Integrating Machine Learning and Multi-Objective Optimization in Biofuel Systems: A Review by Ivan P. Malashin, Dmitry A. Martysyuk, Vadim S. Tynchenko, Andrei P. Gantimurov, Vladimir A. Nelyub, Aleksei S. Borodulin

“…The optimization of biofuel production involves balancing multiple conflicting objectives such as yield maximization, cost minimization, and environmental impact reduction. Recent studies have explored various multi-objective optimization (MOO) techniques integrated with machine learning (ML) models to enhance process efficiency. …”
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Machine-Learning-Driven Approaches for Assessment, Delegation, and Optimization of Multi-Floor Building by Abtin Baghdadi, Harald Kloft

“…This study presents a novel integrated framework for the structural analysis and optimization of multi-floor buildings by combining validated theoretical models with machine learning and evolutionary algorithms. …”
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Modeling seawater intrusion along the Alabama coastline using physical and machine learning models to evaluate the effects of multiscale natural and anthropogenic stresses by Hossein Gholizadeh, T. Prabhakar Clement, Christopher T. Green, Geoffrey R. Tick, Alain M. Plattner, Yong Zhang

“…Results revealed that a 50% increase in groundwater withdrawals caused seawater to advance ~ 320 m inland, whereas a 50% reduction led to a ~ 270-meter retreat. This study highlights the vulnerability of Alabama’s shallow coastal aquifers to seawater intrusion due to storm surges and human activities, and demonstrates that combining physics-based models with machine learning approaches can improve groundwater predictions, though its accuracy depends on the availability of site-specific data.…”
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Predicting the Tensile Properties of Automotive Steels at Intermediate Strain Rates via Interpretable Ensemble Machine Learning by Houchao Wang, Fengyao Lv, Zhenfei Zhan, Hailong Zhao, Jie Li, Kangte Yang

“…In this study, a dataset was constructed by collecting data from high-speed tensile experiments on 65 automotive steels. Five machine learning models, including ridge regression, support vector machine regression, gradient boosted regression tree, random forest, and adaptive boosting regression, were developed to predict the yield strength (YS), ultimate tensile strength (UTS), and fracture elongation (FE) of automotive steels at 100/s using the composition, sample size, and quasi-static mechanical properties of automotive steels as input variables. …”
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Enhancing Engineering and Architectural Design Through Virtual Reality and Machine Learning Integration by Ali Shehadeh, Odey Alshboul

“…This study introduces a framework that leverages the synergistic potential of Virtual Reality (VR) and Machine Learning (ML) to enhance graphical modeling in engineering and architectural design. …”
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Predicting Soccer Player Salaries with Both Traditional and Automated Machine Learning Approaches by Davronbek Malikov, Pilsu Jung, Jaeho Kim

“…To address these challenges, this study adopts machine learning (ML) techniques that model player salaries based on a combination of performance metrics and contextual features. …”
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Feasibility, Advantages, and Limitations of Machine Learning for Identifying Spilled Oil in Offshore Conditions by Seong-Il Kang, Cheol Huh, Choong-Ki Kim, Meang-Ik Cho, Hyuek-Jin Choi

“…This study considers machine learning models that can be applied immediately upon measurement of oil density and viscosity. …”
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Machine Learning Approaches for the Prediction of Displaced Abomasum in Dairy Cows Using a Highly Imbalanced Dataset by Zeinab Asgari, Ali Sadeghi-Sefidmazgi, Abbas Pakdel, Saleh Shahinfar

“…For this purpose, in this study, the ability of five machine learning algorithms, namely Logistic Regression (LR), Naïve Bayes (NB), Decision Tree, Random Forest (RF) and Gradient Boosting Machines (GBM), to predict cases of DA was investigated. …”
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Improved Connected-Mode Discontinuous Reception (C-DRX) Power Saving and Delay Reduction Using Ensemble-Based Traffic Prediction by Ji-Hee Yu, Yoon-Ju Choi, Seung-Hwan Seo, Seong-Gyun Choi, Hye-Yoon Jeong, Ja-Eun Kim, Myung-Sun Baek, Young-Hwan You, Hyoung-Kyu Song

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Machine learning (ML) based reduced order modelling (ROM) for linear and non-linear solid and structural mechanics by Mikhael Tannous, Chady Ghnatios, Eivind Fonn, Trond Kvamsdal, Francisco Chinesta

“…This work introduces a minimally intrusive model order reduction technique that employs machine learning within a Proper Orthogonal Decomposition framework to achieve this alliance. …”
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Personalized prediction model generated with machine learning for kidney function one year after living kidney donation by Rikako Oki, Toshihio Hirai, Kazuhiro Iwadoh, Yu Kijima, Hiroyuki Hashimoto, Yasunori Nishimura, Taro Banno, Kohei Unagami, Kazuya Omoto, Tomokazu Shimizu, Junichi Hoshino, Toshio Takagi, Hideki Ishida, Toshihito Hirai

“…This study aimed to develop a machine learning (ML) model to predict serum creatinine (Cre) levels at one year post-donation using preoperative clinical data, including kidney-, fat-, and muscle-volumetry values from computed tomography. …”
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Advanced Machine Learning Approaches for Predicting Machining Performance in Orthogonal Cutting Process by Sabrina Al Bukhari, Salman Pervaiz

“…We investigated the orthogonal cutting process by using machine learning models to predict its performance. …”
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Seasonal and Meteorological Drivers of Hand, Foot, and Mouth Disease Outbreaks Using Data-Driven Machine Learning Models by Pakorn Lonlab, Suparinthon Anupong, Chalita Jainonthee, Sudarat Chadsuthi

“…This study aimed to identify the high- and low-risk HFMD outbreak areas using machine learning models: Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forests (RF), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost). …”
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Integrating Sentinel-1 SAR and Machine Learning Models for Optimal Soil Moisture Sensor Placement at Catchment Scale by Yi Xie, Guotao Cui, Kaifeng Zheng, Guoping Tang

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Development of an Optimal Machine Learning Model to Predict CO₂ Emissions at the Building Demolition Stage by Gi-Wook Cha, Choon-Wook Park

“…In this study, research on the development of optimal machine learning (ML) models was conducted to predict CO₂ emissions at the demolition stage. …”
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Development of an interpretable machine learning model based on CT radiomics for the prediction of post acute pancreatitis diabetes mellitus by Xiyao Wan, Yuan Wang, Ziyi Liu, Ziyan Liu, Shuting Zhong, Xiaohua Huang

“…Abstract This study sought to establish and validate an interpretable CT radiomics-based machine learning model capable of predicting post-acute pancreatitis diabetes mellitus (PPDM-A), providing clinicians with an effective predictive tool to aid patient management in a timely fashion. …”
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Enhancing weather index insurance through surrogate models: leveraging machine learning techniques and remote sensing data by Sachini Wijesena, Biswajeet Pradhan

“…WII products often rely on a single weather index, which fails to encompass the complex nature of weather events. While machine learning models offer the potential to model the multifaceted nature of factors influencing crop growth, their adoption in WII products has been limited due to their lack of transparency, often perceived as ‘black box models’. …”
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Machine Learning-Based Prediction of Resilience in Green Agricultural Supply Chains: Influencing Factors Analysis and Model Construction by Daqing Wu, Tianhao Li, Hangqi Cai, Shousong Cai

“…Secondly, by integrating configurational analysis with machine learning, it innovatively constructs a resilience level prediction model based on fsQCA-XGBoost. …”
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Machine learning-based predictive model for enteral nutrition-associated diarrhea in ICU patients and its nursing applications by Xiaoying Liao, Chunhua Li, Qunyan Liu, Wang Xia, Zhenglin Liu, Jiamao Zhu, Wei Hu, Qionghua Hong

“…LASSO regression was used for feature selection, and 9 machine learning (ML) algorithms were evaluated. Model performance was assessed using metrics such as the area under the receiver operating characteristic curve (AUC). …”
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Development and interpretation of a machine learning risk prediction model for post-stroke depression in a Chinese population by Xia Zhong, Tianen Zhao, Shimeng Lv, Guangheng Zhang, Jing Li, Donghai Liu, Huachen Jiao

“…After selecting the core predictors of PSD using LASSO regression dimension reduction, six machine learning (ML) algorithms were used to statistically model the risk prediction of PSD. …”
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