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Group decision making with hesitant fuzzy linguistic preference relations based on modified extent measurement

EXPERT SYSTEMS WITH APPLICATIONS 2021年 171卷 114235-114235页

作者： Ren, Peijia Xu, Zeshui Wang, Xinxin Zeng, Xiao-Jun South China Univ Technol Sch Business Adm Guangzhou 510640 Peoples R China Sichuan Univ Business Sch Chengdu 610064 Peoples R China Univ Manchester Sch Comp Sci Manchester M13 9PL Lancs England

The paper presents a consensus model for group decision making (GDM) with hesitant fuzzy linguistic preference relations (HFLPRs), which is composed of two parts: (1) clustering HFLPRs by mapping them into a higher dimension space based on a kernel function;(2) building a consensus model based on measuring the modified extents of decision makers? HFLPRs for reducing the biased judgements existing in their less-familiar ways. The paper further makes comprehensive analyses for the proposed model on: (1) the influence of decision makers? different sensitive attitudes towards the distances between the individual HFLPRs and the overall HFLPRs on decision-making results;(2) the differences and complexities of another model with a different consensus perspective and the proposed model. The experimental analyses provide the support for the maximum modified extent determination in different decision scenarios, and show that the proposed consensus model makes sense. Finally, the proposed model is illustrated by the application in choosing an optimal flood discharge technique for a hydropower station.

关键词： Group decision making Hesitant fuzzy linguistic preference relation clustering algorithm Consensus index Modified extent

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Proposed Model to Intelligent Recommendation System based on Markov Chains and Grouping of Genres

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Procedia Computer Science 2020年 176卷 868-877页

作者： Fatma Mlika Wafa Karoui Université de Sousse ISITCom 4011 Sousse Tunisie Université de Tunis El Manar Institut Supérieur d’Informatique 2080 Tunis Tunisie

A recommendation system (RS) provides users with a list of recommended tags or resources that they may like. Still, most systems are vulnerable with certain limitations and gaps related to the recommendation environment. Several RSs do not take into account the changes of the user preference over time to ensure his satisfaction. Thus, the role of these systems is limited to define the general orientations of each user according to their observed preferences without shading light on the importance of the user preferences in short time. In this regard, we propose a new approach of recommendation for the entertainment industry that permits to guide the user towards suggestions that are more relevant according to their previous interactions. That means, we offer our users suggestions to decrease the time and frustration of discovering engaging content. Thereby, we have created our own solution, a Recommendation System based on Markov Chains and Grouping of Genres (RSMCG). It is a simple method that enables to construct an intelligent system which explores the Markov chains to predict the following actions taking into consideration the most recent actions of the user. Also, we adapt a machine learning algorithm DBSCAN clustering in order to exactly identify the interest of each user and provide adaptable answers. This approach has been studied and evaluated on the basis of a movie collection of the most popular video streaming service “Netflix”. The results of the experiments show the efficiency of our hypothesis in comparison to a sample of the user’s real needs.

关键词： Recommendation system Markov chains Sequential prediction Machine learning clustering algorithm

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Construction of intelligent supervision platform for college students' physical health for intelligent medical service decision-making

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FRONTIERS IN PHYSICS 2023年 11卷

作者： Guo, Fei Harbin Univ Sch Phys Educ Harbin Heilongjiang Peoples R China

Due to the rapid changes in current technology, machine learning and high-performance computing in medical applications also usher in new development opportunities. They are widely used in medical data analysis, diagnostic decision-making, disease prediction, disease assisted diagnosis, disease prognosis evaluation, new drug research and development, health management, and other fields. The impact of medical application on daily life is also increasing, which makes the use of intelligent medical service decision-making more extensive. However, with the continuous improvement and development of the population's physical fitness, the physical fitness of university students is deteriorating. Physical decline has become a common concern. Therefore, it is of great significance to investigate the physical condition of college students and find a more suitable method to promote the physical health of college students. It helps college students better engage in learning and life, enabling them to adapt to work faster and better meet the current social development needs for college students' physical fitness. For this reason, this paper proposes the idea of building a smart supervision platform for college students' physical health through smart medical service decision-making. Through empirical research on this platform, it is found that the method of building the platform proposed in this paper is more conducive to the improvement of college students' physical health. The excellent grade of freshmen in this platform is 5.4% higher than that of the traditional platform, and the excellent grade of sophomores in the test is 6.31% higher than that of the traditional platform, the excellent grade of college students' physical health test on this platform accounts for a higher proportion. The platform provides corresponding personalized sports programs through real-time monitoring of students' physical health, so as to realize teaching students in accordance with their aptitude,

关键词： college students physical health intelligent medical service decision-making clustering algorithm sports culture smart security service system

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Detection of Multiple Salient Objects by Categorizing Regional Features

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2016年第1期10卷 272-287页

作者： Oh, Kang-Han Kim, Soo-Hyung Kim, Young-Chul Lee, Yu-Ra Chonnam Natl Univ Sch Elect & Comp Engn 77 Yongbong Ro Gwangju 500757 South Korea

Recently, various and effective contrast based salient object detection models to focus on a single target have been proposed. However, there is a lack of research on detection of multiple objects, and also it is a more challenging task than single target process. In the multiple target problem, we are confronted by new difficulties caused by distinct difference between properties of objects. The characteristic of existing models depending on the global maximum distribution of data point would become a drawback for detection of multiple objects. In this paper, by analyzing limitations of the existing methods, we have devised three main processes to detect multiple salient objects. In the first stage, regional features are extracted from over-segmented regions. In the second stage, the regional features are categorized into homogeneous cluster using the mean-shift algorithm with the kernel function having various sizes. In the final stage, we compute saliency scores of the categorized regions using only spatial features without the contrast features, and then all scores are integrated for the final salient regions. In the experimental results, the scheme achieved superior detection accuracy for the SED2 and MSRA-ASD benchmarks with both a higher precision and better recall than state-of-the-art approaches. Especially, given multiple objects having different properties, our model significantly outperforms all existing models.

关键词： Multiple salient objects detection Object segmentation clustering algorithm

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RETRACTED: Hybrid cluster made content forwarding and light weight sign encryption IoT based Named Data Network using African Buffalo optimization algorithm (Retracted Article)

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第3期43卷 2891-2905页

作者： Karthik, S. V. Vijaya Selvi, J. Arputha Vijaya Kings Coll Engn Dept ECE Pudukkottai Tamil Nadu India

Information Centric Network (ICN) is a newer technology in handling web content distribution that has recently emerged in order to tackle the risk of data security. For handling content distribution, ICN provides data security via a name-based approach. Named Data Networking (NDN) is an ongoing ICN realisation that was incorporated recently. Named Data Networking (NDN) has recently grown in popularity and significance as a new internet design that solves certain limitations in traditional internet communications. NDN is perfectly adapted for the Internet of Things (IoT), which is today dominated by huge, and emerging applications. In this work, we propose an IoT enabled hybrid cluster-based routing protocol with mitigation of content poisoning attack for information-centric Wireless Sensor Network (WSN)-NDN. In this method, hybrid K-medoids clustering is used with African Buffalo Optimization algorithm (ABOA), which is to find an optimal shortest path between the cluster heads, and light weight encryption. It is developed by using Hyperelliptic Curve Cryptography (HCC) to mitigate content poisoning. Our proposed system has effective data security as it has encrypted data in the cluster head. The smart health care monitoring system has been used for our proposed method. The proposed method has been subjected to extensive analysis by comparing with other existing methods that should improve performance justified in terms of several metrics by introducing the malicious nodes (10%, 20%, and 30%).

关键词： Named data network clustering algorithm content poisoning african buffalo optimization hyperelliptic curve cryptography

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An Improved algorithm of Nearness Degree of Incidence Based on Grey Neural Network

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IEEE ACCESS 2020年 8卷 207044-207053页

作者： Geng, Xinyu Ye, Jinchi Xia, Zhen Mu, Yukun Zhu, Liping Southwest Petr Univ Sch Comp Sci Chengdu 610500 Peoples R China

The traditional model of grey nearness degree of incidence contains some inherent limitations in the calculation of data sequences. It does not consider the impacts of certain data on degree of incidence when there are significant differences in orders of magnitude between adjacent data in the same sequence, and big errors may occur in the calculation of some special oscillation sequences. In response to these problems, we propose a new improved method, which uses the characteristics of the model of grey nearness degree of incidence and introduces a neural network algorithm to define a grey neural network-nearness degree of incidence. Thereby, a model of nearness degree of incidence is established based on grey neural network. Then we apply a new model to the field of data mining. According to the clustering algorithm, we take all the degrees of incidence as the variables of the distance metric function, and use the clustering algorithm of data mining for data analysis. Finally, through simulation experiments, we verify the effectiveness of the clustering algorithm under the new distance metric definition. The experimental results show that, compared with other methods, the computational outcomes of the improved model are more consistent with the actual situation. The cluster algorithm with the model used can deliver results that have a high accuracy, so the new model can be applicated in a wide range of fields.

关键词： clustering algorithms Data models Analytical models Mathematical model Measurement Artificial neural networks Training Grey neural network nearness degree of incidence data mining clustering algorithm

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Multilevel Partitioning with Multiple Strategies for Complex Water Distribution Network

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2022年第12期148卷 04022064-04022064页

作者： Tian, Junren Long, Zhihong Zhu, ZiPeng Xu, Gang Cheng, Weiping Zhejiang Univ Coll Civil Engn & Architecture Hangzhou 310058 Zhejiang Peoples R China Guangzhou Water Supply Co Ltd 12 Zhongshan 1st Rd Guangzhou 510600 Peoples R China

The operation and dispatching of large-scale water supply systems is becoming more and more complex, and effective partitioning is a good response measure to this increasing complexity. This paper presents a multilevel partitioning method using freely configurable strategies suitable for multiobjective situations such as allocation of water sources, pressure regulation, and flow measurement. The method mainly relies on two strategies. Using Strategy A, water supply features are constructed based on water sources and a partition clustering is implemented that is suitable for the overall partitioning of a water supply from multiple sources. Using Strategy B, important pipes are first extracted to simplify the search path and then various constraints (pressure boundary, flow shunt, cross-river management) are used to select control points and partition by graph theory searching. This strategy is suitable for refining partitioning of the structure of a specific source. A combination of the aforementioned strategies was applied to the first-level and second-level partitioning in a case study. By flexibly configuring strategies and constraints, good partitioning is achieved and multiple management goals are accomplished satisfactorily. This research enhances understanding of the operational issues in complex water systems. The overall management of water networks can be disassembled into tasks at all levels and with their own objectives performed in stages. The solution provided in this study has the advantages of flexibility and effectiveness for a variety of complex water distribution situations, contributing insights into the real challenges of partitioning large water distribution networks.

关键词： clustering algorithm District metered area (DMA) Graph theory Large-scale networks Multilevel partitioning Water distribution

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Community energy storage system: Deep learning based optimal energy management solution for residential community

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JOURNAL OF ENERGY STORAGE 2023年 64卷

作者： Alam, Md. Morshed Bin Mofidul, Raihan Jang, Yeong Min Kookmin Univ Dept Elect Engn Seoul South Korea

The concept of community energy storage system (CESS) is required for the efficient and reliable utilization of renewable energy and flexible energy sharing among consumers. This paper proposes a novel approach to assess the practical benefits of CESS deployment in a residential community by decreasing the daily electricity cost and maximizing the self-consumption of PV energy. To this end, a deep-learning-based forecasting model, namely a bi-directional long short-term memory model, is implemented to predict the operational constraints and dependency. Furthermore, a hybrid optimization technique that comprises a clustering and optimization algorithm is developed in which the clustering algorithm ensures appropriate combinations of user groups to develop optimal control policies. Finally, the forecasting model is integrated with the hybrid optimization algorithm to find the optimal solution involving PV-CESS energy utilization. Numerical analyses are performed using real historic data of the energy demand and PV generation for three consecutive days considering different scenarios. The results demonstrate that the electricity costs and self-consumption associated with the CESS are lower and greater than those of an individual ESS system, respectively, with the daily electricity cost decreasing by 21.89%, 13.81%, and 7.66% in the three analyzed scenarios.

关键词： Energy management system Community energy storage system Optimal scheduling Optimization algorithm Bi-directional long short-term memory clustering algorithm

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Novel distance measures for intuitionistic fuzzy sets based on various triangle centers of isosceles triangular fuzzy numbers and their applications

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期191卷 116228-116228页

作者： Garg, Harish Rani, Dimple Deemed Univ Sch Math Thapar Inst Engn & Technol Patiala 147004 Punjab India

The paper aims at introducing novel distance measures for the intuitionistic fuzzy set (IFS) to discuss the decision-making problems. The current work exploits four different notions of centers, namely centroid, orthocenter, circumcenter and incenter of the triangle. First, we mold knowledge embedded in IFSs into isosceles TFN (triangular fuzzy number). Hence, based on these TFNs, we design four-novel distance/similarity measures for IFSs using the structures of the four aforementioned centers and inspect their properties. To avoid the loss of information during the conversion of IFSs into isosceles TFNs, we included the degree of hesitation (t) between the pairs of the membership function in the process. The compensations and authentication of the proposed measures are established with diverse counter-intuitive patterns and decision-making obstacles. Further, a clustering algorithm is also given to match the objects based on confidence levels. The performed analysis shows that the proposed measures give distinguishable and compatible results as contrasted to existing ones.

关键词： IFS Similarity distance measures Centroid Orthocenter Circumcenter Incenter clustering algorithm TFNs

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DETECTION OF LINE-SYMMETRY CLUSTERS

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2011年第8期7卷 5027-5043页

作者： Hsieh, Yi-Zeng Su, Mu-Chun Chou, Chien-Hsing Wang, Pa-Chun Natl Cent Univ Dept Comp Sci & Informat Engn Jhongli 32001 Taoyuan County Taiwan Tamkang Univ Dept Elect Engn New Taipei City 25137 Taiwan Cathay Gen Hosp Qual Management Ctr Taipei Taiwan

Many real-world and man-made objects are symmetry. Therefore, it is reasonable to assume that some kinds of symmetry may exist in data clusters. The most common type of symmetry is line symmetry. In this paper, we propose a line symmetry distance measure. Based on the proposed line symmetry distance, a modified version of the K-means algorithm can be used to partition data into clusters with different geometrical shapes. Several data sets are used to test the performance of the proposed modified version of the K-means algorithm incorporated with the line symmetry distance.

关键词： Cluster analysis clustering algorithm Symmetry Distance measure

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