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アクセスランキング（PKV）

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PKV: 現在知識価値 PKV-F: 高減衰 PKV 月間 年間 トータル レビュー数
順位 タイトル
著者 雑誌 巻 号 ページ 年 PKVアクセス数
22551 iMCRec: A multi-criteria framework for personalized point-of-interest recommendations
Yan Lyu, Chi-Yin Chow, Ran Wang, Victor C.S. Lee Information Sciences 483 None 294–312 2019 1.0
平均評価: / 5 ( 件のレビュー)
22552 Two new reference vector adaptation strategies for many-objective evolutionary algorithms
Zhengping Liang, Weijun Hou, Xiang Huang, Zexuan Zhu Information Sciences 483 None 332–349 2019 1.0
平均評価: / 5 ( 件のレビュー)
22553 A novel method for classification of BCI multi-class motor imagery task based on Dempster–Shafer theory
Sara Razi, Mohammad Reza Karami Mollaei, Jamal Ghasemi Information Sciences 484 None 14–26 2019 1.0
平均評価: / 5 ( 件のレビュー)
22554 Hierarchical attribute based encryption with continuous leakage-resilience
Jiguo Li, Qihong Yu, Yichen Zhang Information Sciences 484 None 113–134 2019 1.0
平均評価: / 5 ( 件のレビュー)
22555 Geometrically invariant color, shape and texture features for object recognition using multiple kernel learning classification approach
Chandan Singh, Jaspreet Singh Information Sciences 484 None 135–152 2019 1.0
平均評価: / 5 ( 件のレビュー)
22556 Stabilization for switched stochastic systems with semi-Markovian switching signals and actuator saturation
Zenghui Hu, Xiaowu Mu Information Sciences 483 None 419–431 2019 1.0
平均評価: / 5 ( 件のレビュー)
22557 Fuzzy forecasting based on linear combinations of independent variables, subtractive clustering algorithm and artificial bee colony algorithm
Shouzhen Zeng, Shyi-Ming Chen, Mario Orlando Teng Information Sciences 484 None 350–366 2019 1.0
平均評価: / 5 ( 件のレビュー)
22558 A novel H∞ tracking control scheme for periodic piecewise time-varying systems
Xiaochen Xie, James Lam, Panshuo Li Information Sciences 484 None 71–83 2019 1.0
平均評価: / 5 ( 件のレビュー)
22559 A novel network optimization partner selection method based on collaborative and knowledge networks
Jing Han, Xinyu Teng, Xun Cai Information Sciences 484 None 269–285 2019 1.0
平均評価: / 5 ( 件のレビュー)
22560 Evaluation of statistical and machine learning models for time series prediction: Identifying the state-of-the-art and the best conditions for the use of each model
Antonio Rafael Sabino Parmezan, Vinicius M.A. Souza, Gustavo E.A.P.A. Batista Information Sciences 484 None 302–337 2019 1.0
平均評価: / 5 ( 件のレビュー)