GLOBE Observer: A Case Study in Advancing Earth System Knowledge with AI-Powered Citizen Science by Peder V. Nelson, Russanne Low, Holli Kohl, David Overoye, Di Yang, Xiao Huang, Sriram Chellappan, Farhat Binte Azam, Ryan M. Carney, Monika Falk, Joan Garriga, Larisa Schelkin, Rebecca Boger, Theresa Schwerin

“…These advances position GLOBE citizen scientist data for discovery and use in environmental and health research, as well as by machine learning scientists working in the general field of GeoAI.…”
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Combined Methods for Diabetic Retinopathy Screening, Using Retina Photographs and Tear Fluid Proteomics Biomarkers by Zsolt Torok, Tunde Peto, Eva Csosz, Edit Tukacs, Agnes M. Molnar, Andras Berta, Jozsef Tozser, Andras Hajdu, Valeria Nagy, Balint Domokos, Adrienne Csutak

“…The results from the tear fluid proteomics analysis and from digital microaneurysm (MA) detection on fundus images were used as the input of a machine learning system. Results. MA detection method alone resulted in 0.84 sensitivity and 0.81 specificity. …”
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Multi-Feature Driver Variable Fusion Downscaling TROPOMI Solar-Induced Chlorophyll Fluorescence Approach by Jinrui Fan, Xiaoping Lu, Guosheng Cai, Zhengfang Lou, Jing Wen

“…Using the Random Forest (RF) model, we downscaled SIF data from 0.05° to 1 km based on invariant spatial scaling theory, focusing on the winter wheat growth cycle. Various machine learning models, including CNN, Stacking, Extreme Random Trees, AdaBoost, and GBDT, were compared, with Random Forest yielding the best performance, achieving R² = 0.931, RMSE = 0.052 mW/m²/nm/sr, and MAE = 0.031 mW/m²/nm/sr for 2018–2019 and R² = 0.926, RMSE = 0.058 mW/m²/nm/sr, and MAE = 0.034 mW/m²/nm/sr for 2019–2020. …”
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Integrating Explainable Artificial Intelligence With Advanced Deep Learning Model for Crowd Density Estimation in Real-World Surveillance Systems by Sultan Refa Alotaibi, Hanan Abdullah Mengash, Mohammed Maray, Faiz Abdullah Alotaibi, Abdulwhab Alkharashi, Ahmad A. Alzahrani, Moneerah Alotaibi, Mrim M. Alnfiai

“…The system assists in detecting and analyzing crowd density in real-time by utilizing artificial intelligence and machine learning (ML) models on surveillance videos. It detects crowded areas, manages crowd flow, and combines automated analysis with human oversight for improved public safety and early intervention. …”
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Comparison of diet and exercise on cardiometabolic factors in young adults with overweight/obesity: multiomics analysis and gut microbiota prediction, a randomized controlled trial by Zongyu Lin, Tianze Li, Fenglian Huang, Miao Wu, Lewei Zhu, Yueqin Zhou, Ying‐An Ming, Zhijun Lu, Wei Peng, Fei Gao, Yanna Zhu

“…Additionally, we used machine learning algorithms to further predict individual responses based on baseline gut microbiota composition, with specific microbial genera guiding targeted intervention selection. …”
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Towards PErsonalised PRognosis for children with traumatic brain injury: the PEPR study protocol by Jaap Oosterlaan, Marsh Königs, Job B M van Woensel, Marc Engelen, Marjan E Steenweg, Petra J W Pouwels, Cece C Kooper, Hilgo Bruining, Arne Popma, Dennis R Buis, Maayke Hunfeld

“…In addition, the potential added value of advanced neuroimaging data and the use of machine learning algorithms in the development of prognostic models will be assessed.Methods and analysis 210 children aged 4–18 years diagnosed with mild-to-severe TBI will be prospectively recruited from a research network of Dutch hospitals. …”
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Dataset of aerial photographs acquired with UAV using a multispectral (green, red and near-infrared) camera for cherry tomato (Solanum lycopersicum var. cerasiforme) monitoringDrya... by Osiris Chávez-Martínez, Sergio Alberto Monjardin-Armenta, Jesús Gabriel Rangel-Peraza, Zuriel Dathan Mora-Felix, Antonio Jesus Sanhouse-García

“…However, this multispectral imagery dataset can also have various uses, such as creating training datasets with accurate labels or classes which can then be used to develop, train, and/or validate machine learning algorithms for image classification, object detection tasks, or change detection analysis.…”
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Marine ecological information prediction by using adjacent location spatiotemporal deep learning model with ensemble learning techniques by Yue-Shan Chang, Shu-Ting Huang, Basanta Haobijam, Satheesh Abimannan, Takayuki Kushida

“…Recently, many studies have used machine learning or deep learning to predict SST and TH. …”
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Biparametric MRI-based radiomics for noninvastive discrimination of benign prostatic hyperplasia nodules (BPH) and prostate cancer nodules: a bio-centric retrospective cohort study by Yangbai Lu, Runqiang Yuan, Yun Su, Zhiying Liang, Hongxing Huang, Qu Leng, Ang Yang, Xuehong Xiao, Zhaoqi Lai, Yongxin Zhang

“…Radiomic models were created by comparing seven machine learning classifiers. The useful clinical variables and radiomic signature were integrated to develop the combined model. …”
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Compact Quantum Cascade Laser-Based Noninvasive Glucose Sensor Upgraded with Direct Comb Data-Mining by Liying Song, Zhiqiang Han, Hengyong Nie, Woon-Ming Lau

“…The sensor data-mines 164 sets of critical singularity strengths, each comprising 4 critical singularity strengths directly from the 9840 million raw signal datapoints, and the 656 critical singularity strengths are subjected to a machine-learning regression model analysis, which yields 164 glucose concentrations. …”
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Predictive Policing and Enhancing Security Performance through Artificial Intelligence Applications by Bensalem Kheira

“…By leveraging comprehensive data analysis and machine learning, AI enables proactive responses to both natural disasters and the globalization of crime. …”
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Diagnosis of lung cancer using salivary miRNAs expression and clinical characteristics by Negar Alizadeh, Hoda Zahedi, Maryam Koopaie, Mahnaz Fatahzadeh, Reza Mousavi, Sajad Kolahdooz

“…Receiver operating characteristic (ROC) curve was utilized to assess the potential significance of miRNAs in saliva for lung cancer diagnosis with the use of multiple logistic regression (MLR), principal component analysis, and machine learning methods. Results Diagnostic odds ratio (DOR) of miR-20a in lung adenocarcinoma diagnosis versus healthy control was higher than miR-221, and DOR of miR-221 was higher than let-7a-2. miR-20a demonstrated a higher DOR for small cell lung carcinoma versus healthy control compared to let-7a-2, which in turn exhibited a higher DOR than miR-221. …”
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Analytical performance of OncoPrism-HNSCC, an RNA-based assay to inform immune checkpoint inhibitor treatment decisions for recurrent/metastatic head and neck squamous cell carcino... by Jeffrey Hiken, Jon Earls, Kevin C. Flanagan, Rachel L. Wellinghoff, Michelle Ponder, David N. Messina, Jarret I. Glasscock, Eric J. Duncavage

“…The assay combines next generation RNA sequencing-based immunomodulatory gene expression signatures with machine learning algorithms to generate an OncoPrism score that classifies patients as having low, medium, or high likelihood of disease control in response to ICI treatment. …”
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Development and Validation of a Routine Electronic Health Record-Based Delirium Prediction Model for Surgical Patients Without Dementia: Retrospective Case-Control Study by Emma Holler, Christina Ludema, Zina Ben Miled, Molly Rosenberg, Corey Kalbaugh, Malaz Boustani, Sanjay Mohanty

“…ObjectiveThis study aimed to develop and externally validate a machine learning-based prediction model for POD using routine electronic health record (EHR) data. …”
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Design of an Integrated Model Combining CycleGAN, PPO, and Vision Transformer for Adaptive Scene Rendering in the Metaverse by Durga Prasad Kavadi, Kailash Chandra Mishra, Sai Babu Veesam, Mudimela Madhusudhan, Palacharla Ravi Kumar, Pannangi Naresh, Yuvaraju Chinnam

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Advancements and trends in mangrove species mapping based on remote sensing: A comprehensive review and knowledge visualization by Yuqi Wu, Chunyan Lu, Kexin Wu, Wenna Gao, Nuocheng Yang, Jingwen Lin

“…Classification algorithm development has evolved four stages, from pixel-based methods to object-oriented approaches, progressing to approaches incorporating machine learning algorithms, and currently advancing towards ensemble learning and deep learning. …”
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Automated Detection of Galactic Rings from Sloan Digital Sky Survey Images by Linn Abraham, Sheelu Abraham, Ajit K. Kembhavi, N. S. Philip, A. K. Aniyan, Sudhanshu Barway, Harish Kumar

“…It is therefore necessary to use AI-based techniques like machine learning and deep learning to find morphological structures quickly and efficiently. …”
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Accuracy and Usability of Smartphone-Based Distance Estimation Approaches for Visual Assistive Technology Development by Giles Hamilton-Fletcher, Mingxin Liu, Diwei Sheng, Chen Feng, Todd E. Hudson, John-Ross Rizzo, Kevin C. Chan

“…<italic>Methods:</italic> We tested five smartphone-based distance-estimation approaches in the image center and periphery at 1&#x2013;3 meters, including machine learning (CoreML), infrared grid distortion (IR_self), light detection and ranging (LiDAR_back), and augmented reality room-tracking on the front (ARKit_self) and back-facing cameras (ARKit_back). …”
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Automatic Recognition of Authors Identity in Persian based on Systemic Functional Grammar by Fatemeh Soltanzadeh, Azadeh Mirzaei, Mohammad Bahrani, Shahram Modarres Khiabani

“…Subsequent feature selection identified the most effective features for the machine learning phase. The results indicated that the relative frequency of function words outperformed SFG-based attributes in terms of effectiveness. …”
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Contrast quality control for segmentation task based on deep learning models—Application to stroke lesion in CT imaging by Juliette Moreau, Juliette Moreau, Laura Mechtouff, Laura Mechtouff, David Rousseau, Omer Faruk Eker, Omer Faruk Eker, Yves Berthezene, Yves Berthezene, Tae-Hee Cho, Tae-Hee Cho, Carole Frindel, Carole Frindel

“…IntroductionAlthough medical imaging plays a crucial role in stroke management, machine learning (ML) has been increasingly used in this field, particularly in lesion segmentation. …”
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