Cardiovascular health in perspective: a comprehensive five-year geodatabase of hospitalizations and environmental factors in Mashhad, Iran by Shahab MohammadEbrahimi, Mohammad Dehghan, Behzad Kiani

“…Using a spatiotemporal dataset of over 52,000 hospitalized CVD patients collected over five years, the study supports approaches like advanced spatiotemporal modeling, artificial intelligence, and machine learning to predict high-risk CVD areas and guide public health interventions. …”
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Exploring prognosis and therapeutic strategies for HBV-HCC patients based on disulfidptosis-related genes by Chuankuo Zhang, Xing Zhang, Shengjie Dai, Wenjun Yang

“…A prognostic model was established and validated using machine learning, with internal datasets and external datasets for verification. …”
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Advances and Mechanisms of RNA–Ligand Interaction Predictions by Chen Zhuo, Chengwei Zeng, Haoquan Liu, Huiwen Wang, Yunhui Peng, Yunjie Zhao

“…However, determining the structures of these complexes experimentally can be technically challenging and often results in low-resolution data. Many machine learning computational approaches have recently emerged to learn multiscale-level RNA features to predict the interactions. …”
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Optimization Method of Underwater Flapping Foil Propulsion Performance Based on Gaussian Process Regression and Deep Reinforcement Learning by YANG Yinghe, WEI Handi, FAN Dixia, LI Ang

“…The Latin hypercube sampling technique is utilized to obtain the samples of multi-dimensional flapping parameters in actual water pool data, and a Gaussian process regression (GPR) machine learning model is established based on these samples to generalize the working environment. …”
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Improving wheat yield prediction through variable selection using Support Vector Regression, Random Forest, and Extreme Gradient Boosting by Juan Carlos Moreno Sánchez, Héctor Gabriel Acosta Mesa, Adrián Trueba Espinosa, Sergio Ruiz Castilla, Farid García Lamont

“…This study uses these data to develop a wheat grain yield (GY) prediction model, using machine learning techniques such as Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost). …”
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CORE-MD clinical risk score for regulatory evaluation of artificial intelligence-based medical device software by Frank E. Rademakers, Elisabetta Biasin, Nico Bruining, Enrico G. Caiani, Rhodri H. Davies, Stephen H. Gilbert, Eric Kamenjasevic, Gearóid McGauran, Gearóid O’Connor, Jean-Baptiste Rouffet, Baptiste Vasey, Alan G. Fraser

“…Abstract The European CORE–MD consortium (Coordinating Research and Evidence for Medical Devices) proposes a score for medical devices incorporating artificial intelligence or machine learning algorithms. Its domains are summarised as valid clinical association, technical performance, and clinical performance. …”
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Temperature and Humidity Monitoring in Hydroponic Cultivation Based on Internet of Things: Dataset Development for Smart Agriculture by Simon Prananta Barus, Jeriko Ichtus Seo

“…Further research will integrate more monitoring parameters, conduct direct hydroponic cultivation trials, and apply artificial intelligence such as machine learning and deep learning to improve efficiency and effectiveness in hydroponic cultivation.…”
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Poly(A)-DG: A deep-learning-based domain generalization method to identify cross-species Poly(A) signal without prior knowledge from target species. by Yumin Zheng, Haohan Wang, Yang Zhang, Xin Gao, Eric P Xing, Min Xu

“…Identifying the cis-determinants of poly(A) signal (PAS) on the DNA sequence is the key to understand the mechanism of translation regulation and mRNA metabolism. Although machine learning methods were widely used in computationally identifying PAS, the need for tremendous amounts of annotation data hinder applications of existing methods in species without experimental data on PAS. …”
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Ensemble Classification Model With CFS-IGWO–Based Feature Selection for Cancer Detection Using Microarray Data by Pinakshi Panda, Sukant Kishoro Bisoy, Sandeep Kautish, Reyaz Ahmad, Asma Irshad, Nadeem Sarwar

“…Cancer is the top cause of death worldwide, and machine learning (ML) has made an indelible mark on the field of early cancer detection, thereby lowering the death toll. …”
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Distinguishing Reality from AI: Approaches for Detecting Synthetic Content by David Ghiurău, Daniela Elena Popescu

“…Key detection approaches reviewed include stylometric analysis, watermarking, pixel prediction techniques, dual-stream networks, machine learning models, blockchain, and hybrid approaches, highlighting their strengths and limitations, as well as their detection accuracy, independent accuracy of 80% for stylometric analysis and up to 92% using multiple modalities in hybrid approaches. …”
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Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation by Yue Wan, Jialu Wu, Tingjun Hou, Chang-Yu Hsieh, Xiaowei Jia

“…However, the data scarcity, combined with the highly non-linear causal relationships between physicochemical and biological properties and conventional molecular featurization schemes, complicates the development of robust molecular machine learning models. Self-supervised learning (SSL) has emerged as a popular solution, utilizing large-scale, unannotated molecular data to learn a foundational representation of chemical space that might be advantageous for downstream tasks. …”
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Forecasting GICs and Geoelectric Fields From Solar Wind Data Using LSTMs: Application in Austria by R. L. Bailey, R. Leonhardt, C. Möstl, C. Beggan, M. A. Reiss, A. Bhaskar, A. J. Weiss

“…We approach this problem with machine learning tools, specifically recurrent neural networks or LSTMs by taking solar wind observations as input and training the models to predict two different kinds of output: first, the geoelectric field components Ex and Ey; and second, the GICs in specific substations in Austria. …”
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Hybrid deep learning approach for brain tumor classification using EfficientNetB0 and novel quantum genetic algorithm by Kerem Gencer, Gülcan Gencer

“…The use of artificial intelligence and machine learning, especially in the automatic classification of brain tumors, is increasing significantly. …”
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Dataset of polarimetric images of mechanically generated water surface waves coupled with surface elevation records by wave gauges linear arrayScienceDB by Noam Ginio, Michael Lindenbaum, Barak Fishbain, Dan Liberzon

“…To address these challenges a novel method was developed, using polarization filter equipped camera as the main sensor and Machine Learning (ML) algorithms for data processing [1,2]. …”
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A Novel MEGNet for Classification of High-Frequency Oscillations in Magnetoencephalography of Epileptic Patients by Jun Liu, Siqi Sun, Yang Liu, Jiayang Guo, Hailong Li, Yuan Gao, Jintao Sun, Jing Xiang

“…After optimized configuration, the accuracy, precision, recall, and F1-score of the proposed detector reached 94%, 95%, 94%, and 94%, which were better than other classical machine learning models. In addition, we used the k-fold cross-validation scheme to test the performance consistency of the model. …”
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Energy consumption prediction for households in a society with an ageing population by Yan Zou, Chen Wang, Hina Najam, Abdelmohsen A. Nassani, Gozal Djuraeva, David Oscar

“…This study addresses the gap in understanding high-frequency impacts of aging on energy use by employing advanced machine learning techniques. Using Gaussian Mixture Models (GMM) and Finite Mixture Models (FMM), we analyze high-frequency hourly energy consumption data from 14,000 households in Shanghai (2016–2023) to identify distinct consumption patterns and their relationship with household characteristics. …”
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Assessing Africa's child survival gains and prospects for attaining SDG target on child mortality. by Sunday A Adedini, Seun Stephen Anjorin, Jacob Wale Mobolaji, Elvis Anyaehiechukwu Okolie, Sanni Yaya

“…Results of the predictions using supervised machine learning on the Bayesian network reveal that the probability of achieving the SDG target 3.2 (i.e., having U5MR of 25 deaths per 1000 live births or less) increases (from 21.6% to 100%) when the contraceptive prevalence increases from 49.8% to 78.5%; and the use of skilled birth attendants increases from 44.8% to 86.3%; and percentage of secondary school completion of female increases from 42.5 to 74.0%. …”
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Advanced Fraud Detection: Leveraging K-SMOTEENN and Stacking Ensemble to Tackle Data Imbalance and Extract Insights by Nurafni Damanik, Chuan-Ming Liu

“…This study proposes an innovative solution for credit card fraud detection, utilizing a stacking ensemble of machine learning classifiers enhanced with sophisticated data resampling techniques. …”
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Revolutionizing Supply Chains: Unleashing the Power of AI-Driven Intelligent Automation and Real-Time Information Flow by Mohammad Shamsuddoha, Eijaz Ahmed Khan, Md Maruf Hossan Chowdhury, Tasnuba Nasir

“…This paper examines how AI, machine learning (ML), and robotic process automation (RPA) influence supply chain operations to adjust to the risks and vulnerabilities. …”
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A Novel Ionospheric Inversion Model: PINN‐SAMI3 (Physics Informed Neural Network Based on SAMI3) by Jiayu Ma, Haiyang Fu, J. D. Huba, Yaqiu Jin

“…The objective of this study is to investigate the feasibility of integrating physical models with machine learning for ionospheric modeling. The PINN‐SAMI3 framework enforces physical laws through the multiple ion species of continuity, momentum, temperature equations in the magnetic dipole coordinate system. …”
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