Hamed Khosravi

Discovering novel materials with desired properties is essential for driving innovation. Industry 4.0 and smart manufacturing have promised transformative advances in this area through real-time data integration and automated production planning and control. However, the reliance on automation alone has often fallen short, lacking the flexibility needed for complex processes. To fully unlock the potential of smart manufacturing, we must evolve from automation to autonomous systems that go beyond rigid programming and can dynamically optimize the search for solutions. Current discovery approaches are often slow, requiring numerous trials to find optimal combinations, and costly, particularly when optimizing multiple properties simultaneously. This paper proposes a Bayesian multi-objective sequential decision-making (BMSDM) framework that can intelligently select experiments as manufacturing progresses, guiding us toward the discovery of optimal design faster and more efficiently. The framework leverages sequential learning through Bayesian Optimization, which iteratively refines a statistical model representing the underlying manufacturing process. This statistical model acts as a surrogate, allowing for efficient exploration and optimization without requiring numerous real-world experiments. This approach can significantly reduce the time and cost of data collection required by traditional experimental designs. The proposed framework is compared with traditional DoE methods and two other multi-objective optimization methods. Using a manufacturing dataset, we evaluate and compare the performance of these approaches across five evaluation metrics. BMSDM comprehensively outperforms the competing methods in multi-objective decision-making scenarios. Our proposed approach represents a significant leap forward in creating an intelligent autonomous platform capable of novel material discovery.

Background and Aims: Urban fires pose a significant threat in terms of property damage and potential loss of life. Firefighters play an important role in managing these incidents, so their safety is a top priority for fire departments and emergency responders. The purpose of this study is to predict the probability of firefighter injury and entrapment before dispatching the operational team or the initial stages of the urban firefighting operations.Methods: In this study, we compare the performance of eight machine learning algorithms in predicting the occurrence of firefighter injuries and entrapment during urban fire incidents. We use data from the Fire Department Operations and Management System (FOMS) of Mashhad's city fire and safety services organization. Specifically, we assess the effectiveness of the generated models through five stages, two of which use additional features calculated using the built‐in FOMS functions.Results: We found that the Multi‐Layer Perceptron and Kernel Naive Bayes algorithms achieved the highest predictive accuracy for the occurrence of firefighter injuries and entrapments, 96.7% and 96.0%, respectively.Conclusions: The generated models can help to reduce the incidence of injuries and entrapments among firefighters by providing the decision‐makers with early prediction of the risk factors associated with an ongoing urban firefighting operation.

Reducing carbon dioxide (CO₂) emissions is vital at both global and national levels, given their significant role in exacerbating climate change. CO₂ emissions, stemming from a variety of industrial and economic activities, are major contributors to the greenhouse effect and global warming, posing substantial obstacles in addressing climate issues. It is imperative to forecast CO₂ emissions trends and classify countries based on their emission patterns to effectively mitigate worldwide carbon emissions. This paper presents an in-depth comparative study on the determinants of CO₂ emissions in twenty countries with high Human Development Index (HDI), exploring factors related to economy, environment, energy use, and renewable resources over a span of 25 years. The study unfolds in two distinct phases: initially, statistical techniques such as Ordinary Least Squares (OLS), fixed effects, and random effects models are applied to pinpoint significant determinants of CO₂ emissions. Following this, the study leverages supervised and unsupervised time series approaches to further scrutinize and understand the factors influencing CO₂ emissions. Seasonal AutoRegressive Integrated Moving Average with eXogenous variables (SARIMAX), a statistical time series forecasting model, is first used to predict emission trends from historical data, offering practical insights for policy formulation. Subsequently, Dynamic Time Warping (DTW), an unsupervised time series clustering approach, is used to group countries by similar emission patterns. The dual-phase approach utilized in this study significantly improves the accuracy of CO₂ emissions predictions while also providing a deeper insight into global emission trends. By adopting this thorough analytical framework, nations can develop more focused and effective carbon reduction policies, playing a vital role in the global initiative to combat climate change.

Suicide is the second leading cause of death among individuals aged 5 to 24 in the United States (US). However, the precursors to suicide often do not surface, making suicide prevention challenging. This study aims to develop a machine learning model for predicting suicide ideation (SI), suicide planning (SP), and suicide attempts (SA) among adolescents in the US during the coronavirus pandemic. We used the 2021 Adolescent Behaviors and Experiences Survey Data. Class imbalance was addressed using the proposed data augmentation method tailored for binary variables, Modified Synthetic Minority Over-Sampling Technique. Five different ML models were trained and compared. SHapley Additive exPlanations analysis was conducted for explainability. The Logistic Regression model, identified as the most effective, showed superior performance across all targets, achieving high scores in recall: 0.82, accuracy: 0.80, and area under the Receiver Operating Characteristic curve: 0.88. Variables such as sad feelings, hopelessness, sexual behavior, and being overweight were noted as the most important predictors. Our model holds promise in helping health policymakers design effective public health interventions. By identifying vulnerable sub-groups within regions, our model can guide the implementation of tailored interventions that facilitate early identification and referral to medical treatment.

Background This study explores the utility of machine learning (ML) models in predicting complicated Ovarian Hyperstimulation Syndrome (OHSS) in patients undergoing infertility treatments, addressing the challenge posed by highly imbalanced datasets. Objective This research fills the existing void by introducing a detailed structure for crafting diverse machine learning models and enhancing data augmentation methods to predict complicated OHSS effectively. Importantly, the research also concentrates on pinpointing critical elements that affect OHSS. Method This retrospective study employed a ML framework to predict complicated OHSS in patients undergoing infertility treatment. The dataset included various patient characteristics, treatment details, ovarian response variables, oocyte quality indicators, embryonic development metrics, sperm quality assessments, and treatment specifics. The target variable was OHSS, categorized as painless, mild, moderate, or severe. The ML framework incorporated Ray Tune for hyperparameter tuning and SMOTE-variants for addressing data imbalance. Multiple ML models were applied, including Decision Trees, Logistic Regression, SVM, XGBoost, LightGBM, Ridge Regression, KNN, and SGD. The models were integrated into a voting classifier, and the optimization process was conducted. The SHAP package was used to interpret model outcomes and feature contributions. Results The best model incorporated IPADE-ID augmentation along with an ensemble of classifiers (SGDClassifier, SVC, RidgeClassifier), reaching a recall of 0.9 for predicting OHSS occurrence and an accuracy of 0.76. SHAP analysis identified key factors: GnRH antagonist use, longer stimulation, female infertility factors, irregular menses, higher weight, hCG triggers, and, notably, higher number of embryos. Conclusion This novel study demonstrates ML’s potential for predicting complicated OHSS. The optimized model provides insights into contributory factors, challenging certain conventional assumptions. The findings highlight the importance of considering patient-specific factors and treatment details in OHSS risk assessment.

In the context of global sustainability, buildings are significant consumers of energy, emphasizing the necessity for innovative strategies to enhance efficiency and reduce environmental impact. This research leverages extensive raw data from building infrastructures to uncover energy consumption patterns and devise strategies for optimizing resource use. We investigate the factors influencing energy efficiency and cost reduction in buildings, utilizing Lasso Regression, Decision Tree, and Random Forest models for accurate energy use forecasting. Our study delves into the factors affecting energy utilization, focusing on primary fuel and electrical energy, and discusses the potential for substantial cost savings and environmental benefits. Significantly, we apply metaheuristic techniques to enhance the Decision Tree algorithm, resulting in improved predictive precision. This enables a more nuanced understanding of the characteristics of buildings with high and low energy efficiency potential. Our findings offer practical insights for reducing energy consumption and operational costs, contributing to the broader goals of sustainable development and cleaner production. By identifying key drivers of energy use in buildings, this study provides a valuable framework for policymakers and industry stakeholders to implement cleaner and more sustainable energy practices.