Authors: Wang, Le | Ding, Jian
Article Type: Research Article
Abstract: The activities in public places are becoming increasingly frequent, and public seats have become an important component of public space and an “auxiliary tool” for work, life, and leisure. The demand for such furniture in China is gradually increasing, but the design and research work of public seats is lagging behind the development status and speed of other civil and office furniture. Usually, public seats can be divided into two parts based on their usage: public indoor seats and public outdoor seats. How to understand the role and impact of public seats on the indoor environment, and how to establish …a new design method for public seats in the context of the overall environmental system, are currently urgent issues to be solved. The chair furniture comfort design evaluation is a classical MADM issues. In such paper, the generalized weighted Bonferroni mean (WBM) operator and power average (PA) is constructed for MADM with single-valued neutrosophic sets (SVNSs). Then, the generalized single-valued neutrosophic number power WBM (GSVNNPWBM) operator is built and then the MADM decision methods are proposed based on the GSVNNPWBM operator. Finally, an example about chair furniture comfort design evaluation and some comparative analysis were given to demonstrate the GSVNNPWBM method. Show more
Keywords: Multiple attribute decision making (MADM), single-valued neutrosophic sets (SVNSs), weighted Bonferroni mean (WBM) operator, power average (PA), chair furniture comfort design evaluation
DOI: 10.3233/KES-230123
Citation: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 27, no. 4, pp. 407-424, 2023
Authors: Feng, Lin | Wang, Le | Jin, Bo
Article Type: Research Article
Abstract: High utility itemsets mining is a hot topic in data stream mining. It is essential that the mining algorithm should be efficient in both time and space for data stream is continuous and unbounded. To the best of our knowledge, the existing algorithms require multiple database scans to mine high utility itemsets, and this hinders their efficiency. In this paper, we propose a new data structure, called UT-Tree (Utility on Tail Tree), for maintaining utility information of transaction itemsets to avoid multiple database scans. The UT-Tree is created with one database scan, and contains a fixed number of transaction itemsets; …utility information is stored on tail-nodes only. Based on the proposed data structure and the sliding window approach, we propose a mining algorithm, called HUM-UT (High Utility itemsets Mining based on UT-Tree), to find high utility itemsets from transactional data streams. The HUM-UT algorithm mines high utility itemsets from the UT-Tree without additional database scan. Experiment results show that our algorithm has better performance and is more stable under different experimental conditions than the state-of-the-art algorithm HUPMS in terms of time and space. Show more
Keywords: Data mining, data streams, frequent itemsets, high utility itemsets
DOI: 10.3233/IDA-130595
Citation: Intelligent Data Analysis, vol. 17, no. 4, pp. 585-602, 2013
Authors: Cheng, Haodong | Han, Meng | Zhang, Ni | Wang, Le | Li, Xiaojuan
Article Type: Research Article
Abstract: The researcher proposed the concept of Top-K high-utility itemsets mining over data streams. Users directly specify the number K of high-utility itemsets they wish to obtain for mining with no need to set a minimum utility threshold. There exist some problems in current Top-K high-utility itemsets mining algorithms over data streams including the complex construction process of the storage structure, the inefficiency of threshold raising strategies and utility pruning strategies, and large scale of the search space, etc., which still can not meet the requirement of real-time processing over data streams with limited time and memory constraints. To solve this …problem, this paper proposes an efficient algorithm based on dataset projection for mining Top-K high-utility itemsets from a data stream. A data structure CIUDataListSW is also proposed, which stores the position of the item in the transaction to effectively obtain the initial projected dataset of the item. In order to improve the projection efficiency, this paper innovates a new reorganization technology for projected transactions in common batches to maintain the sort order of transactions in the process of dataset projection. Dual pruning strategy and transaction merging mechanism are also used to further reduce search space and dataset scanning costs. In addition, based on the proposed CUDH S W structure, an efficient threshold raising strategy CUD is used, and a new threshold raising strategy CUDCB is designed to further shorten the mining time. Experimental results show that the algorithm has great advantages in running time and memory consumption, and it is especially suitable for the mining of high-utility itemsets of dense datasets. Show more
Keywords: Itemset mining, utility mining, high utility itemsets, data streams, Top-K high-utility
DOI: 10.3233/JIFS-210610
Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3317-3338, 2021
Authors: Cheng, Haodong | Han, Meng | Zhang, Ni | Li, Xiaojuan | Wang, Le
Article Type: Research Article
Abstract: Traditional association rule mining has been widely studied, but this is not applicable to practical applications that must consider factors such as the unit profit of the item and the purchase quantity. High-utility itemset mining (HUIM) aims to find high-utility patterns by considering the number of items purchased and the unit profit. However, most high-utility itemset mining algorithms are designed for static databases. In real-world applications (such as market analysis and business decisions), databases are usually updated by inserting new data dynamically. Some researchers have proposed algorithms for finding high-utility itemsets in dynamically updated databases. Different from the batch processing …algorithms that always process the databases from scratch, the incremental HUIM algorithms update and output high-utility itemsets in an incremental manner, thereby reducing the cost of finding high-utility itemsets. This paper provides the latest research on incremental high-utility itemset mining algorithms, including methods of storing itemsets and utilities based on tree, list, array and hash set storage structures. It also points out several important derivative algorithms and research challenges for incremental high-utility itemset mining. Show more
Keywords: Survey, pattern mining, incremental mining, high-utility patterns, frequent itemsets
DOI: 10.3233/JIFS-202745
Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 841-866, 2021
Authors: Han, Meng | Li, Xiaojuan | Wang, Le | Zhang, Ni | Cheng, Haodong
Article Type: Research Article
Abstract: Most data stream ensemble classification algorithms use supervised learning. This method needs to use a large number of labeled data to train the classifier, and the cost of obtaining labeled data is very high. Therefore, the semi supervised learning algorithm using labeled data and unlabeled data to train the classifier becomes more and more popular. This article is the first to review data stream ensemble classification methods from the perspectives of supervised learning and semi-supervised learning. Firstly, basic classifiers such as decision trees, neural networks, and support vector machines are introduced from the perspective of supervised learning and semi-supervised learning. …Secondly, the key technologies in data stream ensemble classification are explained from the two aspects of incremental and online. Finally, the majority voting and weight voting are explained in the ensemble strategies. The different ensemble methods are summarized and the classic algorithms are quantitatively analyzed. Further research directions are given, including the handling of concept drift under supervised and semi-supervised learning, the study of homogeneous ensemble and heterogeneous ensemble, and the classification of data stream ensemble under unsupervised learning. Show more
Keywords: Review, ensemble learning, supervised algorithm, semi-supervised algorithm
DOI: 10.3233/JIFS-211101
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3859-3878, 2022
Authors: Wu, Xingwang | Wang, Wanqin | Wang, Le | Liu, Bin | Yu, Yongqiang | Zhang, Shuai | Gao, Na | Shen, Yun
Article Type: Research Article
Abstract: Purpose: The purpose of this study was to investigate the value of spectral CT monoenergetic imaging for detecting hemoglobin levels. Material and methods: Sixty-five hospitalized patients received chest non-contrast CT scan in gemstone spectral imaging (GSI) mode on a GE Discovery CT750 HD. This study was approved by the hospital ethics committee and informed consent was signed by every patient. Raw data were reconstructed at 1.25 mm and then transferred to a AW4.4 workstation. The CT value of the ascending aorta at 40 keV∼140 keV was measured under GSI viewer of AW4.4. Hemoglobin contents were measured biochemically within 24 hrs …after CT scan. The SPSS 16.0 software package was used to analyze the correlation between the obtained CT value and hemoglobin level. Results: At 40 keV ∼ 100 keV, the CT value decreased followed by the increase of keV and gradually stabilized at 100 keV ∼ 140 keV with an amplitude variation of 10 HU. The CT values measured at 40 keV ∼ 140 keV were well correlated with hemoglobin levels. The optimal correlation was observed at the monoenergetic level of 70 keV (r=0.633, p=0.000). The relationship between CT value and hemoglobin content could be expressed as CT value=13.015+0.245 × hemoglobin content. At 40 keV ∼ 140 keV, there is strong linear correlation between the CT value of the ascending aorta and hemoglobin content. The optimal linear relationship was observed at 70 keV. Conclusion: Spectral CT monoenergetic imaging can be applied for quantitative determination of hemoglobin content within a specified area of the circulatory system. Show more
Keywords: Poly-energetic imaging, monoenergetic imaging, energy spectrum scan, hemoglobin
DOI: 10.3233/XST-2012-00354
Citation: Journal of X-Ray Science and Technology, vol. 20, no. 4, pp. 483-488, 2012
Authors: He, Yu-Chuan | Yuan, Guo-Dong | Li, Nan | Ren, Mei-Fang | Qian-Zhang, | Deng, Kai-Ning | Wang, Le-Chuan | Xiao, Wei-Ling | Ma, Nan | Stamm, Christof | Felthaus, Oliver | Prantl, Lukas | Nie, Jia | Wang, Gang
Article Type: Research Article
Abstract: Myocardial infarction refers to the ischemic necrosis of myocardium, characterized by a sharp reduction or interruption of blood flow in the coronary arteries due to the coronary artery occlusion, resulting in severe and prolonged ischemia in the corresponding myocardium and ultimately leading to ischemic necrosis of the myocardium. Given its high risk, it is considered as one of the most serious health threats today. In current clinical practice, multiple approaches have been explored to diminish myocardial oxygen consumption and alleviate symptoms, but notable success remains elusive. Accumulated clinical evidence has showed that the implantation of mesenchymal stem cell for treating …myocardial infarction is both effective and safe. Nevertheless, there persists controversy and variability regarding the standardizing MSC transplantation protocols, optimizing dosage, and determining the most effective routes of administration. Addressing these remaining issues will pave the way of integration of MSCs as a feasible mainstream cardiac treatment. Show more
Keywords: Myocardial infarction, mesenchymal stem cells, nanomaterials
DOI: 10.3233/CH-249101
Citation: Clinical Hemorheology and Microcirculation, vol. 87, no. 3, pp. 383-398, 2024
