Application of Open-Source, Low-Code Machine-Learning Library in Python to Diagnose Parkinson's Disease Using Voice Signal Features by Daniel Hilário da Silva, Caio Tonus Ribeiro, Leandro Rodrigues da Silva Souza, Adriano Alves Pereira

“…We leveraged the PyCaret Python library's ML algorithms and functions, which were introduced in this article, to demonstrate their simplicity and effectiveness in dealing with real-world data. …”
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Integrating weighted gene co-expression network analysis and machine learning to elucidate neural characteristics in a mouse model of depression by Jinli Gao, Qinglang Wang, Jie Liu, Siqian Zheng, Jiahong Liu, Zhiyong Gao, Cheng Zhu

“…IntroductionAn AI-assisted deep learning strategy was applied to analyze the neurobiological characteristics of depression in mouse models. …”
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BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification by Anderson P. Avila Santos, Breno L. S. de Almeida, Robson P. Bonidia, Peter F. Stadler, Polonca Stefanic, Ines Mandic-Mulec, Ulisses Rocha, Danilo S. Sanches, André C.P.L.F. de Carvalho

“…Recently, deep learning algorithms have provided advancements in ncRNA classification. …”
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HY-Deepnet : A new Optimal Deep transfer learning empowered framework for an autonomous Alzheimer’s disease detection and diagnosis system by S. Veluchamy, R. Bhuvaneswari, K. Ashwini, Samah Alshathri, Walid El-Shafai

“…A progressive neurological disorder, Alzheimer’s disease (AD) predominantly impacts memory and cognitive functions of diseased victims. The disease is usually identified by the buildup of abnormal protein deposits in the brain, resulting in the development of plaques and tangles that interfere with communication between nerve cells. …”
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Prognostic risk modeling of endometrial cancer using programmed cell death-related genes: a comprehensive machine learning approach by Tianshu Chen, Yuhan Yang, Zhizhong Huang, Feng Pan, Zhendi Xiao, Kunxue Gong, Wenguang Huang, Liu Xu, Xueqin Liu, Caiyun Fang

“…This study aimed to develop a robust predictive model integrating programmed cell death-related genes and advanced machine learning techniques. Methods Utilizing transcriptomic data from TCGA-UCEC and GSE119041 datasets, we employed a comprehensive approach involving 117 machine learning algorithms. …”
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Integrative bioinformatics and machine learning approach unveils potential biomarkers linking coronary atherosclerosis and fatty acid metabolism-associated gene by Hong Li, Yongyun Xu, Aiting Wang, Chuanxin Zhao, Man Zheng, Chunyan Xiang

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A Machine Learning‐Based Observation Operator for FY‐4B GIIRS Brightness Temperatures Considering the Uncertainty of Label Data by Yonghui Li, Wei Han, Wansuo Duan, Zeting Li, Hao Li

“…Moreover, since the label data do not represent truth, models trained with loss functions like mean squared error (MSE) or mean absolute error (MAE) fail to account for its uncertainty. …”
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Analysis of Surface Roughness and Machine Learning-Based Modeling in Dry Turning of Super Duplex Stainless Steel Using Textured Tools by Shailendra Pawanr, Kapil Gupta

“…One of the most critical aspects of turning, and machining in general, is the surface roughness of the finished product, which directly influences the performance, functionality, and longevity of machined components. …”
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Construction and clinical visualization application of a predictive model for mortality risk in sepsis patients based on an improved machine learning model by Ting Chen, Xuefeng Zhang, Qunfeng Yu, Qin Yang, Lingmin Yuan, Fei Tong

“…A Self-Weighted Self-Evolutionary Learning Model (SWSELM) was developed to identify independent risk factors for sepsis mortality and to create a visualization system for clinical application.ResultsThe improved algorithm significantly outperformed other algorithms on 23 standard test functions. …”
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Elevating Patient Care With Deep Learning: High-Resolution Cervical Auscultation Signals for Swallowing Kinematic Analysis in Nasogastric Tube Patients by Farnaz Khodami, Amanda S. Mahoney, James L. Coyle, Ervin Sejdic

“…This study aimed to investigate the utility of high-resolution cervical auscultation (HRCA) signals in assessing swallowing functionality of patients using feeding tubes. HRCA, capturing swallowing vibratory and acoustic signals, has been explored as a surrogate for videofluoroscopy image analysis in previous research. …”
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A Summary of Recent Advances in the Literature on Machine Learning Techniques for Remote Sensing of Groundwater Dependent Ecosystems (GDEs) from Space by Chantel Nthabiseng Chiloane, Timothy Dube, Mbulisi Sibanda, Tatenda Dalu, Cletah Shoko

“…Among these, remote sensing and advanced machine learning (ML) techniques have emerged as key tools for improving the evaluation of dryland GDEs. …”
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Hierarchical Reinforcement Learning with Automatic Curriculum Generation for Unmanned Combat Aerial Vehicle Tactical Decision-Making in Autonomous Air Combat by Yang Li, Wenhan Dong, Pin Zhang, Hengang Zhai, Guangqi Li

“…At the low level, three tactical policies (angle, snapshot, and energy tactics) are designed with reward functions informed by expert knowledge, while the high-level policy dynamically terminates current tactics and selects new ones through sparse reward learning, thus overcoming the limitations of fixed-duration tactical execution. …”
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Evaluating ChatGPT-4o for ophthalmic image interpretation: From in-context learning to code-free clinical tool generation by Joon Yul Choi, Tae Keun Yoo

“…Additionally, the model successfully translated schematic clinical algorithms into functional, browser-based diagnostic tools using natural language prompts alone. …”
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Prediction of suicidal attempts among Chinese adolescents with mood disorders: a clinical study using a machine learning approach by Jianbing Li, Yinqiu Zhao, Chi Yang, Wenqing Li, Zhihao Huang, Changhe Fan

“…Abstract Background Machine learning (ML) has been widely used to predict suicidal attempts (SA) in adolescents and adults. …”
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Comprehensive molecular analyses of an autoimmune-related gene predictive model and immune infiltrations using machine learning methods in intracranial aneurysma by Minxue Zhang, Lin Zhou, Yuying Zhao, Yanling Wang, Zhuobo Zhang, Zhan Liu

“…GO, KEGG and GSEA analyses were performed to uncover underlying molecular functions. Three machine learning methods—LASSO logistic regression, random forest (RF), and XGBoost—were employed to identify key genes. …”
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Building Type Classification Using CNN-Transformer Cross-Encoder Adaptive Learning From Very High Resolution Satellite Images by Shaofeng Zhang, Mengmeng Li, Wufan Zhao, Xiaoqin Wang, Qunyong Wu

“…Building type information indicates the functional properties of buildings and plays a crucial role in smart city development and urban socioeconomic activities. …”
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Deciphering organic substrate impacts in Anammox systems: A machine learning driven framework for predictive classification and process mechanism analysis by Zemin Li, Yulun Wu, Tao Chen, Bo Yan, Chaohai Wei

“…In summary, machine learning framework effectively integrates and optimizes the regulation of material stoichiometry, environmental parameters, and microbial functionality, thereby advancing the development of energy-efficient nitrogen removal technologies and enhancing the evaluation system for wastewater treatment processes.…”
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Leveraging machine learning on the role of hospitalizations in the dynamics of dengue spread in Brazil: an ecological study of health systems resilienceResearch in context by Paula de Castro-Nunes, Paloma Palmieri, Patrícia Passos Simões, Paulo Victor Rodrigues de Carvalho, Alessandro Jatobá

“…Summary: Background: The alarming rise in dengue cases and fatalities worldwide necessitates an in-depth analysis of essential public health functions (EPHFs) to fortify the resilience of health systems in the face of upcoming surges. …”
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Deep learning approach with ConvNeXt-SE-attn model for in vitro oral squamous cell carcinoma and chemotherapy analysis by Abhay Nath, Om Roy, Priyanka Silveri, Sanskruti Patel

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Estimating ocean currents from the joint reconstruction of absolute dynamic topography and sea surface temperature through deep learning algorithms by D. Ciani, C. Fanelli, B. Buongiorno Nardelli

“…To address these issues, we developed and tested different deep learning methodologies, specifically convolutional neural network (CNN) models that were originally proposed for single-image super resolution. …”
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