Mobile QR Code QR CODE

※ The user interface design of www.ieiespc.org has been recently revised and updated. Please contact inter@theieie.org for any inquiries regarding paper submission.

Journal Search

  1. Home
  2. Archive
  3. 2023-08 (Vol.12 No.04)
  4. 10.5573/IEIESPC.2023.12.4.341
XML PDF INFO REF

REFERENCES

1 
P. Avent, C. Hughes, H. Garvin. “Applying posterior probability informed thresholds to traditional cranial trait sex estimation methods”. Journal of forensic sciences, vol. 67(2), pp. 440-449, 2021.DOI
2 
L. Chen, et al., “An innovative deep neural network–based approach for internal cavity detection of timber columns using percussion sound”. Structural Health Monitoring, vol. 21(3), pp. 1251-1265, 2022.DOI
3 
M. S. Johnson, S. Sinharay. “The Reliability of the Posterior Probability of Skill Attainment in Diagnostic Classification Models.” Journal of Educational and Behavioral Statistics, vol. 45(1), pp. 5-31, 2020.DOI
4 
L. Sun, B. Zou, S. Fu, et al. “Speech emotion recognition based on DNN-decision tree SVM model.” Speech Communication, vol. 115, pp. 29-37, 2019.DOI
5 
W. Jiang, F. Wen, P. Liu. “Robust Beamforming for Speech Recognition Using DNN-based Time-Frequency Masks Estimation.” IEEE Access, vol. 6, pp. 52385-52392, 2018.URL
6 
H. Seki, K. Yamamoto, T. Akiba, S. Nakagawa, “Discriminative Learning of Filterbank Layer within Deep Neural Network Based Speech Recognition for Speaker Adaptation.” IEICE Transactions on Information and Systems, vol. (2), pp. 364-374, 2019.DOI
7 
Kentaro, SONE, Toru, NAKASHIKA. “Pre-Training of DNN-Based Speech Synthesis Based on Bidirectional Conversion between Text and Speech.” IEICE Transactions on Information and Systems, 2019, vol. E102.D(8), pp. 1546-1553, 2019.DOI
8 
V. M. Praseetha, S. Vadivel. “Deep Learning Models for Speech Emotion Recognition.” Journal of Computer Science, vol. 14(11), pp. 1577-1587, 2018.URL
9 
H. Prafianto, T. Nose, Y. Chiba, A. Ito. “Improving Human Scoring of Prosody Using Parametric Speech Synthesis.” Speech Communication, vol. 111, pp. 14-21, 2019.DOI
10 
J. Liu, L. Lin, X. Liang, “Intelligent system of English composition scoring model based on improved machine learning algorithm.” Journal of Intelligent and Fuzzy Systems, vol. 40(2), pp. 2397-2407, 2021.DOI
11 
F. S. Pribadi, A. E. Permanasari, T. B. Adji, “Short answer scoring system using automatic reference answer generation and geometric average normalized-longest common subsequence (GAN-LCS).” Education & Information Technologies, vol. 23(6), pp. 2855-2866, 2018.DOI
12 
T. Gaillat, et al. “Predicting CEFR levels in learners of English: The use of microsystem criteria features in a machine learning approach.” ReCall, vol. 34(May), pp. 130-146, 2022.DOI
13 
Y. Zhang, “Interactive intelligent teaching and automatic composition scoring system based on linear regression machine learning algorithm.” Journal of Intelligent and Fuzzy Systems, vol. 40(2), pp. 2069-2081, 2021.URL
14 
Westera W. “Comparing Bayesian Statistics and Frequentist Statistics in Serious Games Research.” International Journal of Serious Games, vol. 8(1), pp. 27-44, 2021.DOI
15 
O. M. Crook, C. W. Chung, C. M. Deane, “Challenges and Opportunities for Bayesian Statistics in Proteomics.” Journal of proteome research, vol. 21(4), pp. 849-864, 2022.DOI
16 
J. M. Luningham, Chen J., Tang S., De Jager P. L., Bennett D. A., Buchman A. S., Yang J. “Bayesian Genome-wide TWAS Method to Leverage both cis- and trans-eQTL Information through Summary Statistics.” The American Journal of Human Genetics, vol. 107(4), pp. 714-726, 2020.URL
17 
T. Caelli, J. Mukerjee, A. Mccabe, D. Kirszenblat. “The Situation Awareness Window: a Hidden Markov Model for analyzing Maritime Surveillance missions.” The Journal of Defense Modeling and Simulation, vol. 18(3), pp. 207-215, 2021.DOI
18 
X. Liu, K. Shi, Z. Wang, J. Chen. “Exploit Camera Raw Data for Video Super- Resolution via Hidden Markov Model Inference.” IEEE Transactions on Image Processing, vol. 30, pp. 2127-2140, 2021.DOI
19 
Y. Li, E. Zio, E. Pan. “An MEWMA-based segmental multivariate hidden Markov model for degradation assessment and prediction.” Journal of Risk and Reliability, vol. 235(5), pp. 831-844, 2021.DOI
20 
P. L. Prasanna, “Forecasting Inflation Rate (WPI &CPI) in India with Time Series Model – A Statistical Recurrent Neural Network Approach”. IARJSET, vol. 8(6), pp. 87-90, 2021.URL
21 
X. Dai, J. Liu, Y. Li. “A recurrent neural network using historical data to predict time series indoor PM2.5 concentrations for residential buildings.” Indoor Air, 2021, vol. 31(4), pp. 1228-1237, 2021.DOI
22 
X. Zhang, Y. Huang, Y. Rong, G. Li, H. Wang, C. Liu. “Recurrent neural network based optimal integral sliding mode tracking control for four-wheel independently driven robots.” IET control theory & applications, 2021, vol. 15(10), pp. 1346-1363, 2021.DOI