Screening and Risk Analysis of Atrial Fibrillation After Radiotherapy for Breast Cancer: Protocol for the Cross-Sectional Cohort Study “Watch Your Heart (WATCH)” by Laura Saint-Lary, Baptiste Pinel, Loic Panh, Gaelle Jimenez, Julien Geffrelot, Youlia Kirova, Jeremy Camilleri, David Broggio, Marie-Odile Bernier, Corinne Mandin, Christelle Levy, Serge Boveda, Juliette Thariat, Sophie Jacob

“…Cross-sectional screening for AF at the time of the scheduled 5-year post-RT visit will be conducted by recording data from a Withings ScanWatch smartwatch for 1 month, confirmed by an electrocardiogram (ECG), and validated by a physician. …”
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Lung Cancer Prediction Using an Enhanced Neutrosophic Set Combined with a Machine Learning Approach by Vakeel A. Khan, Asheesh Kumar Yadav, Mohammad Arshad, Nadeem Akhtar

“…To address this issue, we propose an Enhanced Neutrosophic Set (ENS) framework integrated with machine learning algorithms to improve the prediction accuracy of lung cancer. …”
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Prediction of formation pressure in underground gas storage based on data-driven method by SUI Gulei, FU Yujiang, ZHU Hongxiang, LI Zunzhao, WANG Xiaolin

“…The experimental results show that predictive performances of three predictive models are ranked from high to low: SVR, XGBoost, LSTM, among which the predictive performance of SVR is the most stable. Introducing the proportion of gas injection-production to screen pressure monitoring wells can improve the predictive performance of the data-driven model. …”
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Artificial Intelligence and Machine Learning Approaches for Target-Based Drug Discovery: A Focus on GPCR-Ligand Interactions by M. O. Otun

“…This review explores the integration of AI and ML techniques in GPCR-targeted drug discovery, highlighting their potential to accelerate lead identification, optimize ligand binding predictions, and improve structure-activity relationship modeling. …”
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Dynamic Skin: A Systematic Review of Energy-Saving Design for Building Facades by Jian Wang, Shengcai Li, Peng Ye

“…Parametric modeling, computer simulation, and multi-objective algorithms are commonly used to optimize the performance of dynamic skin. …”
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The geriatric 5Ms, artificial intelligence, and Hannah Arendt’s critique: ethical reflections within contemporary gerontology by Virgílio Garcia Moreira, Andréia Pain, Ivan Aprahamian

“…The integration of AI into geriatrics has the potential to improve diagnostic accuracy, optimize therapies, and individualize interventions. …”
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The future of critical care: AI-powered mortality prediction for acute variceal gastrointestinal bleeding and acute non-variceal gastrointestinal bleeding patients by Zhou Liu, Guijun Jiang, Liang Zhang, Palpasa Shrestha, Yugang Hu, Yi Zhu, Guang Li, Yuanguo Xiong, Liying Zhan

“…BackgroundAcute upper gastrointestinal bleeding (AUGIB) is one of the most common critical diseases encountered in the intensive care unit (ICU), with a mortality rate ranging from 15 to 20%. …”
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A prediction method for radiation proctitis based on SAM-Med2D model by Ning Zhang, Haifeng Ling, Wenyu Zhang, Mei Zhang

“…Accurate diagnosis are crucial for optimizing treatment strategies and improving patient outcomes. …”
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Real-Time Common Rust Maize Leaf Disease Severity Identification and Pesticide Dose Recommendation Using Deep Neural Network by Zemzem Mohammed Megersa, Abebe Belay Adege, Faizur Rashid

“…Maize is one of the most widely grown crops in Ethiopia and is a staple crop around the globe; however, common rust maize disease (CRMD) is becoming a serious problem and severely impacts yields. …”
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Predicting child mortality determinants in Uttar Pradesh using Machine Learning: Insights from the National Family and Health Survey (2019–21) by Pinky Pandey, Sacheendra Shukla, Niraj Kumar Singh, Mukesh Kumar

“…Policy measures targeting critical factors, such as promoting breastfeeding, optimizing birth intervals, and improving maternal health and antenatal care, can significantly enhance childhood survival rates in Uttar Pradesh.…”
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ABL-SMOTE: A Novel Resampling Method by Handling Noisy and Borderline Challenge for Imbalanced Dataset for Software Defect Prediction by Kamal Bashir, Sara Abdelwahab Ghorashi, Ali Ahmed, Abdolraheem Khader

“…Machine learning algorithms face important implementation difficulties due to imbalanced learning since the Synthetic Minority Oversampling Technique (SMOTE) helps improve performance through the creation of new minority class examples in feature space before preprocessing. …”
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Cross-sectional and longitudinal Biomarker extraction and analysis for multicentre FLAIR brain MRI by J. DiGregorio, A. Gibicar, H. Khosravani, P. Jabehdar Maralani, J.-C. Tardif, P.N. Tyrrell, A.R. Moody, A. Khademi

“…Despite this, most automated biomarker extraction algorithms are designed for T1-weighted or multi-modal inputs. …”
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A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning by Aleksander Radovan, Leo Mršić, Goran Đambić, Branko Mihaljević

“…The accurate counting of passengers in public transport systems is crucial for optimizing operations, improving service quality, and planning infrastructure. …”
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Comparative Analysis of Machine Learning Models for Predicting Innovation Outcomes: An Applied AI Approach by Marko Martinović, Kristian Dokic, Dalibor Pudić

“…Logistic regression proved to be the most computationally efficient model despite its weaker predictive power. …”
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ON THE SIMULATION OF MODES ОF ELECTRIC POWER SYSTEMS WITH FACTS by E. D. Halilov

“…It is necessary to reduce the power loss, improve the reliability and quality of power supply and increase the power transmission. …”
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Machine learning-based prediction of physical parameters in heterogeneous carbonate reservoirs using well log data by Fuyong Wang, Xianmu Hou

“…The results demonstrate that GPR achieves the highest accuracy in porosity prediction, with a coefficient of determination (R2) value of 0.7342, while RF proves to be the most accurate for permeability prediction. Despite these improvements, accurately predicting low-permeability zones in heterogeneous carbonate rocks remains a significant challenge. …”
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A comprehensive review of data analytics and storage methods in geothermal energy operations by Ali Basem, Ahmed Kateb Jumaah Al-Nussairi, Dana Mohammad Khidhir, Narinderjit Singh Sawaran Singh, Mohammadreza Baghoolizadeh, Mohammad Ali Fazilati, Soheil Salahshour, S. Mohammad Sajadi, Ali Mohammadi Hasanabad

“…The study also delves into the potential of machine learning to optimize geothermal design, monitor performance, improve performance, find errors, and more. …”
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Study on debris flow vulnerability of ensemble learning model based on spy technology A case study of upper Minjiang river basin by Yutao Chen, Ning Li, Fucheng Xing, Han Xiang, Zilong Chen

“…Since it is challenging to predict debris flows with precision using traditional methods, machine learning algorithms have been used more and more in this field in recent years. …”
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Efficient spatio-temporal modeling for sign language recognition using CNN and RNN architectures by Kasian Myagila, Kasian Myagila, Devotha Godfrey Nyambo, Mussa Ally Dida

“…These results show that more effort is required to improve signer independence performance, including the challenges of hand dominance by optimizing spatial features.…”
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Ultrasound combined with serological markers for predicting neonatal necrotizing enterocolitis: a machine learning approach by Yi Yang, Shoulan Zhou, Xiaomin Liu, Yanhong Zhang, Liping Lin, Chenhan Zheng, Xiaohong Zhong

“…SHAP analysis identified bowel peristalsis, C-reactive protein, albumin, bowel thickness, and procalcitonin as the most influential predictors. Decision curve analysis demonstrated a positive relative net benefit of the USPN model compared to the US and serological models in the validation set.ConclusionA machine learning model integrating ultrasound and serological markers significantly improves the prediction of NEC in neonates compared to single-modality approaches. …”
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