Preventive maintenance activities require a tool to be offline for long hour in order to perform the prescribed maintenance activities. Although preventive maintenance is crucial to ensure operational reliability and efficiency of the tool, long hour of preventive maintenance activities increases the cycle time of the semiconductor fabrication foundry (Fab). Therefore, this activity is usually performed when the incoming Work-in-Progress to the equipment is forecasted to be low. The current statistical forecasting approach has low accuracy because it lacks the ability to capture the time-dependent behavior of the Work-in-Progress. In this paper, we present a forecasting model that utilizes machine learning method to forecast the incoming Work-In-Progress. Specifically, our proposed model uses LSTM to forecast multistep ahead incoming Work-in-Progress prediction to an equipment group. The proposed model's prediction results were compared with the results of the current statistical forecasting method of the Fab. The experimental results demonstrated that the proposed model performed better than the statistical forecasting method in both hit rate and Pearson's correlation coefficient, r.
Short Message Service (SMS) spam poses significant risks, including financial scams and phishing attempts. Although numerous datasets from online repositories have been utilized to address this issue, little attention has been given to evaluating their effectiveness and impact on SMS spam detection models. This study fills this gap by assessing the performance of ten SMS spam detection datasets using Decision Tree and Multinomial Naïve Bayes models. Datasets were evaluated based on accuracy and qualitative factors such as authenticity, class imbalance, feature diversity, metadata availability, and preprocessing needs. Due to the multilingual nature of the datasets, experiments were conducted with two stopword removal groups: one in English and another in the respective non-English languages. The key findings of this research have led to the recommendation of Dataset 5 for future SMS spam detection research, as evidence from the dataset's high qualitative assessment score of 3.8 out of 5.0 due to its high feature diversity, real-world complexity, and balanced class distribution, and low detection rate of 86.10% from Multinomial Naïve Bayes. Recommending a dataset that poses challenges for high model performance fosters the development of more robust and adaptable spam detection models capable of handling diverse forms of noise and ambiguity. Furthermore, selecting the dataset with the highest qualitative score enhances research quality, improves model generalizability, and mitigates risks related to bias and inconsistencies.