MRI-based radiomic and machine learning for prediction of lymphovascular invasion status in breast cancer by Cici Zhang, Minzhi Zhong, Zhiping Liang, Jing Zhou, Kejian Wang, Jun Bu

“…Radiomic features were extracted from T2WI and dynamic contrast-enhanced (DCE) of MRI sequences, the optimal feature filter and LASSO algorithm were used to obtain the optimal features, and eight machine learning algorithms, including LASSO, logistic regression, random forest, k-nearest neighbor (KNN), support vector machine, gradient boosting decision tree, extreme gradient boosting, and light gradient boosting machine, were used to construct models for predicating LVI status in BC. …”
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Screening of serum biomarkers in patients with PCOS through lipid omics and ensemble machine learning. by Ji-Ying Chen, Wu-Jie Chen, Zhi-Ying Zhu, Shi Xu, Li-Lan Huang, Wen-Qing Tan, Yong-Gang Zhang, Yan-Li Zhao

“…PI (18:0/20:3)-H and PE (18:1p/22:6)-H were identified as candidate biomarkers. Three machine learning models, logistic regression, random forest, and support vector machine, showed that screened biomarkers had better classification ability and effect. …”
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Driven early detection of chronic kidney cancer disease based on machine learning technique. by Wafa Almukadi, Sayed Abdel-Khalek, Adel A Bahaddad, Ahmed Mohammed Alghamdi

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Automatic sequence identification in multicentric prostate multiparametric MRI datasets for clinical machine-learning by José Guilherme de Almeida, Ana Sofia Castro Verde, Carlos Bilreiro, Inês Santiago, Joana Ip, Manolis Tsiknakis, Kostas Marias, Daniele Regge, Celso Matos, Nickolas Papanikolaou, ProCAncer-I

“…However, dataset-specific data should be included to achieve optimal performance. Critical relevance statement Organising large collections of data is time-consuming but necessary to train clinical machine-learning models. …”
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Predicting the interfacial tension of CO2 and NaCl aqueous solution with machine learning by Kashif Liaqat, Daniel J. Preston, Laura Schaefer

“…This demonstrates that ML models offer a cost-effective and efficient alternative to experimental methods, potentially optimizing designs for CO2 sequestration.…”
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Personalized treatment strategies for breast adenoid cystic carcinoma: A machine learning approach by Sakhr Alshwayyat, Mahmoud Bashar Abu Al Hawa, Mustafa Alshwayyat, Tala Abdulsalam Alshwayyat, Siya sawan, Ghaith Heilat, Hanan M. Hammouri, Sara Mheid, Batool Al Shweiat, Hamdah Hanifa

“…To identify the prognostic variables, we conducted Cox regression analysis and constructed prognostic models using five Machine Learning (ML) algorithms to predict the 5-year survival. …”
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Advancing Kidney Transplantation: A Machine Learning Approach to Enhance Donor–Recipient Matching by Nahed Alowidi, Razan Ali, Munera Sadaqah, Fatmah M. A. Naemi

“…(1) Background: Globally, the kidney donor shortage has made the allocation process critical for patients awaiting a kidney transplant. Adopting Machine Learning (ML) models for donor–recipient matching can potentially improve kidney allocation processes when compared with traditional points-based systems. (2) Methods: This study developed an ML-based approach for donor–recipient matching. …”
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Prediction of Moisture Content in Kiwi (Actinidia deliciosa) Dried Using Machine Learning Approaches by Halil Nusret BULUS, Soner CELEN

“…This prediction technique necessitates the development of numerical drying models. The aim of this research is to compare prediction models developed using two popular machine learning approaches in recent years: Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Artificial Neural Networks (ANN). …”
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Prediction of Gait Neurodegenerative Diseases by Variational Mode Decomposition Using Machine Learning Algorithms by P. Visvanathan, P.M. Durai Raj Vincent

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Prediction of the Control Effect of Fractured Leakage in Unconventional Reservoirs Using Machine Learning Method by Lei Pu, Jianjian Song, Mingbiao Xu, Jun Zhou, Peng Xu, Shanshan Zhou

“…The training was carried out based on the collected field data, the appropriate activation function was set, and the deep well network structure was optimized. By improving the field plugging measures in the later period, the model was verified by these actual cases, and the results showed that the established model produced the highest R2 of 0.974, has a good fit, and predicts well.…”
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Comparative Assessment of Several Effective Machine Learning Classification Methods for Maternal Health Risk by Md Nurul Raihen, Sultana Akter

“…The suggested model outperforms all others in terms of accuracy and efficiency, with an accuracy score of 86.13% for the support vector machine using a 10-fold cross validation technique. …”
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The Effects of Cutting Pick Parameters on Cutting Head Performance in Tunneling–Bolting Combined Machines by Bo Ning, Mingzhu Li, Jinhua Zhang

“…This paper studies the influence of the pick structure on the cutting characteristics of the cutting head when cutting rocks with a tunneling–bolting combined machine. A simulated model of the breaking of soft rock with a cutting pick was established. …”
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Predicting the <i>S. cerevisiae</i> Gene Expression Score by a Machine Learning Classifier by Piotr H. Pawłowski, Piotr Zielenkiewicz

“…To find the most important classifier, a machine learning classifier (random forest) was selected, trained, and optimized on the Waikato Environment for Knowledge Analysis WEKA platform, resulting in the most accurate attribute-dependent prediction of the ES of <i>Saccharomyces cerevisiae</i> genes. …”
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Enhancing Crowdfunding Success With Machine Learning and Visual Analytics: Insights From Chinese Platforms by Wang Yunfeng, Othman Ibrahim

“…Our findings indicate that technological features significantly enhance success rates. The Random Forest model highlights critical factors such as project quality signals and social capital. …”
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Leveraging AHP and transfer learning in machine learning for improved prediction of infectious disease outbreaks by Reham Abdallah, Sayed Abdelgaber, Hanan Ali Sayed

“…Notably, in the context of Chikungunya, this model achieves an optimal balance between precision and recall, with an accuracy of 93.31%, a precision of 57%, and a recall of 63%, highlighting its reliability for effective outbreak prediction.…”
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Research on machine vision online monitoring system for egg production and quality in cage environment by Zhenlong Wu, Hengyuan Zhang, Cheng Fang

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Predicting the Likelihood of Operational Risk Occurrence in the Banking Industry Using Machine Learning Algorithms by Hamed Naderi, Mohammad Ali Rastegar Sorkhe, Bakhtiar Ostadi, Mehrdad Kargari

“…This study investigates and predicts the likelihood of operational risk occurrence in the banking industry using machine learning algorithms. The primary objective is to analyze operational risk data and evaluate the performance of various machine learning models to develop effective tools for enhancing risk management and minimizing financial losses in banks and financial institutions. …”
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Evaluation of machine learning techniques for real-time prediction of implanted lower limb mechanics by Chase Maag, Clare K. Fitzpatrick, Paul J. Rullkoetter

“…IntroductionAccurate prediction of knee biomechanics during total knee replacement (TKR) surgery is crucial for optimal outcomes. This study investigates the application of machine learning (ML) techniques for real-time prediction of knee joint mechanics.MethodsA validated finite element (FE) model of the lower limb was used to generate a dataset of knee joint kinematics, kinetics, and contact mechanics. …”
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An overview of AI in Biofunctional Materials by Dazhou Li

“…The integration of artificial intelligence (AI) into biofunctional materials is transforming material design, synthesis, and optimization for medical applications. Machine learning and deep learning models now predict material properties (e.g., mechanical strength, degradation rate) with > 90% accuracy, dramatically reducing trial-and-error in scaffold and nanoparticle fabrication. …”
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Power Losses in the Multi-Turn Windings of High-Speed PMSM Electric Machine Armatures by Oleksandr Makarchuk, Dariusz Całus

“…The analysis takes into account the actual design of the slot and the conductor, the variable arrangement of individual conductors in the slot, the core saturation and the presence of the excitation field—to represent the main factors that affect the process of additional losses in the slot of the electric machine. The verification of the mathematical model developed in this study was carried out by comparing the distribution of power losses in the slot section of the coil, consisting of several elementary conductors connected in parallel and located in a rectangular open slot, with an identical distribution derived on the basis of an analytical method from the classical circuit theory. …”
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