Machining Scheme Selection of Features Based on Process Knowledge Graph and Improved Cosine Similarity Matching by Lin Wang, Hao Cheng, Rui Wang, Xunzhuo Huang

“…The machining scheme selection (MSS) for features is to choose the optimal machining scheme for a feature before machining. …”
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Machine learning-based approach for reduction of energy consumption in hybrid energy storage electric vehicle by T. Paulraj, Yeddula Pedda Obulesu

“…Abstract This research introduces a novel machine learning-based strategy for generating supercapacitor (SC) reference current to optimize energy distribution in Battery Electric Vehicles (BEV) and Hybrid Battery Electric Vehicles (HBEV). …”
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Machine learning-driven imaging data for early prediction of lung toxicity in breast cancer radiotherapy by Tamás Ungvári, Döme Szabó, András Győrfi, Zsófia Dankovics, Balázs Kiss, Judit Olajos, Károly Tőkési

“…Machine learning models and HPF can also provide effective diagnostic and therapeutic support for other diseases.…”
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Fault diagnosis of ZDJ7 railway point machine based on improved DCNN and SVDD classification by Zengshu Shi, Yiman Du, Xinwen Yao

“…When the sample distribution is unbalanced, the performance indexes obtained by the proposed model are the best.…”
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Decoupling Implantation Prediction and Embryo Ranking in Machine Learning: The Impact of Clinical Data and Discarded Embryos by Itay Erlich, Sotirios H. Saravelos, Cristina Hickman, Assaf Ben‐Meir, Iris Har‐Vardi, James A. Grifo, Semra Kahraman, Assaf Zaritsky

“…Unlike clinicians who rank embryos from the same IVF cycle cohort based on the embryos visual quality and determine how many embryos to transfer based on clinical factors, machine learning solutions usually combine these steps by optimizing for implantation prediction and using the same model for ranking the embryos within a cohort. …”
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Analysis of the exercise intention-behavior gap among college students using explainable machine learning by Cui Cui, Cui Cui, Jixin Yin

“…A critical challenge in improving student fitness is addressing the intention-behavior gap–the disconnect between students' intentions to engage in physical activity and their actual behavior.MethodsThis study utilized survey data from TikTok-using college students, incorporating variables such as gender, academic grade, health belief perceptions, and planned behavior perceptions. Multiple machine learning models were developed to predict the presence of the intention-behavior gap. …”
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Dementia ascertainment in India and development of nation‐specific cutoffs: A machine learning and diagnostic analysis by Danny Maupin, Hongxin Gao, Emma Nichols, Alden Gross, Erik Meijer, Haomiao Jin

“…Dementia ascertainment was conducted by an online panel. A machine learning (ML) model was trained on these classifications, with explainable artificial intelligence to assess feature importance and inform cutoffs that were assessed across demographic groups. …”
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A Topical Review of Quantum and Classical Machine Learning Approaches to Disaster Escape Routing Problems by A. Vinil, Parameswaran Iyer, Jetain Chetan, Aniket Bembale, Nagendra Singh

“…Our problem, termed the Disaster Escape Routing problem, aims to find the optimal path within this dynamic graph. We review and analyze an existing hybrid quantum-classical machine learning model alongside classical machine learning models specifically for this problem. …”
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Interpretable material descriptors for critical pitting temperature in austenitic stainless steel via machine learning by Faguo Hou, Hong-Hui Wu, Dexin Zhu, Jinyong Zhang, Liudong Hou, Shuize Wang, Guilin Wu, Junheng Gao, Jing Ma, Xinping Mao

“…Utilizing interpretable machine learning techniques, a predictive model for CPT is developed and confirmed via cross-validation, demonstrating superior predictive accuracy. …”
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Simulation and Analysis of Double Compound CAM Pusher Tool Changing Mechanism for NC Machine Tool by Xinbo Zhang, Xiaobing Zhang, Xigui Wang, Zhongping Lai, Wenxue Zhu, Yujie Zhang

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Machine learning for efficient CO2 sequestration in cementitious materials: a data-driven method by Yanjie SUN, Chen ZHANG, Yuan-Hao WEI, Haoliang JIN, Peiliang SHEN, Chi Sun POON, He YAN, Xiao-Yong WEI

“…The results show that the XGBoost model significantly outperforms traditional linear regression approaches. …”
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Developing a hybrid feature selection method to detect botnet attacks in IoT devices by Alshaeaa H.Y., Ghadhban Z.M., Ministry of Education, Iraq

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Application of multimodal machine learning-based analysis for the biomethane yields of NaOH-pretreated biomass by Oluwatobi Adeleke, Kehinde O. Olatunji, Daniel M. Madyira, Tien-Chien Jen

“…A comprehensive data-driven insight was gained through correlation-mapping, SHAP-based XAI for feature-ranking, cluster analysis for bio-digestion operational dataset using k-means integrated with Principal Component Analysis (PCA). Optimal hyperparameter settings in four different ML models, namely Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT), were conducted for predicting the biomethane yield. …”
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Machine learning and multi-omics analysis reveal key regulators of proneural–mesenchymal transition in glioblastoma by Can Xu, Jin Yang, Huan Xiong, Xiaoteng Cui, Yuhao Zhang, Mingjun Gao, Lei He, Qiuyue Fang, Changxi Han, Wei Liu, Yangyang Wang, Jin Zhang, Ying Yuan, Zhaomu Zeng, Ruxiang Xu

“…The Lasso, Cox, and Step machine learning algorithms were used to construct and screen the optimal risk assessment prognostic model. …”
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How machine learning on real world clinical data improves adverse event recording for endoscopy by Stefan Wittlinger, Isabella C. Wiest, Mahboubeh Jannesari Ladani, Jakob Nikolas Kather, Matthias P. Ebert, Fabian Siegel, Sebastian Belle

“…Accurate and comprehensive documentation is crucial for enhancing patient safety and optimizing clinical outcomes; however, adverse events remain underreported. …”
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Application-Wise Review of Machine Learning-Based Predictive Maintenance: Trends, Challenges, and Future Directions by Christos Tsallis, Panagiotis Papageorgas, Dimitrios Piromalis, Radu Adrian Munteanu

“…This systematic literature review (SLR) provides a comprehensive application-wise analysis of machine learning (ML)-driven predictive maintenance (PdM) across industrial domains. …”
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Machine learning-based prediction of 90-day prognosis and in-hospital mortality in hemorrhagic stroke patients by Ahmad A. Abujaber, Ibrahem Albalkhi, Yahia Imam, Said Yaseen, Abdulqadir J. Nashwan, Naveed Akhtar, Ibrahim M. Alkhawaldeh

“…Abstract This study aims to predict hemorrhagic stroke outcomes, including 90-day prognosis and in-hospital mortality, using machine learning models and SHapley Additive exPlanations (SHAP) analysis. …”
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Explainable Machine Learning and Predictive Statistics for Sustainable Photovoltaic Power Prediction on Areal Meteorological Variables by Sajjad Nematzadeh, Vedat Esen

“…Precisely predicting photovoltaic (PV) output is crucial for reliable grid integration; so far, most models rely on site-specific sensor data or treat large meteorological datasets as black boxes. …”
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A guide to neural ordinary differential equations: Machine learning for data-driven digital engineering by Joseph M. Worsham, Jugal K. Kalita

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PilY1 regulates the dynamic architecture of the type IV pilus machine in Pseudomonas aeruginosa by Shuaiqi Guo, Yunjie Chang, Yves V. Brun, P. Lynne Howell, Lori L. Burrows, Jun Liu

“…These findings point to a hypothetical model where the interplay between the secretin protein PilQ and the PilY1-minor-pilin priming complex is important for optimizing conformations of the T4P machine in P. aeruginosa, suggesting a gate-keeping mechanism that regulates pilus dynamics.…”
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