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Breast and prostate cancer patients may experience physical and psychological distress, and a possible decrease in sleep quality. Subjective and objective methods measure different aspects of sleep quality. Our study attempted to determine differences between objective and subjective measurements of sleep quality using bivariate and Pearson’s correlation data analysis. Forty breast (n = 20) and prostate (n = 20) cancer patients were recruited in this observational study. Participants were given an actigraphy device (ACT) and asked to continuously wear it for seven consecutive days, for objective data collection. Following this period, they filled out the Pittsburgh Sleep Quality Index Questionnaire (PSQI) to collect subjective data on sleep quality. The correlation results showed that, for breast cancer patients, PSQI sleep duration was moderately correlated with ACT total sleeping time (TST) (r = −0.534, p< 0.05), and PSQI daytime dysfunction was related to ACT efficiency (r = 0.521, p< 0.05). For prostate cancer patients, PSQI sleep disturbances were related to ACT TST (r = 0.626, p< 0.05). Both objective and subjective measurements are important in validating and determining details of sleep quality, with combined results being more insightful, and can also help in personalized care to further improve quality of life among cancer patients.
Diana Barsasella; Shabbir Syed-Abdul; Shwetambara Malwade; Terry Kuo; Ming-Jen Chien; Francisco Núñez-Benjumea; Gi-Ming Lai; Ruey-Ho Kao; Hung-Jen Shih; Yu-Ching Wen; Yu-Chuan Li; Iván Carrascosa; Kuan-Jen Bai. Sleep Quality among Breast and Prostate Cancer Patients: A Comparison between Subjective and Objective Measurements. Healthcare 2021, 9, 785 .
AMA StyleDiana Barsasella, Shabbir Syed-Abdul, Shwetambara Malwade, Terry Kuo, Ming-Jen Chien, Francisco Núñez-Benjumea, Gi-Ming Lai, Ruey-Ho Kao, Hung-Jen Shih, Yu-Ching Wen, Yu-Chuan Li, Iván Carrascosa, Kuan-Jen Bai. Sleep Quality among Breast and Prostate Cancer Patients: A Comparison between Subjective and Objective Measurements. Healthcare. 2021; 9 (7):785.
Chicago/Turabian StyleDiana Barsasella; Shabbir Syed-Abdul; Shwetambara Malwade; Terry Kuo; Ming-Jen Chien; Francisco Núñez-Benjumea; Gi-Ming Lai; Ruey-Ho Kao; Hung-Jen Shih; Yu-Ching Wen; Yu-Chuan Li; Iván Carrascosa; Kuan-Jen Bai. 2021. "Sleep Quality among Breast and Prostate Cancer Patients: A Comparison between Subjective and Objective Measurements." Healthcare 9, no. 7: 785.
The United Nations Agenda 2030 established 17 Sustainable Development Goals (SDGs) as a guideline to guarantee a sustainable worldwide development. Recent advances in artificial intelligence and other digital technologies have already changed several areas of modern society, and they could be very useful to reach these sustainable goals. In this paper we propose a novel decision making model based on surveys that ranks recommendations on the use of different artificial intelligence and related technologies to achieve the SDGs. According to the surveys, our decision making method is able to determine which of these technologies are worth investing in to lead new research to successfully tackle with sustainability challenges.
Sergio Alonso; Rosana Montes; Daniel Molina; Iván Palomares; Eugenio Martínez-Cámara; Manuel Chiachio; Juan Chiachio; Francisco Melero; Pablo García-Moral; Bárbara Fernández; Cristina Moral; Rosario Marchena; Javier Pérez de Vargas; Francisco Herrera. Ordering Artificial Intelligence Based Recommendations to Tackle the SDGs with a Decision-Making Model Based on Surveys. Sustainability 2021, 13, 6038 .
AMA StyleSergio Alonso, Rosana Montes, Daniel Molina, Iván Palomares, Eugenio Martínez-Cámara, Manuel Chiachio, Juan Chiachio, Francisco Melero, Pablo García-Moral, Bárbara Fernández, Cristina Moral, Rosario Marchena, Javier Pérez de Vargas, Francisco Herrera. Ordering Artificial Intelligence Based Recommendations to Tackle the SDGs with a Decision-Making Model Based on Surveys. Sustainability. 2021; 13 (11):6038.
Chicago/Turabian StyleSergio Alonso; Rosana Montes; Daniel Molina; Iván Palomares; Eugenio Martínez-Cámara; Manuel Chiachio; Juan Chiachio; Francisco Melero; Pablo García-Moral; Bárbara Fernández; Cristina Moral; Rosario Marchena; Javier Pérez de Vargas; Francisco Herrera. 2021. "Ordering Artificial Intelligence Based Recommendations to Tackle the SDGs with a Decision-Making Model Based on Surveys." Sustainability 13, no. 11: 6038.
Iván Palomares; James Neve; Carlos Porcel; Luiz Pizzato; Ido Guy; Enrique Herrera-Viedma. Corrigendum to “Reciprocal Recommender Systems: Analysis of state-of-art literature, challenges and opportunities towards social recommendation” Information Fusion Volume 69 (2020) 103-127. Information Fusion 2021, 75, 101 .
AMA StyleIván Palomares, James Neve, Carlos Porcel, Luiz Pizzato, Ido Guy, Enrique Herrera-Viedma. Corrigendum to “Reciprocal Recommender Systems: Analysis of state-of-art literature, challenges and opportunities towards social recommendation” Information Fusion Volume 69 (2020) 103-127. Information Fusion. 2021; 75 ():101.
Chicago/Turabian StyleIván Palomares; James Neve; Carlos Porcel; Luiz Pizzato; Ido Guy; Enrique Herrera-Viedma. 2021. "Corrigendum to “Reciprocal Recommender Systems: Analysis of state-of-art literature, challenges and opportunities towards social recommendation” Information Fusion Volume 69 (2020) 103-127." Information Fusion 75, no. : 101.
There exist situations of decision-making under information overload in the Internet, where people have an overwhelming number of available options to choose from, e.g. products to buy in an e-commerce site, or restaurants to visit in a large city. Recommender systems arose as a data-driven personalized decision support tool to assist users in these situations: they are able to process user-related data, filtering and recommending items based on the user’s preferences, needs and/or behaviour. Unlike most conventional recommender approaches where items are inanimate entities recommended to the users and success is solely determined upon the end user’s reaction to the recommendation(s) received, in a Reciprocal Recommender System (RRS) users become the item being recommended to other users. Hence, both the end user and the user being recommended should accept the “matching” recommendation to yield a successful RRS performance. The operation of an RRS entails not only predicting accurate preference estimates upon user interaction data as classical recommenders do, but also calculating mutual compatibility between (pairs of) users, typically by applying fusion processes on unilateral user-to-user preference information. This paper presents a snapshot-style analysis of the extant literature that summarizes the state-of-the-art RRS research to date, focusing on the algorithms, fusion processes and fundamental characteristics of RRS, both inherited from conventional user-to-item recommendation models and those inherent to this emerging family of approaches. Representative RRS models are likewise highlighted. Following this, we discuss the challenges and opportunities for future research on RRSs, with special focus on (i) fusion strategies to account for reciprocity and (ii) emerging application domains related to social recommendation.
Iván Palomares; Carlos Porcel; Luiz Pizzato; Ido Guy; Enrique Herrera-Viedma. Reciprocal Recommender Systems: Analysis of state-of-art literature, challenges and opportunities towards social recommendation. Information Fusion 2020, 69, 103 -127.
AMA StyleIván Palomares, Carlos Porcel, Luiz Pizzato, Ido Guy, Enrique Herrera-Viedma. Reciprocal Recommender Systems: Analysis of state-of-art literature, challenges and opportunities towards social recommendation. Information Fusion. 2020; 69 ():103-127.
Chicago/Turabian StyleIván Palomares; Carlos Porcel; Luiz Pizzato; Ido Guy; Enrique Herrera-Viedma. 2020. "Reciprocal Recommender Systems: Analysis of state-of-art literature, challenges and opportunities towards social recommendation." Information Fusion 69, no. : 103-127.
The last decade witnessed tremendous developments in social media and e-democracy technologies. A fundamental aspect in these paradigms is that the number of decision makers allowed to partake in a decision making event drastically increases. As a result Large Scale Decision Making (LSDM) has established itself as an emerging and rapidly developing research field, attracting comprehensive studies in the last decade. LSDM events are a complex class of decision making problems, in which multiple and highly diverse stakeholders are involved and the provided alternatives are assessed considering multiple criteria/attributes. Since some of the extant LSDM research was extended from group decision making scenarios, there is no established definition for a LSDM problem as of yet. We firstly propose a clear definition and characterization of LSDM events as a basis for characterizing this emerging family of decision frameworks. Secondly, a classification of LSDM literature is provided. Effectively solving an LSDM problem is usually a complex and challenging process, in which reaching a high consensus or accounting for the agreement or conflict relationships between participants becomes critical. Accordingly, we present a taxonomy and an overview of LSDM models, predicated on their key elements, i.e. the procedures and specific steps followed by the existing models: consensus measurement, subgroup clustering, behavior management, and consensus building mechanisms. Finally, we provide a discussion in which we identify research challenges and propose future research directions under a triple perspective: key LSDM methodologies, AI and data fusion for LSDM, and innovative applications. The potential rise of AI-based LSDM is particularly highlighted in the discussion provided.
Ru-Xi Ding; Iván Palomares; Xueqing Wang; Guo-Rui Yang; Bingsheng Liu; Yucheng Dong; Enrique Herrera-Viedma; Francisco Herrera. Large-Scale decision-making: Characterization, taxonomy, challenges and future directions from an Artificial Intelligence and applications perspective. Information Fusion 2020, 59, 84 -102.
AMA StyleRu-Xi Ding, Iván Palomares, Xueqing Wang, Guo-Rui Yang, Bingsheng Liu, Yucheng Dong, Enrique Herrera-Viedma, Francisco Herrera. Large-Scale decision-making: Characterization, taxonomy, challenges and future directions from an Artificial Intelligence and applications perspective. Information Fusion. 2020; 59 ():84-102.
Chicago/Turabian StyleRu-Xi Ding; Iván Palomares; Xueqing Wang; Guo-Rui Yang; Bingsheng Liu; Yucheng Dong; Enrique Herrera-Viedma; Francisco Herrera. 2020. "Large-Scale decision-making: Characterization, taxonomy, challenges and future directions from an Artificial Intelligence and applications perspective." Information Fusion 59, no. : 84-102.
Individual consistency and group consensus are both important when seeking reliable and satisfying solutions for group decision making (GDM) problems using additive preference relations (APRs). In this paper, two new algorithms are proposed to facilitate the consensus reaching process, the first of which is used to improve the individual consistency level, and the second of which is designed to assist the group to achieve a predefined consensus level. Unlike previous GDM studies for consistency and consensus building, the proposed algorithms are essentially heuristic, modify only some of the elements in APRs to reduce the number of preference modifications in the consistency and consensus process, and have modified preferences that belong to the original evaluation scale to make the generated suggestions easier to understand. In particular, the consensus algorithm ensures that the individual consistency level is still acceptable when the predefined consensus level is achieved. Finally, classical examples and simulations are given to demonstrate the effectiveness of the proposed approaches.
Zhibin Wu; Jie Xiao; Iván Palomares. Direct Iterative Procedures for Consensus Building with Additive Preference Relations Based on the Discrete Assessment Scale. Group Decision and Negotiation 2019, 28, 1167 -1191.
AMA StyleZhibin Wu, Jie Xiao, Iván Palomares. Direct Iterative Procedures for Consensus Building with Additive Preference Relations Based on the Discrete Assessment Scale. Group Decision and Negotiation. 2019; 28 (6):1167-1191.
Chicago/Turabian StyleZhibin Wu; Jie Xiao; Iván Palomares. 2019. "Direct Iterative Procedures for Consensus Building with Additive Preference Relations Based on the Discrete Assessment Scale." Group Decision and Negotiation 28, no. 6: 1167-1191.
Practical two-sided matching decision making problems, such as marriage matching and person-job matching, are often characterized by a lack of knowledge and time constraints. Therefore, matching objects tend to provide comparative preferential information over other matching objects represented by incomplete fuzzy preference relations. In this paper, it is proposed a new approach to stable two-sided matching decision making with incomplete fuzzy preference relations based on disappointment theory. In the proposed approach, the subjective satisfaction degrees of each matching object on one side over matching objects on the other side are first calculated based on priority weight vectors derived from incomplete fuzzy preference relations. Based on disappointment theory, both the disappointment and elation degrees associated with each matching object over matching objects on the other side are calculated. This process is undertaken by considering the probability of each possible matching pair, which are further used to derive the adjusted satisfaction degrees of matching objects. Afterwards, a stable matching optimization model that aims to maximize the total adjusted satisfaction degrees of both sides is constructed by considering stable matching conditions under incomplete information. The optimal stable matching result can be further determined by solving the optimization model. Finally, a numerical example and some comparative studies are presented to demonstrate the characteristics, innovations and added value of the proposed approach.
Zhen Zhang; Xinyue Kou; Iván Palomares; Wenyu Yu; Junliang Gao. Stable two-sided matching decision making with incomplete fuzzy preference relations: A disappointment theory based approach. Applied Soft Computing 2019, 84, 105730 .
AMA StyleZhen Zhang, Xinyue Kou, Iván Palomares, Wenyu Yu, Junliang Gao. Stable two-sided matching decision making with incomplete fuzzy preference relations: A disappointment theory based approach. Applied Soft Computing. 2019; 84 ():105730.
Chicago/Turabian StyleZhen Zhang; Xinyue Kou; Iván Palomares; Wenyu Yu; Junliang Gao. 2019. "Stable two-sided matching decision making with incomplete fuzzy preference relations: A disappointment theory based approach." Applied Soft Computing 84, no. : 105730.
Managing comparative linguistic expressions (CLEs) information is a key issue in group decision-making (GDM). A transformation approach has been previously defined to convert CLEs into hesitant fuzzy linguistic terms sets (HFLTSs). However, it is noted that the occurring possibilities of the linguistic terms in the HFLTSs are assumed equal. This assumption might sometimes not capture the real opinions of the decision makers. Linguistic distribution assessments (LDAs) are an effective way to deal with this issue. This paper develops a linguistic distribution-based optimization approach for converting CLEs into LDAs, in which we assume that decision makers provide their opinions using preference relations with CLEs. Particularly, the proposed optimization approach is based on the use of a consistency-driven methodology, which seeks to minimize the inconsistency level of LDA preference relations obtained by transforming the original CLE preference relations elicited from decision makers. The linguistic distribution-based optimization approach is further developed to transform CLEs into interval LDAs to increase their flexibility. Moreover, society and technology trends make it possible to involve and manage large groups of decision makers in GDM environment. So, a large-scale GDM framework with CLE information is designed based on the linguistic distribution-based optimization approach. To justify the effectiveness and applicability of the proposed methodology, it is applied to solve a real large-scale GDM problem, pertaining the selection of best sustainable disinfection technique for wastewater reuse projects. A comparison against a baseline method is likewise provided to highlight the advantages and innovations of our proposal.
Hengjie Zhang; Jing Xiao; Ivan Palomares; Haiming Liang; Yucheng Dong. Linguistic Distribution-Based Optimization Approach for Large-Scale GDM With Comparative Linguistic Information: An Application on the Selection of Wastewater Disinfection Technology. IEEE Transactions on Fuzzy Systems 2019, 28, 376 -389.
AMA StyleHengjie Zhang, Jing Xiao, Ivan Palomares, Haiming Liang, Yucheng Dong. Linguistic Distribution-Based Optimization Approach for Large-Scale GDM With Comparative Linguistic Information: An Application on the Selection of Wastewater Disinfection Technology. IEEE Transactions on Fuzzy Systems. 2019; 28 (2):376-389.
Chicago/Turabian StyleHengjie Zhang; Jing Xiao; Ivan Palomares; Haiming Liang; Yucheng Dong. 2019. "Linguistic Distribution-Based Optimization Approach for Large-Scale GDM With Comparative Linguistic Information: An Application on the Selection of Wastewater Disinfection Technology." IEEE Transactions on Fuzzy Systems 28, no. 2: 376-389.
The paper proposes a Trust Relationship-based Conflict Detection and Elimination decision making (TR-CDE) model, applicable for Large-scale Group Decision Making (LSGDM) problems in social network contexts. The TR-CDE model comprises three processes: a trust propagation process; a conflict detection and elimination process; and a selection process. In the first process, we propose a new relationship strength-based trust propagation operator, which allows to construct a complete social network by considering the impact of relationship strength on propagation efficiency. In the second process, we define the concept of conflict degree and quantify the collective conflict degree by combining the assessment information and trust relationships among decision makers in the large group. We use social network analysis and a nonlinear optimization model to detect and eliminate conflicts among decision makers. By finding the optimal solution to the proposed nonlinear optimization model, we promote the modification of the assessments from the DM who exhibits the highest degree of conflict in the process, as well as guaranteeing that a sufficient reduction of the group conflict degree is achieved. In the third and last process, we propose a new selection method for LSGDM that determines decision makers’ weights based on their conflict degree. A numerical example and a practical scenario are implemented to show the feasibility of the proposed TR-CDE model.
Bingsheng Liu; Qi Zhou; Ru-Xi Ding; Iván Palomares; Francisco Herrera. Large-scale group decision making model based on social network analysis: Trust relationship-based conflict detection and elimination. European Journal of Operational Research 2018, 275, 737 -754.
AMA StyleBingsheng Liu, Qi Zhou, Ru-Xi Ding, Iván Palomares, Francisco Herrera. Large-scale group decision making model based on social network analysis: Trust relationship-based conflict detection and elimination. European Journal of Operational Research. 2018; 275 (2):737-754.
Chicago/Turabian StyleBingsheng Liu; Qi Zhou; Ru-Xi Ding; Iván Palomares; Francisco Herrera. 2018. "Large-scale group decision making model based on social network analysis: Trust relationship-based conflict detection and elimination." European Journal of Operational Research 275, no. 2: 737-754.
In consensus-based multiple attribute group decision making (MAGDM) problems, it is frequent that some experts exhibit non-cooperative behaviors owing to the different areas to which they may belong and the different (sometimes conflicting) interests they might present. This may adversely affect the overall efficiency of the consensus reaching process, especially when some uncooperative behaviors by experts arise. To this end, this paper develops a novel consensus framework based on Social Network Analysis (SNA) to deal with non-cooperative behaviors. In the proposed SNA-based consensus framework, a trust propagation and aggregation mechanism to yield experts’ weights from the social trust network is presented, and the obtained weights of experts are then integrated into the consensus-based MAGDM framework. Meanwhile, a non-cooperative behavior analysis module is designed to analyze the behaviors of experts. Based on the results of such analysis during the consensus process, each expert can express and modify the trust values pertaining other experts in the social trust network. As a result, both the social trust network and the weights of experts derived from it are dynamically updated in parallel. A simulation and comparison study is presented to demonstrate the efficiency of the SNA-based consensus framework for coping with non-cooperative behaviors.
Hengjie Zhang; Iván Palomares; Yucheng Dong; Weiwei Wang. Managing non-cooperative behaviors in consensus-based multiple attribute group decision making: An approach based on social network analysis. Knowledge-Based Systems 2018, 162, 29 -45.
AMA StyleHengjie Zhang, Iván Palomares, Yucheng Dong, Weiwei Wang. Managing non-cooperative behaviors in consensus-based multiple attribute group decision making: An approach based on social network analysis. Knowledge-Based Systems. 2018; 162 ():29-45.
Chicago/Turabian StyleHengjie Zhang; Iván Palomares; Yucheng Dong; Weiwei Wang. 2018. "Managing non-cooperative behaviors in consensus-based multiple attribute group decision making: An approach based on social network analysis." Knowledge-Based Systems 162, no. : 29-45.
Failure mode and effect analysis (FMEA) is an effective risk-management tool, which has been extensively utilized to manage failure modes (FMs) of products, processes, systems, and services. Almost all FMEA models are concerned with how to get a complete risk order of FMs from highest to lowest risk. However, in many situations, it may be sufficient to classify the FMs into several ordinal risk classes. Meanwhile, generating a consensual decision is crucial for the FMEA problem because 1) reaching consensus will enhance the connections among FMEA participants, and 2) a highly accepted group solution to the FMEA problem can be generated. Thus, this study proposes a consensus-based group decision-making framework for FMEA with the aim of classifying FMs into several ordinal risk classes in which we assumed that FMEA participants provide their preferences in a linguistic way using possibilistic hesitant fuzzy linguistic information. In the FMEA framework, a consensus-driven methodology is presented to generate the weights of risk factors. Following this, an optimization-based consensus rule guided by a minimum adjustment distance policy is devised, and an interactive model for reaching consensus is developed to generate consensual FM risk classes. In order to justify its validity of the proposal, our framework is applied for the risk evaluation of proton beam radiotherapy.
Hengjie Zhang; Yucheng Dong; Ivan Palomares-Carrascosa; Haiwei Zhou. Failure Mode and Effect Analysis in a Linguistic Context: A Consensus-Based Multiattribute Group Decision-Making Approach. IEEE Transactions on Reliability 2018, 68, 566 -582.
AMA StyleHengjie Zhang, Yucheng Dong, Ivan Palomares-Carrascosa, Haiwei Zhou. Failure Mode and Effect Analysis in a Linguistic Context: A Consensus-Based Multiattribute Group Decision-Making Approach. IEEE Transactions on Reliability. 2018; 68 (2):566-582.
Chicago/Turabian StyleHengjie Zhang; Yucheng Dong; Ivan Palomares-Carrascosa; Haiwei Zhou. 2018. "Failure Mode and Effect Analysis in a Linguistic Context: A Consensus-Based Multiattribute Group Decision-Making Approach." IEEE Transactions on Reliability 68, no. 2: 566-582.
Zijian Shi; Xueqing Wang; Iván Palomares; Sijia Guo; Ru-Xi Ding. A novel consensus model for multi-attribute large-scale group decision making based on comprehensive behavior classification and adaptive weight updating. Knowledge-Based Systems 2018, 158, 196 -208.
AMA StyleZijian Shi, Xueqing Wang, Iván Palomares, Sijia Guo, Ru-Xi Ding. A novel consensus model for multi-attribute large-scale group decision making based on comprehensive behavior classification and adaptive weight updating. Knowledge-Based Systems. 2018; 158 ():196-208.
Chicago/Turabian StyleZijian Shi; Xueqing Wang; Iván Palomares; Sijia Guo; Ru-Xi Ding. 2018. "A novel consensus model for multi-attribute large-scale group decision making based on comprehensive behavior classification and adaptive weight updating." Knowledge-Based Systems 158, no. : 196-208.
This paper investigates the problem of measuring consensus in multi-perspective Multi-Criteria Group Decision Making (MCGDM) problems, in which participants have individual views on the relative importance of different evaluation criteria. A novel dual consensus measure for multi-perspective MCGDM problems is introduced. The proposed measure determines the level of agreement between participants' opinions based on: (i) the global performance or satisfaction of alternatives, (ii) their partial performances of alternatives under each criterion, and (iii) the similarity between the perspectives of participants regarding criteria weights. Preliminary experiments are conducted for an example multi-perspective MCGDM scenario. The degree to which global and partial performance information are jointly taken into account - together with the actual pairwise distances between the opinions of participants - are shown to directly affect the overall measurement of consensus in the group. An application example is introduced in a MCGDM problem on selecting the safest logistic route to transport hazardous materials.
Ivan Palomares; Michael Crosscombe; Zhen-Song Chen; Jonathan Lawry. Dual Consensus Measure for Multi-perspective Multi-criteria Group Decision Making. 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2018, 3313 -3318.
AMA StyleIvan Palomares, Michael Crosscombe, Zhen-Song Chen, Jonathan Lawry. Dual Consensus Measure for Multi-perspective Multi-criteria Group Decision Making. 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 2018; ():3313-3318.
Chicago/Turabian StyleIvan Palomares; Michael Crosscombe; Zhen-Song Chen; Jonathan Lawry. 2018. "Dual Consensus Measure for Multi-perspective Multi-criteria Group Decision Making." 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) , no. : 3313-3318.
Online dating constitutes one out of myriad popular services that can be accessed via the Internet nowadays. This paper introduces a novel detection system for identifying dubious users, i.e. users who utilize a Japanese online dating service for purposes besides dating. Examples of such purposes include sales and multi-level marketing, amongst others. More specifically, the proposed detection is characterized by simultaneously analyzing: (i) user profile data; (ii) user actions over their first few hours; and (iii) data retrieved from Facebook in order to find the likelihood that the user is a spammer. The resulting system successfully detects a number of spammers every day, thereby becoming a valuable tool for the customer service team in Eureka Inc, where it has been deployed.
James Neve; Ivan Palomares. Arikui - A Dubious User Detection System for Online Dating in Japan. 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2018, 2299 -2304.
AMA StyleJames Neve, Ivan Palomares. Arikui - A Dubious User Detection System for Online Dating in Japan. 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 2018; ():2299-2304.
Chicago/Turabian StyleJames Neve; Ivan Palomares. 2018. "Arikui - A Dubious User Detection System for Online Dating in Japan." 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) , no. : 2299-2304.
Nowadays, with the development of information communication technology and Internet, more and more people receive information and exchange their opinions with others via online environments (e.g. Twitter, Facebook, Weibo, and WeChat). According to eMarketer Report [Worldwide Internet and Mobile Users: eMarketer’s Updated Estimates and Forecast for 2015–2020 (eMarketer Report). Published October 11, 2016, https://www.emarketer.com/Report/Worldwide-Internet-Mobile-Users-eMarketers-Updated-Estimates-Forecast-20152020/2001897 ).], by the end of 2016, more than 3.2 billion individuals worldwide will use the Internet regularly, accounting for nearly 45% of the world population. By contrast, the other half of the global population still obtain information and regularly exchange their opinions in a more traditional way (e.g. face to face). Generally, the speed at which information spreads and opinions are exchanged and updated in an online environment is much faster than in an offline environment. This paper focuses on jointly investigating the challenge of consensus formation in opinion dynamics with online and offline interactions. Without loss of generality, we assume the speed at which information spreads and opinions are exchanged and updated in an online environment is [Formula: see text] times as fast as in an offline environment. We demonstrate that the update speed ratio in mixed online and offline environments (i.e. [Formula: see text]) strongly impacts the consensus formation at complex networks: a large update speed ratio of online and offline environments (i.e. [Formula: see text]) makes it difficult for all agents to reach consensus in opinion dynamics. Furthermore, these effects are often further intensified as the number of online participating agents increases.
Zhaogang Ding; Yucheng Dong; Gang Kou; Ivan Palomares; Shui Yu. Consensus formation in opinion dynamics with online and offline interactions at complex networks. International Journal of Modern Physics C 2018, 29, 1 .
AMA StyleZhaogang Ding, Yucheng Dong, Gang Kou, Ivan Palomares, Shui Yu. Consensus formation in opinion dynamics with online and offline interactions at complex networks. International Journal of Modern Physics C. 2018; 29 (7):1.
Chicago/Turabian StyleZhaogang Ding; Yucheng Dong; Gang Kou; Ivan Palomares; Shui Yu. 2018. "Consensus formation in opinion dynamics with online and offline interactions at complex networks." International Journal of Modern Physics C 29, no. 7: 1.
Iván Palomares; Fiona Browne; Peadar Davis. Multi-view fuzzy information fusion in collaborative filtering recommender systems: Application to the urban resilience domain. Data & Knowledge Engineering 2018, 113, 64 -80.
AMA StyleIván Palomares, Fiona Browne, Peadar Davis. Multi-view fuzzy information fusion in collaborative filtering recommender systems: Application to the urban resilience domain. Data & Knowledge Engineering. 2018; 113 ():64-80.
Chicago/Turabian StyleIván Palomares; Fiona Browne; Peadar Davis. 2018. "Multi-view fuzzy information fusion in collaborative filtering recommender systems: Application to the urban resilience domain." Data & Knowledge Engineering 113, no. : 64-80.
Hamza Sellak; Brahim Ouhbi; Bouchra Frikh; Iván Palomares. Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support. Renewable and Sustainable Energy Reviews 2017, 80, 1544 -1577.
AMA StyleHamza Sellak, Brahim Ouhbi, Bouchra Frikh, Iván Palomares. Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support. Renewable and Sustainable Energy Reviews. 2017; 80 ():1544-1577.
Chicago/Turabian StyleHamza Sellak; Brahim Ouhbi; Bouchra Frikh; Iván Palomares. 2017. "Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support." Renewable and Sustainable Energy Reviews 80, no. : 1544-1577.
In this paper we investigate the consensus reaching problem for Large Group Multi-Criteria Decision Making (MCLGDM). We present an adaptive, semi-supervised consensus model for MCLGDM problems with preferences expressed as Comparative Linguistic Expressions. Specifically, our work introduces an adaptive, semi-supervised feedback mechanism that, depending on the positions of decision makers' preferences and their level of uncertainty caused by hesitancy, requests human supervision to modify their preferences or updates them automatically. The proposed consensus model effectively handles large amounts of linguistic-natured information in consensus processes involving large groups. The methodology is illustrated and experimentally validated through a MCLGDM problem for candidate assessment in recruiting processes. Like-wise, a theoretical comparison with similar works is provided.
Ivan Palomares; Hamza Sellak; Brahim Ouhbi; Bouchra Frikh. Adaptive semi-supervised consensus model for multi-criteria large group decision making in a linguistic setting. 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) 2017, 1 -9.
AMA StyleIvan Palomares, Hamza Sellak, Brahim Ouhbi, Bouchra Frikh. Adaptive semi-supervised consensus model for multi-criteria large group decision making in a linguistic setting. 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE). 2017; ():1-9.
Chicago/Turabian StyleIvan Palomares; Hamza Sellak; Brahim Ouhbi; Bouchra Frikh. 2017. "Adaptive semi-supervised consensus model for multi-criteria large group decision making in a linguistic setting." 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) , no. : 1-9.
With the increase in volume, heterogeneity and uncertainty in data, conventional analytics approaches for monitoring users behavior in organisations are no longer sufficient for the effective and reliable detection of malicious activities. This motivates the need for introducing additional analysis techniques. This paper introduces an intelligent fusion method based on fuzzy aggregation functions typically utilized in multi-criteria decision making. The proposed method, which can be integrated with analytics systems, undertakes temporal and multi-criteria fusion processes on pre-analyzed data, to enhance effective monitoring and decision-making. An application to a prominent area of research in the cyber-security domain, the insider threat problem, is shown to validate the usefulness of our method.
Ivan Palomares; Harsha Kalutarage; Yan Huang; Paul Miller Robert McCausland; Gavin McWilliams. A fuzzy multicriteria aggregation method for data analytics: Application to insider threat monitoring. 2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS) 2017, 1 -6.
AMA StyleIvan Palomares, Harsha Kalutarage, Yan Huang, Paul Miller Robert McCausland, Gavin McWilliams. A fuzzy multicriteria aggregation method for data analytics: Application to insider threat monitoring. 2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS). 2017; ():1-6.
Chicago/Turabian StyleIvan Palomares; Harsha Kalutarage; Yan Huang; Paul Miller Robert McCausland; Gavin McWilliams. 2017. "A fuzzy multicriteria aggregation method for data analytics: Application to insider threat monitoring." 2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS) , no. : 1-6.
Iván Palomares; Sergey V. Kovalchuk. Multi-View Data approaches in Recommender Systems: an Overview. Procedia Computer Science 2017, 119, 30 -41.
AMA StyleIván Palomares, Sergey V. Kovalchuk. Multi-View Data approaches in Recommender Systems: an Overview. Procedia Computer Science. 2017; 119 ():30-41.
Chicago/Turabian StyleIván Palomares; Sergey V. Kovalchuk. 2017. "Multi-View Data approaches in Recommender Systems: an Overview." Procedia Computer Science 119, no. : 30-41.