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

“…Large-scale, automated cross-sectional and longitudinal cerebral biomarker extraction from FLAIR datasets could progress disease characterization, improve disease monitoring, and help to determine optimal intervention times. Despite this, most automated biomarker extraction algorithms are designed for T1-weighted or multi-modal inputs. …”
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Identification of Spambots and Fake Followers on Social Network via Interpretable AI-Based Machine Learning by Danish Javed, Noor Zaman Zaman, Navid Ali Khan, Sayan Kumar Ray, Arafat Al-Dhaqm, Victor R. Kebande

“…To this end, we propose an interpretable machine learning (ML) framework, leveraging multiple ML algorithms with hyperparameters optimized through cross-validation, to enhance the detection process. …”
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Advanced Classifiers and Feature Reduction for Accurate Insomnia Detection Using Multimodal Dataset by Ameya Chatur, Mostafa Haghi, Nagarajan Ganapathy, Nima TaheriNejad, Ralf Seepold, Natividad Martinez Madrid

“…Our results demonstrate that the ensemble classifiers generalize well on the dataset regardless of the feature count, while other algorithms are hindered by the curse of dimensionality.…”
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A Piezoelectric Sensor Based on MWCNT-Enhanced Polyvinyl Chloride Gel for Contact Perception of Grippers by Qiyun Zhong, Qingsong He, Diyi Liu, Xinyu Lu, Siyuan Liu, Yuze Ye, Yefu Wang

“…The optimal PMPG (PVC:DBA = 1:5, 1 wt% MWCNTs) exhibited outstanding performance. …”
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A Systematic Integration of Artificial Intelligence Models in Appendicitis Management: A Comprehensive Review by Ivan Maleš, Marko Kumrić, Andrea Huić Maleš, Ivan Cvitković, Roko Šantić, Zenon Pogorelić, Joško Božić

“…In diagnostics, ML algorithms incorporating clinical, laboratory, imaging, and demographic data have improved accuracy and reduced uncertainty. …”
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Literature Review of Prognostic Factors in Secondary Generalized Peritonitis by Valerii Luțenco, Adrian Beznea, Raul Mihailov, George Țocu, Verginia Luțenco, Oana Mariana Mihailov, Mihaela Patriciu, Grigore Pascaru, Liliana Baroiu

“…The Mannheim Peritonitis Index (MPI) remains a widely validated prognostic tool, while APACHE II and SOFA scores also provide valuable risk estimates. …”
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Enhanced Conformer-Based Speech Recognition via Model Fusion and Adaptive Decoding with Dynamic Rescoring by Junhao Geng, Dongyao Jia, Zihao He, Nengkai Wu, Ziqi Li

“…Speech recognition is widely applied in fields like security, education, and healthcare. While its development drives global information infrastructure and AI strategies, current models still face challenges such as overfitting, local optima, and inefficiencies in decoding accuracy and computational cost. …”
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Comparative study of machine learning methods for mapping forest fire areas using Sentinel-1B and 2A imagery by Xinbao Chen, Xinbao Chen, Yaohui Zhang, Shan Wang, Zecheng Zhao, Chang Liu, Junjun Wen

“…This study provides technical support and empirical evidence for extracting and mapping forest fire areas while assessing damage caused by fires.…”
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Diagnostic Models for Differentiating COVID-19-Related Acute Ischemic Stroke Using Machine Learning Methods by Eylem Gul Ates, Gokcen Coban, Jale Karakaya

“…Various feature selection algorithms were applied to identify the most relevant features, which were then used to train and evaluate machine learning classification models. …”
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Development and validation of a machine learning model for online predicting the risk of in heart failure: based on the routine blood test and their derived parameters by Jianchen Pu, Yimin Yao, Xiaochun Wang

“…In addition, eight different machine learning algorithms were applied for prediction, and the prediction performances of these algorithms were comprehensively evaluated using the receiver operating characteristic curve, area under the curve (AUC), calibration curve analysis, and decision curve analysis and confusion matrix.ConclusionsUsing LASSO regression analysis, leukocyte, neutrophil, red blood cell, hemoglobin, platelet, and monocyte-to-lymphocyte ratios were identified as risk factors for HF. …”
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REVOLUTIONIZING LUXURY: THE ROLE OF AI AND MACHINE LEARNING IN ENHANCING MARKETING STRATEGIES WITHIN THE TOURISM AND HOSPITALITY LUXURY SECTORS by Maria Nascimento CUNHA, Manuel PEREIRA, António CARDOSO, Jorge FIGUEIREDO, Isabel OLIVEIRA

“…AI and ML applications, such as chatbots for 24/7 customer service and predictive analytics for tailoring travel recommendations, have greatly improved customer interaction and operational efficiencies. While the industry benefits from technological advancements, there are ongoing challenges such as concerns over data privacy and the need for constant updates to algorithms to keep pace with evolving market conditions.…”
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Theory of an Automatic Seepage Meter and Ramifications for Applications by Vitaly A. Zlotnik, D. Kip Solomon, David P. Genereux, Troy E. Gilmore, C. Eric Humphrey, Aaron R. Mittelstet, Anatoly V. Zlotnik

“…We quantify how the accuracy of parameter estimation depends on test duration and noise amplitude and propose how our analysis can be used to optimize field test protocols. On this basis, changing the ASM geometry by increasing the radius and decreasing tube insertion depth may enable ASM field test protocols that estimate interface flux and hydraulic conductivity faster while maintaining desired accuracy. …”
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Long-term planning optimisation of sustainable energy systems: A systematic review and meta-analysis of trends, drivers, barriers, and prospects by Soheil Mohseni, Alan C. Brent

“…These integrated resource planning endeavours primarily aim to minimise total discounted system costs while adhering to a network of interconnected technical constraints, encompassing considerations of reliability, resilience, and the integration of renewable energy sources. …”
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Assessing the temporal transferability of machine learning models for predicting processing pea yield and quality using Sentinel-2 and ERA5-land data by Michele Croci, Manuele Ragazzi, Alessandro Grassi, Giorgio Impollonia, Stefano Amaducci

“…TR prediction was more challenging while RF showed promising results in LOGOCV (nRMSE = 22.1 %), all ML models were outperformed by the NullModel in the more realistic LOYOCV scenario. …”
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Enhanced Position-Aided Beam Prediction Using Real-World Data and Enhanced-Convolutional Neural Networks by Ahmed Abd El Moaty Mohamed Gouda, Ehab K. I. Hamad, Aziza I. Hussein, M. Mourad Mabrook, A. A. Donkol

“…For 16-beams, the accuracy increased from 86.17% to 94.64 %, while for 8-beams, the accuracy increased from 90.24% to 97.11%. …”
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A Comparative Performance Evaluation of OFDM, GFDM, and OTFS in Impulsive Noise Channels by Mohsen Sheikh-Hosseini, Farhad Rahdari, Hazhir Ghasemnezhad, Somayeh Ahmadi, Murat Uysal

“…This method examines the impact of variations in the precoder order and explores the application of iterative algorithms for more optimal designing of the precoder. …”
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Uterine hydatidosis: casuistry is possible by A. L. Tikhomirov, V. V. Kazenashev, A. A. Dubinin, R. R. Sadikova, M. V. Maminova, J. S. Globa, A. V. Bukharov

“…Compared with common gynecological disease such as uterine fibroids, ovarian cyst and malignancies uterine hydatidosis may be identified only in 0.16 % cases.Aim: to present a clinical case of uterine hydatid cyst in order to optimize algorithms for differential diagnosis of primary pelvic echinococcosis and gynecological pathology, which is necessary for successfully conducted timely surgical treatment.Clinical case. …”
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Development of an ensemble prediction model for acute graft-versus-host disease in allogeneic transplantation based on machine learning by Lin Song, Xingwei Wu, Mengjia Xu, Ling Xue, Xun Yu, Zongqi Cheng, Chenrong Huang, Liyan Miao

“…Thus, the purpose of this study was to develop and optimize models by Cox regression and machine learning algorithms to predict the risk of aGVHD in which cyclosporin A exposure and common clinical factors were included as variables. …”
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Regularized Kaczmarz Solvers for Robust Inverse Laplace Transforms by Marta González-Lázaro, Eduardo Viciana, Víctor Valdivieso, Ignacio Fernández, Francisco Manuel Arrabal-Campos

“…Quantitative evaluation via mean squared error (MSE), Wasserstein distance, total variation, peak signal-to-noise ratio (PSNR), and runtime demonstrates that Wasserstein–Kaczmarz attains an optimal balance of speed (0.53 s per inversion) and accuracy (MSE = <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>4.7</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>8</mn></mrow></msup></mrow></semantics></math></inline-formula>), while TRAIn achieves the highest fidelity (MSE = <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.5</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>8</mn></mrow></msup></mrow></semantics></math></inline-formula>) at a modest computational cost. …”
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Construction and SHAP interpretability analysis of a risk prediction model for feeding intolerance in preterm newborns based on machine learning by Hui Xu, Xingwang Peng, Ziyu Peng, Rui Wang, Rui Zhou, Lianguo Fu

“…Second, ML models were constructed based on the logistic regression (LR), decision tree (DT), support vector machine (SVM) and eXtreme Gradient Boosting (XGBoost) algorithms, after which random sampling and tenfold cross-validation were separately used to evaluate and compare these models and identify the optimal model. …”
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