Senatore, Enrique Herrera-Viedma
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2020)
Article
Computer Science, Artificial Intelligence
Barbara Cardone, Ferdinando Di Martino, Sabrina Senatore
Summary: This paper proposes a framework for emotion-based classification from social streams, such as Twitter, according to Plutchik's wheel of emotions. Through experiments, it has been shown that the EwFCM algorithm provides high accuracy and efficiency in classifying primary and secondary emotions, outperforming other fuzzy clustering-based approaches.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Danilo Cavaliere, Sabrina Senatore
Summary: Precision agriculture systems use spectral images from satellites and drones to monitor vegetation and soil conditions. This article introduces a system model that detects anomalies based on area phenology and historical vegetation trends. The model combines image processing and learning to classify vegetation anomalies.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2022)
Article
Computer Science, Information Systems
Mariarosaria Falanga, Enza De Lauro, Simona Petrosino, Diego Rincon-Yanez, Sabrina Senatore
Summary: This article presents an IoT-oriented framework for collecting and processing seismic data and storing them in a knowledge base. It utilizes Semantic Web technologies and ontologies to enhance data quality and achieve syntactic and semantic interoperability. The framework has been validated using monitoring networks at Mt. Vesuvius and Colima volcano, successfully detecting various seismic events.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Chemistry, Multidisciplinary
Diego Rincon-Yanez, Enza De Lauro, Simona Petrosino, Sabrina Senatore, Mariarosaria Falanga
Summary: This study focuses on analyzing the background seismic noise acquired at several volcanoes and proposes a machine learning approach to recognize the fingerprint of each volcano. The experimental results demonstrate the effectiveness of this approach in identifying the noisy background signal, suggesting its potential as a practical tool for real-time volcano monitoring.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Giuseppina Di Paolo, Diego Rincon-Yanez, Sabrina Senatore
Summary: Due to the rapid growth of knowledge graphs (KG) as representational learning methods, question-answering approaches using KG have gained attention. This paper proposes a question-answering approach that translates natural language queries into graph triples and uses knowledge graph embedding (KGE) models to retrieve answers. The system outperforms existing literature and provides a fast knowledge extraction system and answer prediction model. A use case example demonstrates the generated KG in a graphical interface.
Proceedings Paper
Computer Science, Artificial Intelligence
Carmen Fucile, Danilo Cavaliere, Sabrina Senatore
Summary: The climate change emergency has a significant impact on vegetation growth in terrestrial ecosystems. Vegetation monitoring is crucial for assessing environmental changes and protecting wildlife. A fuzzy set-based framework is proposed to evaluate vegetation health, with an agent-based modeling optimizing the control and flow of activation rules.
2022 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Danilo Cavaliere, Vincenzo Loia, Sabrina Senatore
2020 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Diego Rincon-Yanez, Enza De Lauro, Mariarosaria Falanga, Sabrina Senatore, Simona Petrosino
2020 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI)
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Danilo Cavaliere, Sabrina Senatore
2020 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI)
(2020)
Proceedings Paper
Computer Science, Cybernetics
Jacek Filipczuk, Nicola Felice Capece, Sabrina Senatore, Ugo Erra
2019 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC)
(2019)
Article
Computer Science, Information Systems
Danilo Cavaliere, Vincenzo Loia, Sabrina Senatore
Article
Computer Science, Artificial Intelligence
Hao Yang, Min Wang, Zhengfei Yu, Hang Zhang, Jinshen Jiang, Yun Zhou
Summary: In this paper, a novel method called CSTTA is proposed for test time adaptation (TTA), which utilizes confidence-based optimization and sample reweighting to better utilize sample information. Extensive experiments demonstrate the effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Jin Liu, Ju-Sheng Mi, Dong-Yun Niu
Summary: This article focuses on a novel method for generating a canonical basis for decision implications based on object-induced operators (OE operators). The logic of decision implication based on OE operators is described, and a method for obtaining the canonical basis for decision implications is given. The completeness, nonredundancy, and optimality of the canonical basis are proven. Additionally, a method for generating true premises based on OE operators is proposed.
KNOWLEDGE-BASED SYSTEMS
(2024)
Review
Computer Science, Artificial Intelligence
Kun Bu, Yuanchao Liu, Xiaolong Ju
Summary: This paper discusses the importance of sentiment analysis and pre-trained models in natural language processing, and explores the application of prompt learning. The research shows that prompt learning is more suitable for sentiment analysis tasks and can achieve good performance.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangjun Cai, Dagang Li
Summary: This paper presents a new decomposition mechanism based on learned decomposition mapping. By using a neural network to learn the relationship between original time series and decomposed results, the repetitive computation overhead during rolling decomposition is relieved. Additionally, extended mapping and partial decomposition methods are proposed to alleviate boundary effects on prediction performance. Comparative studies demonstrate that the proposed method outperforms existing RDEMs in terms of operation speed and prediction accuracy.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xu Wu, Yang Liu, Jie Tian, Yuanpeng Li
Summary: This paper proposes a blockchain-based privacy-preserving trust management architecture, which adopts federated learning to train task-specific trust models and utilizes differential privacy to protect device privacy. In addition, a game theory-based incentive mechanism and a parallel consensus protocol are proposed to improve the accuracy of trust computing and the efficiency of consensus.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zaiyang Yu, Prayag Tiwari, Luyang Hou, Lusi Li, Weijun Li, Limin Jiang, Xin Ning
Summary: This study introduces a 3D view-based approach that effectively handles occlusions and leverages the geometric information of 3D objects. The proposed method achieves state-of-the-art results on occluded ReID tasks and exhibits competitive performance on holistic ReID tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Yongliang Shi, Runyi Yang, Zirui Wu, Pengfei Li, Caiyun Liu, Hao Zhao, Guyue Zhou
Summary: Neural implicit representations have gained attention due to their expressive, continuous, and compact properties. However, there is still a lack of research on city-scale continual implicit dense mapping based on sparse LiDAR input. In this study, a city-scale continual neural mapping system with a panoptic representation is developed, incorporating environment-level and instance-level modeling. A tailored three-layer sampling strategy and category-specific prior are proposed to address the challenges of representing geometric information in city-scale space and achieving high fidelity mapping of instances under incomplete observation.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ruihan Hu, Zhi-Ri Tang, Rui Yang, Zhongjie Wang
Summary: Mesh data is crucial for 3D computer vision applications worldwide, but traditional deep learning frameworks have struggled with handling meshes. This paper proposes MDSSN, a simple mesh computation framework that models triangle meshes and represents their shape using face-based and edge-based Riemannian graphs. The framework incorporates end-to-end operators inspired by traditional deep learning frameworks, and includes dedicated modules for addressing challenges in mesh classification and segmentation tasks. Experimental results demonstrate that MDSSN outperforms other state-of-the-art approaches.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Buliao Huang, Yunhui Zhu, Muhammad Usman, Huanhuan Chen
Summary: This paper proposes a novel semi-supervised conditional normalizing flow (SSCFlow) algorithm that combines unsupervised imputation and supervised classification. By estimating the conditional distribution of incomplete instances, SSCFlow facilitates imputation and classification simultaneously, addressing the issue of separated tasks ignoring data distribution and label information in traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Deeksha Varshney, Asif Ekbal, Erik Cambria
Summary: This paper focuses on the neural-based interactive dialogue system that aims to engage and retain humans in long-lasting conversations. It proposes a new neural generative model that combines step-wise co-attention, self-attention-based transformer network, and an emotion classifier to control emotion and knowledge transfer during response generation. The results from quantitative, qualitative, and human evaluation show that the proposed models can generate natural and coherent sentences, capturing essential facts with significant improvement over emotional content.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Junchen Ye, Weimiao Li, Zhixin Zhang, Tongyu Zhu, Leilei Sun, Bowen Du
Summary: Modeling multivariate time series has long been a topic of interest for scholars in various fields. This paper introduces MvTS, an open library based on Pytorch, which provides a unified framework for implementing and evaluating these models. Extensive experiments on public datasets demonstrate the effectiveness and universality of the models reproduced by MvTS.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Reham R. Mostafa, Ahmed M. Khedr, Zaher Al Aghbari, Imad Afyouni, Ibrahim Kamel, Naveed Ahmed
Summary: Feature selection is crucial in classification procedures, but it faces challenges in high-dimensional datasets. To overcome these challenges, this study proposes an Adaptive Hybrid-Mutated Differential Evolution method that incorporates the mechanics of the Spider Wasp Optimization algorithm and the concept of Enhanced Solution Quality. Experimental results demonstrate the effectiveness of the method in terms of accuracy and convergence speed, and it outperforms contemporary cutting-edge algorithms.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ti Xiang, Pin Lv, Liguo Sun, Yipu Yang, Jiuwu Hao
Summary: This paper introduces a Track Classification Model (TCM) based on marine radar, which can effectively recognize and classify shipping tracks. By using a feature extraction network with multi-feature fusion and a dataset production method to address missing labels, the classification accuracy is improved, resulting in successful engineering application in real scenarios.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zhihao Zhang, Yuan Zuo, Chenghua Lin, Junjie Wu
Summary: This paper proposes a novel unsupervised context-aware quality phrase mining framework called LMPhrase, which is built upon large pre-trained language models. The framework mines quality phrases as silver labels using a parameter-free probing technique on the pre-trained language model BERT, and formalizes the phrase tagging task as a sequence generation problem by fine-tuning on the Sequence to-Sequence pre-trained language model BART. The results of extensive experiments show that LMPhrase consistently outperforms existing competitors in two different granularity phrase mining tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Kemal Buyukkaya, M. Ozan Karsavuran, Cevdet Aykanat
Summary: The study aims to investigate the hybrid parallelization of the Stochastic Gradient Descent (SGD) algorithm for solving the matrix completion problem on a high-performance computing platform. A hybrid parallel decentralized SGD framework with asynchronous inter-process communication and a novel flexible partitioning scheme is proposed to achieve scalability up to hundreds of processors. Experimental results on real-world benchmark datasets show that the proposed algorithm achieves 6x higher throughput on sparse datasets compared to the state-of-the-art, while achieving comparable throughput on relatively dense datasets.
KNOWLEDGE-BASED SYSTEMS
(2024)