In vitro machine learning-based CAR T immunological synapse quality measurements correlate with patient clinical outcomes. by Alireza Naghizadeh, Wei-Chung Tsao, Jong Hyun Cho, Hongye Xu, Mohab Mohamed, Dali Li, Wei Xiong, Dimitri Metaxas, Carlos A Ramos, Dongfang Liu

“…The human immune system consists of a highly intelligent network of billions of independent, self-organized cells that interact with each other. Machine learning (ML) is an artificial intelligence (AI) tool that automatically processes huge amounts of image data. …”
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The implementation of random survival forests in conflict management data: An examination of power sharing and third party mediation in post-conflict countries. by Andrew B Whetten, John R Stevens, Damon Cann

“…We implement both methods for simulated time-to-event data and the Power-Sharing Event Dataset (PSED) to assist researchers in evaluating the merits of machine learning duration models. …”
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Intellectual code analysis in automation grading by Denys Seliutin, Elena Yashyna

“…The syntactic analysis enabled the detection of problematic and erroneous code blocks and the identification of fraudulent attempts (manipulating the program's output instead of performing the algorithm). …”
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The Use of Artificial Intelligence in Combating Financial Crimes and Money Laundering in International Trade A Data-Driven Analysis (2010–2024) by Balouz Mohamed

“…The primary objectives are to assess the development and effectiveness of AI-driven algorithms in detecting illicit transactions, analyze the role of machine learning in real-time monitoring and predictive analytics, and investigate regulatory and ethical challenges that constrain AI’s full potential in financial crime prevention. …”
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Metabolomics and machine learning identify urine metabolic characteristics and potential biomarkers for severe Mycoplasma pneumoniae pneumonia by Lin Li, Wang haijun, Chen Tianyu, Wang Wenjing, Wang Zi, Cheng yibing

“…Abstract To study the differences in the urine metabolome between pediatric patients with severe Mycoplasma pneumoniae pneumonia (SMPP) and those with general Mycoplasma pneumoniae pneumonia (GMPP) via non-targeted metabolomics method, and potential biomarkers were explored through machine learning (ML) algorithms. The urine metabonomics data of 48 children with SMPP and 85 children with GMPP were collected via high performance liquid chromatography‒mass spectrometry (HPLC-MS/MS). …”
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Optimizing cervical cancer classification using transfer learning with deep gaussian processes and support vector machines by Emmanuel Ahishakiye, Fredrick Kanobe

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Detection of Damage on Inner and Outer Races of Ball Bearings Using a Low-Cost Monitoring System and Deep Convolution Neural Networks by Handeul You, Dongyeon Kim, Juchan Kim, Keunu Park, Sangjin Maeng

“…As a result, considerable research attention has been directed toward the early detection of bearing faults. With recent rapid advancements in machine learning algorithms, there is increasing interest in proactively diagnosing bearing faults by analyzing signals obtained from bearings. …”
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Synergistic use of satellite, legacy, and in situ data to predict spatio-temporal patterns of the invasive Lantana camara in a savannah ecosystem by Lilly Theresa Schell, Emma Evers, Sarah Schönbrodt-Stitt, Konstantin Müller, Maximilian Merzdorf, Drew Arthur Bantlin, Insa Otte

“…Spatiotemporal patterns of Lantana camara from 2015 to 2023 were predicted at a 30-m spatial resolution using a presence-only species distribution model, implementing a Random Forest classification algorithm and set up in the Google Earth Engine. The model incorporated Sentinel-1 SAR, Sentinel-2 multispectral data, anthropogenic predictors, and in situ presence data of Lantana camara. …”
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CNN Based Fault Classification and Predition of 33kw Solar PV System with IoT Based Smart Data Collection Setup by K. Punitha, G. Sivapriya, T. Jayachitra

“…Convolutional Neural Networks (CNNs) are a class of deep learning algorithms most commonly applied. They are particularly powerful for tasks involving data recognition, classification, and analysis due to their ability to automatically and adaptively learn spatial hierarchies of features. …”
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A Neutrosophic Logic Ruled Based Machine Learning Approaches for Chronic Kidney Disease Risk Prediction by Bhabani S. Mohanty, Krishna Priya R, Liya Alias, R. Vijaya Kumar Reddy, Prasanta Kumar Raut, Samson Isaac, Manibharathi.D, C. Saravanakumar, Nihar Ranjan Panda, Said Broumi

“…This research suggests a novel machine-learning technique to create a reliable and accurate CKD risk prediction model by combining neutrosophic logic with various classification algorithms. …”
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Real-time perception and intelligent early warning technology for power grid control operations based on multi-dimensional data fusion by Wei Li, Chao Hu, Bo Zhou, Weibo Peng

“…Therefore, this article proposes a real-time perception and intelligent warning technology for power grid regulation operations based on multidimensional data fusion. This technology integrates dynamic data from factories and stations, operation ticket content, and 3D virtual environment information, and uses deep learning and reinforcement learning algorithms to construct real-time perception and intelligent warning models. …”
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Machine learning and AVO class II workflow for hydrocarbon prospectivity in the Messinian offshore Nile Delta Egypt by Nadia Abd-Elfattah, Aia Dahroug, Manal El Kammar, Ramy Fahmy

“…This method is particularly useful for identifying low seismic amplitude anomalies, which are often challenging to detect with conventional seismic analysis. (1) This study developed a workflow to detect low seismic amplitude gas fields in near-field exploration. (2) It uses a machine learning algorithm to classify and explore low-seismic-amplitude gas sand reservoirs. (3) This approach helps estimate the likelihood of success and reduces the risk associated with hydrocarbon exploration wells.…”
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A Radio Frequency Interference Screening Framework—From Quick-Look Detection Using Statistics-Assisted Network to Raw Echo Tracing by Jiayuan Shen, Bing Han, Yang Li, Zongxu Pan, Di Yin, Yugang Feng, Guangzuo Li

“…The existing RFI detection method usually only uses a single type of data for detection, ignoring the information association between the data at all levels of the real SAR product, resulting in some computational redundancy. …”
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Exploring Transfer Learning for Anthropogenic Geomorphic Feature Extraction from Land Surface Parameters Using UNet by Aaron E. Maxwell, Sarah Farhadpour, Muhammad Ali

“…However, the operationalization of these supervised classification methods is limited by a lack of large volumes of quality training data. This study explores the use of transfer learning, where information learned from another, and often much larger, dataset is used to potentially reduce the need for a large, problem-specific training dataset. …”
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Applying interpretable machine learning to assess intraspecific trait divergence under landscape‐scale population differentiation by Sambadi Majumder, Chase M. Mason

“…Methods Recursive feature elimination was applied to functional trait data from the HeliantHOME database, followed by the application of the Boruta algorithm to detect the traits that are most predictive of ecoregion. …”
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Diagnostic Models for Differentiating COVID-19-Related Acute Ischemic Stroke Using Machine Learning Methods by Eylem Gul Ates, Gokcen Coban, Jale Karakaya

“…Various feature selection algorithms were applied to identify the most relevant features, which were then used to train and evaluate machine learning classification models. …”
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Transfer learning nonlinear plasma dynamic transitions in low dimensional embeddings via deep neural networks by Zhe Bai, Xishuo Wei, William Tang, Leonid Oliker, Zhihong Lin, Samuel Williams

“…Deep learning algorithms provide a new paradigm to study high-dimensional dynamical behaviors, such as those in fusion plasma systems. …”
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Machine Learning-Driven Prediction of Brain Age for Alzheimer’s Risk: APOE4 Genotype and Gender Effects by Carter Woods, Xin Xing, Subash Khanal, Ai-Ling Lin

“…This study aimed to use machine learning (ML) algorithms to predict brain age and assess AD risk by considering the effects of the APOE4 genotype and gender. …”
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Integrating single-cell RNA sequencing, WGCNA, and machine learning to identify key biomarkers in hepatocellular carcinoma by Gang Wang, Jiaxing Zhang, Yirong Li, Yuyu Zhang, Weiwei Dong, Hengquan Wu, Jinglan Wang, Peiqing Liao, Ziqiang Yuan, Tao Liu, Wenting He

“…Additionally, AutoDockTools were used for molecular docking to investigate the association between the most sensitive drug and the core proteins. 44 genes were obtained by intersecting the WGCNA results, marker genes from scRNA-seq data, and up-regulated DEGs. Three machine-learning algorithms refined CDKN3, PPIA, PRC1, GMNN, and CENPW as hub biomarkers. …”
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ML‐UrineQuant: A machine learning program for identifying and quantifying mouse urine on absorbent paper by Warren G. Hill, Bryce MacIver, Gary A. Churchill, Mariana G. DeOliveira, Mark L. Zeidel, Marcelo Cicconet

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