## 3 Methodology
### iForest Outlier Detection
_Sensors_**2021**, _21_, 3880. [CrossRef]
* (27) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] An Innovative Hourly Water Demand Forecasting Preprocessing Framework with Local Outlier Correction and Adaptive Decomposition Techniques. _Water_**2021**, _13_, 582. [CrossRef]
* (28) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] An Anomaly Detection Method for Wireless Sensor Networks Based on the Improved Isolation Forest. _Appl. Sci._**2023**, _13_, 702. [CrossRef]
* (29) [PERSON]; [PERSON]; [PERSON]; [PERSON] SMOTE: Synthetic Minority Over-Sampling Technique. _J. Artif. Intell. Res._**2002**, _16_, 321-357. [CrossRef]
* (30) [PERSON]; [PERSON]; [PERSON]; [PERSON] Imbalanced Enterprise Credit Evaluation with DTE-SBD: Decision Tree Ensemble Based on SMOTE and Bagging with Differentiated Sampling Rates. _Inf. Sci._**2018**, _425_, 76-91. [CrossRef]
* (31) [PERSON]; [PERSON]; [PERSON]; [PERSON] Oversampling Techniques for Bankruptcy Prediction: Novel Features from a Transaction Dataset. _Symmetry_**2018**, _10_, 79. [CrossRef]
* (32) [PERSON]; [PERSON]; [PERSON]; [PERSON] Recognition of New and Old Banknotes Based on SMOTE and SVM. In Proceedings of the 2015 IEEE 12 th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12 th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15 th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom), Beijing, China, 10-14 August 2015; IEEE: Piscataway, NJ, USA, 2015; pp. 213-220. * (33) [PERSON]; [PERSON]; [PERSON] Isolation Forest. In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, Pisa, Italy, 15-19 December 2008; IEEE: Piscataway, NJ, USA, 2008; pp. 413-422. * (34) [PERSON] Random Forest. _Mach. Learn._**2001**, _45_, 5-32. [CrossRef]
* (35) [PERSON] RFID Reading Dataset. Available online: [[https://github.com/ganjar87/RFID_reading_dataset](https://github.com/ganjar87/RFID_reading_dataset)]([https://github.com/ganjar87/RFID_reading_dataset](https://github.com/ganjar87/RFID_reading_dataset)) (accessed on 18 February 2023). * (36) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; et al. Scikit-Learn: Machine Learning in Python. _[PERSON]. [PERSON]. Learn. Res._**2012**, _12_, 2825-2830. [CrossRef]
* (37) [PERSON]; [PERSON] [PERSON] A Systematic Analysis of Performance Measures for Classification Tasks. _Inf. Process. Manag._**2009**, _45_, 427-437. [CrossRef]
* (38) [PERSON]; [PERSON]; [PERSON]; [PERSON] Decreasing False-Positive RFID Tag Reads by Improved Portal Antenna Setups. In Proceedings of the 2012 3 rd IEEE International Conference on the Internet of Things, Wuxi, China, 24-26 October 2012; IEEE: Piscataway, NJ, USA, 2012; pp. 99-106. * [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON], [PERSON] Development of a Web-Based Liver Cancer Prediction Model for Type II Diabetes Patients by Using an Artificial Neural Network. _Comput. Methods Programs Biomed._**2016**, _125_, 58-65. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Machine Learning Based Diabetes Prediction and Development of Smart Web Application. _Int. J. Cogn. Comput. Eng._**2021**, \(2\), 229-241. [CrossRef]
* (39)**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.