Mapping Nationwide Subfield Division Dynamics in Saudi Arabia Using Temporal Patterns of Sentinel-2 NDVI and Machine Learning by Ting Li, Oliver Miguel Lopez Valencia, Matthew F. McCabe

“…Object-based analysis is widely used for extracting information from satellite data using machine learning, offering reduced sensitivity to fine-scale variability, noise, and computational cost compared to pixel-based methods. …”
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Developing data driven framework to model earthquake induced liquefaction potential of granular terrain by machine learning classification models by Kennedy C. Onyelowe, Viroon Kamchoom, Tammineni Gnananandarao, Krishna P. Arunachalam

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Machine Learning Framework for Early Detection of Chronic Kidney Disease Stages Using Optimized Estimated Glomerular Filtration Rate by Samit Kumar Ghosh, Namareq Widatalla, Ahsan H. Khandoker

“…Conventional CKD diagnosis relies on estimated Glomerular Filtration Rate (eGFR), a measure of kidney function derived from serum biomarkers such as serum creatinine (SCr) and cystatin C (SCysC). …”
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A Novel LSTM Architecture for Automatic Modulation Recognition: Comparative Analysis With Conventional Machine Learning and RNN-Based Approaches by Sam Ansari, Soliman Mahmoud, Sohaib Majzoub, Eqab Almajali, Anwar Jarndal, Talal Bonny

“…Experimental results demonstrate that the model achieves a recognition accuracy of 99.87% at an SNR of -5 dB, outperforming several conventional machine learning techniques, including multi-layer perceptron (MLP), radial basis function (RBF) networks, adaptive neuro-fuzzy inference systems (ANFIS), decision trees (DT), naïve Bayes (NB), support vector machines (SVM), probabilistic neural networks (PNN), k-nearest neighbors (KNN), and ensemble learning models, as well as recurrent neural networks (RNNs). …”
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Web-SpikeSegNet: Deep Learning Framework for Recognition and Counting of Spikes From Visual Images of Wheat Plants by Tanuj Misra, Alka Arora, Sudeep Marwaha, Ranjeet Ranjan Jha, Mrinmoy Ray, Rajni Jain, A. R. Rao, Eldho Varghese, Shailendra Kumar, Sudhir Kumar, Aditya Nigam, Rabi Narayan Sahoo, Viswanathan Chinnusamy

“…Computer vision with deep learning is emerging as a significant approach for non-invasive and non-destructive plant phenotyping. …”
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Physics-enhanced machine learning for predicting strength of high-carbon chromium steel during thermomechanical processing and spheroidizing annealing by Changqing Shu, Shasha Zhang, Peiheng Ding, Yaxin Sun, Xuewei Tao, Xiaolin Zhu, Qiuhao Gu, Liukai Hua, Song Xue, Zhengjun Yao

“…This study presents a physics-enhanced machine learning framework to predict yield strength and ultimate tensile strength. …”
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CardioGenAI: a machine learning-based framework for re-engineering drugs for reduced hERG liability by Gregory W. Kyro, Matthew T. Martin, Eric D. Watt, Victor S. Batista

“…In addition, the framework incorporates novel state-of-the-art discriminative models for predicting hERG, NaV1.5 and CaV1.2 channel activity, which can function independently as effective components of virtual screening pipelines.…”
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Combining multi-omics analysis with machine learning to uncover novel molecular subtypes, prognostic markers, and insights into immunotherapy for melanoma by Songyun Zhao, Zihao Li, Kaibo Liu, Gaoyi Wang, Quanqiang Wang, Hua Yu, Wanying Chen, Hao Dai, Yijun Li, Jiaheng Xie, Yucang He, Liqun Li

“…Finally, the oncogenic role of AGPAT2 in melanoma cell lines was successfully confirmed through cell function assays and subcutaneous tumor formation assays in nude mice. …”
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Toward automated plantar pressure analysis: machine learning-based segmentation and key point detection across multicenter data by Carlo Dindorf, Jonas Dully, Steven Simon, Dennis Perchthaler, Stephan Becker, Hannah Ehmann, Christian Diers, Christoph Garth, Michael Fröhlich

“…Plantar pressure analysis is a pivotal tool for assessing foot function, diagnosing deformities, and characterizing gait patterns. …”
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Learning EEG Representations With Weighted Convolutional Siamese Network: A Large Multi-Session Post-Stroke Rehabilitation Study by Shuailei Zhang, Kai Keng Ang, Dezhi Zheng, Qianxin Hui, Xinlei Chen, Yang Li, Ning Tang, Effie Chew, Rosary Yuting Lim, Cuntai Guan

“…Most current feature extractors in BCI are classification-based methods, yet very few works from literature use metric learning based methods to learn representations for BCI. …”
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Integration of Hybrid Machine Learning and Multi-Objective Optimization for Enhanced Turning Parameters of EN-GJL-250 Cast Iron by Yacine Karmi, Haithem Boumediri, Omar Reffas, Yazid Chetbani, Sabbah Ataya, Rashid Khan, Mohamed Athmane Yallese, Aissa Laouissi

“…This study aims to optimize the turning parameters for EN-GJL-250 grey cast iron using hybrid machine learning techniques integrated with multi-objective optimization algorithms. …”
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Machine Learning‐Based Radiomics in Malignancy Prediction of Pancreatic Cystic Lesions: Evidence from Cyst Fluid Multi‐Omics by Sihang Cheng, Ge Hu, Shenbo Zhang, Rui Lv, Limeng Sun, Zhe Zhang, Zhengyu Jin, Yanyan Wu, Chen Huang, Lu Ye, Yunlu Feng, Zhe‐Sheng Chen, Zhiwei Wang, Huadan Xue, Aiming Yang

“…This models can effectively and accurately predict the malignant potential of PCLs, with multi‐omics evidence suggesting the biological mechanisms involving secretion function and lipid metabolism underlying clinical‐radiomic models.…”
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DM-YOLO: improved YOLOv9 model for tomato leaf disease detection by Abudukelimu Abulizi, Junxiang Ye, Halidanmu Abudukelimu, Wenqiang Guo

“…Specifically, firstly, lightweight dynamic up-sampling DySample is incorporated into the feature fusion backbone network to enhance the ability to extract features of small lesions and suppress the interference from the background environment; secondly, the MPDIoU loss function is used to enhance the learning of the details of overlapping lesion margins in order to improve the accuracy of localizing overlapping lesion margins. …”
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Issues and solutions on inventive problems-solving skills (IPSS) in invention course by Tee Tze Kiong, Nuraliah Abd Nasir, Nurulwahida Azid, Mimi Mohaffyza Mohamad, Wu Ming Chang, Charanjit Kaur Swaran Singh

“…Majority teachers support the application of the Theory of Inventive Problem-Solving Skills using Function Analysis for root cause identification (TRIZ). …”
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Surface EMG Sensing and Granular Gesture Recognition for Rehabilitative Pouring Tasks: A Case Study by Congyi Zhang, Dalin Zhou, Yinfeng Fang, Naoyuki Kubota, Zhaojie Ju

“…These results highlight the potential of our approach in rehabilitation technologies and assistive systems for restoring motor functions in daily activities.…”
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Machine Learning and Experimental Validation Reveal MYH11 as a Novel Prognostic Biomarker and Therapeutic Target in Bladder Cancer by Tan Z, Chen X, Fu S, Huang Y, Li H, Gong C, Lv D, Yang C, Wang J, Ding M, Wang H

“…Finally, single-cell analysis identified key cells involved in BCa pathogenesis, and in vitro experiments validated the expression and function of key genes.Results: The risk model constructed by 8 prognostic genes identified using 101 algorithms effectively predicted the survival outcomes of BCa patients. …”
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A hybrid model to overcome landslide inventory incompleteness issue for landslide susceptibility prediction by Jiayao Tan, Chi Yang, Yuzhou Wang, Hanxiang Xiong, Chuanming Ma

“…Our findings underscore the necessity of evaluating LII before applying statistical or machine learning methods in LSM.…”
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MRI-derived quantification of hepatic vessel-to-volume ratios in chronic liver disease using a deep learning approach by Alexander Herold, Daniel Sobotka, Lucian Beer, Nina Bastati, Sarah Poetter-Lang, Michael Weber, Thomas Reiberger, Mattias Mandorfer, Georg Semmler, Benedikt Simbrunner, Barbara D. Wichtmann, Sami A. Ba-Ssalamah, Michael Trauner, Ahmed Ba-Ssalamah, Georg Langs

“…Abstract Background We aimed to quantify hepatic vessel volumes across chronic liver disease stages and healthy controls using deep learning-based magnetic resonance imaging (MRI) analysis, and assess correlations with biomarkers for liver (dys)function and fibrosis/portal hypertension. …”
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Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts. by Naeimeh Atabaki-Pasdar, Mattias Ohlsson, Ana Viñuela, Francesca Frau, Hugo Pomares-Millan, Mark Haid, Angus G Jones, E Louise Thomas, Robert W Koivula, Azra Kurbasic, Pascal M Mutie, Hugo Fitipaldi, Juan Fernandez, Adem Y Dawed, Giuseppe N Giordano, Ian M Forgie, Timothy J McDonald, Femke Rutters, Henna Cederberg, Elizaveta Chabanova, Matilda Dale, Federico De Masi, Cecilia Engel Thomas, Kristine H Allin, Tue H Hansen, Alison Heggie, Mun-Gwan Hong, Petra J M Elders, Gwen Kennedy, Tarja Kokkola, Helle Krogh Pedersen, Anubha Mahajan, Donna McEvoy, Francois Pattou, Violeta Raverdy, Ragna S Häussler, Sapna Sharma, Henrik S Thomsen, Jagadish Vangipurapu, Henrik Vestergaard, Leen M 't Hart, Jerzy Adamski, Petra B Musholt, Soren Brage, Søren Brunak, Emmanouil Dermitzakis, Gary Frost, Torben Hansen, Markku Laakso, Oluf Pedersen, Martin Ridderstråle, Hartmut Ruetten, Andrew T Hattersley, Mark Walker, Joline W J Beulens, Andrea Mari, Jochen M Schwenk, Ramneek Gupta, Mark I McCarthy, Ewan R Pearson, Jimmy D Bell, Imre Pavo, Paul W Franks

“…Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. …”
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Exploratory integration of near-infrared spectroscopy with clinical data: a machine learning approach for HCV detection in serum samples by Eloy Pérez-Gómez, José Gómez, José Gómez, Jennifer Gonzalo, Sergio Salgüero, Daniel Riado, María Luisa Casas, María Luisa Gutiérrez, Elena Jaime, Enrique Pérez-Martínez, Rafael García-Carretero, Javier Ramos, Conrado Fernández-Rodríguez, Conrado Fernández-Rodríguez, Myriam Catalá, Myriam Catalá, Luca Martino, Óscar Barquero-Pérez

“…Feature importance analysis highlighted specific wavelengths near 1,150 nm, 1,410 nm, and 1,927 nm, associated with water molecular states and liver function biomarkers (GPT, GOT, GGT), reinforcing the biological relevance of this approach.ConclusionsThese findings suggest that integrating NIRS and clinical data through machine learning enhances HCV diagnostic capabilities, offering a scalable and non-invasive alternative for early detection and risk assessment.…”
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