Comparative analysis of machine learning models and explainable AI for agriculture drought prediction: A case study of the Ta-pieh mountains by Lichang Xu, Shaowei Ning, Xiaoyan Xu, Shenghan Wang, Le Chen, Rujian Long, Shengyi Zhang, Yuliang Zhou, Min Zhang, Bhesh Raj Thapa

“…The analysis examined interactions between key factors and spatial patterns, showing how their contributions varied with drought severity and location. …”
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Leveraging machine learning and Landsat time series for high-resolution mapping of mining-induced vegetation changes in Ouagadougou, Burkina Faso by Oussama Himmy, Thanh Thi Nguyen, Katharina Salomea Hemmler, Safia Loulad, Hassan Rhinane, Andreas Buerkert

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A human-machine collaborative approach for high-resolution monitoring of suspended sediment dynamics in data-scarce and optically complex waters by Hai Sun, Yanan Chu, Bingchen Liang, Huiqian Wang, Chao Fan

“…We propose a transfer-based human–machine collaborative learning approach that embeds expert knowledge to significantly reduce reliance on field data. …”
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Characterization of Irrigated Rice Cultivation Cycles and Classification in Brazil Using Time Series Similarity and Machine Learning Models with Sentinel Imagery by Andre Dalla Bernardina Garcia, Ieda Del’Arco Sanches, Victor Hugo Rohden Prudente, Kleber Trabaquini

“…We identified four distinct irrigated rice cropping patterns across Santa Catarina, where the northern region favors double cropping, the south predominantly adopts single cropping, and the central region shows both, a flattened single and double cropping. …”
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A Hybrid Regression–Kriging–Machine Learning Framework for Imputing Missing TROPOMI NO₂ Data over Taiwan by Alyssa Valerio, Yi-Chun Chen, Chian-Yi Liu, Yi-Ying Chen, Chuan-Yao Lin

“…These results demonstrate the model’s robustness in capturing intra-seasonal patterns and nonlinear trends in NO₂ distribution. …”
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Brown adipose tissue machine learning nnU-Net V2 network using TriDFusion (3DF) by Daniel Lafontaine, Stephanie Chahwan, Gustavo Barraza, Burcin Agridag Ucpinar, Gunjan Kayal, Nicolás Gómez-Banoy, Paul Cohen, John L. Humm, Heiko Schöder

“…In the context of cancer care, artificial intelligence (AI)-driven BAT detection holds immense promise for rapid and automatic differentiation between malignant lesions and non-malignant BAT confounds. By leveraging machine learning to discern intricate patterns in imaging data, this study aims to advance the automation of BAT recognition and provide precise quantitative assessment of radiographic features. …”
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Objective approach to diagnosing attention deficit hyperactivity disorder by using pixel subtraction and machine learning classification of outpatient consultation videos by Yi-Hung Chiu, Ying-Han Lee, San-Yuan Wang, Chen-Sen Ouyang, Rong-Ching Wu, Rei-Cheng Yang, Lung-Chang Lin

“…ADHD is typically characterized by persistent patterns of inattention or hyperactivity–impulsivity, and it is diagnosed on the basis of the criteria outlined in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, through subjective observations and information provided by parents and teachers. …”
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Climate drivers of forest ecosystem services supply in the hilly mountainus regions of southern China based on SHAP-enhanced machine learning by Qi Mengjuan, Guo Luo, Liu Wenshu, Wang Weiyin, Jiang Chunqian, Bai Yanfeng

“…Analyzing the spatiotemporal patterns of forest ecosystem services (FESs) and their climatic drivers in the hilly mountainous regions of southern China (CSHR) is crucial for advancing regional ecological conservation. …”
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Flow dynamics in a vertical pipe with internal fins exposed to sunlight – A machine learning based evaluation of thermal signature by Assmaa Abd-Elmonem, Zill E Shams, Mariam Imtiaz, Kashif Ali, Sohail Ahmad, Wasim Jamshed, Fayza Abdel Aziz ElSeabee, Neissrien Alhubieshi, Syed M. Hussain, Hijaz Ahmad

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Ultrasound Imaging and Machine Learning to Detect Missing Hand Motions for Individuals Receiving Targeted Muscle Reinnervation for Nerve-Pain Prevention by Anna Rita E. Moukarzel, Justin Fitzgerald, Marcus Battraw, Clifford Pereira, Andrew Li, Paul Marasco, Wilsaan M. Joiner, Jonathon Schofield

“…We found that attempted missing hand movements resulted in unique patterns of deformation in the reinnervated muscles and applying a K-nearest neighbors machine learning algorithm, we could predict 4-10 hand movements for each participant with 83.3-99.4% accuracy. …”
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Simple Yet Powerful: Machine Learning-Based IoT Intrusion System With Smart Preprocessing and Feature Generation Rivals Deep Learning by Kazim Kivanc Eren, Kerem Kucuk, Fatih Ozyurt, Omar H. Alhazmi

“…With the rapid advancements in deep learning, IoT intrusion detection systems have increasingly adopted deep learning models as the state-of-the-art solution due to their ability to handle complex data patterns. However, these solutions introduce the risk of overengineering, in which the complexity of the model outweighs its practical benefits. …”
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Machine learning-based source apportionment and source-oriented probabilistic ecological risk assessment of heavy metals in urban green spaces by Jun Li, Jia-Yi Lu, Xin-Ying Tuo, Chao Wang, Jun-Zhuo Liu, Zhan-Dong Gao, Cun-Hao Yu, Fei Zang

“…Elevated concentrations, particularly of Zn, Cd, Pb, and Hg, displayed distinct spatial patterns linked to industrial activities and urban development. …”
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Clinical characteristics of COVID-19 in children and adolescents: insights from an Italian paediatric cohort using a machine-learning approach by Carlo Giaquinto, Daniela Paolotti, Daniele Donà, Stefania Fiandrino, Piero Poletti, Michael Davis Tira, Costanza Di Chiara

“…It aims to identify patterns in COVID-19 morbidity by clustering individuals based on symptom similarities and duration of symptoms and develop a machine-learning tool to classify new cases into risk groups.Methods We propose a data-driven approach to explore changes in COVID-19 characteristics by analysing data from 581 children and adolescents collected within a paediatric cohort at the University Hospital of Padua. …”
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Identification of novel metabolism-related biomarkers of Kawasaki disease by integrating single-cell RNA sequencing analysis and machine learning algorithms by Chenhui Feng, Zhimiao Wei, Xiaohui Li, Xiaohui Li

“…The cellular expression patterns of signature genes were further validated using our own scRNA-seq dataset. …”
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Brain functional connectivity analysis of fMRI-based Alzheimer's disease data by Maitha S. Alarjani, Badar A. Almarri

“…The core of this framework discovers and analyzes functional connectivity among regions of interest (ROIs) of a human brain. Multivariate Pattern Analysis (MVPA) is applied to extract features that reveal complex functional connectivity patterns in the brain. …”
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Oxidative balance score predicts chronic kidney disease risk in overweight adults: a NHANES-based machine learning study by Leying Zhao, Leying Zhao, Cong Zhao, Cong Zhao, Yuchen Fu, Yuchen Fu, Xiaochang Wu, Xiaochang Wu, Xuezhe Wang, Xuezhe Wang, Yaoxian Wang, Yaoxian Wang, Yaoxian Wang, Huijuan Zheng

“…Restricted cubic spline regression examined dose–response patterns, and subgroup analyses evaluated effect modifiers. …”
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Exploring Mortality and Prognostic Factors of Heart Failure with In-Hospital and Emergency Patients by Electronic Medical Records: A Machine Learning Approach by Yu CS, Wu JL, Shih CM, Chiu KL, Chen YD, Chang TH

“…To improve the explainability of the AI models, Shapley Additive Explanations methods were also conducted.Conclusion: Exploring HF mortality and its patterns related to clinical risk factors by machine learning models can help physicians make appropriate decisions when monitoring HF patients’ health quality in the hospital.Keywords: mortality, risk factor, cardiovascular disease, multivariate statistical analysis, machine learning, artificial intelligence…”
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Analysis of the Predictors of Mortality from Ischemic Heart Diseases in the Southern Region of Brazil: A Geographic Machine-Learning-Based Study by Amanda de Carvalho Dutra, Lincoln Luis Silva, Isadora Martins Borba, Amanda Gubert Alves dos Santos, Diogo Pinetti Marquezoni, Matheus Henrique Arruda Beltrame, Rogério do Lago Franco, Ualid Saleh Hatoum, Juliana Harumi Miyoshi, Gustavo Cezar Wagner Leandro, Marcos Rogério Bitencourt, Oscar Kenji Nihei, João Ricardo Nickenig Vissoci, Luciano de Andrade

“…The findings showed patterns of vulnerability in southern Brazil, suggesting the formulation of health policies to improve access to diagnostic and therapeutic resources, with the potential to reduce IHD mortality.…”
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Development and Internal Validation of Machine Learning Algorithms for Predicting Subsequent Contralateral Slipped Capital Femoral Epiphysis in Patients With Unilateral Slips by David P. VanEenenaam, Jr., BS, Carter Hall, BS, Daniel A. Maranho, MD, PhD, Christopher J. DeFrancesco, MD, Eduardo N. Novais, MD, Wudbhav N. Sankar, MD

“…Background: Controversy remains about whether to pin the contralateral side in cases of unilateral slipped capital femoral epiphysis (SCFE). Machine learning (ML) algorithms can be leveraged to identify complex, nonlinear patterns in data and allow for more accurate predictions on which patients may need a prophylactic pin. …”
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Preparation of land subsidence susceptibility map using machine learning methods based on decision tree (case study: Isfahan–Borkhar) by Negar Ghasemi, Iman Khosravi, Ali Bahrami

“…InSAR techniques allow for high-precision measurements of surface deformation across large areas, making them invaluable in understanding and modeling subsidence patterns. When combined with machine learning approaches, these technologies offer even greater potential for developing predictive models and susceptibility maps, enabling proactive risk management. …”
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