Technologies | Highly Cited Papers in 2022–2023 in the Field of Artificial Intelligence
As all of the articles published in Technologies (ISSN: 2227-7080) are presented in an open access format, everyone has free and unlimited access to the full texts. We welcome you to read our most highly cited papers published in 2022 and 2023 listed below:
1. “Utilization of Artificial Neural Networks for Precise Electrical Load Prediction”
by Christos Pavlatos, Evangelos Makris, Georgios Fotis, Vasiliki Vita and Valeri Mladenov
Technologies 2023, 11(3), 70; https://doi.org/10.3390/technologies11030070
Available online: https://www.mdpi.com/2227-7080/11/3/70
2. “A Comparative Analysis on Suicidal Ideation Detection Using NLP, Machine, and Deep Learning”
by Rezaul Haque, Naimul Islam, Maidul Islam and Md Manjurul Ahsan
Technologies 2022, 10(3), 57; https://doi.org/10.3390/technologies10030057
Available online: https://www.mdpi.com/2227-7080/10/3/57
3. “Continuous Emotion Recognition for Long-Term Behavior Modeling through Recurrent Neural Networks”
by Ioannis Kansizoglou, Evangelos Misirlis, Konstantinos Tsintotas and Antonios Gasteratos
Technologies 2022, 10(3), 59; https://doi.org/10.3390/technologies10030059
Available online: https://www.mdpi.com/2227-7080/10/3/59
4. “How to Bell the Cat? A Theoretical Review of Generative Artificial Intelligence towards Digital Disruption in All Walks of Life”
by Subhra Mondal, Subhankar Das, and Vasiliki G. Vrana
Technologies 2023, 11(2), 44; https://doi.org/10.3390/technologies11020044
Available online: https://www.mdpi.com/2227-7080/11/2/44
5. “A Review of Deep Transfer Learning and Recent Advancements”
by Mohammadreza Iman, Hamid Reza Arabnia and Khaled Rasheed
Technologies 2023, 11(2), 40; https://doi.org/10.3390/technologies11020040
Available online: https://www.mdpi.com/2227-7080/11/2/40
6. “Developments and Applications of Artificial Intelligence in Music Education”
by Xiaofei Yu, Ning Ma, Lei Zheng, Licheng Wang and Kai Wang
Technologies 2023, 11(2), 42; https://doi.org/10.3390/technologies11020042
Available online: https://www.mdpi.com/2227-7080/11/2/42
7. “Traffic Flow Prediction for Smart Traffic Lights Using Machine Learning Algorithms”
by Alfonso Navarro-Espinoza, Oscar Roberto López-Bonilla, Enrique Efrén García-Guerrero, Esteban Tlelo-Cuautle, Didier López-Mancilla, Carlos Hernández-Mejía and Everardo Inzunza-González
Technologies 2022, 10(1), 5; https://doi.org/10.3390/technologies10010005
Available online: https://www.mdpi.com/2227-7080/10/1/5
8. “Multimodal Semantic Segmentation in Autonomous Driving: A Review of Current Approaches and Future Perspectives”
by Giulia Rizzoli, Francesco Barbato and Pietro Zanuttigh
Technologies 2022, 10(4), 90; https://doi.org/10.3390/technologies10040090
Available online: https://www.mdpi.com/2227-7080/10/4/90
9. “Evaluation of Machine Learning Algorithms for Classification of EEG Signals”
by Francisco Javier Ramírez-Arias, Enrique Efren García-Guerrero, Esteban Tlelo-Cuautle, Juan Miguel Colores-Vargas, Eloisa García-Canseco, Oscar Roberto López-Bonilla, Gilberto Manuel Galindo-Aldana and Everardo Inzunza-González
Technologies 2022, 10(4), 79; https://doi.org/10.3390/technologies10040079
Available online: https://www.mdpi.com/2227-7080/10/4/79
10. “An Advanced Solution Based on Machine Learning for Remote EMDR Therapy”
by Francesca Fiani, Samuele Russo and Christian Napoli
Technologies 2023, 11(6), 172; https://doi.org/10.3390/technologies11060172
Available online: https://www.mdpi.com/2227-7080/11/6/172