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Citations to this Journal [166 citations: 1–100 of 166 articles]

Articles published in Advances in Artificial Intelligence have been cited 166 times. The following is a list of the 166 articles that have cited the articles published in Advances in Artificial Intelligence.

  • Alejandro Bellogin, Pablo Castells, and Ivan Cantador, “Neighbor Selection and Weighting in User-Based Collaborative Filtering: A Performance Prediction Approach,” Acm Transactions on The Web, vol. 8, no. 2, 2014. View at Publisher · View at Google Scholar
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  • Marin Silic, Goran Delac, Ivo Krka, and Sinisa Srbljic, “Scalable and Accurate Prediction of Availability of Atomic Web Services,” Ieee Transactions on Services Computing, vol. 7, no. 2, pp. 252–264, 2014. View at Publisher · View at Google Scholar
  • Jun-ze Wang, Zheng Yan, Laurence T. Yang, and Ben-xiong Huang, “An Approach to Rank Reviews by Fusing and Mining Opinions based on Review Pertinence,” Information Fusion, 2014. View at Publisher · View at Google Scholar
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  • Mahmood Narimani, Seyed Hossein Hosseinian, and Behrooz Vahidi, “A modified methodology in electricity tracing problems based on Bialek’s method,” International Journal of Electrical Power & Energy Systems, vol. 60, pp. 74–81, 2014. View at Publisher · View at Google Scholar
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  • Mahdi Naderi-Beni, Ehsan Ghobadian, Sadoullah Ebrahimnejad, and Reza Tavakkoli-Moghaddam, “Fuzzy bi-objective formulation for a parallel machine scheduling problem with machine eligibility restrictions and sequence-dependent setup times,” International Journal of Production Research, pp. 1–24, 2014. View at Publisher · View at Google Scholar
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  • Hongli Lin, Xuedong Yang, Weisheng Wang, and Jiawei Luo, “A Performance Weighted Collaborative Filtering Algorithm for Personalized Radiology Education,” Journal of Biomedical Informatics, 2014. View at Publisher · View at Google Scholar
  • Luca Mesin, and Paolo Costa, “Prognostic value of EEG indexes for the Glasgow outcome scale of comatose patients in the acute phase,” Journal of Clinical Monitoring and Computing, vol. 28, no. 4, pp. 377–385, 2014. View at Publisher · View at Google Scholar
  • Shui-Guang Deng, Long-Tao Huang, and Zhao-Hui Wu, “Trust-Based Personalized Service Recommendation: A Network Perspective,” Journal of Computer Science and Technology, vol. 29, no. 1, pp. 69–80, 2014. View at Publisher · View at Google Scholar
  • Fuzhi Zhang, and Shuangxia Sun, “A Robust Collaborative Recommendation Algorithm Based on Least Median Squares Estimator,” Journal of Computers, vol. 9, no. 2, 2014. View at Publisher · View at Google Scholar
  • Ruzhi Xu, Shuaiqiang Wang, Xuwei Zheng, and Yinong Chen, “Distributed collaborative filtering with singular ratings for large scale recommendation,” Journal of Systems and Software, 2014. View at Publisher · View at Google Scholar
  • Amin Javari, and Mahdi Jalili, “A probabilistic model to resolve diversity–accuracy challenge of recommendation systems,” Knowledge and Information Systems, 2014. View at Publisher · View at Google Scholar
  • Nouman Azam, and JingTao Yao, “Game-theoretic Rough Sets for Recommender Systems,” Knowledge-Based Systems, 2014. View at Publisher · View at Google Scholar
  • Deng-Feng Li, and Shu-Ping Wan, “A fuzzy inhomogenous multiattribute group decision making approach to solve outsourcing provider selection problems,” Knowledge-Based Systems, 2014. View at Publisher · View at Google Scholar
  • Feng Xie, Zhen Chen, Jiaxing Shang, and Geoffrey C. Fox, “Grey Forecast model for accurate recommendation in presence of data sparsity and correlation,” Knowledge-Based Systems, 2014. View at Publisher · View at Google Scholar
  • Minor P. Hertwin, Olivares-Benítez Elías, Tapia-Olvera Ruben, and Martínez Flores J. Luis, “Variations in the Flow Approach to CFCLP-TC for Multiobjective Supply Chain Design,” Mathematical Problems in Engineering, vol. 2014, pp. 1–13, 2014. View at Publisher · View at Google Scholar
  • Eric A Stone, “Predictor performance with stratified data and imbalanced classes,” Nature Methods, vol. 11, no. 8, pp. 782–783, 2014. View at Publisher · View at Google Scholar
  • Sudhir Kumar, Jieping Ye, and Li Liu, “Reply to: "Proper reporting of predictor performance",” Nature Methods, vol. 11, no. 8, pp. 781–782, 2014. View at Publisher · View at Google Scholar
  • Jing Wang, and Liangwen Ke, “Feature subspace transfer for collaborative filtering,” Neurocomputing, 2014. View at Publisher · View at Google Scholar
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  • Chu-Xu Zhang, Lu Yu, Chuang Liu, Hao Liu, and Xiao-Yong Yan, “Information filtering via collaborative user clustering modeling,” Physica A-Statistical Mechanics and Its Applications, vol. 396, pp. 195–203, 2014. View at Publisher · View at Google Scholar
  • Zi-Ke Zhang, Lu Yu, Kuan Fang, Zhi-Qiang You, Chuang Liu, Hao Liu, and Xiao-Yong Yan, “Website-oriented recommendation based on heat spreading and tag-aware collaborative filtering,” Physica A-Statistical Mechanics and Its Applications, vol. 399, pp. 82–88, 2014. View at Publisher · View at Google Scholar
  • Rajani Chulyadyo, and Philippe Leray, “A Personalized Recommender System from Probabilistic Relational Model and Users’ Preferences,” Procedia Computer Science, vol. 35, pp. 1063–1072, 2014. View at Publisher · View at Google Scholar
  • Milan Ojstersek, Janez Brezovnik, Mojca Kotar, Marko Ferme, Goran Hrovat, Albin Bregant, and Mladen Borovic, “Establishing of a Slovenian open access infrastructure: a technical point of view,” Program-Electronic Library and Information Systems, vol. 48, no. 4, pp. 394–412, 2014. View at Publisher · View at Google Scholar
  • Salvador E. Barbosa, and Mikel D. Petty, “Exploiting spatio-temporal patterns using partial-state reinforcement learning in a synthetically augmented environment,” Progress in Artificial Intelligence, 2014. View at Publisher · View at Google Scholar
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  • Yongli Ren, Gang Li, Jun Zhang, and Wanlei Zhou, “The maximum imputation framework for neighborhood-based collaborative filtering,” Social Network Analysis and Mining, vol. 4, no. 1, 2014. View at Publisher · View at Google Scholar
  • Xin Liu, Hai-hong E, Jun-jie Tong, and Mei-na Song, “Collaborative recommendation based on social community detection,” The Journal of China Universities of Posts and Telecommunications, vol. 21, pp. 20–45, 2014. View at Publisher · View at Google Scholar
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  • M. J. Mahmoodabadi, M. Taherkhorsandi, and A. Bagheri, “Pareto Design of State Feedback Tracking Control of a Biped Robot via Multiobjective PSO in Comparison with Sigma Method and Genetic Algorithms: Modified NSGAII and MATLAB’s Toolbox,” The Scientific World Journal, vol. 2014, pp. 1–8, 2014. View at Publisher · View at Google Scholar
  • Seok-Ho Yoon, Ji-Soo Kim, Jiwoon Ha, Sang-Wook Kim, Minsoo Ryu, and Ho-Jin Choi, “Link-Based Similarity Measures Using Reachability Vectors,” The Scientific World Journal, vol. 2014, pp. 1–13, 2014. View at Publisher · View at Google Scholar
  • Manuel Schmitt, and Rolf Wanka, “Particle swarm optimization almost surely finds local optima,” Theoretical Computer Science, 2014. View at Publisher · View at Google Scholar
  • Tomislav Shuminoski, and Toni Janevski, “Radio Network Aggregation for 5G Mobile Terminals in Heterogeneous Wireless and Mobile Networks,” Wireless Personal Communications, 2014. View at Publisher · View at Google Scholar
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  • E. Chatzimichail, E. Paraskakis, and A. Rigas, “Predicting Asthma Outcome Using Partial Least Square Regression and Artificial Neural Networks,” Advances in Artificial Intelligence, vol. 2013, pp. 1–7, 2013. View at Publisher · View at Google Scholar
  • Faisal Kaleem, Abolfazl Mehbodniya, Kang K. Yen, and Fumiyuki Adachi, “A Fuzzy Preprocessing Module for Optimizing the Access Network Selection in Wireless Networks,” Advances in Fuzzy Systems, vol. 2013, pp. 1–9, 2013. View at Publisher · View at Google Scholar
  • Ariel Gómez, Carlos León, Jorge Ropero, Alejandro Carrasco, and Joaquín Luque, “Sabio: Soft Agent For Extended Information Retrieval,” Applied Artificial Intelligence, vol. 27, no. 4, pp. 249–277, 2013. View at Publisher · View at Google Scholar
  • Misato Tanaka, Yasunari Sasaki, Mitsunori Miki, and Tomoyuki Hiroyasu, “Crossover Method for Interactive Genetic Algorithms to Estimate Multimodal Preferences,” Applied Computational Intelligence and Soft Computing, vol. 2013, pp. 1–16, 2013. View at Publisher · View at Google Scholar
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