Publications

The criterion of largest entropy can provide a suitable interpretation for how to choose the optimal outcome of reasoning mechanism. Enlightened by such idea, in this study the entropy-based symmetric implicational (EBSI) method is presented. First of all, the novel EBSI criterion is provided, which is embodied as a kind of enhanced form of the fuzzy entropy full implication criterions. Moreover, the fundamental properties of outcome of such strategy are explored. Furthermore, unified outcomes of such strategy are constructed, which is based upon some properties of these fuzzy implications. Then the ideal outcomes are given for some kinds of implications and particular fuzzy implications.

Anomaly (outlier) detection is one of the most important problems of modern data analysis. Anomalies can be the results of database users' mistakes, operational errors or just missing values. The problem is important because of fast growth of the large data sets. Therefore, we present the initial results of work on a Granular Computing approach to data imputation and missing data analysis. Our proposal brings intuitive and interpretable solutions. Finally, in a series of experiments, we demonstrate its effectiveness for a large dataset in the area of transport.

Information granularity is an essential concept of Granular Computing that has successfully been applied in group decision-making to improve the consensus. Unlike the existing approaches, in which a uniform distribution of information granularity has been employed, this study proposes to improve the consensus by invoking a process of an optimal information granularity distribution across the experts' assessments provided in the form of fuzzy preference relations. An illustrative example and some simulated group decision-making problems are conducted to show evidence of its effectiveness. The results demonstrate that this optimal process, by being more flexible than the ones assuming that the information granularity is distributed in a uniform way, reaches a higher level of consensus.

Communications satellite network (CSN), as an integral component of the next generation of communication systems, has the capability to offer services globally. Data transmission in this network primarily relies on two modes: inter-satellite communication and satellite-to-ground station communication. The latter directly impacts the successful reception of data by users. However, due to resource and task limitations, finding a satisfactory solution poses a significant challenge. The communication satellite-ground station network scheduling problem (CS-GSNSP) aims to optimize CSN effectiveness by devising a plan that maximizes link construction time while considering constraints associated with satellite operation modes. The large number of tasks and numerous constraints in the problem result in a time-consuming evaluation of fitness function values. To address this issue, we propose a fuzzy fitness evaluation method (FFEM) that employs fuzzy or real evaluation methods based on individual similarity degrees. Additionally, we introduce an evolutionary algorithm based on FFEM, called evolutionary algorithm based on FFEM (FFEEA), for iteratively searching high-quality network construction schemes. In FFEEA, an adaptive crossover approach is used for efficient population search. Finally, extensive experiments are conducted to demonstrate that our proposed fuzzy fitness evaluation method and other improvement strategies significantly enhance satellite network service time. The study introduces a novel approach to enhance the efficiency of solving combinatorial optimization problems, such as CS-GSNSP, by mitigating the complexity associated with fitness evaluation.

This survey paper explores the transformative role of Machine Learning (ML) and Artificial Intelligence (AI) in Cardiopulmonary Resuscitation (CPR). It examines the evolution from traditional CPR methods to innovative ML-driven approaches, highlighting the impact of predictive modeling, AI-enhanced devices, and real-time data analysis in improving resuscitation outcomes. The paper provides a comprehensive overview, classification, and critical analysis of current applications, challenges, and future directions in this emerging field.

Federated clustering (FC) is an emerging and important topic that clustering all the data from many different heterogeneous clients/devices while prohibiting clients from sharing raw data. However, for existing works, there are some problems: (1) federated learning performs well in independent and identically distributed (IID) scenarios, but for Non-IID scenarios (Non-Identical class distribution), it is hard to collaboratively train a clustering algorithm based on global similarity measure while keeping all data local. (2) Some federated clustering algorithms have good performance, but their communication costs are high. It is difficult to balance communication costs and clustering effectiveness. In this paper, we propose new federated k-means clustering framework to solve the above two problems and balance communication costs and clustering effectiveness. (1) For the clients, we use cluster centers (representative points) genearate by K-means to represent the corresponding clusters because these representative points form density backbone of clusters and can effectively preserve the structure of the local data. (2) For the server, we propose two methods to reprocess these uploaded encrypted representative points to obtain better final cluster centers, one uses K-means and the other takes the improved density peaks (density cores) as final centers, and then send them back to the clients. Finally, each client assign local data to the nearest centers. The experimental results show that the proposed method performs better than some centralized (non-federated) classical clustering algorithms (K-means, DBSCAN and density peak clustering) and state-of-the-art (SOTA) centralized clustering algorithms in most cases. In particular, the proposed algorithms performs better than SOTA federated clustering framework k-FED (ICML2021) and MUFC (ICLR2023).