Achievements of Rseslib KNN reported in independent reviewed publications
Rank |
Out of |
Task |
Reference |
1 |
9 |
Environmental sound recognition |
Grama, L., & Rusu, C. (2017). Choosing an accurate number of mel frequency cepstral coefficients for audio classification purpose. In Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis (pp. 225-230). IEEE. |
2 |
8 |
Acoustic-based environment monitoring |
Rusu, C., & Grama, L. (2017). Recent developments in acoustical signal classification for monitoring. In 2017 5th International Symposium on Electrical and Electronics Engineering (ISEEE) (pp. 1-10). IEEE. |
1 |
21 |
Facebook content recognition |
Dey, N., Borah, S., Babo, R., & Ashour, A. (2018). Social network analytics: computational research methods and techniques. Academic Press. |
2 |
8 |
Context awareness of a service robot |
Grama, L., & Rusu, C. (2018). Adding audio capabilities to TIAGo service robot. In 2018 International Symposium on Electronics and Telecommunications (ISETC) (pp. 1-4). IEEE. |
5 |
47 |
Student performance prediction |
Almasri, A., Celebi, E., & Alkhawaldeh, R. S. (2019). EMT: Ensemble meta-based tree model for predicting student performance. Scientific Programming, 2019. |
2 |
47 |
Metabolic pathway prediction of plant enzymes |
de Oliveira Almeida, R., & Valente, G. T. (2020). Predicting metabolic pathways of plant enzymes without using sequence similarity: Models from machine learning. In The Plant Genome (Vol. 13, Issue 3). Wiley. |
2 |
13 |
Phlebopathic patient screening |
D'Angelantonio, E., Lucangeli, L., Camomilla, V., & Pallotti, A. (2022). Smart sock-based machine learning models development for phlebopathic patient screening. IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT) (pp. 137-142). IEEE. |
2 |
5 |
Student performance prediction |
Niu, K., Jia, B. T., Zou, Y. H., & Lu, G. Q. (2022). A hybrid model for predicting academic performance of engineering undergraduates. International Journal of Modeling, Simulation, and Scientific Computing (Vol. 14, No. 02, 2350030). |
2 |
10 |
Gait analysis for rehabilitation planning |
Galasso, S., Baptista, R., Molinara, M., Pizzocaro, S., Calabro, R. S., & De Nunzio, A. M. (2023). Predicting physical activity levels from kinematic gait data using machine learning techniques. Engineering Applications of Artificial Intelligence (Vol. 123, 106487). |