Forecasting Stock Market Volatility Using Housing Market Indicators: A Reinforcement Learning-Based Feature Selection Approach by Pourya Zareeihemat, Samira Mohamadi, Jamal Valipour, Seyed Vahid Moravvej

“…This study tackles the complex challenge of accurately predicting stock market volatility through indicators from the housing market. …”
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Multilevel determinants of racial/ethnic disparities in severe maternal morbidity and mortality in the context of the COVID-19 pandemic in the USA: protocol for a concurrent triang... by Bankole Olatosi, Jiajia Zhang, Xiaoming Li, Chen Liang, Jihong Liu, Peiyin Hung, Shan Qiao, Berry A Campbell, Myriam E Torres, Neset Hikmet

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A lightweight knowledge graph-driven question answering system for field-based mineral resource survey by Mingguo Wang, Chengbin Wang, Jianguo Chen, Bo Wang, Wei Wang, Xiaogang Ma, Jiangtao Ren, Zichen Li, Yicai Ye, Jiakai Zhang, Yue Wang

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Targeted urinary metabolomics combined with machine learning to identify biomarkers related to central carbon metabolism for IBD by Miao-Lin Lei, Guan-Wei Bi, Xiao-Lin Yin, Xiao-Lin Yin, Yue Wang, Yue Wang, Yue Wang, Zi-Ru Sun, Zi-Ru Sun, Zi-Ru Sun, Xin-rui Guo, Hui-peng Zhang, Xiao-han Zhao, Feng Li, Feng Li, Yan-Bo Yu

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Machine learning based calculation of refractive index of polyethylene glycol polymer by Walid Abdelfattah, Munthar Kadhim Abosaoda, Hardik Doshi, H.S. Shreenidhi, Manoranjan Parhi, Devendra Singh, Prabhjot Singh, Bilakshan Purohit, Kamal Kant Joshi, Ahmad Abumalek

“…This study develops advanced machine learning algorithms to accurately predict the refractive index of polyethylene glycol (PEG) polymers using temperature and molecular weight as key input variables. …”
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AI Innovations in Liver Transplantation: From Big Data to Better Outcomes by Eleni Avramidou, Dominik Todorov, Georgios Katsanos, Nikolaos Antoniadis, Athanasios Kofinas, Stella Vasileiadou, Konstantina-Eleni Karakasi, Georgios Tsoulfas

“…As a result, algorithms are being developed to assess steatosis in pre-implantation biopsies and predict liver graft function, with AI applications displaying great accuracy across various studies included in this review. …”
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Opportunities and limitations of introducing artificial intelligence technologies into reproductive medicine by V. A. Lebina, O. Kh. Shikhalakhova, A. A. Kokhan, I. Yu. Rashidov, K. A. Tazhev, A. V. Filippova, E. P. Myshinskaya, Yu. V. Symolkina, Yu. I. Ibuev, A. A. Mataeva, A. N. Sirotenko, T. T. Gabaraeva, A. I. Askerova

“…AI can analyze vast amounts of data, including medical histories and research results, to more accurately predict pregnancy outcomes. This enables doctors to make more justified clinical decisions. …”
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Development and validation of a population pharmacokinetic model to guide perioperative tacrolimus dosing after lung transplantation by Todd A. Miano, PharmD, PhD, Athena F. Zuppa, MD, MSCE, Rui Feng, PhD, Stephen Griffiths, BS, Laurel Kalman, BA, Michelle Oyster, MS, Edward Cantu, MD, MSCE, Wei Yang, PhD, Joshua M. Diamond, MD, MSCE, Jason D. Christie, MD, MSCE, Marc H. Scheetz, PharmD, MSC, Michael G.S. Shashaty, MD, MSCE

“…The strongest predictor of tacrolimus clearance was postoperative day, with median predicted clearance increasing more than 3-fold over the 14-day study period. …”
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METHOD OF IMAGE PROCESSING IN THE PROBLEM OF DETECTING MOVING OBJECTS IN OPTICAL-ELECTRONIC SURVEILLANCE SYSTEMS OF THERMAL IMAGING TYPE by E. I. Mikhnionok

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QoS Routing in Telecommunications Networks by N. I. Listopad, O. A. Lavshuk

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Exploration of the clinicopathological and prognostic significance of BRCA1 in gastric cancer by Hongrong Zhang, Qi Xu, Hongxing Kan, Yinfeng Yang, Yunquan Cai

“…To explore potential biomarkers for GC, GC patient transcriptome data were subjected to a comprehensive approach involving machine learning, binary nomogram prediction model construction, the topological algorithm of CytoHubba, and Kaplan–Meier and Mendelian randomization (MR) analyses. …”
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Quantitative Detection of Quartz Sandstone SiO2 Grade Using Polarized Infrared Absorption Spectroscopy with Convolutional Neural Network Model by Banglong Pan, Hongwei Cheng, Shuhua Du, Hanming Yu, Shaoru Feng, Yi Tang, Juan Du, Huaming Xie

“…Then, generalized regression neural network (GRNN), partial least squares regression (PLSR), and convolutional neural network (CNN) were employed to establish a hyperspectral prediction model of SiO2 grade. The results show that the quantitative model by the PCA-CNN algorithm has the better prediction precision for the reciprocal logarithm data, with a coefficient of determination (R2), root mean square error (RMSE), and ratio of performance to interquartile range (RPIQ) of 0.907, 0.023, and 5.11, respectively. …”
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Intelligence model-driven multi-stress adaptive reliability enhancement testing technology by Shouqing Huang, Beichen He, Jing Wang, Xiaoyang Li, Rui Kang, Fangyong Li

“…In terms of mathematical models, we propose a Tuna Swarm Optimization–Gaussian Process Regression (TSO-GPR) model, which combines the global search capability of the tuna swarm optimization algorithm and the accurate prediction capability of Gaussian process regression, effectively handling the complex nonlinear relationships between multiple stresses and failure characteristic. …”
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Elemental numerical descriptions to enhance classification and regression model performance for high-entropy alloys by Yan Zhang, Cheng Wen, Pengfei Dang, Xue Jiang, Dezhen Xue, Yanjing Su

“…Moreover, these new numerical descriptions for phase classification can be directly applied to regression model predictions of HEAs, reducing the error by 22% and improving the R 2 value from 0.79 to 0.88 in hardness prediction. …”
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No-Reference Stereoscopic IQA Approach: From Nonlinear Effect to Parallax Compensation by Ke Gu, Guangtao Zhai, Xiaokang Yang, Wenjun Zhang

“…Third, the saliency based parallax compensation, resulted from different stereoscopic image contents, is considerably valid to improve the prediction performance of image quality metrics. Experimental results confirm that our proposed stereoscopic image quality assessment paradigm has superior prediction accuracy as compared to state-of-the-art competitors.…”
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Multiobjective Optimization of Milling Parameters for Ultrahigh-Strength Steel AF1410 Based on the NSGA-II Method by Jin Xu, Fuwu Yan, Yan Li, Zhenchao Yang, Long Li

“…The influence of milling parameters (milling speed, each tooth feed, radial depth of cut, and axial depth of cut) on milling force and surface roughness is studied by ANOVA and established prediction model. Multiobjective optimization of milling parameters is accomplished based on nondominated sorting genetic algorithm II (NSGA-II) with milling force, surface roughness, and material removal rate as optimization objectives. …”
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Unsupervised clustering for sepsis identification in large-scale patient data: a model development and validation study by Na Li, Kiarash Riazi, Jie Pan, Kednapa Thavorn, Jennifer Ziegler, Bram Rochwerg, Hude Quan, Hallie C. Prescott, Peter M. Dodek, Bing Li, Alain Gervais, Allan Garland

“…Cluster membership in the validation cohort was assigned using an XGBoost model trained to predict cluster membership in the development cohort. …”
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Artificial intelligence (AI) in restorative dentistry: current trends and future prospects by Mariya Najeeb, Shahid Islam

“…Key challenges include data privacy concerns, algorithmic bias, interpretability of AI decision-making processes, and the need for standardized AI training programs in dental education. …”
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FPGA-Accelerated Sparse Subset Segmentation Using ADMM for High-Resolution Imagery by Rupali Karthikeyan, Deep Amit Lodaya, Rama Muni Reddy Yanamala, Rayappa David Amar Raj, K. Krishna Prakasha, T. Subeesh, V. Anandkumar, Archana Pallakonda

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Disinformation in the Digital Age: Climate Change, Media Dynamics, and Strategies for Resilience by Andrea Tomassi, Andrea Falegnami, Elpidio Romano

“…Our findings indicate that social media algorithms and user dynamics can amplify false scientific claims, as seen in case studies of viral misinformation campaigns on vaccines and climate change. …”
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