Predicting changes of incisor and facial profile following orthodontic treatment: a machine learning approach by Jing Peng, Yan Zhang, Mengyu Zheng, Yanyan Wu, Guizhen Deng, Jun Lyu, Jianming Chen

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Integrating transcriptomics and hybrid machine learning enables high-accuracy diagnostic modeling for nasopharyngeal carcinoma by Hehe Wang, Junge Zhang, Peng Cheng, Lujie Yu, Chunlin Li, Yaowen Wang

“…Immune infiltration patterns and functional enrichment were analyzed using CIBERSORT and GSEA/GSVA, respectively. …”
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Machine learning approaches for predicting feed intake in Australian Merino, Corriedale, and Dohne Merino sheep by Fernando Amarilho-Silveira, Ignacio De Barbieri, Elly A. Navajas, Jaime Araujo Cobuci, Gabriel Ciappesoni

“…This indicates that support vector machines effectively captures the underlying patterns of feed intake distribution. …”
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Identification and Immunological Characterization of Cuproptosis Related Genes in Preeclampsia Using Bioinformatics Analysis and Machine Learning by Tiantian Yu, Guiying Wang, Xia Xu, Jianying Yan

“…The current study aims to elucidate the gene expression matrix and immune infiltration patterns of cuproptosis‐related genes (CRGs) in the context of PE. …”
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Mapping Gridded GDP Distribution of China Based on Remote Sensing Data and Machine Learning Methods by Saimiao Liu, Wenliang Liu, Yi Zhou, Shixin Wang, Futao Wang, Zhenqing Wang

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Identification and validation of pyroptosis-related genes in Alzheimer’s disease based on multi-transcriptome and machine learning by Yuntai Wang, Yuntai Wang, Yilin Li, Lu Zhou, Yihuan Yuan, Chuanfei Liu, Zimeng Zeng, Yuanqi Chen, Qi He, Zhuoze Wu

“…Additionally, we validated the expression patterns of these key genes using the expression data from AD mice and constructed potential regulatory networks through time series and correlation analysis.ResultsWe identified 91 PRGs in AD using the weighted gene co-expression network analysis (WGCNA) and differentially expressed genes analysis. …”
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Performance of unmarked abundance models with data from machine‐learning classification of passive acoustic recordings by Cameron J. Fiss, Samuel Lapp, Jonathan B. Cohen, Halie A. Parker, Jeffery T. Larkin, Jeffery L. Larkin, Justin Kitzes

“…Our findings were consistent across two species with differing relative abundance and habitat use patterns. The higher precision of models fit using ARU data is likely due to higher cumulative detection probability, which itself may be the result of greater survey effort using ARUs and machine‐learning classifiers to sample significantly more time for focal species at any given point. …”
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CIAO: A machine-learning algorithm for mapping Arctic Ocean Chlorophyll-a from space by Maria Laura Zoffoli, Vittorio Brando, Gianluca Volpe, Luis González Vilas, Bede Ffinian Rowe Davies, Robert Frouin, Jaime Pitarch, Simon Oiry, Jing Tan, Simone Colella, Christian Marchese

“…Furthermore, CIAO produced consistent spatial patterns without artifacts and provided more reliable Chl-a estimates in coastal waters, where other algorithms tend to overestimate. …”
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Comparative analysis of machine learning models for predicting water quality index in Dhaka’s rivers of Bangladesh by Mosaraf Hosan Nishat, Md. Habibur Rahman Bejoy Khan, Tahmeed Ahmed, Syed Nahin Hossain, Amimul Ahsan, M. M. El-Sergany, Md. Shafiquzzaman, Monzur Alam Imteaz, Mohammad T. Alresheedi

“…Furthermore, an Adjusted R 2 value of 0.965 further confirmed its ability to capture complex patterns in water quality data with remarkable accuracy. …”
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Machine learning-derived asthma and allergy trajectories in children: a systematic review and meta-analysis by Daniil Lisik, Saliha Selin Özuygur Ermis, Gregorio Paolo Milani, Giulia Carla Immacolata Spolidoro, Selin Ercan, Michael Salisu, Faozyat Odetola, Daniele Giovanni Ghiglioni, Danylo Pylov, Emma Goksör, Rani Basna, Göran Wennergren, Hannu Kankaanranta, Bright I. Nwaru

“…Introduction Numerous studies have characterised trajectories of asthma and allergy in children using machine learning, but with different techniques and mixed findings. …”
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Machine learning helps reveal key factors affecting tire wear particulate matter emissions by Zhenyu Jia, Jiawei Yin, Tiange Fang, Zhiwen Jiang, Chongzhi Zhong, Zeping Cao, Lin Wu, Ning Wei, Zhengyu Men, Lei Yang, Qijun Zhang, Hongjun Mao

“…Avoiding strenuous driving behaviors (TTF < 400 N, TLF < 400 N), reducing tread temperature (T < 45℃), and minimizing the number of small tread patterns are feasible ways to reduce TWPs.…”
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Prediksi Kesiapan Sekolah Menggunakan Machine Learning Berbasis Kombinasi Adam dan Nesterov Momentum by Indah Mustika Rahayu, Ahmad Yusuf, Mujib Ridwan

“…Meanwhile, teachers and parents who have a role in providing support and stimulation to children cannot use these instrument. Machine learning is a technique that uses algorithms to find useful patterns in data. …”
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Algorithmically Enhanced Wearable Multimodal Emotion Sensor by Anand Babu, Getnet Kassahun, Isabelle Dufour, Dipankar Mandal, Damien Thuau

“…The study's approach provides a pathway to understanding complex human emotions and enhances the capabilities of effective human–machine interaction.…”
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A STUDY ON THE MONITORING MANAGEMENT SYSTEM FOR ANALYZING THE HEALTH STATUS OF LIVESTOCK CATTLE BASED ON MACHINE LEARNING by Cho Jeong Hyun

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Data-driven price trends prediction of Ethereum: A hybrid machine learning and signal processing approach by Ebenezer Fiifi Emire Atta Mills, Yuexin Liao, Zihui Deng

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Prediction of groundwater level and potential zone identification in Keonjhar, Odisha based on machine learning and GIS techniques by B. Ritushree, Shubhshree Panda, Abinash Sahoo, Sandeep Samantaray, Deba P Satapathy

“…Population growth, change in climate, changing land use pattern, and increase in mining activities causes over exploitation of groundwater in Keonjhar district to fulfill the freshwater demand. …”
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Machine Learning-enhanced loT and Wireless Sensor Networks for predictive analysis and maintenance in wind turbine systems by Lei Gong, Yanhui Chen

“…The PM-C-LSTM model combines CNN for recognizing spatial patterns and LSTM networks for analyzing sequential data in a way that doesn't affect the accuracy of WT-PM. …”
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Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models by Benedictor Alexander Nguchu, Benedictor Alexander Nguchu, Yifei Han, Yanming Wang, Peter Shaw

“…The features were specifically the gray matter volume and dopaminergic features of the neostriatum, i.e., the caudate, putamen, and anterior putamen. We use machine learning (ML) algorithms, including Random Forest, Logistic Regression, and Support Vector Machine, to evaluate the diagnostic power of the brain features and network patterns in differentiating the PD subtypes and distinguishing PD from HC. …”
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Machine Learning-Based Identification of Phonological Biomarkers for Speech Sound Disorders in Saudi Arabic-Speaking Children by Deema F. Turki, Ahmad F. Turki

“…SHAP analysis revealed that articulation patterns and phonological patterns were the most influential features for distinguishing between Atypical and TD categories. …”
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Effective Machine Learning Techniques for Non-English Radiology Report Classification: A Danish Case Study by Alice Schiavone, Lea Marie Pehrson, Silvia Ingala, Rasmus Bonnevie, Marco Fraccaro, Dana Li, Michael Bachmann Nielsen, Desmond Elliott

“…Large language models can outperform traditional pattern-matching methods.…”
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