Identification of immune-associated biomarkers in diagnosing allergic rhinitis by Qi Cheng, PhD, Jie Fei, MS, Yuanfen Liao, MS, Hang Lin, MD, Yubao Cui, PhD

“…Additionally, 5 machine-learning algorithms were integrated to screen for novel diagnostic biomarkers for AR. …”
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Integrated network toxicology and bioinformatics reveal PFAS-driven prognostic biomarkers and molecular mechanisms in breast cancer: insights from transcriptome analysis and molecu... by Yingqiang Fu, Yiyang Liu, Ziqi Sui

“…Enrichment analysis, network construction, and survival analysis were performed to elucidate the biological mechanisms and prognostic value. The CIBERSORT algorithm assessed immune cell infiltration, and molecular docking evaluated interactions between PFAS compounds and key genes. …”
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Integrating human services and criminal justice data with claims data to predict risk of opioid overdose among Medicaid beneficiaries: A machine-learning approach. by Wei-Hsuan Lo-Ciganic, Julie M Donohue, Eric G Hulsey, Susan Barnes, Yuan Li, Courtney C Kuza, Qingnan Yang, Jeanine Buchanich, James L Huang, Christina Mair, Debbie L Wilson, Walid F Gellad

“…This study aimed to improve the accuracy of a machine-learning algorithm to predict opioid overdose risk by integrating human services and criminal justice data with health claims data to capture the social determinants of overdose risk. …”
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An Integrated Fault Diagnosis Method for Rotating Machinery Based on Improved Multivariate Multiscale Amplitude-Aware Permutation Entropy and Uniform Phase Empirical Mode Decomposi... by Jiancheng Gong, Xiaoqiang Yang, Fan Pan, Wuqiang Liu, Fuming Zhou

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Identification of Key Genes Related to Intramuscular Fat Content of Psoas Major Muscle in Saba Pigs by Integrating Bioinformatics and Machine Learning Based on Transcriptome Data by Zixia Huang, Yongli Yang, Jinhua Lai, Qiang Chen, Xiaoyi Wang, Shuyan Wang, Mingli Li, Shaoxiong Lu

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Coalition Game-Based Reinforcement Learning in Underwater Acoustic Networks by Khouloud Gharsalli, Sameh Najeh, Thierry Le Pors, Leila Najjar, Pierre-Jean Bouvet, Hichem Besbes

“…Simulation results demonstrate that the U-CoCRL method outperforms baseline algorithms across all interference levels. It achieves up to 18.1% higher global utility and 33.9% better performance indices under high interference, 13.6% and 33.9% improvements under medium interference, and 6.1% and 27.7% gains under low interference, respectively. …”
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A Machine Learning Model for Predicting Breast Cancer Recurrence and Supporting Personalized Treatment Decisions Through Comprehensive Feature Selection and Explainable Ensemble Le... by Lee TF, Shiau JP, Chen CH, Yun WP, Wuu CS, Huang YJ, Yeh SA, Chen HC, Chao PJ

“…An ensemble learning model was developed based on multiple machine learning algorithms, with SHAP (Shapley additive explanations) used for interpretability.Results: The ensemble model achieved an AUC of 0.817, outperforming the best single model (AUC 0.711), demonstrating improved predictive accuracy and stability. …”
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Recovery of Implied Volatility in a Spatial-Fractional Black–Scholes Equation Under a Finite Moment Log Stable Model by Xiaoying Jiang, Chunmei Shi, Yujie Wei

“…Using a linearization technique, we rigorously derive a mathematical formulation of the inverse problem in terms of a Fredholm integral equation of the first kind. Based on an integral equation, an efficient numerical reconstruction algorithm is proposed to recover the coefficient. …”
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Application of Fuzzy System on Settlement of Shallow Footing on Granular Soil by Soroush Lesani

“…The proposed models integrate the reptile search algorithm (RSA) with support vector regression (SVR) analysis and adaptive neuro-fuzzy inference system (ANFIS). …”
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Development of a Self-Updating System for the Prediction of Steel Mechanical Properties in a Steel Company by Machine Learning Procedures by Valerio Zippo, Elisa Robotti, Daniele Maestri, Pietro Fossati, David Valenza, Stefano Maggi, Gennaro Papallo, Masho Hilawie Belay, Simone Cerruti, Giorgio Porcu, Emilio Marengo

“…The results proved to be very promising for all the types of steel investigated, with very good RMSE and R² values both in fitting and in prediction. The application here presented is being integrated into Total Quality Tutor (TQT) software, developed in-house in C# language, for predicting the mechanical properties of steel.…”
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Energy optimization and digitization of the PV energy balance between production and consumption by Abdellatif Hraich, Ali Haddi

“…This work had permitted to enhance the conversion efficiency of the photovoltaic panels by using the tracking technique and the Sun Algorithm Position via Astronomic calculation in the same line, we had built a database permitting to store the details about the different positions of tracking system with their equivalent energy values in their real actual reading times. …”
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Enhancing automated detection and classification of dementia in individuals with cognitive impairment using artificial intelligence techniques by Shoayee Dlaim Alotaibi, Abeer A. K. Alharbi

“…Finally, the improved salp swarm algorithm (ISSA) is implemented to choose the WNN technique’s hyperparameters optimally. …”
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Error Analysis and Compensation of 3‒PTT Parallel Robot by CHEN Mingfang, LIANG Hongjian, WEI Songpo, HE Chaoyin

“…Thus, the algorithm is more feasible. In addition, to improve the efficiency of the aforementioned error compensation algorithm, the standard particle swarm optimization algorithm is further enhanced by integrating the dynamic inertia weight value and dynamic learning factor, thus overcoming the problems of precocious convergence to a local optimum and slow convergence in the later iteration of the standard particle swarm optimization algorithm. …”
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A comparative study of bone density in elderly people measured with AI and QCT by Min Guo, Min Guo, Yu Zhang, Yu Zhang, XinXin Gu, XinXin Gu, Xuhui Liu, Xuhui Liu, Fei Peng, Fei Peng, Zongjun Zhang, Zongjun Zhang, Mei Jing, Mei Jing, Yingxia Fu, Yingxia Fu

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Machine learning algorithms to predict khat chewing practice and its predictors among men aged 15 to 59 in Ethiopia: further analysis of the 2011 and 2016 Ethiopian Demographic and... by Mequannent Sharew Melaku, Lamrot Yohannes, Eliyas Addisu Taye, Nebebe Demis Baykemagn

“…Effective khat prevention strategies should focus on the following: preserving rural norms that discourage khat use and expanding these to urban areas; targeted interventions for young and middle-aged men, including youth programs and economic empowerment initiatives as alternative opportunities; strengthening family values through marriage counseling and spouse involvement to help reduce khat chewing; integrating khat education into reproductive health programs and engaging religious leaders in awareness efforts; and, finally, implementing media campaigns, school-based education, and policy measures—such as restricting sales near schools and enforcing community bylaws—to further curb khat consumption while promoting healthier economic alternatives.…”
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Implementation of Chaotic Synchronization and Artificial Neural Networks in Modified OTP Scheme for Image Encryption by Hristina Stoycheva, Georgi Mihalev, Stanimir Sadinov, Krasen Angelov

“…This paper presents a modified image encryption scheme based on the OTP (One-Time Pad) algorithm, consisting of chaotic synchronization and artificial neural networks (ANNs) for improved security and efficiency. …”
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Evaluation of Eight Decomposition-Hybrid Models for Short-Term Daily Reference Evapotranspiration Prediction by Yunfei Chen, Zuyu Liu, Ting Long, Xiuhua Liu, Yaowei Gao, Sibo Wang

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Interpretable Machine Learning for Multi-Energy Supply Station Revenue Forecasting: A SHAP-Driven Framework to Accelerate Urban Carbon Neutrality by Zhihui Zhao, Minjuan Wang, Jin Wei, Xiao Cen, Shengnan Du, Ziwen Wu, Huanying Liu, Weiqiang Wang

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Compressive strength prediction of fly ash/slag-based geopolymer concrete using EBA-optimised chemistry-informed interpretable deep learning model by Yang Yu, Iman Munadhil Abbas Al-Damad, Stephen Foster, Ali Akbar Nezhad, Ailar Hajimohammadi

“…To optimise model accuracy, the enhanced bat algorithm (EBA) is designed for metaparameter tuning. …”
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Reconstruction of the object inhomogeneity parameters from near-field measurements in microwave tomography problem using neural networks by A.V. Medvedev, M.Yu. Medvedik

“…The problem posed is reduced to the Lippmann-Schwinger integral equation. A two-step algorithm is used to search for inhomogeneity. …”