Predicting p53 Status in IDH‐Mutant Gliomas Using MRI‐Based Radiomic Model by Jiamin Li, Zhihong Lan, Xiao Zhang, Xiaoyun Liang, Hanwei Chen, Xiangrong Yu

“…A total of 962 radiomic features were extracted for each VOI, followed by feature selection and modeling (Rad_VOIT, Rad_VOIPE, and Rad_VOIT + PE models) using machine learning algorithms. A nomogram was developed to integrate significant clinical factors and radiomic predictors for p53 status prediction. …”
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Impacts of Spatial Expansion of Urban and Rural Construction on Typhoon-Directed Economic Losses: Should Land Use Data Be Included in the Assessment? by Siyi Zhou, Zikai Zhao, Jiayue Hu, Fengbao Liu, Kunyuan Zheng

“…Results demonstrate three key findings: (1) By introducing prototype learning, a meta-learning approach, to guide model updates, we achieved precise assessments with small training samples, attaining an MAE of 1.02, representing 58.5–76.1% error reduction compared to conventional machine learning algorithms. This reveals that implicitly classifying typhoon disaster loss types through prototype learning can significantly improve assessment accuracy in data-scarce scenarios. (2) By designing a dual-path uncertainty quantification mechanism, we realized high-reliability risk assessment, with 95.45% of actual loss values falling within predicted confidence intervals (theoretical expectation: 95%). …”
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On the Total Version of Triple Roman Domination in Graphs by Juan Carlos Valenzuela-Tripodoro, Maria Antonia Mateos-Camacho, Martin Cera, Maria Pilar Alvarez-Ruiz

“…We investigate the algorithmic complexity of the associated decision problem, establish sharp bounds regarding graph structural parameters, and obtain the exact values for several graph families.…”
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Energy-Accuracy Trade-Offs for Resistive In-Memory Computing Architectures by Saion K. Roy, Naresh R. Shanbhag

“…This result clearly implies the need for other approaches, e.g., algorithmic- and learning-based methods, to improve the inference accuracy of resistive IMC architectures.…”
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Machine learning prediction model for functional prognosis of acute ischemic stroke based on MRI radiomics of white matter hyperintensities by Yayuan Xia, Linhui Li, Peipei Liu, Tianxu Zhai, Yibing Shi

“…Radiomic models for the three ROIs were developed using six machine learning algorithms. The clinical model was built by identifying clinical risk factors through univariate and multivariate logistic regression analyses. …”
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The effect of materials and methods of the sella turcica bottom plastic surgery after transsphenoidal pituitary adenoma surgery on the incidence of nasal liquorrhea by V. Yu. Cherebillo, Yu. I. Ryumina, A. V. Dubinina

“…Thus, over the past five years, we can note a significant decrease in the frequency of postoperative nasal liquorrhea after transsphenoidal surgery of pituitary adenomas, the emergence of many options and algorithms for closing the postoperative defect of the base of the skull to prevent and treat it. …”
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Non-Invasive Bioelectrical Characterization of Strawberry Peduncles for Post-Harvest Physiological Maturity Classification by Jonnel Alejandrino, Ronnie Concepcion, Elmer Dadios, Ryan Rhay Vicerra, Argel Bandala, Edwin Sybingco, Laurence Gan Lim, Raouf Naguib

“…Feature selection reduced these to 6 parameters: phase angle at 50 kHz (<i>θ</i> (<i>50</i> kHz)); relaxation time (<i>τ</i>); impedance ratio (|<i>Z</i>₁_k/<i>Z</i>₂₅₀_k|); dispersion coefficient (<i>α</i>); membrane capacitance (<i>Cm</i>); and intracellular resistivity (<i>ρi</i>). Four algorithms (TabPFN, CatBoost, GPC, EBM) were evaluated with Monte Carlo cross-validation with five iterations, ensuring robust evaluation. …”
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Acute Respiratory Infections Identification With Cough Sounds and Overlapping Patch Modulated Vision Transformers by P. V. V. Kishore, D. Anil Kumar, Pasupuleti Sasikiran, Kaja Krishna Mohan, P. Praveen Kumar, Mogadala Vinod Kumar

“…Variable cough sound vs silent intervals between samples of a class in MFCC spectral images has shown to influence training algorithms to learn meaningful patterns for classification. …”
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State preparation by shallow circuits using feed forward by Harry Buhrman, Marten Folkertsma, Bruno Loff, Niels M. P. Neumann

“…This model fits naturally between quantum algorithms in the NISQ era and full-fledged fault-tolerant quantum computation. …”
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Association of marital status with DNA methylation-based biological age among US older adults by Xian Cui, Jianghua Huo, Yaqian Xu, Hui Zhang, Chongyu Ding, Jun Du, Xiangwei Li

“…BackgroundDNA methylation (DNAm) algorithms, such as GrimAge and GrimAge2, have emerged as reliable measures of biological age acceleration and may offer additional insights into health outcomes. …”
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Combining a Risk Factor Score Designed From Electronic Health Records With a Digital Cytology Image Scoring System to Improve Bladder Cancer Detection: Proof-of-Concept Study by Sandie Cabon, Sarra Brihi, Riadh Fezzani, Morgane Pierre-Jean, Marc Cuggia, Guillaume Bouzillé

“…MethodsThe first step relied on designing a predictive model based on clinical data (ie, risk factors identified in the literature) extracted from the clinical data warehouse of the Rennes Hospital and machine learning algorithms (logistic regression, random forest, and support vector machine). …”
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Mechanisms of drug resistance to neoadjuvant chemotherapy in breast cancer by K. A. Aliev, E. Yu. Zyablitskaya, T. P. Makalish, L. E. Sorokina, E. R. Asanova

“…The presented results can serve as the basis for the creation of diagnostic algorithms that have predictive value regarding the effectiveness of NCT, and also to help identify new targets to justify the use of combined breast cancer treatments in the early stage.…”
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Vibration Signal Analysis for Intelligent Rotating Machinery Diagnosis and Prognosis: A Comprehensive Systematic Literature Review by Ikram Bagri, Karim Tahiry, Aziz Hraiba, Achraf Touil, Ahmed Mousrij

“…In the context of fault detection, support vector machines (SVMs), convolutional neural networks (CNNs), Long Short-Term Memory (LSTM) networks, k-nearest neighbors (KNN), and random forests have been identified as the five most frequently employed algorithms. Meanwhile, transformer-based models are emerging as a promising venue for the prediction of RUL values, along with data transformation. …”
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Safety evaluation of baloxavir marboxil: analysis and discussion utilizing real adverse events from the FAERS database by Xiaolong Lai, Liuyin Jin, Yixia Zhou, Yang Li, Lindan Sheng, Guomin Xie, Jianjiang Fang

“…A disproportionality analysis was performed using four validated pharmacovigilance algorithms: reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and multi-item gamma Poisson shrinker (MGPS). …”
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THEORETICAL AND METHODOLOGICAL ASPECTS OF USING INFORMATION ANDCOGNITIVE TECHNOLOGIES IN THE TRAINING OF TRANSPORT SPECIALISTS by Lavrentieva Olena, Krupskyi Oleksandr

“…It has been established that information-cognitive technologies combine methods and algorithms based on insights into the cognition processes, learning, communication, and information processing, grounded in the achievements of neuroscience, digital and information technologies, and the mathematical modelling of consciousness. …”
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The UK Biobank mental health enhancement 2022: Methods and results. by Katrina A S Davis, Jonathan R I Coleman, Mark Adams, Gerome Breen, Na Cai, Helena L Davies, Kelly Davies, Alexandru Dregan, Thalia C Eley, Elaine Fox, Jo Holliday, Christopher Hübel, Ann John, Aliyah S Kassam, Matthew J Kempton, William Lee, Danyang Li, Jared Maina, Rose McCabe, Andrew M McIntosh, Sian Oram, Marcus Richards, Megan Skelton, Fenella Starkey, Abigail R Ter Kuile, Laura M Thornton, Rujia Wang, Zhaoying Yu, Johan Zvrskovec, Matthew Hotopf

“…It includes established measures, such as the PHQ-9 for current depression and CIDI-SF for lifetime panic, as well as bespoke questions. Algorithms and R code were developed to facilitate analysis.…”
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A machine learning model revealed that exosome small RNAs may participate in the development of breast cancer through the chemokine signaling pathway by Jun-luan Mo, Xi Li, Lin Lei, Ji Peng, Xiong-shun Liang, Hong-hao Zhou, Zhao-qian Liu, Wen-xu Hong, Ji-ye Yin

“…We used machine learning algorithms to analyze small RNAs with significant differences between the two groups, fit the model through training sets, and optimize the model through testing sets. …”
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%diag_test: a generic SAS macro for evaluating diagnostic accuracy measures for multiple diagnostic tests by Jacques K. Muthusi, Peter W. Young, Frankline O. Mboya, Samuel M. Mwalili

“…We also used the macro to reproduce results of published work on evaluating performance of multiple classification machine learning algorithms for predicting coronary artery disease. Conclusion The SAS macro presented here is a powerful analytic tool for analyzing data from multiple diagnostic tests. …”
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Development of an Artificial Neural Network-Based Image Retrieval System for Lung Disease Classification and Identification by Atul Pratap Singh, Ajeet Singh, Amit Kumar, Himanshu Agarwal, Sapna Yadav, Mohit Gupta

“…The CNN architecture is then meticulously constructed, with attention to layer configurations, activation functions, and optimization algorithms to facilitate effective learning and classification. …”
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AI-powered visual E-monitoring system for cattle health and wealth by Aung Si Thu Moe, Pyke Tin, Masaru Aikawa, Ikuo Kobayashi, Thi Thi Zin

“…Through integrated deep learning algorithms, the platform performs key health-related tasks, including ear-tag, body-based, and face-based cattle identification, body condition scoring (BCS), lameness detection, feeding time estimation, and real-time localization. …”
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