Adaptive filter for detection outlier data on electronic nose signal

2006

Ghozali, 2016

Kuswandi, 2017, Immuno strip test for detection of pork adulteration in cooked meatballs, Food Biosci., 19, 1, 10.1016/j.fbio.2017.05.001

Yang, 2018, Rapid identification of pork adulterated in the beef and mutton by infrared spectroscopy, J. Spectrosc., 2018, 10.1155/2018/2413874

Sabilla, 2017, Estimating gas concentration using artificial neural network for electronic nose, Procedia Comp. Sci., 124, 181, 10.1016/j.procs.2017.12.145

Swapna, 2018, Diabetes detection using deep learning algorithms, ICT Express, 4, 243, 10.1016/j.icte.2018.10.005

Cruz, 2006

Saktiawati, 2021, eNose-TB: A trial study protocol of electronic nose for tuberculosis screening in Indonesia, PLoS One, vol. 16, 10.1371/journal.pone.0249689

Hariyanto, 2018, Detection of diabetes from gas analysis of human breath using e-Nose, vol. 2018, 241

Mirshahi, 2018, 45, 15, 2788

Pardo, 2000, Data preprocessing enhances the classification of different brands of Espresso coffee with an electronic nose, Sensors Actuators B Chem., 69, 397, 10.1016/S0925-4005(00)00499-8

Shao, 2015, Comparison of different classification methods for analyzing electronic nose data to characterize sesame oils and blends, Sensors, 15, 26726, 10.3390/s151026726

Yan, 2015, Electronic nose feature extraction methods: a review, Sensors, Vol. 15, 27804, 10.3390/s151127804

Alimohammadi, 2022, Performance evaluation of outlier detection techniques in production timeseries: a systematic review and meta-analysis, Expert Syst. Appl., 191, 116371, 10.1016/j.eswa.2021.116371

Baldomero-Naranjo, 2021, A robust SVM-based approach with feature selection and outliers detection for classification problems, Expert Syst. Appl., vol. 178, 10.1016/j.eswa.2021.115017

Pang, 2021, Deep Learning for Anomaly Detection, ACM Comput. Surv., vol. 54, 10.1145/3439950

Hinton, 2006, Reducing the dimensionality of data with neural networks, Science, vol. 313, 504, 10.1126/science.1127647

Christ, 2018, Time series FeatuRe extraction on basis of scalable hypothesis tests (tsfresh – a Python package), Neurocomputing, vol. 307, 72, 10.1016/j.neucom.2018.03.067

Heisterberg, 2011, Deodorants are the leading cause of allergic contact dermatitis to fragrance ingredients, Contact Dermatitis, vol. 64, 258, 10.1111/j.1600-0536.2011.01889.x

MQ-2 Datasheet | HANWEI ELETRONICS

MQ-3 Datasheet | HANWEI ELETRONICS

Learning, 2016, 技術者が知っておきたい Deep Learning の基礎と 組込みでの利用 ~ 今さら聞いてください Deep Learning ~, Nature, vol. 26, 436

Häse, 2016, An overview of gradient descent optimization algorithms, undefined, vol. 4, 1134

Wijaya, 2021, DWTLSTM for electronic nose signal processing in beef quality monitoring, Sensors Actuators B Chem., 326, 10.1016/j.snb.2020.128931

Sarno, 2020, Detecting pork adulteration in beef for halal authentication using an optimized electronic nose system, IEEE Access, 10.1109/ACCESS.2020.3043394

Chikodili, 2021, vol. 1350

Luque, 2019, The impact of class imbalance in classification performance metrics based on the binary confusion matrix, Pattern Recogn., vol. 91, 216, 10.1016/j.patcog.2019.02.023

Mortaz, 2020, Imbalance accuracy metric for model selection in multi-class imbalance classification problems, Knowledge-Based Syst., vol. 210, 10.1016/j.knosys.2020.106490