| Predictive modeling of biomass gasification with machine learning-based regression methods F Elmaz, Ö Yücel, AY Mutlu Energy 191, 116541, 2020 | 162 | 2020 |
| Smartphone-based colorimetric detection via machine learning AY Mutlu, V Kılıç, GK Özdemir, A Bayram, N Horzum, ME Solmaz Analyst 142 (13), 2434-2441, 2017 | 119 | 2017 |
| Quantifying colorimetric tests using a smartphone app based on machine learning classifiers ME Solmaz, AY Mutlu, G Alankus, V Kılıç, A Bayram, N Horzum Sensors and Actuators B: Chemical 255, 1967-1973, 2018 | 116 | 2018 |
| An artificial intelligence based approach to predicting syngas composition for downdraft biomass gasification AY Mutlu, O Yucel Energy 165, 895-901, 2018 | 107 | 2018 |
| A time-frequency-based approach to phase and phase synchrony estimation S Aviyente, AY Mutlu IEEE Transactions on Signal Processing 59 (7), 3086-3098, 2011 | 72 | 2011 |
| Single-image-referenced colorimetric water quality detection using a smartphone V Kılıç, G Alankus, N Horzum, AY Mutlu, A Bayram, ME Solmaz ACS omega 3 (5), 5531-5536, 2018 | 70 | 2018 |
| Detection of epileptic dysfunctions in EEG signals using Hilbert vibration decomposition AY Mutlu Biomedical Signal Processing and Control 40, 33-40, 2018 | 51 | 2018 |
| Hilbert vibration decomposition-based epileptic seizure prediction with neural network B Büyükçakır, F Elmaz, AY Mutlu Computers in biology and medicine 119, 103665, 2020 | 49 | 2020 |
| A signal-processing-based approach to time-varying graph analysis for dynamic brain network identification AY Mutlu, E Bernat, S Aviyente Computational and mathematical methods in medicine 2012, 2012 | 49 | 2012 |
| Multivariate empirical mode decomposition for quantifying multivariate phase synchronization AY Mutlu, S Aviyente EURASIP Journal on Advances in Signal Processing 2011, 1-13, 2011 | 46 | 2011 |
| Classification of solid fuels with machine learning F Elmaz, B Büyükçakır, Ö Yücel, AY Mutlu Fuel 266, 117066, 2020 | 34 | 2020 |
| Evaluating the effect of blending ratio on the co-gasification of high ash coal and biomass in a fluidized bed gasifier using machine learning F Elmaz, Ö Yücel, AY Mutlu Mugla Journal of Science and Technology 5 (1), 1-12, 2019 | 17 | 2019 |
| Machine learning based smartphone spectrometer for harmful dyes detection in water AY Mutlu, V Kılıç 2018 26th Signal Processing and Communications Applications Conference (SIU …, 2018 | 14 | 2018 |
| A measure of multivariate phase synchrony using hyperdimensional geometry M Al-Khassaweneh, M Villafañe-Delgado, AY Mutlu, S Aviyente IEEE Transactions on Signal Processing 64 (11), 2774-2787, 2016 | 13 | 2016 |
| Inferring effective connectivity in the brain from EEG time series using dynamic bayesian networks AY Mutlu, S Aviyente 2009 Annual International Conference of the IEEE Engineering in Medicine and …, 2009 | 9 | 2009 |
| Machine learning based approach for predicting of higher heating values of solid fuels using proximity and ultimate analysis F ELMAZ, Ö YÜCEL, AY MUTLU International Journal of Advances in Engineering and Pure Sciences 32 (2 …, 2020 | 7 | 2020 |
| Hyperspherical phase synchrony for quantifying multivariate phase synchronization AY Mutlu, S Aviyente 2012 IEEE Statistical Signal Processing Workshop (SSP), 888-891, 2012 | 7 | 2012 |
| Predictive modeling of the syngas production from methane dry reforming over cobalt catalyst with statistical and machine learning based approaches F ELMAZ, Ö YÜCEL, AY MUTLU International Journal of Advances in Engineering and Pure Sciences 32 (1), 8-14, 2020 | 6 | 2020 |
| Comparison of Hilbert vibration decomposition with empirical mode decomposition for classifying epileptic seizures B Büyükçakir, AY Mutlu 2018 52nd Asilomar Conference on Signals, Systems, and Computers, 357-362, 2018 | 5 | 2018 |
| Subspace analysis for characterizing dynamic functional brain networks AY Mutlu, S Aviyente 2013 IEEE International Conference on Acoustics, Speech and Signal …, 2013 | 5 | 2013 |
