Application of the outlier detection method for web-based blood glucose level monitoring system

Recent advancements in biosensors have empowered individuals with diabetes to autonomously monitor their blood glucose levels through continuous glucose monitoring (CGM) sensors. Nevertheless, the data collected from these sensors may occasionally include outliers due to the inherent imperfections of the sensor devices. Consequently, the identification of these outliers is critical to determine whether blood glucose levels deviate significantly from the norm, necessitating further action. This study employs an outlier detection approach based on the 3-sigma method and the interquartile range (IQR), along with the application of the Winsorizing technique to correct the identified outliers. Additionally, a web-based system for visualizing blood glucose levels is developed, utilizing both outlier detection methods. In order to assess the system's performance, two types of testing are conducted: black box testing and load testing. The results of black box testing indicate that all test scenarios operate as anticipated. As for the load testing response times, it is observed that the 3-sigma visualization page loads an average of 606.75 milliseconds faster compared to the IQR visualization page. This study's outcomes are expected to enhance data quality, enhance the precision of analyses, and facilitate more informed decision-making by identifying and addressing extreme data points

Utilizing Random Forest with iForest-based outlier detection and SMOTE to detect movement and direction of RFID tags

In recent years, radio frequency identification (RFID) technology has been utilized to monitor product movements within a supply chain in real time. By utilizing RFID technology, the products can be tracked automatically in real-time. However, the RFID cannot detect the movement and direction of the tag. This study investigates the performance of machine learning (ML) algorithms to detect the movement and direction of passive RFID tags. The dataset utilized in this study was created by considering a variety of conceivable tag motions and directions that may occur in actual warehouse settings, such as going inside and out of the gate, moving close to the gate, turning around, and static tags. The statistical features are derived from the received signal strength (RSS) and the timestamp of tags. Our proposed model combined Isolation Forest (iForest) outlier detection, Synthetic Minority Over Sampling Technique (SMOTE) and Random Forest (RF) has shown the highest accuracy up to 94.251% as compared to other ML models in detecting the movement and direction of RFID tags. In addition, we demonstrated the proposed classification model could be applied to a web-based monitoring system, so that tagged products that move in or out through a gate can be correctly identified. This study is expected to improve the RFID gate on detecting the status of products (being received or delivered) automatically.Web of Science153art. no. 10

Web-based Sentiment Analysis System Using SVM and TF-IDF with Statistical Feature

Social media's tendency for instant reactions can be harnessed by companies and organizations to gather feedback. Nevertheless, effectively analyzing vast amounts of social media data poses a challenge. This issue can be addressed through the use of sentiment analysis technology. In this study, a sentiment analysis model is developed, employing Support Vector Machine (SVM) and Term Frequency-Inverse Document Frequency (TF-IDF) algorithms. The study aims to investigate the impact of feature engineering on TF-IDF, by incorporating statistical features into the SVM model's sentiment analysis performance. The experimental results reveal that the prediction model utilizing the conventional TFIDF approach achieves an SVM model with an F-measure score of 84.55%. Through the implementation of feature engineering, by adding max, min, and sum features, the model's performance shows a noticeable improvement, with an increase of 0.65% in the F-measure score difference. Consequently, the proposed feature engineering method positively enhances the capability of the SVM-based sentiment analysis model. To facilitate the acquisition of sentiment analysis results through user interfaces, the trained SVM model is integrated into a web-based sentiment analysis application. By doing so, the findings of this study contribute to streamlining the process of obtaining sentiment analysis results from social media data