Research of application of DFT-modulated filter bank in systems with significant spectral component amplification by N. S. Sanko, M. I. Vashkevich

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Evaluation of liver fibrosis in patients with metabolic dysfunction-associated steatotic liver disease using ultrasound controlled attenuation parameter combined with clinical feat... by LIU Chunyu, TANG Jingkuan, ZHAO Wei

“…Features were selected using the Boruta algorithm, and a predictive model combining CAP and clinical features was constructed. …”
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ScRNA-seq reveals novel immune-suppressive T cells and investigates CMV-TCR-T cells cytotoxicity against GBM by Wei Gao, Qing Liu, Xiang Lin, Yiming Zhao, Minghua Wu, Shuai Chen, Peng Du, Meng Huang, Cheng Huang, Xiangyu Wang, Xinmiao Long, Zuping Zhang, Yuzhe Li, Kun Deng, Xiaoling She, Minfu Zhang, Shiyi Wang, Yinfei Du

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LimeSoDa: A dataset collection for benchmarking of machine learning regressors in digital soil mapping by Jonas Schmidinger, Sebastian Vogel, Viacheslav Barkov, Anh-Duy Pham, Robin Gebbers, Hamed Tavakoli, Jose Correa, Tiago R. Tavares, Patrick Filippi, Edward J. Jones, Vojtech Lukas, Eric Boenecke, Joerg Ruehlmann, Ingmar Schroeter, Eckart Kramer, Stefan Paetzold, Masakazu Kodaira, Alexandre M.J.-C. Wadoux, Luca Bragazza, Konrad Metzger, Jingyi Huang, Domingos S.M. Valente, Jose L. Safanelli, Eduardo L. Bottega, Ricardo S.D. Dalmolin, Csilla Farkas, Alexander Steiger, Taciara Z. Horst, Leonardo Ramirez-Lopez, Thomas Scholten, Felix Stumpf, Pablo Rosso, Marcelo M. Costa, Rodrigo S. Zandonadi, Johanna Wetterlind, Martin Atzmueller

“…We demonstrated the use of LimeSoDa for benchmarking by comparing the predictive performance of four learning algorithms across all datasets. …”
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Long-term prognosis of 47 pediatric patients with Blau syndrome in China by Xinwei Shi, Jianghong Deng, Junmei Zhang, Xiaozhen Zhao, Yinan Zhao, Li Li, Fengqiao Gao, Weiying Kuang, Jiang Wang, Xiaohua Tan, Chao Li, Shipeng Li, Caifeng Li

“…A Bayesian network was constructed to integrate prediction algorithms of genetic mutations and clinical manifestations, exploring the complex relationship between genotype and phenotype through R (Version 4.4.1, R Core Development Team). …”
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Detecting Botrytis Cinerea Control Efficacy via Deep Learning by Wenlong Yi, Xunsheng Zhang, Shiming Dai, Sergey Kuzmin, Igor Gerasimov, Xiangping Cheng

“…The innovations include (1) combining channel attention mechanism, multi-head self-attention mechanism, and multi-scale feature extractor to improve prediction accuracy and (2) introducing the Shapley value algorithm to achieve a precise quantitative analysis of environmental variables’ contribution to colony growth. …”
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Learning atomic forces from uncertainty-calibrated adversarial attacks by Henrique Musseli Cezar, Tilmann Bodenstein, Henrik Andersen Sveinsson, Morten Ledum, Simen Reine, Sigbjørn Løland Bore

“…While already providing great practical value, little is known about the actual prediction errors of MLIPs on adversarial structures and whether these errors can be controlled. …”
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PATH CONTROL AIRCRAFT OF TRAJECTORY UNDER THE INFLUENCE OF DISTURBANCES ON AN DUAL CHANNEL by S. A. Kabanov, F. V. Mitin

“…The problem is solved using the algorithm with the predictive model.…”
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Using Time Clusters for Following Users’ Shifts in Rating Practices by Dionisis Margaris, Costas Vassilakis

“…In that sense, the practice of using a single mean value for adjusting users’ ratings is inadequate, since it fails to follow such shifts in users’ rating practices, leading to decreased rating prediction accuracy. In this work, we address this issue by using the concept of dynamic averages introduced earlier and we extend earlier work by (1) introducing the concept of rating time clusters and (2) presenting a novel algorithm for calculating dynamic user averages and exploiting them in user-user collaborative, filtering implementations. …”
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Detection of Aflatoxin B1 in Maize Silage Based on Hyperspectral Imaging Technology by Lina Guo, Haiqing Tian, Daqian Wan, Yang Yu, Kai Zhao, Xinglu Zheng, Haijun Li, Jianying Sun

“…Among the models, SVR_SD_Mixup_PCA achieved the best performance, with an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mrow><mi mathvariant="normal">R</mi></mrow><mrow><mi mathvariant="normal">p</mi></mrow><mrow><mn>2</mn></mrow></msubsup></mrow></semantics></math></inline-formula> of 0.9458, RMSEP of 3.1259 μg/kg, and RPD of 4.2969, indicating high prediction accuracy and generalization capability. This study fills the gap of hyperspectral image technology fused with artificial intelligence algorithm in the application of quantitative detection of AFB1 content in maize silage and provides a new technical method and theoretical basis for nondestructive testing of corn silage feed.…”
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Risk Limiting Based Resilient Operation Method for Power Grid by Xiyue WANG, Hao ZHANG, Chaoyi PENG

“…Finally, based on the IEEE 6-node system and an actual 25981-node regional grid examples, we analyze the relationship between system flexibility, prediction accuracy, and system resilience, and it is concluded that improving the operational resilience of the new power system is premised on sufficient system flexibility and prediction accuracy.…”
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LogProb: Online Parsing Evolving Logs With Complex Parameters by Yan Li, Ziying Wang, Jiaming Mao, Zhimin Gu, Haitao Jiang

“…The extraction process includes two stages: identifying candidate templates with predicted static tokens, and using a fast matching algorithm to determine the final template. …”
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AIoT Monitoring for Early Identification of Diseases in Grapevines: Complete Study by Mihaela Hnatiuc, Domnica Alpetri, Sorin-Robertino Sintea, Bogdan Hnatiuc, Gabriel Margarit Raicu, Mirel Paun, Ionica Dina

“…Machine learning (ML) algorithms, running on a server with an NVIDIA R3900 card, process this data to predict potential infections caused by pathogens such as Plasmopara viticola, Uncinula necator, and Botrytis. …”
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The role and prognostic value of PANoptosis-related genes in skin cutaneous melanoma by Huijing Feng, Linzi Jia, Yanan Ma, Pengmin Liu, Xiaoling Yang, Lina Hu, Kai Xu, Fan Yang, Dongfeng Zhang, Jian Li, Qi Mei, Fei Han

“…The risk model and nomogram showed excellent predictive abilities for SKCM patients. Genes in both high - and low - risk groups were linked to cytokine - regulated immune responses, with nine differential immune cells identified between the groups. …”
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Real‐Time Coseismic Slip Estimation via the GNSS Carrier Phase to Fault Slip Approach: A Case Study of the 2016 Kumamoto Earthquake by Yusuke Tanaka, Yusaku Ohta, Shin'ichi Miyazaki

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Genetic variants and molecular profiling of 46,XY gonadal dysgenesis using whole-exome sequencing by Ning Zhang, Ning Zhang, Ning Zhang, Shuoming You, Shuoming You, Jingjing Guo, Jingjing Guo, Xingyu Chang, Xingyu Chang, Junjun Qiu, Junjun Qiu, Keqin Hua, Keqin Hua

“…These variant sites are conserved among species and were predicted to be damaging according to functional algorithms and protein analyses. …”
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Use of ICT to Confront COVID-19 by Yousry Saber El Gamal

“…Risk prediction is one of the important AI applications during the Pandemic. …”
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Estimation of Biophysical Parameters of Forage Cactus Under Different Agricultural Systems Through Vegetation Indices and Machine Learning Using RGB Images Acquired with Unmanned A... by Gabriel Italo Novaes da Silva, Alexandre Maniçoba da Rosa Ferraz Jardim, Wagner Martins dos Santos, Alan Cézar Bezerra, Elisiane Alba, Marcos Vinícius da Silva, Jhon Lennon Bezerra da Silva, Luciana Sandra Bastos de Souza, Gabriel Thales Barboza Marinho, Abelardo Antônio de Assunção Montenegro, Thieres George Freire da Silva

“…The RGBVI and E<i>x</i>GR indices stood out for presenting greater correlations with FM and DM. The prediction analysis using the Random Forest algorithm, highlighting DM, which presented a mean absolute error of 1.39, 0.99, and 1.72 Mg ha⁻¹ in experimental units I and II, III, and IV, respectively. …”
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Research on Soft-Sensing Method Based on Adam-FCNN Inversion in <i>Pichia pastoris</i> Fermentation by Bo Wang, Wenyu Ma, Hui Jiang, Shaowen Huang

“…Finally, a composite pseudo-linear system is formed by cascading the inverse model with the original system, achieving decoupling and the high-accuracy prediction of key parameters. Experimental results demonstrate that the proposed method significantly reduces prediction errors and enhances generalization capabilities compared to traditional models, validating the effectiveness of the proposed method in complex bioprocesses.…”
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From Tweets to Trades: A Bibliometric and Systematic Review of Social Media’s Influence on Cryptocurrency by Sheela Sundarasen, Farida Saleem

“…This study finds that social media sentiment plays a crucial role in cryptocurrency price forecasting, with machine learning and natural language processing (NLP) techniques enhancing prediction accuracy. Thematic analysis reveals four primary areas of focus: sentiment analysis and market prediction, machine learning-driven algorithmic trading, blockchain investment risks, and influencer-driven market behavior. …”
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