Oversampling technique in student performance classification from engineering course

The first year of an engineering student was important to take proper academic planning. All subjects in the first year were essential for an engineering basis. Student performance prediction helped academics improve their performance better. Students checked performance by themselves. If they were aware that their performance are low, then they could make some improvement for their better performance. This research focused on combining the oversampling minority class data with various kinds of classifier models. Oversampling techniques were SMOTE, Borderline-SMOTE, SVMSMOTE, and ADASYN and four classifiers were applied using MLP, gradient boosting, AdaBoost and random forest in this research. The results represented that Borderline-SMOTE gave the best result for minority class prediction with several classifiers

An assistive model of obstacle detection based on deep learning: YOLOv3 for visually impaired people

The World Health Organization (WHO) reported in 2019 that at least 2.2 billion people were visual-impairment or blindness. The main problem of living for visually impaired people have been facing difficulties in moving even indoor or outdoor situations. Therefore, their lives are not safe and harmful. In this paper, we proposed an assistive application model based on deep learning: YOLOv3 with a Darknet-53 base network for visually impaired people on a smartphone. The Pascal VOC2007 and Pascal VOC2012 were used for the training set and used Pascal VOC2007 test set for validation. The assistive model was installed on a smartphone with an eSpeak synthesizer which generates the audio output to the user. The experimental result showed a high speed and also high detection accuracy. The proposed application with the help of technology will be an effective way to assist visually impaired people to interact with the surrounding environment in their daily life

Time Series Analysis for Fail Spare Part Prediction: Case of ATM Maintenance

Prediction of failed spare parts is an interesting issue in inventory management. Our work applied predictive analytic to forecast future amount of failed spare parts. This research used maintenance time series data from year 2013 to 2016 to train and test data for a prediction model. In the preprocessing step, we looked into new features based on historical data set. Then, we added the day of week feature into the example of the data set. The day of week feature had an impact to spare parts prediction model. Moving average and windowing methods were used in the preprocessing phase before sending through the prediction model. Artificial neural network and support vector machine for regression were applied to predict the amount of failed spare parts. The experiments demonstrated the average accuracy of failed spare part prediction. The result represented that the support vector machine for regression showed the best accuracy at 88.24%. SVR yielded the highest prediction accuracy at 92.7%

An Analytics Prediction Model of Monthly Rainfall Time Series: Case of Thailand

Rainfall prediction is regarded as a challenging task in an agricultural country like Thailand. A time series data especially rainfall and temperature needs analytics technologies to return a valuable knowledge. It has been recognized that a high accuracy of rainfall prediction model will be helpful for agriculturist and water management. The study area of this research is located in Thailand, which the daily rainfall and temperature time series data collected from five regions of Thailand were taken by Meteorological Department of Thailand from years 2000 to 2015. In this research, analytics method is proposed in the preprocessing steps, which are composed of data cleansing and data transform. Principal component analysis in feature selection step and weighted moving average are applied. In the prediction modeling, support vector regression (SVR) and artificial neural network (ANN) are employed. The results of the experiment showed the comparison of overall accuracy between ANN and SVR in five data sets over the area of study. The results of the experiment showed that the two prediction models gave a high overall accuracy, although SVR plays an important advantage in less computational time than ANN. This experiment is extremely useful not only as the most effective way to manage the amount of rainfall in water management for Thai agriculturist, but the proposed model can also become a representative in the monthly rainfall prediction model used in Thailand