Emergency Department Blood Pressure Management in Type B Aortic Dissection: An Analysis with Machine Learning by Nelson Chen, Jessica V. Downing, Jacob Epstein, Samira Mudd, Angie Chan, Sneha Kuppireddy, Roya Tehrani, Isha Vashee, Emily Hart, Emily Esposito, Rose Chasm, Quincy K. Tran

“…We used random forest (RF) algorithms, a machine-learning tool that uses clusters of decision trees to predict a categorical outcome, to identify predictors of achieving HR and SBP goals prior to ED departure, defined as the time point at which patients left the referring ED to come to our institution. …”
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Implementation of Machine Vision Methods for Cattle Detection and Activity Monitoring by Roman Bumbálek, Tomáš Zoubek, Jean de Dieu Marcel Ufitikirezi, Sandra Nicole Umurungi, Radim Stehlík, Zbyněk Havelka, Radim Kuneš, Petr Bartoš

“…The goal of this research was to implement machine vision algorithms in a cattle stable to detect cattle in stalls and determine their activities. …”
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Use of a convolutional neural network for direct detection of acid-fast bacilli from clinical specimens by Paul English, Muir J. Morrison, Blaine Mathison, Elizabeth Enrico, Ryan Shean, Brendan O'Fallon, Deven Rupp, Katie Knight, Alexandra Rangel, Jeffrey Gilivary, Amanda Vance, Haleina Hatch, Leo Lin, David P. Ng, Salika M. Shakir

“…A machine learning computer vision model was trained using 11,411 annotated organisms across 109 WSI. Model predictions were correlated with final culture-confirmed results. …”
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Exploring the Global and Regional Factors Influencing the Density of <i>Trachurus japonicus</i> in the South China Sea by Mingshuai Sun, Yaquan Li, Zuozhi Chen, Youwei Xu, Yutao Yang, Yan Zhang, Yalan Peng, Haoda Zhou

“…Leveraging advanced machine learning algorithms and causal inference, our robust experimental design uncovered nine key global and regional factors affecting the distribution of <i>T. japonicus</i> density. …”
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Low-intensity behavioral activation smartphone app to support low mood: description, engagement and log data analysis by Paul Farrand, Patrick J. Raue, Earlise Ward, Sandra Healey, Jonathan Baker

“…For those completing the PHQ-8 and GAD-7 outcome measures, there was a significant reduction in PHQ-8 (t(48) = 5.2; p < 0.01) and GAD-7 (t(48) = 3.5; p < 0.01) scores, respectively, with reductions greater with higher baseline severity. …”
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Exploring the role of mitochondrial metabolism and immune infiltration in myocardial infarction: novel insights from bioinformatics and experimental validation by Jingyi Hou, Jingyi Hou, Jingyi Hou, Lihan Wang, Lihan Wang, Naiqiang Zhu, Xueli Li

“…A protein-protein interaction (PPI) network of mitoDEGs was constructed, and hub mitoDEGs associated with MI were identified using CytoHubba and molecular complex detection (MCODE) algorithms. Transcription factor (TF) and microRNA (miRNA) targets of hub mitoDEGs were predicted using iRegulon and miRWalk plug-ins, respectively, and a regulatory network involving TFs, hub mitoDEGs, and miRNA was established. …”
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A Large-Scale Inter-Comparison and Evaluation of Spatial Feature Engineering Strategies for Forest Aboveground Biomass Estimation Using Landsat Satellite Imagery by John B. Kilbride, Robert E. Kennedy

“…However, this improvement came at the cost of increased model prediction bias.…”
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PhA-MOE: Enhancing Hyperspectral Retrievals for Phytoplankton Absorption Using Mixture-of-Experts by Weiwei Wang, Bingqing Liu, Song Gao, Jiang Li, Yueling Zhou, Songyang Zhang, Zhi Ding

“…The proposed PhA-MOE for <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>a</mi><mrow><mi>p</mi><mi>h</mi><mi>y</mi></mrow></msub></semantics></math></inline-formula> prediction is tailored to both past and current hyperspectral missions, including EMIT and PACE. …”
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Unveiling lipoprotein subfractions signature in high-FNPO PCOS: implications for PCOM diagnosis and risk assessment using advanced machine learning models by Xueqi Yan, Ziyi Yang, Hui Zhao, Gengchen Feng, Shumin Li, Yimeng Li, Yu Sun, Jinlong Ma, Han Zhao, Xueying Gao, Shigang Zhao

“…Ten machine learning algorithms and recursive feature elimination with logistic regression were used to construct the effective model to predict PCOM based on the new guideline. …”
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Analysing Time Series of Land Subsidence in the West of Tehran Province (Shahriar Plain) and its Relation to Groundwater Discharge by InSAR Technique by Ramin Papi, ُSara Attarchi, Masoud Soleimani

“…The Small BAseline Subset (SBAS) algorithm was applied to analyze the time series of land surface displacement and to generate the mean displacement velocity map. …”
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Risk Factors for Gastrointestinal Bleeding in Patients With Acute Myocardial Infarction: Multicenter Retrospective Cohort Study by Yanqi Kou, Shicai Ye, Yuan Tian, Ke Yang, Ling Qin, Zhe Huang, Botao Luo, Yanping Ha, Liping Zhan, Ruyin Ye, Yujie Huang, Qing Zhang, Kun He, Mouji Liang, Jieming Zheng, Haoyuan Huang, Chunyi Wu, Lei Ge, Yuping Yang

“…Propensity score matching was adjusted for demographics, and the Boruta algorithm identified key predictors. A total of 7 ML algorithms—logistic regression, k-nearest neighbors, support vector machine, decision tree, random forest (RF), extreme gradient boosting, and neural networks—were trained using 10-fold cross-validation. …”
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Pharmacological potential of 6,11-dihydro[1,2,4]triazolo[4’,3’:1,6]-pyrido[3,4-b]-5-carboxylic acid and its esters by S. O. Fedotov, A. S. Hotsulia

“…The molecular docking method, based on computational algorithms, has been used to predict and study interactions, including the identification of potential binding sites, estimation of energy parameters and spatial configuration of molecules. …”
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Implementasi Algoritma Catboost Dan Shapley Additive Explanations (SHAP) Dalam Memprediksi Popularitas Game Indie Pada Platform Steam by Mohammad Teddy Syamkalla, Siti Khomsah, Yohani Setya Rafika Nur

“…The SHAP method reveals the influence of features on prediction results. The existence of steam trading cards category, RPG genre and compatibility on mac operating system will increase the popularity. …”
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Molecular subtype of recurrent implantation failure reveals distinct endometrial etiology of female infertility by Jing Yang, Lingtao Yang, Ying Zhou, Fengyang Cao, Hongkun Fang, Huan Ma, Jun Ren, Chunyu Huang, Lianghui Diao, Qiyuan Li, Qionghua Chen

“…A molecular classifier (MetaRIF) was developed using the optimal F-score from 64 combinations of machine learning algorithms. Candidate therapeutic compounds were predicted using the Connectivity Map (CMap) database. …”
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Editorial by Bulent Cavas

“…The eighth article, “Prediction of Middle School Students’ Recycling Behaviors with Machine Learning Algorithms,” by Mustafaoğlu and Alkan (Türkiye), applies machine learning techniques to identify factors predicting students&#39; recycling behavior. …”
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Advancing the deployment and information management of direct air capture: A solution enabled by integrating consortium blockchain system by Zihan Chen, Yiyu Liu, Eryu Wang, Huajie You, Qi Gao, Fan David Yeung, Jia Li

“…(Source: Investopedia) ECDSA encryption,22 Elliptic Curve Digital Signature Algorithm (ECDSA): In cryptography, the Elliptic Curve Digital Signature Algorithm (ECDSA) offers a variant of the Digital Signature Algorithm (DSA) which uses elliptic-curve cryptography. …”
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Fibrosis-4plus score: a novel machine learning-based tool for screening high-risk varices in compensated cirrhosis (CHESS2004): an international multicenter study by Bingtian Dong, Ruiling He, Shenghong Ju, Yuping Chen, Ivica Grgurevic, Jianzhong Ma, Ying Guo, Huizhen Fan, Qiang Yan, Chuan Liu, Huixiong Xu, Anita Madir, Kristian Podrug, Jia Wang, Linxue Qian, Zhengzi Geng, Shanghao Liu, Tao Ren, Guo Zhang, Kun Wang, Meiqin Su, Fei Chen, Sumei Ma, Liting Zhang, Zhaowei Tong, Yonghe Zhou, Xin Li, Fanbin He, Hui Huan, Wenjuan Wang, Yunxiao Liang, Juan Tang, Fang Ai, Tingyu Wang, Liyun Zheng, Zhongwei Zhao, Jiansong Ji, Wei Liu, Jiaojiao Xu, Bo Liu, Xuemei Wang, Yao Zhang, Qiong Yan, Hui Liu, Xiaomei Chen, Shuhua Zhang, Yihua Wang, Yang Liu, Li Yin, Yanni Liu, Yanqing Huang, Li Bian, Ping An, Xin Zhang, Shaoting Zhang, Jinhua Shao, Xiangman Zhang, Wei Rao, Chaoxue Zhang, Christoph Frank Dietrich, Won Kim, Xiaolong Qi

“…Shapley Additive exPlanations method was used to interpret the model predictions. Results We analyzed data from 502 patients with compensated cirrhosis who underwent EGD screening. …”
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Explainable artificial intelligence visions on incident duration using eXtreme Gradient Boosting and SHapley Additive exPlanations by Khaled Hamad, Emran Alotaibi, Waleed Zeiada, Ghazi Al-Khateeb, Saleh Abu Dabous, Maher Omar, Bharadwaj R.K. Mantha, Mohamed G. Arab, Tarek Merabtene

“…This study introduces an application of Explainable Artificial Intelligence (XAI) using eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) to analyze the complexities of traffic incident duration prediction. Utilizing a substantial dataset of over 366,000 records from the Houston traffic management center, the study innovates in the domain of traffic analytics by predicting incident durations and revealing the contribution of each predictive variable. …”
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Transdimensional Inference for Gravitational-wave Astronomy with Bilby by Hui Tong, Nir Guttman, Teagan A. Clarke, Paul D. Lasky, Eric Thrane, Ethan Payne, Rowina Nathan, Ben Farr, Maya Fishbach, Gregory Ashton, Valentina Di Marco

“…The tBilby package allows users to set up transdimensional inference calculations using the existing Bilby architecture with off-the-shelf nested samplers and/or Markov Chain Monte Carlo algorithms. Transdimensional models are particularly helpful when seeking to test theoretically uncertain predictions described by phenomenological models. …”
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Improving the Incident Management Process Based on a Use Case Approach by A. A. Mikryukov, A. V. Kuular

“…The analysis of statistical data is carried out; the efficiency is estimated as a result of the application of the algorithm based on the use case analysis. The assessment showed a significant reduction in unnecessary escalations of incidents to the second support line, so the application of the base of use cases in resolving incidents allowed for improving the incident management process.Conclusion. …”
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