The citation analysis helps highlight the papers that have been the most influential in this research area (Table 3). As the links in VOSviewer are undirected – i.e., no distinction is made as to which paper at the end of a link contains the citation – the most cited articles are found by counting the number of linked articles that were published after any item. For example, the item with the highest link strength is that published by Tsompanas et al. (2019). Only two of its 10 linked articles were published after 2019, however, meaning that it was only cited twice. On the other hand, six of the eight links for the paper written by Lesnik & Liu (2017) were published after it, meaning that it was cited six times and was, therefore, more influential.
Top 5 most influential articles in the field of AI applied in microbial fuel cell research
| Article title . | Journal . | Authors . | Year . | Times cited* . |
|---|---|---|---|---|
| Performance evaluation of microbial fuel cell by artificial intelligence methods | Expert Systems with Applications | Garg, A.; Vijayaraghavan, V.; Mahapatra, S. S.; Tai, K.; Wong, C. H. | 2014 | 6 |
| Predicting microbial fuel cell biofilm communities and bioreactor performance using artificial neural networks | Environmental Science & Technology | Lesnik, Keaton Larson; Liu, Hong | 2017 | 6 |
| Use of artificial neural network for the prediction of bioelectricity production in a membrane less microbial fuel cell | Fuel | Tardast, Ali; Rahimnejad, Mostafa; Najafpour, Ghasem; Ghoreyshi, Ali; Premier, Giuliano C.; Bakeri, Gholamreza; Oh, Sang-Eun | 2014 | 5 |
| Prediction of sustainable electricity generation in microbial fuel cell by neural network: Effect of anode angle with respect to flow direction | Journal of Electrochemical Chemistry | Jaeel, Ali J.; Al-wared, Abeer I.; Ismail, Zainab Z. | 2016 | 5 |
| Neural network and neuro-fuzzy modeling to investigate the power density and Columbic efficiency of microbial fuel cell | Journal of the Taiwan Institute of Chemical Engineers | Esfandyari, Morteza; Fanaei, Mohammad Ali; Gheshlaghi, Reza; Mahdavi, Mahmood Akhavan | 2016 | 4 |
| Article title . | Journal . | Authors . | Year . | Times cited* . |
|---|---|---|---|---|
| Performance evaluation of microbial fuel cell by artificial intelligence methods | Expert Systems with Applications | Garg, A.; Vijayaraghavan, V.; Mahapatra, S. S.; Tai, K.; Wong, C. H. | 2014 | 6 |
| Predicting microbial fuel cell biofilm communities and bioreactor performance using artificial neural networks | Environmental Science & Technology | Lesnik, Keaton Larson; Liu, Hong | 2017 | 6 |
| Use of artificial neural network for the prediction of bioelectricity production in a membrane less microbial fuel cell | Fuel | Tardast, Ali; Rahimnejad, Mostafa; Najafpour, Ghasem; Ghoreyshi, Ali; Premier, Giuliano C.; Bakeri, Gholamreza; Oh, Sang-Eun | 2014 | 5 |
| Prediction of sustainable electricity generation in microbial fuel cell by neural network: Effect of anode angle with respect to flow direction | Journal of Electrochemical Chemistry | Jaeel, Ali J.; Al-wared, Abeer I.; Ismail, Zainab Z. | 2016 | 5 |
| Neural network and neuro-fuzzy modeling to investigate the power density and Columbic efficiency of microbial fuel cell | Journal of the Taiwan Institute of Chemical Engineers | Esfandyari, Morteza; Fanaei, Mohammad Ali; Gheshlaghi, Reza; Mahdavi, Mahmood Akhavan | 2016 | 4 |
*Cited in articles in the same area studied in this work.