Prediction of coronary heart disease based on klotho levels using machine learning by Yuan Yao, Ying Zhao, Haifeng Li, Yanlin Han, Yue Wu, Renwei Guo, Mingfeng Ma, Lixia Bu

“…We evaluated the ability of five models constructed using logistic regression, neural networks, random forest, support vector machine, and eXtreme Gradient Boosting to predict CHD. …”
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A Rapid Design Method for Centrifugal Pump Impellers Based on Machine Learning by Y. Chen, W. Li, Y. Luo, L. Ji, S. Li, Y. Long

“…Once these parameters are defined, the main dimensions of the impeller can be calculated. …”
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Electrical discharge machining: Recent advances and future trends in modeling, optimization, and sustainability by Muhamad Taufik Ulhakim, Sukarman, Khoirudin, Dodi Mulyadi, Hendri Susilo, Rohman, Muji Setiyo

“…Advanced modeling techniques, such as finite element analysis (FEA) and artificial intelligence (AI)-driven simulations, have improved the accuracy of process predictions, enabling real-time adjustments and precise control of machining parameters. Optimization approaches, including machine learning-based algorithms, multi-objective optimization, and hybrid methods, have enhanced key performance indicators, such as material removal rate (MRR), surface quality, and tool wear, thereby increasing process efficiency and reducing machining time. …”
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Evaluation of machine learning models for predicting performance metrics of aero-engine combustors by Huan Yang, Shu Guo, Haolin Xie, Jian Wen, Jiarui Wang

“…As environmental standards tighten and aero-engine performance improves, credible mapping between structural parameters and performance metrics is crucial for optimizing combustion chamber design. …”
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A web-based machine learning framework for building energy efficiency prediction by B.S.S.V. Ramana, S. Chanikya Kumar, N. Bharath Kumar, Attuluri R. Vijay Babu

“…This paper presents a web-based machine learning framework for estimating heating and cooling loads using static design parameters. …”
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Machine learning-based prediction of shear strength in interior beam-column joints by Iman Kattoof Harith, Wissam Nadir, Mustafa S. Salah, Ahmed Y. Mohammed, Mohammed L. Hussien

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Transfer Kernel Extreme Learning Machine Based on Bidirectional Cross Domain Approximation by Yuanxiao Zeng, Huimin Li, Yanbing Song

“…Kernel extreme learning machine (KELM) not only inherits the high learning efficiency of the traditional extreme learning machine (ELM), but also mitigates the instability caused by random initialization of input layer parameters, thus establishing its success as a machine learning model. …”
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Predicting Sugar Yield From Sugarcane Using Machine Learning for Jaggery Production by Kathirvel Narayanasamy, Ilayaraja Venkatachalam

“…This study presents a machine learning approach for rapid and accurate prediction of sugar yield per metric ton of sugarcane, using multiple physicochemical and environmental parameters. …”
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Comparison of ECG Between Gameplay and Seated Rest: Machine Learning-Based Classification by Emi Yuda, Hiroyuki Edamatsu, Yutaka Yoshida, Takahiro Ueno

“…Grid search was applied to optimize parameters for LGR, RF, and XGB, while default settings were used for the other classifiers. …”
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Prediction of Corroded Pipeline Failure Pressure Based on Empirical Knowledge and Machine Learning by Hongbo Liu, Xiangzhao Meng

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Effectiveness of machine learning models in diagnosis of heart disease: a comparative study by Waleed Alsabhan, Abdullah Alfadhly

“…Our study employs a wide range of ML algorithms, such as Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), K-Nearest Neibors (KNN), AdaBoost (AB), Gradient Boosting Machine (GBM), Light Gradient Boosting Machine (LGBM), CatBoost (CB), Linear Discriminant Analysis (LDA), and Artificial Neural Network (ANN) to assess the predictive performance of these algorithms in the context of heart disease detection. …”
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Voice biomarkers as prognostic indicators for Parkinson’s disease using machine learning techniques by Ifrah Naeem, Allah Ditta, Tehseen Mazhar, Muhammad Anwar, Mamoon M. Saeed, Habib Hamam

“…The study uses different parameters to evaluate the model’s classification results. …”
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Predictive Framework for Sustainable Engineering through Machine Learning and Cross-Sector Collaboration by Choudhary Abhik, Adhikari Upasana, Roy Dipankar, Gupta Subir, Roy Priyanka, Bhaduri Aparna

“…This research proposes a new methodology based on ensemble machine learning, assessing, and predicting the outcomes of engineering projects with real-time open-source data focused on sustainability. …”
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Condition-Based Health Monitoring of Electrical Machines Using DWT and LDA Classifier by Faraz Ahmed Shaikh, Muhammad Zuhaib Kamboh, Bilal Ahmad Alvi, Sheroz Khan, Farhat Muhammad Khan

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Multi-Objective Machine Learning Optimization of Cylindrical TPMS Lattices for Bone Implants by Mansoureh Rezapourian, Ali Cheloee Darabi, Mohammadreza Khoshbin, Irina Hussainova

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Machining Process Optimization Using a Model Based on Criterial Functional Dependence by Peter Pavol Monka, Katarina Monkova, Ondrej Bilek, Martin Reznicek

“…This research deals with the optimization of the machining process using a model based on criterial functional dependence hypothesis. …”
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Light is sufficient to compensate for random positioning machine-simulated microgravity in plant roots by Rakesh David, Apriadi Situmorang, Nam Nghiep Tran, Thiri Maythwe, Volker Hessel, Philip B. Brewer

“…To explore this issue, we designed a 3D-printed mini-phytotron with adjustable light-emitting diodes to use with a random positioning machine (RPM). Simulated microgravity in the RPM, together with darkness, caused Arabidopsis roots to lose vertical perception, resulting in significantly altered root morphology parameters consistent with gravity loss. …”
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Evaluation of machine learning-based regression techniques for prediction of diabetes levels fluctuations by Badriah Alkalifah, Muhammad Tariq Shaheen, Johrah Alotibi, Tahani Alsubait, Hosam Alhakami

“…This paper suggests using continuous glucose monitoring (CGM) of 14733 patients, with three assistance factors to predict blood glucose levels independently of other parameters, hence reducing the burden on the patients. …”
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Fault Diagnosis in Power Generators: A Comparative Analysis of Machine Learning Models by Quetzalli Amaya-Sanchez, Marco Julio del Moral Argumedo, Alberto Alfonso Aguilar-Lasserre, Oscar Alfonso Reyes Martinez, Gustavo Arroyo-Figueroa

“…This work presents a comparative analysis of machine learning (ML) models for the generator fault diagnosis. …”
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Machine Learning Approach for Assessment of Compressive Strength of Soil for Use as Construction Materials by Yassir M. H. Mustafa, Yakubu Sani Wudil, Mohammad Sharif Zami, Mohammed A. Al-Osta

“…This study investigates the use of machine learning techniques to predict the unconfined compressive strength (UCS) of both stabilized and unstabilized soils. …”
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