Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest

A Robust Framework for Self-Care Problem Identification for Children with Disability

Recently, a standard dataset namely SCADI (Self-Care Activities Dataset) based on the International Classification of Functioning, Disability, and Health for Children and Youth framework for self-care problems identification of children with physical and motor disabilities was introduced. This is a very interesting, important and challenging topic due to its usefulness in medical diagnosis. This study proposes a robust framework using a sampling technique and extreme gradient boosting (FSX) to improve the prediction performance for the SCADI dataset. The proposed framework first converts the original dataset to a new dataset with a smaller number of dimensions. Then, our proposed framework balances the new dataset in the previous step using oversampling techniques with different ratios. Next, extreme gradient boosting was used to diagnose the problems. The experiments in terms of prediction performance and feature importance were conducted to show the effectiveness of FSX as well as to analyse the results. The experimental results show that FSX that uses the Synthetic Minority Over-sampling Technique (SMOTE) for the oversampling module outperforms the ANN (Artificial Neural Network) -based approach, Support vector machine (SVM) and Random Forest for the SCADI dataset. The overall accuracy of the proposed framework reaches 85.4%, a pretty high performance, which can be used for self-care problem classification in medical diagnosis.

An Affordable Fast Early Warning System for Edge Computing in Assembly Line

Maintaining product quality is essential for smart factories, hence detecting abnormal events in assembly line is important for timely decision-making. This study proposes an affordable fast early warning system based on edge computing to detect abnormal events during assembly line. The proposed model obtains environmental data from various sensors including gyroscopes, accelerometers, temperature, humidity, ambient light, and air quality. The fault model is installed close to the facilities, so abnormal events can be timely detected. Several performance evaluations are conducted to obtain the optimal scenario for utilizing edge devices to improve data processing and analysis speed, and the final proposed model provides the highest accuracy in terms of detecting abnormal events compared to other classification models. The proposed model was tested over four months of operation in a Korean automobile parts factory, and provided significant benefits from monitoring assembly line, as well as classifying abnormal events. The model helped improve decision-making by reducing or preventing unexpected losses due to abnormal events.

Adaptive Data Boosting Technique for Robust Personalized Speech Emotion in Emotionally-Imbalanced Small-Sample Environments

Personalized emotion recognition provides an individual training model for each target user in order to mitigate the accuracy problem when using general training models collected from multiple users. Existing personalized speech emotion recognition research has a cold-start problem that requires a large amount of emotionally-balanced data samples from the target user when creating the personalized training model. Such research is difficult to apply in real environments due to the difficulty of collecting numerous target user speech data with emotionally-balanced label samples. Therefore, we propose the Robust Personalized Emotion Recognition Framework with the Adaptive Data Boosting Algorithm to solve the cold-start problem. The proposed framework incrementally provides a customized training model for the target user by reinforcing the dataset by combining the acquired target user speech with speech from other users, followed by applying SMOTE (Synthetic Minority Over-sampling Technique)-based data augmentation. The proposed method proved to be adaptive across a small number of target user datasets and emotionally-imbalanced data environments through iterative experiments using the IEMOCAP (Interactive Emotional Dyadic Motion Capture) database.