Self-driving lab for the photochemical synthesis of plasmonic nanoparticles with targeted structural and optical properties by Tianyi Wu, Sina Kheiri, Riley J. Hickman, Huachen Tao, Tony C. Wu, Zhi-Bo Yang, Xin Ge, Wei Zhang, Milad Abolhasani, Kun Liu, Alan Aspuru-Guzik, Eugenia Kumacheva

“…Here, we introduce the Autonomous Fluidic Identification and Optimization Nanochemistry (AFION) self-driving lab that integrates a microfluidic reactor, in-flow spectroscopic nanoparticle characterization, and machine learning for the exploration and optimization of the multidimensional chemical space for the photochemical synthesis of plasmonic nanoparticles. …”
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Convolutional Neural Networks for Direction of Arrival Estimation Compared to Classical Estimators and Bounds by Christopher J. Bell, Kaushallya Adhikari, Andrew Brown

“…Recently, there has been a proliferation of applied machine learning (ML) research, including the use of convolutional neural networks (CNNs) for direction of arrival (DoA) estimation. …”
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Mapping urban green structures using object-based analysis of satellite imagery: A review by Shivesh Kishore Karan, Bjørn Tobias Borchsenius, Misganu Debella-Gilo, Jonathan Rizzi

“…For classification, the review covers machine learning techniques such as random forests, support vector machines, and convolutional neural networks, among others. …”
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Follow-Up and Risk Assessment in Patients with Myocardial Infarction Using Artificial Neural Networks by Tatjana Gligorijević, Zoran Ševarac, Branislav Milovanović, Vlado Đajić, Marija Zdravković, Saša Hinić, Marina Arsić, Milica Aleksić

“…Artificial neural networks (ANNs) are machine learning technique, inspired by the principles found in biological neurons. …”
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Impact and optimization of vehicle charging scheduling on regional clean energy power supply network management by Penghui Xu, Xiaobo Wang, Zhichao Li

“…In particular, the demand forecasting model based on machine learning effectively solves the dilemma of matching the charging load with a clean energy supply. …”
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Cell Differentiation Trajectory Predicts Prognosis and Immunotherapeutic Response in Clear Cell Renal Cell Carcinoma by Jin Xu, Xi Chen, Yinyu Chen, Qiushuang Wang, Yingliang Jin, Huashuo Zhao

“…Combined with bulk RNA-seq data, we classified patients into two clusters and found that this classification was closely correlated with patient prognosis and immunotherapeutic responses. Based on machine learning, we identified a prognostic risk model composed of 14 DRGs, including BTG2, CDKN1A, COL6A1, CPM, CYB5D2, FOSB, ID2, ISG15, PLCG2, SECISBP2, SOCS3, TES, ZBTB16, and ZNF704, to predict the survival rate of patients and then constructed a nomogram model integrating clinicopathological characteristics and risk score for clinical practice. …”
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SurvBeNIM: The Beran-Based Neural Importance Model for Explaining Survival Models by Lev V. Utkin, Danila Y. Eremenko, Andrei V. Konstantinov, Vladimir A. Muliukha

“…It aims to explain predictions of machine learning survival models, which are in the form of survival or cumulative hazard functions. …”
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Modelling the flowing bottom hole pressure of oil and gas wells using multivariate adaptive regression splines by Okorie Ekwe Agwu, Saad Alatefi, Ahmad Alkouh, Raja Rajeswary Suppiah

“…Traditional methods, such as using downhole gauges or relying on empirical and mechanistic models, have limitations, prompting the exploration of alternative approaches such as machine learning (ML). However, most ML models operate as black box models, lacking transparency and interpretability. …”
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Predicting occupational musculoskeletal disorders in South Korean male office workers using a robust and sparse twin support vector machine by Haewon Byeon

“…The RSTSVM model was developed and compared with traditional machine learning models, including Support Vector Machine (SVM) and Gradient Boosting Machine (GBM), to predict the risk of MSDs. …”
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Predictive Modeling and Experimental Analysis of Cyclic Shear Behavior in Sand–Fly Ash Mixtures by Özgür Yıldız, Ali Fırat Çabalar

“…Furthermore, the integration of advanced machine learning techniques such as NARX models offers a powerful tool for predicting the behavior of soil mixtures, facilitating more effective and data-driven decision-making in geotechnical applications. …”
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Finding influential nodes in complex networks based on Kullback–Leibler model within the neighborhood by Guan Wang, Zejun Sun, Tianqin Wang, Yuanzhe Li, Haifeng Hu

“…Abstract As a research hot topic in the field of network security, the implementation of machine learning, such as federated learning, involves information interactions among a large number of distributed network devices. …”
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Advancing Sustainable Development Goals through Earth Observation Satellite Data: Current Insights and Future Directions by Qiang Zhao, Le Yu

“…Despite existing challenges in data standardization, accessibility, and cross-platform compatibility, advancements in artificial intelligence, machine learning, and collaborative frameworks are anticipated to optimize EO data use. …”
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Interval Prediction of Photovoltaic Power Using Improved NARX Network and Density Peak Clustering Based on Kernel Mahalanobis Distance by Wen-He Chen, Long-Sheng Cheng, Zhi-Peng Chang, Han-Ting Zhou, Qi-Feng Yao, Zhai-Ming Peng, Li-Qun Fu, Zong-Xiang Chen

“…Comparative research of point forecasting is implemented to evaluate the machine learning and deep learning methods, with the proposed SAE-BRNARX under four different periods. …”
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Indoor Positioning System in Learning Approach Experiments by Dodo Zaenal Abidin, Siti Nurmaini, Erwin, Errissya Rasywir, Yovi Pratama

“…The DNN method can estimate the actual space and get better position results, whereas machine learning methods such as the DNN algorithm can handle more effectively large data and produce more accurate data. …”
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Predicting metabolite response to dietary intervention using deep learning by Tong Wang, Hannah D. Holscher, Sergei Maslov, Frank B. Hu, Scott T. Weiss, Yang-Yu Liu

“…Existing prediction methods are typically limited to traditional machine learning models. Deep learning methods dedicated to such tasks are still lacking. …”
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Random Forests in Count Data Modelling: An Analysis of the Influence of Data Features and Overdispersion on Regression Performance by Ciza Arsène Mushagalusa, Adandé Belarmain Fandohan, Romain Glèlè Kakaï

“…Machine learning algorithms, especially random forests (RFs), have become an integrated part of the modern scientific methodology and represent an efficient alternative to conventional parametric algorithms. …”
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Refined matrix completion for spectrum estimation of heart rate variability by Lei Lu, Tingting Zhu, Ying Tan, Jiandong Zhou, Jenny Yang, Lei Clifton, Yuan-Ting Zhang, David A. Clifton

“…The results underscore the potential of our developed matrix completion-based statistical machine learning model in providing reliable HRV spectrum uncertainty estimation.…”
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LSTM+MA: A Time-Series Model for Predicting Pavement IRI by Tianjie Zhang, Alex Smith, Huachun Zhai, Yang Lu

“…However, the existing research on IRI prediction mainly focuses on using linear regression or traditional machine learning, which cannot take into account the historical effects of IRI caused by climate, traffic, pavement construction and intermittent maintenance. …”
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Unveiling the intricate interplay: Exploring biological bridges between renal ischemia-reperfusion injury and T cell-mediated immune rejection in kidney transplantation. by Xinyi Xia, Xinrui Fan, Shan Jiang, Yuhan Liao, Yang Sun

“…Shared genes were used for TCMR consensus clustering, differentially expressed genes (DEGs) were identified, and gene set enrichment analysis (GSEA) was conducted. Three machine learning algorithms screened for hub genes, which underwent miRNA prediction and transcription factor analysis. …”
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Identifying Coronal Mass Ejection Active Region Sources: An Automated Approach by Julio Hernandez Camero, Lucie M. Green, Alex Piñel Neparidze

“…The resulting catalog of CME–source region associations is made publicly available, offering a valuable resource for statistical studies and machine learning applications in solar physics and space weather forecasting.…”
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