Engineered feature embeddings meet deep learning: A novel strategy to improve bone marrow cell classification and model transparency by Jonathan Tarquino, Jhonathan Rodríguez, David Becerra, Lucia Roa-Peña, Eduardo Romero

“…Deep learning algorithms are a promising approach to automate this bone marrow cell evaluation. …”
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Identification and validation of the nicotine metabolism-related signature of bladder cancer by bioinformatics and machine learning by Yating Zhan, Min Weng, Yangyang Guo, Dingfeng Lv, Feng Zhao, Zejun Yan, Junhui Jiang, Yanyi Xiao, Lili Yao

“…Integrative machine learning combination based on 10 machine learning algorithms was used for the construction of robust signature. …”
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Optimized Time-domain Feature Extraction for Early Onset Diagnosis of Parkinson Disease From EEG Signals by Delshad Ghavami, Moein Radman, Ali Chaibakhsh

“…Objectives: This study aims to develop a diagnostic method for PD by combining signal processing techniques with machine learning (ML) algorithms. Materials & Methods: Electroencephalography (EEG) signals were initially segmented into smaller windows using a windowing technique. …”
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Using Vector Databases for the Selection of Related Occupations: An Empirical Evaluation Using O*NET by Lino Gonzalez-Garcia, Miguel-Angel Sicilia, Elena García-Barriocanal

“…Vector databases offer an opportunity to find related occupations based on large pre-trained word and sentence embeddings and their associated retrieval algorithms for similarity search. …”
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Enhancing Slip, Trip, and Fall Prevention: Real-World Near-Fall Detection with Advanced Machine Learning Technique by Moritz Schneider, Kevin Seeser-Reich, Armin Fiedler, Udo Frese

“…Slips, trips, and falls (STFs) are a major occupational hazard that contributes significantly to workplace injuries and the associated financial costs. The application of traditional fall detection techniques in the real world is limited because they are usually based on simulated falls. …”
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Diagnosing Autoimmune Bullous Diseases—An Indian Perspective by Adhyatm Bhandari, Dipankar De, Shikha Shah, Debajyoti Chatterjee, Vinod Kumar, Rahul Mahajan, Sanjeev Handa

“…It also provides with practically applicable diagnostic algorithms for pragmatic diagnosis of AIBDs in Indian scenario.…”
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AI-based classification of anticancer drugs reveals nucleolar condensation as a predictor of immunogenicity by Giulia Cerrato, Peng Liu, Liwei Zhao, Adriana Petrazzuolo, Juliette Humeau, Sophie Theresa Schmid, Mahmoud Abdellatif, Allan Sauvat, Guido Kroemer

“…Abstract Background Immunogenic cell death (ICD) inducers are often identified in phenotypic screening campaigns by the release or surface exposure of various danger-associated molecular patterns (DAMPs) from malignant cells. …”
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A lightweight knowledge graph-driven question answering system for field-based mineral resource survey by Mingguo Wang, Chengbin Wang, Jianguo Chen, Bo Wang, Wei Wang, Xiaogang Ma, Jiangtao Ren, Zichen Li, Yicai Ye, Jiakai Zhang, Yue Wang

“…Geoscience data associated with mineral resource surveys have become essential digital assets for governments and mining companies. …”
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A data-centric and interpretable EEG framework for depression severity grading using SHAP-based insights by Anruo Shen, Jingnan Sun, Xiaogang Chen, Xiaorong Gao

“…By shifting the focus from algorithmic complexity to data transparency and feature-level insight, the model offers a practical and trustworthy path toward real-world mental health assessment.…”
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UAV With the Ability to Control with Sign Language and Hand by Image Processing by Hediyeh Hojaji, Alireza Delisnav, Mohammad Hossein Ghafouri Moghaddam, Fariba Ghorbani, Shadi Shafaghi, Masoud Shafaghi

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Unveiling new insights into migraine risk stratification using machine learning models of adjustable risk factors by Yu-Chen Liu, Ye-Hai Liu, Hai-Feng Pan, Wei Wang

“…Second, we trained ensemble machine learning (ML) algorithms that incorporated these factors, with Shapley Additive exPlanations (SHAP) value analysis quantifying predictor importance. …”
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A machine learning framework for predicting cognitive impairment in aging populations using urinary metal and demographic data by Fengchun Ren, Xiao Zhao, Qin Yang, Huaqiang Liao, Yudong Zhang, Xuemei Liu

“…Six machine learning algorithms were trained and evaluated using sensitivity (SN), specificity (SP), accuracy (ACC), Matthews correlation coefficient (MCC) and AUC.ResultsThe eXtreme gradient boosting (XGBoost) model demonstrated superior performance across all metrics (SN = 0.78, SP = 0.84, ACC = 0.81, MCC = 0.62, AUC = 0.90), and was selected for subsequent interpretation. …”
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Scalable Clustering of Complex ECG Health Data: Big Data Clustering Analysis with UMAP and HDBSCAN by Vladislav Kaverinskiy, Illya Chaikovsky, Anton Mnevets, Tatiana Ryzhenko, Mykhailo Bocharov, Kyrylo Malakhov

“…The study aims to apply unsupervised clustering algorithms to ECG data to detect latent risk profiles related to heart failure, based on distinctive ECG features. …”
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Advancing coal fire detection model for large-scale areas based on RS indices and machine learning by Jinglong Liu, Feng Zhao, Yunjia Wang, Yanan Wang, Sen Du, Libo Dang, Jordi J. Mallorqui

“…The model is capable of identifying large-scale coal fire target areas without relying on deformation associated with coal fires. CFDM outperformed other ML algorithms, achieving Recall, Precision, F1-score, and Kappa coefficient values of 0.89, 0.94, 0.93, and 0.92, respectively. …”
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Relationship Between Weight Status and Health-Related Quality of Life in School-age Children in China by Mandana Zanganeh, Peymané Adab, Bai Li, Miranda Pallan, Wei J. Liu, Lin Rong, Wei Liu, James Martin, Kar K. Cheng, Emma Frew

“…CHU-9D-CHN utility scores were generated using 2 scoring algorithms (UK and Chinese tariffs). Height and weight measures were taken at school by trained researchers using standardized methods, and BMI _z_ scores were calculated using the World Health Organization 2007 growth charts. …”
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Unsupervised machine learning identifies biomarkers of disease progression in post-kala-azar dermal leishmaniasis in Sudan. by Ana Torres, Brima Musa Younis, Samuel Tesema, Jose Carlos Solana, Javier Moreno, Antonio J Martín-Galiano, Ahmed Mudawi Musa, Fabiana Alves, Eugenia Carrillo

“…This approach could prove instrumental to train future supervised algorithms based on larger patient cohorts both for a more precise diagnosis and to gain insight into fundamental aspects of this complication of visceral leishmaniasis.…”
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Development and validation of a risk prediction model for depression in patients with chronic obstructive pulmonary disease by Tong Feng, PeiPei Li, Ran Duan, Zhi Jin

“…Feature selection was performed with Boruta and least absolute shrinkage and selection operator (LASSO) algorithms, identifying key predictors from demographic, lifestyle, medical history, and laboratory variables. …”
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Body-part-based individual feral cat identification from camera trap images using deep learning by Rio Rifqi Syah Akbar, Matthew W. Rees, Patricia A. Fleming, Ferdous Sohel

“…This study proposes a body-part-based computer algorithmic approach that uses deep learning for individual identification from photos that can address a common challenge associated with using camera trapping, where often only a partial or obscured view of the objects of interest is presented. …”
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Different environmental factors predict the occurrence of tick-borne encephalitis virus (TBEV) and reveal new potential risk areas across Europe via geospatial models by Patrick H. Kelly, Rob Kwark, Harrison M. Marick, Julie Davis, James H. Stark, Harish Madhava, Gerhard Dobler, Jennifer C. Moïsi

“…Region-specific ML models were defined via K-means clustering and trained according to the distribution of extracted geocoordinates relative to explanatory variables in each region. …”
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Use of a convolutional neural network for direct detection of acid-fast bacilli from clinical specimens by Paul English, Muir J. Morrison, Blaine Mathison, Elizabeth Enrico, Ryan Shean, Brendan O'Fallon, Deven Rupp, Katie Knight, Alexandra Rangel, Jeffrey Gilivary, Amanda Vance, Haleina Hatch, Leo Lin, David P. Ng, Salika M. Shakir

“…Fast detection is extremely important to reduce transmission and mortality associated with these infectious agents. Manual smear microscopy is a cost-effective tool for diagnosing and monitoring of these organisms; however, it is labor-intensive and requires highly-trained personnel. …”
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