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Citations to this Journal [430 citations: 1–100 of 429 articles]

Articles published in Advances in Artificial Intelligence have been cited 430 times. The following is a list of the 429 articles that have cited the articles published in Advances in Artificial Intelligence.

  • Shanshan Feng, Jian Cao, Jie Wang, and Shiyou Qian, “Recommendations Based on Comprehensively Exploiting the Latent Factors Hidden in Items’ Ratings and Content,” ACM Transactions on Knowledge Discovery from Data, vol. 11, no. 3, pp. 1–27, 2017. View at Publisher · View at Google Scholar
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  • Lihong Peng, Bo Liao, Wen Zhu, Zejun Li, and Keqin Li, “Predicting Drug–Target Interactions With Multi-Information Fusion,” IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 2, pp. 561–572, 2017. View at Publisher · View at Google Scholar
  • Xiwang Yang, Chao Liang, Miao Zhao, Hongwei Wang, Hao Ding, Yong Liu, Yang Li, and Junlin Zhang, “Collaborative Filtering-Based Recommendation of Online Social Voting,” IEEE Transactions on Computational Social Systems, vol. 4, no. 1, pp. 1–13, 2017. View at Publisher · View at Google Scholar
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  • Da Zheng, Disa Mhembere, Vince Lyzinski, Joshua T. Vogelstein, Carey E. Priebe, and Randal Burns, “Semi-External Memory Sparse Matrix Multiplication for Billion-Node Graphs,” IEEE Transactions on Parallel and Distributed Systems, vol. 28, no. 5, pp. 1470–1483, 2017. View at Publisher · View at Google Scholar
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  • Serkan Ballı, and Mustafa Tuker, “A Fuzzy Multi-Criteria Decision Analysis Approach for the Evaluation of the Network Service Providers in Turkey,” Intelligent Automation & Soft Computing, pp. 1–7, 2017. View at Publisher · View at Google Scholar
  • Yitao Wu, Xingming Zhang, Hong Yu, Shuai Wei, and Wei Guo, “Collaborative filtering recommendation algorithm based on user fuzzy similarity,” Intelligent Data Analysis, vol. 21, no. 2, pp. 311–327, 2017. View at Publisher · View at Google Scholar
  • Anis Sharafoddini, Joel A Dubin, and Joon Lee, “Patient Similarity in Prediction Models Based on Health Data: A Scoping Review,” JMIR Medical Informatics, vol. 5, no. 1, pp. e7, 2017. View at Publisher · View at Google Scholar
  • Luis Omar Colombo-Mendoza, Rafael Valencia-García, Alejandro Rodríguez-González, Ricardo Colomo-Palacios, and Giner Alor-Hernández, “Towards a knowledge-based probabilistic and context-aware social recommender system,” Journal of Information Science, pp. 016555151769878, 2017. View at Publisher · View at Google Scholar
  • Alexandra Oliveira, Brígida Mónica Faria, A. Rita Gaio, and Luís Paulo Reis, “Data Mining in HIV-AIDS Surveillance System,” Journal of Medical Systems, vol. 41, no. 4, 2017. View at Publisher · View at Google Scholar
  • Shantanu Pal, “Evaluating the impact of network loads and message size on mobile opportunistic networks in challenged environments,” Journal of Network and Computer Applications, vol. 81, pp. 47–58, 2017. View at Publisher · View at Google Scholar
  • Abdusselam Altunkaynak, and Tewodros Assefa Nigussie, “Monthly Water Consumption Prediction Using Season Algorithm and Wavelet Transform–Based Models,” Journal of Water Resources Planning and Management, pp. 04017011, 2017. View at Publisher · View at Google Scholar
  • Chaochao Chen, Kevin Chen-Chuan Chang, and Xiaolin Zheng, “Towards Context-Aware Social Recommendation via Individual Trust,” Knowledge-Based Systems, 2017. View at Publisher · View at Google Scholar
  • Jianling Sun, Chenghao Liu, Tao Jin, Steven C. H. Hoi, and Peilin Zhao, “Collaborative topic regression for online recommender systems: an online and Bayesian approach,” Machine Learning, 2017. View at Publisher · View at Google Scholar
  • Rahul Katarya, and Om Prakash Verma, “Efficient music recommender system using context graph and particle swarm,” Multimedia Tools and Applications, 2017. View at Publisher · View at Google Scholar
  • Wenjuan Cui, Pengfei Wang, Yi Du, Xin Chen, Danhuai Guo, Jianhui Li, and Yuanchun Zhou, “An Algorithm for Event Detection Based on Social Media Data,” Neurocomputing, 2017. View at Publisher · View at Google Scholar
  • Xingyi Ren, Meina Song, Haihong E, and Junde Song, “Context-aware probabilistic matrix factorization modeling for point-of-interest recommendation,” Neurocomputing, 2017. View at Publisher · View at Google Scholar
  • Wen-Jun Li, Qiang Dong, Yang-Bo Shi, Yan Fu, and Jia-Lin He, “Effect of recent popularity on heat-conduction based recommendation models,” Physica A: Statistical Mechanics and its Applications, vol. 474, pp. 334–343, 2017. View at Publisher · View at Google Scholar
  • Sunil Kr. Jha, Jasmin Bilalovic, Anju Jha, Nilesh Patel, and Han Zhang, “Renewable energy: Present research and future scope of Artificial Intelligence,” Renewable and Sustainable Energy Reviews, vol. 77, pp. 297–317, 2017. View at Publisher · View at Google Scholar
  • Hongtao Wang, Hui Wen, Feng Yi, Hongsong Zhu, and Limin Sun, “Road Traffic Anomaly Detection via Collaborative Path Inference from GPS Snippets,” Sensors, vol. 17, no. 3, pp. 550, 2017. View at Publisher · View at Google Scholar
  • Paris V. Giampouras, Athanasios A. Rontogiannis, Konstantinos E. Themelis, and Konstantinos D. Koutroumbas, “Online Sparse and Low-Rank Subspace Learning from Incomplete Data: A Bayesian View,” Signal Processing, 2017. View at Publisher · View at Google Scholar
  • Dae-Young Kim, Young-Sik Jeong, and Seokhoon Kim, “Data-Filtering System to Avoid Total Data Distortion in IoT Networking,” Symmetry, vol. 9, no. 1, pp. 16, 2017. View at Publisher · View at Google Scholar
  • Ankhtuya Ochirbat, Timothy K. Shih, Chalothon Chootong, Worapot Sommool, W.K.T.M. Gunarathne, Wang Hai-Hui, and Ma Zhao-Heng, “Hybrid Occupation Recommendation for Adolescents on Interest, Profile, and Behavior,” Telematics and Informatics, 2017. View at Publisher · View at Google Scholar
  • Wei-Ta Chu, and Ya-Lun Tsai, “A hybrid recommendation system considering visual information for predicting favorite restaurants,” World Wide Web, 2017. View at Publisher · View at Google Scholar
  • Liang Hu, Longbing Cao, Jian Cao, Zhiping Gu, Guandong Xu, and Dingyu Yang, “Learning Informative Priors from Heterogeneous Domains to Improve Recommendation in Cold-Start User Domains,” ACM Transactions on Information Systems, vol. 35, no. 2, pp. 1–37, 2016. View at Publisher · View at Google Scholar
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  • Le Wu, Qi Liu, Enhong Chen, Nicholas Jing Yuan, Guangming Guo, and Xing Xie, “Relevance Meets Coverage,” ACM Transactions on Intelligent Systems and Technology, vol. 7, no. 3, pp. 1–30, 2016. View at Publisher · View at Google Scholar
  • Ming Yan, Jitao Sang, Changsheng Xu, and M. Shamim Hossain, “A Unified Video Recommendation by Cross-Network User Modeling,” ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 12, no. 4, pp. 1–24, 2016. View at Publisher · View at Google Scholar
  • Hugo LÓPez-FernÁNdez, Miguel Reboiro-Jato, José A. PÉRez RodrÍGuez, Florentino Fdez-Riverola, and Daniel Glez-PeÑA, “The Artificial Intelligence Workbench: a retrospective review,” Adcaij: Advances In Distributed Computing And Artificial Intelligence Journal, vol. 5, no. 1, pp. 73, 2016. View at Publisher · View at Google Scholar
  • Suwoong Lee, Soono Kwon, Youngwoo Kim, and Kangwon Lee, “A method of vertical and horizontal force estimation by using air-filled material and camera for soft physical human-robot interaction: fundamental experiments,” Advanced Robotics, pp. 1–8, 2016. View at Publisher · View at Google Scholar
  • E. Earl Eiland, and Lorie M. Liebrock, “Efficacious Discriminant Analysis (Classifier) Measures for End Users,” Advances in Artificial Intelligence, vol. 2016, pp. 1–17, 2016. View at Publisher · View at Google Scholar
  • Divya Tomar, and Sonal Agarwal, “Twin Support Vector Machine for Multiple Instance Learning Based on Bag Dissimilarities,” Advances in Artificial Intelligence, vol. 2016, pp. 1–18, 2016. View at Publisher · View at Google Scholar
  • Mustafa Mısır, and Michèle Sebag, “Alors: An algorithm recommender system,” Artificial Intelligence, 2016. View at Publisher · View at Google Scholar
  • Maria-Iuliana Dascalu, Constanta-Nicoleta Bodea, Monica Nastasia Mihailescu, Elena Alice Tanase, and Patricia Ordoñez de Pablos, “Educational recommender systems and their application in lifelong learning,” Behaviour & Information Technology, pp. 1–8, 2016. View at Publisher · View at Google Scholar
  • Fredrick M. Mobegi, Aldert Zomer, Marien I. de Jonge, and Sacha A. F. T. van Hijum, “Advances and perspectives in computational prediction of microbial gene essentiality,” Briefings in Functional Genomics, 2016. View at Publisher · View at Google Scholar
  • A. Prithiviraj, K. Krishnamoorthy, and B. Vinothini, “Fuzzy Logic Based Decision Making Algorithm to Optimize the Handover Performance in HetNets,” Circuits and Systems, vol. 07, no. 11, pp. 3756–3777, 2016. View at Publisher · View at Google Scholar
  • M. Gopila, and I. Gnanambal, “An Effective Detection of Inrush and Internal Faults in Power Transformers Using Bacterial Foraging Optimization Technique,” Circuits and Systems, vol. 07, no. 08, pp. 1569–1580, 2016. View at Publisher · View at Google Scholar
  • Oyebade K. Oyedotun, and Adnan Khashman, “Banknote recognition: investigating processing and cognition framework using competitive neural network,” Cognitive Neurodynamics, 2016. View at Publisher · View at Google Scholar
  • Mehdi Elahi, Francesco Ricci, and Neil Rubens, “A survey of active learning in collaborative filtering recommender systems,” Computer Science Review, 2016. View at Publisher · View at Google Scholar
  • Nikolaos Polatidis, and Christos K. Georgiadis, “A dynamic multi-level collaborative filtering method for improved recommendations,” Computer Standards & Interfaces, 2016. View at Publisher · View at Google Scholar
  • Janghyeok Yoon, Wonchul Seo, Byoung-Youl Coh, Inseok Song, and Jae-Min Lee, “Identifying product opportunities using collaborative filtering-based patent analysis,” Computers & Industrial Engineering, 2016. View at Publisher · View at Google Scholar
  • Minsung Hong, and Jason J. Jung, “MyMovieHistory: Social Recommender System by Discovering Social Affinities Among Users,” Cybernetics and Systems, vol. 47, no. 1-2, pp. 88–110, 2016. View at Publisher · View at Google Scholar
  • Michiel Stock, Krzysztof Dembczyński, Bernard De Baets, and Willem Waegeman, “Exact and efficient top-K inference for multi-target prediction by querying separable linear relational models,” Data Mining and Knowledge Discovery, 2016. View at Publisher · View at Google Scholar
  • Rahul Katarya, and Om Prakash Verma, “An effective collaborative movie recommender system with cuckoo search,” Egyptian Informatics Journal, 2016. View at Publisher · View at Google Scholar
  • Maria Rodriguez Fernandez, Adolfo Cortes Garcia, Ignacio Gonzalez Alonso, and Eduardo Zalama Casanova, “Using the Big Data generated by the Smart Home to improve energy efficiency management,” Energy Efficiency, vol. 9, no. 1, pp. 249–260, 2016. View at Publisher · View at Google Scholar
  • Longbing Cao, “Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting,” Engineering, vol. 2, no. 2, pp. 212–224, 2016. View at Publisher · View at Google Scholar
  • Li-Chen Cheng, Yen-Liang Chen, and Yu-Chia Chiang, “Identifying conflict patterns to reach a consensus – A novel group decision approach,” European Journal of Operational Research, 2016. View at Publisher · View at Google Scholar
  • Thomas Hart, and Lei Xie, “Providing data science support for systems pharmacology and its implications to drug discovery,” Expert Opinion on Drug Discovery, pp. 1–16, 2016. View at Publisher · View at Google Scholar
  • Diego Sánchez-Moreno, Ana B. Gil González, M. Dolores Muñoz Vicente, Vivian F. López Batista, and María N. Moreno García, “A collaborative filtering method for music recommendation using playing coefficients for artists and users,” Expert Systems with Applications, vol. 66, pp. 234–244, 2016. View at Publisher · View at Google Scholar
  • Edjalma Queiroz da Silva, Celso G. Camilo-Junior, Luiz Mario L. Pascoal, and Thierson C. Rosa, “An evolutionary approach for combining results of recommender systems techniques based on collaborative filtering,” Expert Systems with Applications, 2016. View at Publisher · View at Google Scholar
  • Yueshen Xu, Jianwei Yin, Shuiguang Deng, Neal N. Xiong, and Jianbin Huang, “Context-aware QoS Prediction for Web Service Recommendation and Selection,” Expert Systems with Applications, 2016. View at Publisher · View at Google Scholar
  • Zhongchen Miao, Junchi Yan, Kai Chen, Xiaokang Yang, Hongyuan Zha, and Wenjun Zhang, “Joint Prediction of Rating and Popularity for Cold-Start Item by Sentinel User Selection,” IEEE Access, vol. 4, pp. 8500–8513, 2016. View at Publisher · View at Google Scholar
  • Zhe Yang, Bing Wu, Kan Zheng, Xianbin Wang, and Lei Lei, “A Survey of Collaborative Filtering-Based Recommender Systems for Mobile Internet Applications,” IEEE Access, vol. 4, pp. 3273–3287, 2016. View at Publisher · View at Google Scholar
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  • Giuseppe Araniti, Igor Bisio, Mauro De Sanctis, Antonino Orsino, and John Cosmas, “Multimedia Content Delivery for Emerging 5G-Satellite Networks,” IEEE Transactions on Broadcasting, vol. 62, no. 1, pp. 10–23, 2016. View at Publisher · View at Google Scholar
  • Shu Wu, Weiyu Guo, Song Xu, Yongzhen Huang, Liang Wang, and Tieniu Tan, “Coupled Topic Model for Collaborative Filtering With User-Generated Content,” IEEE Transactions on Human-Machine Systems, vol. 46, no. 6, pp. 908–920, 2016. View at Publisher · View at Google Scholar
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  • Cheng Chen, Lan Zheng, Venkatesh Srinivasan, Alex Thomo, Kui Wu, and Anthony Sukow, “Conflict-Aware Weighted Bipartite B-Matching and Its Application to E-Commerce,” IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 6, pp. 1475–1488, 2016. View at Publisher · View at Google Scholar
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  • You Ma, Shangguang Wang, Patrick C.K. Hung, Ching-Hsien Hsu, Qibo Sun, and Fangchun Yang, “A Highly Accurate Prediction Algorithm for Unknown Web Service QoS Values,” IEEE Transactions on Services Computing, vol. 9, no. 4, pp. 511–523, 2016. View at Publisher · View at Google Scholar
  • Yue Meng, Chunxiao Jiang, Lei Xu, Yong Ren, and Zhu Han, “User Association in Heterogeneous Networks: A Social Interaction Approach,” IEEE Transactions on Vehicular Technology, vol. 65, no. 12, pp. 9982–9993, 2016. View at Publisher · View at Google Scholar
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  • Yi Huang, “Personalized Recommendation of Coupon Deals by Keywords Association Rules,” Journal of Industrial and Intelligent Information, 2016. View at Publisher · View at Google Scholar
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