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Smart Agriculture System based on Internet of Things using Kernel K-Means with Support Vector Machine Conference paper
Zhong, Junhong, Lai, Qi. Smart Agriculture System based on Internet of Things using Kernel K-Means with Support Vector Machine[C]:Institute of Electrical and Electronics Engineers Inc., 2024.
Authors:  Zhong, Junhong;  Lai, Qi
Favorite | TC[Scopus]:0 | Submit date：2024/09/03
Data Cleaning  Internet Of Things  Kernel K-means  Smart Agriculture  Support Vector Machine  
QFINCH: Quick hierarchical clustering using k-means and first neighbor relations Conference paper
Zhang, Jiajun, Yang, Geping, Yang, Yiyang, Lu, Juan. QFINCH: Quick hierarchical clustering using k-means and first neighbor relations[C]:Institute of Electrical and Electronics Engineers Inc., 2024, 1992-1997.
Authors:  Zhang, Jiajun;  Yang, Geping;  Yang, Yiyang;  Lu, Juan
Favorite | TC[Scopus]:0 | Submit date：2024/08/05
Data Mining  First Neighbor  Hierarchical Clustering  K-means  
Comparative Analysis of Offshore Wind Power Prediction Models and Clustering-Based Daily Output Classification Conference paper
Ou, Zhongxi, Lan, Wei, Zhang, Liang, Tong, Zhu, Liu, Dundun, Liu, Zhaoxi. Comparative Analysis of Offshore Wind Power Prediction Models and Clustering-Based Daily Output Classification[C]:Institute of Electrical and Electronics Engineers Inc., 2024, 1482-1487.
Authors:  Ou, Zhongxi;  Lan, Wei;  Zhang, Liang;  Tong, Zhu;  Liu, Dundun; et al.
Favorite | TC[Scopus]:1 | Submit date：2024/09/03
Gru  K-means Clustering  Lstm  Offshore Wind Power  Offshore Wind Power Forecasting  Prediction Accuracy  Prediction Models  
Unsupervised Color-Based Nuclei Segmentation in Histopathology Images with Various Color Spaces and K Values Selection Journal article
Zhang, Qi, Ying, Zuobin, Shen, Jian, Kou, Seng Ka, Sun, Jingzhang, Zhang, Bob. Unsupervised Color-Based Nuclei Segmentation in Histopathology Images with Various Color Spaces and K Values Selection[J]. International Journal of Image and Graphics, 2024, 2550061.
Authors:  Zhang, Qi;  Ying, Zuobin;  Shen, Jian;  Kou, Seng Ka;  Sun, Jingzhang; et al.
Favorite | TC[WOS]:0 TC[Scopus]:0  IF:0.8/0.8 | Submit date：2024/05/16
Color Space  Fuzzy C-means  Histopathology  K-means  Nuclei Segmentation  
基于行为影响因子的非侵入式负荷实时分解算法 Journal article
Cai, Yu, Dong, Shufeng, Xu, Hang, Mao, Hangyin, Song, Yonghua. 基于行为影响因子的非侵入式负荷实时分解算法[J]. 电力自动化设备, 2021, 41(12), 193-199.
Authors:  Cai, Yu;  Dong, Shufeng;  Xu, Hang;  Mao, Hangyin;  Song, Yonghua
Favorite | TC[Scopus]:8 | Submit date：2022/01/14
负荷分解  超状态  隐马尔科夫模型  自适应k-means  行为模式  
Self-Adaptive Multiprototype-Based Competitive Learning Approach: A k-Means-Type Algorithm for Imbalanced Data Clustering Journal article
Lu, Yang, Cheung, Yiu Ming, Tang, Yuan Yan. Self-Adaptive Multiprototype-Based Competitive Learning Approach: A k-Means-Type Algorithm for Imbalanced Data Clustering[J]. IEEE Transactions on Cybernetics, 2021, 51(3), 1598-1612.
Authors:  Lu, Yang;  Cheung, Yiu Ming;  Tang, Yuan Yan
Favorite | TC[WOS]:40 TC[Scopus]:51  IF:9.4/10.3 | Submit date：2021/12/07
Class Imbalance Learning  Competitive Learning  Data Clustering  Internal Validation Measure  K-means-type Algorithm  Multiprototype Clustering  
Abnormal dynamic functional connectivity and brain states in Alzheimer's diseases: functional near-infrared spectroscopy study Journal article
Haijing Niu, Zhaojun Zhu, Mengjing Wang, Xuanyu Li, Zhen Yuan, Yu Sun, Ying Han. Abnormal dynamic functional connectivity and brain states in Alzheimer's diseases: functional near-infrared spectroscopy study[J]. NEUROPHOTONICS, 2019, 6(2).
Authors:  Haijing Niu;  Zhaojun Zhu;  Mengjing Wang;  Xuanyu Li;  Zhen Yuan; et al.
Favorite | TC[WOS]:32 TC[Scopus]:32  IF:4.8/4.3 | Submit date：2020/06/24
Resting State  Functional Connectivity  Sliding-window  K-means Clustering  Brain Network  Alzheimer's Disease  
Abnormal dynamic functional connectivity and brain states in Alzheimer’s diseases: functional near-infrared spectroscopy study Journal article
Haijing Niu, Zhaojun Zhu, Mengjing Wan, Xuanyu Li, Zhen Yuan, Yu Sun, Ying Han. Abnormal dynamic functional connectivity and brain states in Alzheimer’s diseases: functional near-infrared spectroscopy study[J]. Neurophotonics, 2019, 6(2), 025010.
Authors:  Haijing Niu;  Zhaojun Zhu;  Mengjing Wan;  Xuanyu Li;  Zhen Yuan; et al.
Adobe PDF | Favorite | TC[WOS]:32 TC[Scopus]:32 | Submit date：2022/08/21
CommunicAtion WithIn The BraIn Is Highly Dynamic. AlzheI.e.’s dIseAse (Ad) ExhibIts Dynamic.progression COrrespondIng To a DeclIne In MemOry And Cognition. HoWever, Little Is Known Of wheTher BraIn Dynamic. Are dIsrupted In Ad And Its Prodromal Stage, Mild CognitI.e.impairment (Mci). FOr Our Study, We AcquI.e. High samplIng RAte Functional near-InfrAred Spectroscopy imagIng DAta At Rest From The EntI.e.cOrtex Of 23 pAtients With Ad Dementia, 25 pAtients With Amnestic Mild CognitI.e.impairment (aMci), And 30 age-mAtched Healthy Controls (Hcs). slidIng-wIndow cOrrelAtion And K-means clusterIng Analyses Were Used To Construct Dynamic.Functional Connectivity (Fc) Maps FOr Each Participant. We dIscovered thAt The BraIn’s Dynamic.Fc Variability Strength (q) Significantly IncreAsed In Both aMci And Ad Group As compAred To Hcs. usIng The q Value As a meAsurement, The clAssificAtion perFOrmance ExhibI.e. a Good poWer In differentiAtIng aMci [Area Under The Curve (Auc ¼ 82.5%)] Or Ad (Auc ¼ 86.4%) From Hcs. furThermOre, We Identified Two abnOrmal BraIn Fc stAtes In The Ad Group, Of Which The Occurrence Frequency (f) ExhibI.e. a Significant decreAse FOr The First-level Fc stAte (stAte 1) And a Significant IncreAse FOr The Second-level Fc stAte (stAte 2). We Also Found thAt The abnOrmal f In These Two stAtes Significantly cOrrelAted With The CognitI.e.impairment In pAtients. These fIndIngs provI.e.The First EvI.e.ce To demonstRAte The dIsruptions Of Dynamic.BraIn Connectivity In aMci And Ad And Extend The trAditional stAtic (I.e., tI.e.averaged) Fc fIndIngs In The dIseAse (I.e., dIsconnection Syndrome) And Thus provI.e.Insights InTo UnderstAndIng The pAthophysiological mechanIsms occurrIng In aMci And Ad.  
Data Clustering Using the Cooperative Search Based Artificial Bee Colony Algorithm Conference paper
Guo, Chen, Tang, Heng, Lee, Chang Boon, Niu, Ben. Data Clustering Using the Cooperative Search Based Artificial Bee Colony Algorithm[C], 2019, 660-671.
Authors:  Guo, Chen;  Tang, Heng;  Lee, Chang Boon;  Niu, Ben
Favorite | TC[Scopus]:1 | Submit date：2019/11/27
Data Clustering  K-means  Artificial Bee Colony Algorithm  
Clustering based Image Segmentation via Weighted Fusion of Non-local and Local Information Conference paper
Guo，Li, Chen，Long, Philip Chen，C. L., Li，Tianjun, Zhou，Jin. Clustering based Image Segmentation via Weighted Fusion of Non-local and Local Information[C], 2018, 299-303.
Authors:  Guo，Li;  Chen，Long;  Philip Chen，C. L.;  Li，Tianjun;  Zhou，Jin
Favorite | TC[Scopus]:1 | Submit date：2021/03/09
automatically balance  image segmentation  K-means  non-local information