Anxiety and depression are psychological disorders characterized by persistent and impairing symptoms. They affect millions of people worldwide and have a significant impact on individuals' well-being and daily functioning. Although highly effective treatments exist, delayed diagnoses and limited access to mental health care contribute to a significant number of undiagnosed individuals. Therefore, it is important to explore predictive modeling to anticipate and address potential issues before the symptoms increase. In that context, this study proposes a machine learning approach to predict anxiety and depression scores based on the Generalized Anxiety Disorder (GAD-7) and Patient Health Questionnaire (PHQ-9). In a regression scenario the proposed multi-layer perceptron (MLP) achieved the lowest MAE values of 5.3924 for anxiety and 5.06 for depression, as well as the lowest MAPE values of 0.1101 for anxiety and 0.1043 for depression. For a classification scenario the best-performing models were the random forest (RF) and LightGBM with an F1score of 0.8997 and 0.8918 for anxiety, respectively, and 0.7593 and 0.7480 for depression. These results highlight the potential of neural network-based models to outperform traditional ensemble and kernel-based approaches to predict mental disorder scores. Additionally, the classification results also suggest that tree and kernel-based models can effectively maintain balanced predictive performance.

The High-Pressure Die Casting (HPDC) process is a critical manufacturing technique for producing complex and high-precision parts, but it faces significant challenges related to equipment longevity, predictive maintenance, and decision support. This paper addresses three major challenges in HPDC environments: predicting the remaining useful life (RUL) of dies, developing an integrated pipeline for anomaly detection and maintenance forecasting, and implementing a real-time dashboard for operational decision-making. A predictive maintenance framework was proposed, combining traditional machine learning models as Light Gradient Boosting Machine (LightGBM) with advanced Large Language Models (LLMs) enhanced by Retrieval-Augmented Generation (RAG). This framework enables proactive fault detection, anomaly classification, failure impact foresight, and operational interruption forecasting. To ensure that these predictive insights are accessible to industry practitioners, a customized dashboard was developed to integrate real-time monitoring, predictive alerts, and maintenance recommendations. The proposed approach enhances operational efficiency, optimizes die usage, and promotes a data-driven maintenance culture in HPDC industries.

A mental disorder is a clinically significant disturbance in an individual's cognition, emotional, or behavioral functioning.Mental disorders such as anxiety and depression can be accessed by psychiatrists using auxiliary tools such as the depression anxiety stress scale (DASS), patient reported outcome (PRO), patient reported outcome measures (PROMs) and patient reported outcomes measurement information system (PROMIS ® ).However, many individuals affected by the symptoms of mental disorders do not receive a proper diagnosis.In that context, this work proposes a machine learning approach to predict the score of anxiety and depression using PROMIS ® questionnaires by performing a comparative study between supervised learning models to estimate the scores of anxiety and depression from individuals.Through the proposed model an average MAPE of 6.31%, R² of 0.76, and Spearman coefficient of 88.86 were achieved, outperforming widely used linear models such as support vector machines (SVM), random forest (RF), and gradient boosting (GB).In conclusion, the utilization of machine learning algorithms with PROMIS ® questionnaires has shown promise as a methodology for assessing anxiety and depression scores from the participants' perspective, aligning with their perceptions of well-being.