Biodiversity characteristics of large forest plots in Qinghai area of Qilian Mountain National Park by WANG Dinghui, SUONAN Cairang, YU Hongyan, DU Yangong

“…The coefficients of determination for the training and testing sets of the machine learning model were 0.95 and 0.93, respectively, with root mean square errors of only 0.06 and 0.08. …”
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Merging metabolic modeling and imaging for screening therapeutic targets in colorectal cancer by Niki Tavakoli, Emma J. Fong, Abigail Coleman, Yu-Kai Huang, Mathias Bigger, Michael E. Doche, Seungil Kim, Heinz-Josef Lenz, Nicholas A. Graham, Paul Macklin, Stacey D. Finley, Shannon M. Mumenthaler

“…This process involved a novel machine learning-based method to computationally screen, in a high-throughput manner, the effects of enzyme perturbations predicted by a computational model of CRC metabolism. …”
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Prediction and influence factors analysis of IP backbone network traffic based on Prophet model and variance reduction by Xuan Wei

“…Despite the rise of popular machine learning models, traditional statistical models maintain significant advantages in interpretability, controllability and simplicity, retaining an essential role in contemporary communication network traffic prediction. …”
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Facilitating or Hindering? The Impact of Low-Carbon Pilot Policies on Socio-Ecological Resilience in Resource-Based Cities by Yanran Peng, Zhong Wang, Yunhui Zhang, Wei Wang

“…Focusing on a panel of 114 resource-based cities in China, spanning from 2003 to 2022, this study employs a range of methodologies, including kernel density estimation, the Difference-in-Differences Model, Spatial Difference-in-Differences, Mediation Analysis, K-means Clustering, and Dual Machine Learning to assess the consequences of low-carbon pilot policies on socio-ecological resilience. …”
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Enhancing breast cancer prediction through stacking ensemble and deep learning integration by Fatih Gurcan

“…In addressing this challenge, the importance of machine learning and deep learning technologies is increasingly recognized. …”
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Smart Filter Performance Monitoring System by Chenxing Pei, Weiqi Chen, Qisheng Ou, David Y. H. Pui

“…Moreover, filter monitoring data can establish a database for researchers to validate the filter models or train a machine learning model for filter performance prediction. …”
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Prediction of traditional Chinese medicine for diabetes based on the multi-source ensemble method by Bin Yang, Qingyun Chi, Xiang Li, Jinglong Wang

“…IntroductionTraditional Chinese medicine (TCM) prescriptions are generally formulated by experienced TCM researchers based on their expertise and data statistical methods.MethodsIn order to predict TCM formulas for diabetes more accurately, this paper proposes a novel multi-source ensemble prediction method that combines machine learning ensemble techniques and multi-source data. …”
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Competitiveness of Russian Universities in the Global System of Higher Education: Quantitative Analysis by D. A. Endovitsky, V. V. Korotkikh, M. V. Voronova

“…Parametric and non-parametric methods for data analysis and machine learning.Results. The authors figured out the hidden determinants of international competitiveness of Russian universities and the national educational system of the Russian Federation. …”
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Properties of the new N $$ \mathcal{N} $$ = 1 AdS4 vacuum of maximal supergravity by Nikolay Bobev, Thomas Fischbacher, Krzysztof Pilch

“…Abstract The recent comprehensive numerical study of critical points of the scalar potential of four-dimensional N $$ \mathcal{N} $$ = 8, SO(8) gauged supergravity using Machine Learning software in [1] has led to a discovery of a new N $$ \mathcal{N} $$ = 1 vacuum with a triality-invariant SO(3) symmetry. …”
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Twitter Sentiment Analysis Towards Candidates of the 2024 Indonesian Presidential Election by Rhoma Cahyanti, Desiana Nurul Maftuhah, Aris Budi Santoso, Indra Budi

“…To predict the sentiment, four different machine-learning methods were used and compared to each other. …”
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Convolutional Neural Decoder for Surface Codes by Hyunwoo Jung, Inayat Ali, Jeongseok Ha

“…Recently, various decoding algorithms based on machine learning have been proposed to improve the decoding performance and latency of QEC codes. …”
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An estimation method of sound speed profile based on grouped dilated convolution informer model by Siyuan Qin, Yi Zhang, Zhou Chen

“…Traditional methods for acquiring SSP data are often time-consuming and costly. Machine learning techniques provide a more efficient alternative for SSP inversion, effectively addressing the limitations of conventional approaches.MethodsThis study proposes a novel SSP inversion model based on a grouped dilated convolution (GDC) Informer architecture. …”
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Recent Web Platforms for Multi-Omics Integration Unlocking Biological Complexity by Eugenia Papadaki, Ioannis Kakkos, Panagiotis Vlamos, Ourania Petropoulou, Stavros T. Miloulis, Stergios Palamas, Aristidis G. Vrahatis

“…The review also discusses the challenges these platforms face, such as data complexity, standardization issues, and the need for improved machine learning and AI-based analytics. Finally, the paper proposes directions for future research and development, emphasizing the importance of more advanced, user-friendly, and secure platforms that can better serve comprehensive healthcare needs.…”
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Winter Wheat Yield Prediction Using Satellite Remote Sensing Data and Deep Learning Models by Hongkun Fu, Jian Lu, Jian Li, Wenlong Zou, Xuhui Tang, Xiangyu Ning, Yue Sun

“…The results indicate that the IGWO-CNN model outperforms traditional machine learning approaches and standalone CNN models in terms of prediction accuracy, achieving the highest performance with an R² of 0.7587, an RMSE of 593.6 kg/ha, an MAE of 486.5577 kg/ha, and an MAPE of 11.39%. …”
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The Prediction Method and Application of Off-Road Mobility for Ground Vehicles: A Review by Chen Hua, Wencheng Zhang, Hanghao Fu, Yuhao Zhang, Biao Yu, Chunmao Jiang, Yuliang Wei, Ziyu Chen, Xinkai Kuang

“…We begin by discussing the concept of vehicle mobility, followed by a systematic and thorough summary of the primary prediction methods, including empirical, semi-empirical, numerical simulation, and machine learning approaches. The strengths and weaknesses of these methods are compared and analyzed in detail. …”
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A Review of Reinforcement Learning for Fixed-Wing Aircraft Control Tasks by David J. Richter, Ricardo A. Calix, Kyungbaek Kim

“…A lot of that can be attributed to the recent advancements in machine learning (ML) and deep learning (DL) as a whole, the power of deep neural networks and the incorporation of them into reinforcement learning algorithms and techniques. …”
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Current development and future prospects of multi-target assignment problem: A bibliometric analysis review by Shuangxi Liu, Zehuai Lin, Wei Huang, Binbin Yan

“…Subsequently, existing solution algorithms for the MTA problem are reviewed, generally falling into three categories: exact algorithms, heuristic algorithms, and machine learning algorithms. Finally, a development framework is proposed based on the ''HIGH'' model (high-speed, integrated, great, harmonious) to guide future research and intelligent weapon system development concerning the MTA problem. …”
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Progress on responsive neurostimulation in treatment of drug⁃resistant epilepsy by FAN Xiu⁃liang, BAI Yu⁃tong, YANG An⁃chao, ZHANG Kai

“…This paper aims to review the composition of the RNS, optimization and dyanmic adjustment of stimulation parameters, long⁃term data recording and analysis, fusion with machine learning, clinical efficacy, and comparison with vagus nerve stimulation (VNS). …”
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RETRACTED ARTICLE: Screening and identification of susceptibility genes for cervical cancer via bioinformatics analysis and the construction of an mitophagy-related genes diagnosti... by Zhang Zhang, Fangfang Chen, Xiaoxiao Deng

“…Furthermore, using machine learning algorithms, we constructed a clinical prognostic model and validated and optimized it via extensive clinical data. …”
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Neural Density Functional Theory of Liquid-Gas Phase Coexistence by Florian Sammüller, Matthias Schmidt, Robert Evans

“…We use supervised machine learning together with the concepts of classical density functional theory to investigate the effects of interparticle attraction on the pair structure, thermodynamics, bulk liquid-gas coexistence, and associated interfacial phenomena in many-body systems. …”
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