Machine learning technique-based four-autoantibody test for early detection of esophageal squamous cell carcinoma: a multicenter, retrospective study with a nested case–control stu... by Yi-Wei Xu, Yu-Hui Peng, Can-Tong Liu, Hao Chen, Ling-Yu Chu, Hai-Lu Chen, Zhi-Yong Wu, Wen-Qiang Wei, Li-Yan Xu, Fang-Cai Wu, En-Min Li

“…Then, we validated the ability of plsRglm model in predicting preclinical ESCC by a nested case–control study (24 preclinical ESCCs and 112 matched controls) within a population-based prospective cohort study. …”
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Large Language Models and Artificial Neural Networks for Assessing 1-Year Mortality in Patients With Myocardial Infarction: Analysis From the Medical Information Mart for Intensive... by Boqun Shi, Liangguo Chen, Shuo Pang, Yue Wang, Shen Wang, Fadong Li, Wenxin Zhao, Pengrong Guo, Leli Zhang, Chu Fan, Yi Zou, Xiaofan Wu

“…An artificial neural network (ANN) algorithm derived from the SWEDEHEART (Swedish Web System for Enhancement and Development of Evidence-Based Care in Heart Disease Evaluated According to Recommended Therapies) registry, termed SWEDEHEART-AI, can predict patient prognosis following acute myocardial infarction (AMI). …”
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A Fault Analysis Method for Three-Phase Induction Motors Based on Spiking Neural P Systems by Zhu Huang, Tao Wang, Wei Liu, Luis Valencia-Cabrera, Mario J. Pérez-Jiménez, Pengpeng Li

“…Moreover, to realize the parallel data computing and information reasoning in the fault prediction and diagnosis process, three reasoning algorithms for the rMFRSNPS are proposed: the pulse value reasoning algorithm, the forward fault prediction reasoning algorithm, and the backward abductive fault diagnosis reasoning algorithm. …”
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How Gait Nonlinearities in Individuals Without Known Pathology Describe Metabolic Cost During Walking Using Artificial Neural Network and Multiple Linear Regression by Arash Mohammadzadeh Gonabadi, Farahnaz Fallahtafti, Judith M. Burnfield

“…Six nonlinear metrics—Lyapunov Exponents based on Rosenstein’s algorithm (LyER), Detrended Fluctuation Analysis (DFA), the Approximate Entropy (ApEn), the correlation dimension (CD), the Sample Entropy (SpEn), and Lyapunov Exponents based on Wolf’s algorithm (LyEW)—were utilized to predict the metabolic cost during walking. …”
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Glucagon-like peptide-1 receptor agonist use prior to spinal surgery results in reduced postoperative length of stay: A propensity-score matched analysis by Samuel N. Goldman, BS, Kyle Mani, BE, Thomas Scharfenberger, BS, Emily Kleinbart, BA, Aaron T. Hui, BS, Rafael De la Garza Ramos, MD, Mitchell S. Fourman, MD, MPhil, Ananth S. Eleswarapu, MD

“…GLP-1 RA use was associated with a significant reduction in median postoperative LOS (3 days vs. 4 days; p = 0.036), particularly among patients undergoing lumbar fusion. …”
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Methods to Quantitatively Evaluate the Effect of Shale Gas Fracturing Stimulation Based on Least Squares by DENG Cai, SUN Kexin, WEN Huan, HU Chaolang

“…Advanced optimization algorithms were employed to efficiently address both the fitting and calculation tasks. …”
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POSSIBILITIES OF CARIES PROGNOSIS IN CHILDREN OF SCHOOL-AGE ACCORDING TO DATA GAINED FROM THEM AND THEIR PARENTS QUESTIONNAIRE by L.F. Kaskova, T.B. Mandziuk, L.P. Ulasevych, L.D. Korovina, M.A. Sadovski

“…Therefore, the purpose of our study was to identify the possibility of predicting caries in preschool children according to questionnaires of surveyed children and their parents. …”
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Hybrid PI and Fuzzy Logic Control for Energy Optimization in Train Operations by Hwan-Hee Cho, Jae-Won Kim, Min-Sup Song, Chi-Myeong Yun, Gyu-Jung Cho, Zhongbei Tian

“…This paper presents a Fuzzy logic-based train control algorithm designed to enhance energy efficiency across a complete railway route. …”
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Modeling of settlement of shallow-founded rocking structures using explainable physics-guided machine learning by Sivapalan Gajan, Christopher Kantor

“…SHAP analysis reveals that the PGML model predictions and their dependency on input features are consistent with the existing domain knowledge, and that the inclusion of physics in PGML models help improve the prediction accuracy, especially in cases where other input features fail to capture the combined complex interaction among the variables involved.…”
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A Comparative study of the Envisaged and Definite Stock Prices of BSE SMEs Using RNN during the COVID-19 Pandemic by S. Kaur, A. Munde, A. K. Goyal

“…The stock market is unstable, but the use of machine learning algorithms allows to predict its future dynamics before spending. …”
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Development of standard fuel models in boreal forests of Northeast China through calibration and validation. by Longyan Cai, Hong S He, Zhiwei Wu, Benard L Lewis, Yu Liang

“…Understanding the fire prediction capabilities of fuel models is vital to forest fire management. …”
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Resolving the conformational ensemble of a membrane protein by integrating small-angle scattering with AlphaFold. by Samuel Eriksson Lidbrink, Rebecca J Howard, Nandan Haloi, Erik Lindahl

“…Several strategies have recently been developed to drive the machine learning algorithm AlphaFold2 (AF) to sample multiple conformations, but it is more challenging to a priori predict what states are stabilized in particular conditions and how the transition occurs. …”
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Explainable post hoc portfolio management financial policy of a Deep Reinforcement Learning agent. by Alejandra de-la-Rica-Escudero, Eduardo C Garrido-Merchán, María Coronado-Vaca

“…In this work, driven by the motivation of making DRL explainable, we developed a novel Explainable DRL (XDRL) approach for PM, integrating the Proximal Policy Optimization (PPO) DRL algorithm with the model agnostic explainable machine learning techniques of feature importance, SHAP and LIME to enhance transparency in prediction time. …”
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Clinical interpretation of variants identified in RNU4ATAC, a non-coding spliceosomal gene. by Clara Benoit-Pilven, Alicia Besson, Audrey Putoux, Claire Benetollo, Clément Saccaro, Justine Guguin, Gabriel Sala, Audric Cologne, Marion Delous, Gaetan Lesca, Richard A Padgett, Anne-Louise Leutenegger, Vincent Lacroix, Patrick Edery, Sylvie Mazoyer

“…As RNU4ATAC has a single non-coding exon, the bioinformatic prediction algorithms assessing the effect of sequence variants on splicing or protein function are irrelevant, which makes variant interpretation challenging to molecular diagnostic laboratories. …”
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Ship path planning and tracking based on rapidly exploring random tree star and convex optimization by Chang ZHOU, Te YU, Jiapeng LIU, Dihua LU, Qingshan ZENG

“…Finally, model predictive control (MPC) algorithm is employed to generate ship control output sequences, enabling safe and efficient navigation around obstacles from the starting point to the destination. …”
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Anomaly Detection and Radio-frequency Interference Classification with Unsupervised Learning in Narrowband Radio Technosignature Searches by Ben Jacobson-Bell, Steve Croft, Carmen Choza, Alex Andersson, Daniel Bautista, Vishal Gajjar, Matthew Lebofsky, David H. E. MacMahon, Caleb Painter, Andrew P. V. Siemion

“…Choza et al. turboSETI -only search of 97 nearby galaxies at the L band, demonstrating a false-positive hit reduction rate of 93.1% and a false-positive event reduction rate of 99.3%.…”
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Congestion forecast framework based on probabilistic power flow and machine learning for smart distribution grids by Alejandro Hernandez-Matheus, Kjersti Berg, Vinicius Gadelha, Mònica Aragüés-Peñalba, Eduard Bullich-Massagué, Samuel Galceran-Arellano

“…This work proposes a framework to predict grid asset congestions on a daily basis. A congestion forecast framework is proposed by combining probabilistic power flows and machine learning algorithms to support DSOs in their daily decision-making. …”
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Combined Allocation of Renewable-Based Distributed Generators and Shunt Capacitor Banks in Distribution Networks with Electric Vehicle Load Penetration by Abhilasha Pawar, Rajkumar Viral, Divya Asija, Mohit Bansal

“…Compared to other optimization algorithms, the APO shows superior performance. …”
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A Computationally Efficient Learning-Based Control of a Three-Phase AC/DC Converter for DC Microgrids by Ran Li, Wendong Feng, Tianhao Qie, Yulin Liu, Tyrone Fernando, Herbert HoChing Iu, Xinan Zhang

“…This paper presents a novel learning-based control algorithm for three-phase AC/DC converters, which are key components in DC microgrids, for reliable power conversion. …”
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DEVELOPMENT METHODS FOR CALCULATING POWER LOSSES AND THE VOLTAGE LEVELS IN COMPLEX DISTRIBUTION NETWORKS by I. M. Valeev, Ha. D. Nguyen

“…The paper presents an analysis and evaluation of the effectiveness different methods for reduction the power loss and voltage in the distribution networks by changing and building a new network topology using the software PSS/ADEAPT. …”
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