In industrial settings, unplanned machine downtime is a serious risk to profitability, operational effectiveness, and production. In order to predict machine breakdowns before they occur, this research offers a machine learning-based predictive maintenance framework that enables early prediction of machine downtime. The research is carried out using recorded data sets of industrial machines that operate according to various factors or reasons for downtime. Based on these values, prediction of downtime is possible. To guarantee data quality and consistency, several preprocessing techniques, such as imputation and normalization, were used on a dataset of 2,500 records and 16 features, ranging... More >

Rice plays a crucial role globally, as it is widely consumed across nations. Therefore, studying rice prices is vital, since fluctuations in the price can affect both its consumption and availability. This study analyzes time-series models using an international dataset. After preprocessing, the dataset comprises 71,856 samples and eight input features from six countries. The original dataset contained 300,816 rows and 23 columns. This study aims to predict rice inflation rates using time series models such as ARIMA, LSTM, and BiLSTM. The ARIMA model achieved the best combination of values (4,1,4)(0,0,0). Various statistical techniques that calculate inflation rates require expert knowledge... More >

Greenhouse gas Methane (CH$_4$) has 86 times more impact on global warming than carbon dioxide (CO$_2$). The emission of methane gas into the atmosphere is increasing due to the reliance on fossil-based resources in post-industrial energy consumption, along with the rise in food demand and the generation of organic waste that accompanies a growing human population. CH$_4$ acts as a vital pollutant in the air. The problem addressed in this study was to accurately estimate CH$_4$ emissions from functional urban areas. This study aims to predict CH$_4$ emissions using Time Series (TS) and Machine Learning (ML) models such as Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SAR... More >