{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T02:04:32Z","timestamp":1772589872714,"version":"3.50.1"},"reference-count":72,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T00:00:00Z","timestamp":1664236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["1DP3DK101075"],"award-info":[{"award-number":["1DP3DK101075"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["1R01DK130049"],"award-info":[{"award-number":["1R01DK130049"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["2-SRA-2017-506-M-B"],"award-info":[{"award-number":["2-SRA-2017-506-M-B"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008871","name":"JDRF","doi-asserted-by":"publisher","award":["1DP3DK101075"],"award-info":[{"award-number":["1DP3DK101075"]}],"id":[{"id":"10.13039\/100008871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008871","name":"JDRF","doi-asserted-by":"publisher","award":["1R01DK130049"],"award-info":[{"award-number":["1R01DK130049"]}],"id":[{"id":"10.13039\/100008871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008871","name":"JDRF","doi-asserted-by":"publisher","award":["2-SRA-2017-506-M-B"],"award-info":[{"award-number":["2-SRA-2017-506-M-B"]}],"id":[{"id":"10.13039\/100008871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Detection and classification of acute psychological stress (APS) and physical activity (PA) in daily lives of people with chronic diseases can provide precision medicine for the treatment of chronic conditions such as diabetes. This study investigates the classification of different types of APS and PA, along with their concurrent occurrences, using the same subset of feature maps via physiological variables measured by a wristband device. Random convolutional kernel transformation is used to extract a large number of feature maps from the biosignals measured by a wristband device (blood volume pulse, galvanic skin response, skin temperature, and 3D accelerometer signals). Three different feature selection techniques (principal component analysis, partial least squares\u2013discriminant analysis (PLS-DA), and sequential forward selection) as well as four approaches for addressing imbalanced sizes of classes (upsampling, downsampling, adaptive synthetic sampling (ADASYN), and weighted training) are evaluated for maximizing detection and classification accuracy. A long short-term memory recurrent neural network model is trained to estimate PA (sedentary state, treadmill run, stationary bike) and APS (non-stress, emotional anxiety stress, mental stress) from wristband signals. The balanced accuracy scores for various combinations of data balancing and feature selection techniques range between 96.82% and 99.99%. The combination of PLS\u2013DA for feature selection and ADASYN for data balancing provide the best overall performance. The detection and classification of APS and PA types along with their concurrent occurrences can provide precision medicine approaches for the treatment of diabetes.<\/jats:p>","DOI":"10.3390\/a15100352","type":"journal-article","created":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T21:23:27Z","timestamp":1664313807000},"page":"352","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Detection and Classification of Unannounced Physical Activities and Acute Psychological Stress Events for Interventions in Diabetes Treatment"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0642-6865","authenticated-orcid":false,"given":"Mohammad Reza","family":"Askari","sequence":"first","affiliation":[{"name":"Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4237-847X","authenticated-orcid":false,"given":"Mahmoud","family":"Abdel-Latif","sequence":"additional","affiliation":[{"name":"Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4521-0872","authenticated-orcid":false,"given":"Mudassir","family":"Rashid","sequence":"additional","affiliation":[{"name":"Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3662-0255","authenticated-orcid":false,"given":"Mert","family":"Sevil","sequence":"additional","affiliation":[{"name":"Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1607-9943","authenticated-orcid":false,"given":"Ali","family":"Cinar","sequence":"additional","affiliation":[{"name":"Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA"},{"name":"Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109119","DOI":"10.1016\/j.diabres.2021.109119","article-title":"IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045","volume":"183","author":"Sun","year":"2022","journal-title":"Diabetes Res. Clin. Pract."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1177\/1932296818789951","article-title":"Incorporating unannounced meals and exercise in adaptive learning of personalized models for multivariable artificial pancreas systems","volume":"12","author":"Hajizadeh","year":"2018","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.jprocont.2019.05.003","article-title":"Adaptive personalized multivariable artificial pancreas using plasma insulin estimates","volume":"80","author":"Hajizadeh","year":"2019","journal-title":"J. Process Control"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ollander, S., Godin, C., Campagne, A., and Charbonnier, S. (2016, January 9\u201312). A comparison of wearable and stationary sensors for stress detection. Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Budapest, Hungary.","DOI":"10.1109\/SMC.2016.7844917"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Sandulescu, V., Andrews, S., Ellis, D., Bellotto, N., and Mozos, O.M. (2015, January 1\u20135). Stress detection using wearable physiological sensors. Proceedings of the International Work-Conference on the Interplay between Natural and Artificial Computation, Elche, Spain.","DOI":"10.1007\/978-3-319-18914-7_55"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"103139","DOI":"10.1016\/j.jbi.2019.103139","article-title":"Stress detection in daily life scenarios using smart phones and wearable sensors: A survey","volume":"92","author":"Can","year":"2019","journal-title":"J. Biomed. Inform."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Minguillon, J., Perez, E., Lopez-Gordo, M.A., Pelayo, F., and Sanchez-Carrion, M.J. (2018). Portable system for real-time detection of stress level. Sensors, 18.","DOI":"10.3390\/s18082504"},{"key":"ref_8","unstructured":"Sun, F.T., Kuo, C., Cheng, H.T., Buthpitiya, S., Collins, P., and Griss, M. (2010, January 25\u201328). Activity-aware mental stress detection using physiological sensors. Proceedings of the International Conference on Mobile Computing, Applications, and Services, Santa Clara, CA, USA."},{"key":"ref_9","unstructured":"Haak, M., Bos, S., Panic, S., and Rothkrantz, L. (2009, January 21\u201322). Detecting stress using eye blinks and brain activity from EEG signals. Proceedings of the 1st Driver Car Interaction and Interface (DCII 2008), New York, NY, USA."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Cvetkovic, B., Gjoreski, M., Sorn, J., Maslov, P., Kosiedowski, M., Bogdanski, M., Stroinski, A., and Lustrek, M. (2017, January 11\u201315). Real-time physical activity and mental stress management with a wristband and a smartphone. Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers, Maui, HI, USA.","DOI":"10.1145\/3123024.3123184"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhai, J., and Barreto, A. (September, January 30). Stress detection in computer users based on digital signal processing of noninvasive physiological variables. Proceedings of the 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, New York City, NY, USA.","DOI":"10.1109\/IEMBS.2006.259421"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4857","DOI":"10.1109\/TIE.2010.2103538","article-title":"A stress-detection system based on physiological signals and fuzzy logic","volume":"58","author":"Avila","year":"2011","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kurniawan, H., Maslov, A.V., and Pechenizkiy, M. (2013, January 20\u201322). Stress detection from speech and galvanic skin response signals. Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, Porto, Portugal.","DOI":"10.1109\/CBMS.2013.6627790"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1177\/1932296818763959","article-title":"Adaptive and personalized plasma insulin concentration estimation for artificial pancreas systems","volume":"12","author":"Hajizadeh","year":"2018","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1177\/19322968211059149","article-title":"Incorporating Prior Information in Adaptive Model Predictive Control for Multivariable Artificial Pancreas Systems","volume":"16","author":"Sun","year":"2022","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1177\/1932296818820550","article-title":"Improving Glucose Prediction Accuracy in Physically Active Adolescents With Type 1 Diabetes","volume":"13","author":"Hobbs","year":"2019","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"105898","DOI":"10.1016\/j.cmpb.2020.105898","article-title":"Discrimination of simultaneous psychological and physical stressors using wristband biosignals","volume":"199","author":"Sevil","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"188","DOI":"10.3390\/signals1020011","article-title":"Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data","volume":"1","author":"Sevil","year":"2020","journal-title":"Signals"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"12859","DOI":"10.1109\/JSEN.2020.3000772","article-title":"Determining Physical Activity Characteristics From Wristband Data for Use in Automated Insulin Delivery Systems","volume":"20","author":"Sevil","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2318","DOI":"10.1021\/acs.iecr.9b04824","article-title":"Artifact Removal from Data Generated by Nonlinear Systems: Heart Rate Estimation from Blood Volume Pulse Signal","volume":"59","author":"Askari","year":"2020","journal-title":"Ind. Eng. Chem. Res."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"McCarthy, C., Pradhan, N., Redpath, C., and Adler, A. (2016, January 29\u201331). Validation of the Empatica E4 wristband. Proceedings of the 2016 IEEE EMBS International Student Conference (ISC), Ottawa, ON, Canada.","DOI":"10.1109\/EMBSISC.2016.7508621"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1136\/bjsports-2016-096990","article-title":"Comparison of four Fitbit and Jawbone activity monitors with a research-grade ActiGraph accelerometer for estimating physical activity and energy expenditure","volume":"52","author":"Imboden","year":"2018","journal-title":"Br. J. Sports Med."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hosseini, S.A., and Khalilzadeh, M.A. (2010, January 23\u201325). Emotional stress recognition system using EEG and psychophysiological signals: Using new labelling process of EEG signals in emotional stress state. Proceedings of the 2010 International Conference on Biomedical Engineering and Computer Science, Wuhan, China.","DOI":"10.1109\/ICBECS.2010.5462520"},{"key":"ref_24","first-page":"605","article-title":"Detecting emotions through non-invasive wearables","volume":"26","author":"Rincon","year":"2018","journal-title":"Log. J. IGPL"},{"key":"ref_25","unstructured":"Zheng, B.S., Murugappan, M., and Yaacob, S. (2012, January 23\u201326). Human emotional stress assessment through Heart Rate Detection in a customized protocol experiment. Proceedings of the 2012 IEEE Symposium on Industrial Electronics and Applications, Bandung, Indonesia."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Rincon, J.A., Costa, A., Novais, P., Julian, V., and Carrascosa, C. (2016, January 19\u201321). Using non-invasive wearables for detecting emotions with intelligent agents. Proceedings of the International Joint Conference SOCO\u201916-CISIS\u201916-ICEUTE\u201916, San Sebasti\u00e1n, Spain.","DOI":"10.1007\/978-3-319-47364-2_8"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Karthikeyan, P., Murugappan, M., and Yaacob, S. (2011, January 4\u20136). A review on stress inducement stimuli for assessing human stress using physiological signals. Proceedings of the 2011 IEEE 7th International Colloquium on Signal Processing and Its Applications, Penang, Malaysia.","DOI":"10.1109\/CSPA.2011.5759914"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1518\/001872098779480569","article-title":"Driver stress and performance on a driving simulator","volume":"40","author":"Matthews","year":"1998","journal-title":"Hum. Factors"},{"key":"ref_29","unstructured":"Shi, Y., Nguyen, M.H., Blitz, P., French, B., Fisk, S., De la Torre, F., Smailagic, A., Siewiorek, D.P., al\u2019Absi, M., and Ertin, E. (2010, January 26\u201330). Personalized stress detection from physiological measurements. Proceedings of the International Symposium on Quality of Life Technology, Las Vegas, NV, USA."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1017\/S0263574702004484","article-title":"Online stress detection using psychophysiological signals for implicit human-robot cooperation","volume":"20","author":"Rani","year":"2002","journal-title":"Robotica"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"de Santos Sierra, A., Avila, C.S., del Pozo, G.B., and Casanova, J.G. (2011, January 19\u201321). Stress detection by means of stress physiological template. Proceedings of the 2011 Third World Congress on Nature and Biologically Inspired Computing, Salamanca, Spain.","DOI":"10.1109\/NaBIC.2011.6089448"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1764","DOI":"10.3389\/fphys.2018.01764","article-title":"Accuracy and precision of the COSMED K5 portable analyser","volume":"9","author":"Fezzardi","year":"2018","journal-title":"Front. Physiol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.2044-8260.1992.tb00997.x","article-title":"The development of a six-item short-form of the state scale of the Spielberger State\u2014Trait Anxiety Inventory (STAI)","volume":"31","author":"Marteau","year":"1992","journal-title":"Br. J. Clin. Psychol."},{"key":"ref_34","unstructured":"Spielberger, C.D., Sydeman, S.J., Owen, A.E., and Marsh, B.J. (1999). Measuring Anxiety and Anger with the State-Trait Anxiety Inventory (STAI) and the State-Trait Anger Expression Inventory (STAXI), Lawrence Erlbaum Associates Publishers."},{"key":"ref_35","first-page":"70","article-title":"Measuring anxiety, anger, depression, and curiosity as emotional states and personality traits with the STAI, STAXI, and STPI","volume":"2","author":"Spielberger","year":"2003","journal-title":"Compr. Handb. Psychol. Assess."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/0021-9290(85)90043-0","article-title":"The frequency content of gait","volume":"18","author":"Antonsson","year":"1985","journal-title":"J. Biomech."},{"key":"ref_37","first-page":"797","article-title":"cvxEDA: A Convex Optimization Approach to Electrodermal Activity Processing","volume":"63","author":"Greco","year":"2016","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Sainath, T.N., Vinyals, O., Senior, A., and Sak, H. (2015, January 19\u201324). Convolutional, long short-term memory, fully connected deep neural networks. Proceedings of the 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), South Brisbane, Australia.","DOI":"10.1109\/ICASSP.2015.7178838"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1454","DOI":"10.1007\/s10618-020-00701-z","article-title":"ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels","volume":"34","author":"Dempster","year":"2020","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_40","first-page":"1","article-title":"pyts: A Python Package for Time Series Classification","volume":"21","author":"Faouzi","year":"2020","journal-title":"J. Mach. Learn. Res."},{"key":"ref_41","unstructured":"Bai, S., Kolter, J.Z., and Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"16161","DOI":"10.1016\/j.ifacol.2020.12.605","article-title":"Application of Neural Networks for Heart Rate Monitoring","volume":"53","author":"Askari","year":"2020","journal-title":"IFAC-PapersOnLine"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1111\/1467-9868.00196","article-title":"Probabilistic principal component analysis","volume":"61","author":"Tipping","year":"1999","journal-title":"J. R. Stat. Soc. Ser. B (Stat. Methodol.)"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1109\/TITS.2009.2026312","article-title":"PPCA-Based Missing Data Imputation for Traffic Flow Volume: A Systematical Approach","volume":"10","author":"Qu","year":"2009","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_45","first-page":"1957","article-title":"Practical approaches to principal component analysis in the presence of missing values","volume":"11","author":"Ilin","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1002\/wics.101","article-title":"Principal component analysis","volume":"2","author":"Abdi","year":"2010","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/0169-7439(87)80084-9","article-title":"Principal component analysis","volume":"2","author":"Wold","year":"1987","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Jolliffe, I. (2005). Principal component analysis. Encyclopedia of Statistics in Behavioral Science, John Wiley & Sons, Ltd.","DOI":"10.1002\/0470013192.bsa501"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1038\/nbt0308-303","article-title":"What is principal component analysis?","volume":"26","year":"2008","journal-title":"Nat. Biotechnol."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Izenman, A.J. (2013). Linear discriminant analysis. Modern Multivariate Statistical Techniques, Springer.","DOI":"10.1007\/978-0-387-78189-1_8"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Xanthopoulos, P., Pardalos, P.M., and Trafalis, T.B. (2013). Linear discriminant analysis. Robust Data Mining, Springer.","DOI":"10.1007\/978-1-4419-9878-1"},{"key":"ref_52","first-page":"1","article-title":"Linear discriminant analysis\u2014A brief tutorial","volume":"18","author":"Balakrishnama","year":"1998","journal-title":"Inst. Signal Inf. Process."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Marcano-Cede\u00f1o, A., Quintanilla-Dom\u00ednguez, J., Cortina-Januchs, M., and Andina, D. (2010, January 7\u201310). Feature selection using sequential forward selection and classification applying artificial metaplasticity neural network. Proceedings of the IECON 2010\u201436th Annual Conference on IEEE Industrial Electronics Society, Glendale, AZ, USA.","DOI":"10.1109\/IECON.2010.5675075"},{"key":"ref_54","unstructured":"Ververidis, D., and Kotropoulos, C. (2005, January 4\u20138). Sequential forward feature selection with low computational cost. Proceedings of the 2005 13th European Signal Processing Conference, Antalya, Turkey."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2451","DOI":"10.1162\/089976600300015015","article-title":"Learning to forget: Continual prediction with LSTM","volume":"12","author":"Gers","year":"2000","journal-title":"Neural Comput."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1109\/TPAMI.2008.137","article-title":"A novel connectionist system for unconstrained handwriting recognition","volume":"31","author":"Graves","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Dahl, G.E., Sainath, T.N., and Hinton, G.E. (2013, January 26\u201331). Improving deep neural networks for LVCSR using rectified linear units and dropout. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6639346"},{"key":"ref_59","unstructured":"He, H., Bai, Y., Garcia, E.A., and Li, S. (2008, January 1\u20138). ADASYN: Adaptive synthetic sampling approach for imbalanced learning. Proceedings of the 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), Hong Kong, China."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Kurniawati, Y.E., Permanasari, A.E., and Fauziati, S. (2018, January 7\u20138). Adaptive synthetic-nominal (ADASYN-N) and adaptive synthetic-KNN (ADASYN-KNN) for multiclass imbalance learning on laboratory test data. Proceedings of the 2018 4th International Conference on Science and Technology (ICST), Yogyakarta, Indonesia.","DOI":"10.1109\/ICSTC.2018.8528679"},{"key":"ref_61","unstructured":"Kukar, M., and Kononenko, I. (1998, January 23\u201328). Cost-sensitive learning with neural networks. Proceedings of the ECAI, Brighton, UK."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1109\/TKDE.2006.17","article-title":"Training cost-sensitive neural networks with methods addressing the class imbalance problem","volume":"18","author":"Zhou","year":"2005","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1613\/jair.1.11192","article-title":"SMOTE for learning from imbalanced data: Progress and challenges, marking the 15-year anniversary","volume":"61","author":"Garcia","year":"2018","journal-title":"J. Artif. Intell. Res."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1689","DOI":"10.3758\/s13428-020-01516-y","article-title":"NeuroKit2: A Python toolbox for neurophysiological signal processing","volume":"53","author":"Makowski","year":"2021","journal-title":"Behav. Res. Methods"},{"key":"ref_66","unstructured":"Mosley, L. (2013). A Balanced Approach to the Multi-Class Imbalance Problem. [Ph.D. Thesis, Iowa State University of Science and Technology]."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Guyon, I., Bennett, K., Cawley, G., Escalante, H.J., Escalera, S., Ho, T.K., Maci\u00e0, N., Ray, B., Saeed, M., and Statnikov, A. (2015, January 12\u201317). Design of the 2015 chalearn automl challenge. Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland.","DOI":"10.1109\/IJCNN.2015.7280767"},{"key":"ref_68","unstructured":"Kelleher, J.D., Mac Namee, B., and D\u2019arcy, A. (2020). Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies, MIT Press."},{"key":"ref_69","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_70","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). TensorFlow: A system for Large-Scale machine learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), Savannah, GA, USA."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"638","DOI":"10.21105\/joss.00638","article-title":"MLxtend: Providing machine learning and data science utilities and extensions to Python\u2019s scientific computing stack","volume":"3","author":"Raschka","year":"2018","journal-title":"J. Open Source Softw."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"2251","DOI":"10.1109\/TBME.2020.3049109","article-title":"Physical Activity and Psychological Stress Detection and Assessment of Their Effects on Glucose Concentration Predictions in Diabetes Management","volume":"68","author":"Sevil","year":"2020","journal-title":"IEEE Trans. Biomed. 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