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NTIS 바로가기지능정보연구 = Journal of intelligence and information systems, v.29 no.3, 2023년, pp.57 - 78
김민경 ((주)데이터월드) , 김건우 (한밭대학교 융합경영학과) , 최근호 (한밭대학교 융합경영학과)
COVID-19, which started in Wuhan, China in November 2019, spread beyond China in 2020 and spread worldwide in March 2020. It is important to prevent a highly contagious virus like COVID-19 in advance and to actively treat it when confirmed, but it is more important to identify the confirmed fact qui...
김가혜, 이소현. (2022). 코로나 전후 행복 이슈?변화 분석 및 행복 증진 방안 연구. 지능정보?연구, 28(3), 81-103.
손명진. (2022). 기침소리 데이터를 이용한 COVID-19?감염자 진단. 상명대학교 석사학위논문.
오상우. (2019). 인공지능 기반의 음성분석을 통한?우울증, 불안증, 조기치매, 또는 자살 징후?조기판별 시스템. 특허등록번호: 10-2041848.
유성주, 김재윤. (2022). 기침 파형 패턴을 활용한?COVID-19 확진자 식별 딥러닝 모델. 한국통신학회 동계종합학슬발표회, 1299-1300.
유소연, 임규건. (2021). 텍스트 마이닝과 의미?네트워크 분석을 활용한 뉴스 의제 분석:?코로나 19 관련 감정을 중심으로. 지능정보?연구, 27(1), 47-64.
이예진. (2022). 기침 소리를 이용한 COVID-19?진단에 최적화된 feature들 제안. 상명대학교?석사학위논문.
이혜빈, 박현진. (2021). 기침 음성 데이터를 이용한 COVID-19 분류. 대한전자공학회 하계학술대회논문집, 1701-1703.
정연길, 서수민, 강현민. (2022). 토픽 모델링을?활용한 코로나19 초기 생활체육 이슈 분석.?지능정보연구, 28(3), 57-79.
최혜진. (2022). VGG16모델을 활용한 코로나 진단에 관한 연구. 한양대학교 석사학위논문.
Abeyratne, U. R., Swarnkar, V., Setyati, A., &?Triasih, R. (2013). Cough sound analysis can?rapidly diagnose childhood pneumonia. Annals?of Biomedical Engineering, 41(11), 2448-2462.
Alqudaihi, K. S., Aslam, N., Khan, I. U.,?Almuhaideb, A. M., Alsunaidi, S. J., Ibrahim,?N. M. A. R., ... & Alshahrani, M. S. (2021).?Cough sound detection and diagnosis using?artificial intelligence techniques: challenges and?opportunities. Ieee Access, 9, 102327-102344.
Amrulloh, Y., Abeyratne, U., Swarnkar, V.,?& Triasih, R. (2015). Cough sound analysis?for pneumonia and asthma classification in?pediatric population. In 2015 6th International?Conference on Intelligent Systems, Modelling?and Simulation, Kuala Lumpur, Malaysia,?127-131.
Brown, C., Chauhan, J., Grammenos, A., Han, J.,?Hasthanasombat, A., Spathis, D., Xia, T., Cicuta,?P., & Mascolo, C. (2020). Exploring automatic?diagnosis of COVID-19 from crowdsourced?respiratory sound data. In Proceedings of the?26th ACM SIGKDD International Conference?on Knowledge Discovery & Data Mining,?3474-3484.
Chen, T., & Guestrin, C. (2016). Xgboost: A scalable?tree boosting system. In Proceedings of the?22nd acm sigkdd international conference on?knowledge discovery and data mining, 785-794.
Daoud, E. (2019). Comparison between XGBoost,?LightGBM and CatBoost Using a Home Credit?Dataset. International Journal of Computer?and Information Engineering, 13(1), 6 - 10.
Dave, N. (2013). Feature extraction methods?LPC, PLP, and MFCC in speech recognition.?International Journal for Advance Research?in Engineering and Technology, 1(6), 1-5.
Dhaliwal, S. S., Nahid, A. A., Abbas, R.(2018)?Effective Intrusion Detection System Using?XGBoost. Information, 9(7), 149.
Doulah, A. B. M. S. U., & Fattah, S. A. (2014).?Neuromuscular disease classification based on?mel frequency cepstrum of motor unit action?potential. In 2014 International Conference?on Electrical Engineering and Information &?Communication Technology, 1-4.
Fakhry, A., Jiang, X., Xiao, J. Chaudhari, G.,?Han, A., & Khanzada, A. (2021). Virufy:?A multi-branch deep learning network for?automated detection of COVID-19. Interspeech.
Gaurav, R., Yahia-Cherif, L., Pyatigorskaya, N.,?Mangone, G., Biondetti, E., Valabregue, R.,?... & Lehericy, S. (2021). Longitudinal changes?in neuromelanin MRI signal in Parkinson's?disease: a progression marker. Movement?Disorders, 36(7), 1592-1602.
He, K., Zhang, X., Ren, S., & Sun, J. (2016).?Deep residual learning for image recognition.?In 2016 IEEE Conference on Computer Vision?and Patatern Recognition, 770-778.
Imran, A., Posokhova, I., Qureshi, H. N., Masood,?U., Riaz, M. S., Ali, K., John, C. N., Hussain,?M. I., & Nabeel, M. (2020). AI4COVID-19:?AI enabled preliminary diagnosis for COVID-19?from cough samples via an app. Informatics?in Medicine Unlocked, 20, 100378.
Issa, D., Demirci, M. F., & Yazici, A. (2020). Speech?emotion recognition with deep convolutional?neural networks. Biomedical Signal Processing?and Control, 59, 101894.
Ittichaichareon, C., Suksri, S., & Yingthawornsuk,?T. (2012). Speech recognition using MFCC.
International Conference on Computer Graphics,?Simulation and Modeling, Pattaya, Thailand.?Jana, B., & Nath, P. K. (2022). A Single-Chip?Solution for Diagnosing Peripheral Arterial?Disease. IEEE Transactions on Very Large?Scale Integration (VLSI) Systems, 30(5), 671-675.
Jana, B., Oswal, K., Mitra, S., Saha, G., &?Banerjee, S. (2019). Detection of peripheral?arterial disease using Doppler spectrogram based?expert system for Point-of-Care applications.?Biomedical Signal Processing and Control,?54, 101599.
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W.,?Ma, W., & Liu, T. Y. (2017). Lightgbm: A?highly efficient gradient boosting decision tree.?Advances in Neural Information Processing?Systems, 30.
Kumar, A., Abhishek, K., Ghalib, M. R., Nerurkar,?P., Shah, K., Chandane, M., Bhirud, S., Patel,?D., & Busnel, Y. (2022). Towards cough sound?analysis using the internet of things and deep?learning for pulmonary disease prediction.?Transactions on Emerging Telecommunications?Technologies, 33(10), e4184.
McFee, B., Raffel, C., Liang, D., Ellis, D. P.,?McVicar, M., Battenberg, E., & Nieto, O.?(2015). Librosa:audio and music signal analysis?in python, In Proceedings of the 14th Python?in Science Conference. Austin, TX, 18-24.
Mishra, D., & Sahu, B. (2011). Feature selection?for cancer classification: a signal-to-noise ratio?approach. International Journal of Scientific?& Engineering Research, 2(4), 1-7.
Muda, L., Begam, M., & Elamvazuthi, I. (2010). Voice?recognition algorithms using mel frequency?cepstral coefficient (MFCC) and dynamic?time warping (DTW) techniques. Journal of?Computing, 2(3), 138-143.
Ogunleye, A., and Wang, Q. G.(2020). XGBoost?Model for Chronic Kidney Disease Diagnosis.?In IEEE/ACM Transactions on Computational?Biology and Bioinformatics, 17(6), 2131-2140.
Orlandic, L., Teijeiro, T., & Atienza, D. (2021). The?COUGHVID crowdsourcing dataset: A corpus?for the study of large-scale cough analysis?algorithms. Science Data 8, 156, https://doi.org/10.1038/s41597-021-00937-4.
Pramono, R. X. A., Imtiaz, S. A., & Rodriguez-Villegas, E. (2016). A cough-based algorithm?for automatic diagnosis of pertussis. PloS One,?11(9), e0162128.
Rezaee, K., Savarkar, S., Yu, X., & Zhang, J.?(2022). A hybrid deep transfer learning-based?approach for Parkinson's disease classification?in surface electromyography signals. Biomedical?Signal Processing and Control, 71, 103161.
Rezende, E., Ruppert, G., Carvalho, T., Ramos, F., &?Geus, P.(2017). Malicious Software Classification?Using Transfer Learning of ResNet-50 Deep?Neural Network. In Proceedings of 16th?IEEE International Conference on Machine?Learning and Applications (ICMLA), Cancun,?Mexico, 1011-1014.
Sadi, T. M., & Hassan, R. (2020). Development of?classification methods for wheeze and crackle?using mel frequency cepstral coefficient (MFCC):?A deep learning approach. International Journal?on Perceptive and Cognitive Computing, 6(2),?107-114.
Singh, Y., & Biswas, A. (2022). Robustness of?musical features on deep learning models for?music genre classification. Expert Systems with?Applications, 199, 116879.
Su, Y., Zhang, K., Wang, J., & Madani, K. (2019).?Environment sound classification using a two-stream CNN based on decision-level fusion.?Sensors, 19(7), 1733.
Tadesse, G. A., Javed, H., Thanh, N. L. N., Thi,?H. D. H., Thwaites, L., Clifton, D. A., &?Zhu, T. (2020). Multi-modal diagnosis of?infectious diseases in the developing world.?IEEE Journal of Biomedical and Health?Informatics, 24(7), 2131-2141.
Tiwari, V. (2010). MFCC and its applications in?speaker recognition. International Journal on?Emerging Technologies, 1(1), 19-22.
Wang, D., Zhang, Y., & Zhao, Y.(2017). LightGBM:?An effective miRNA classification method in?breast cancer patients. In Proceedings of the?2017 International Conference on Computational?Biology and Bioinformatics, 7-11.
Wen, L., Li, X., & Gao, L. A.(2020). Transfer?convolutional neural network for fault diagnosis?based on ResNet-50. Neural Computing and?Applications, 32, 6111-6124.
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