SSVEP Enhancement in Mixed Reality Environment for Brain–Computer Interfaces by Jieyu Wu, Feng He, Xiaolin Xiao, Runyuan Gao, Lin Meng, Xiuyun Liu, Minpeng Xu, Dong Ming

“…The proposed SA-xTRCA significantly outperformed the other four traditional algorithms. The online average information transfer rate (ITR) achieved 57.58 ± 5.31 bits/min. …”
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Children with autoimmune hepatitis receiving standard-of-care therapy demonstrate long-term obesity and linear growth delay by Or Steg Saban, Shannon M. Vandriel, Syeda Aiman Fatima, Celine Bourdon, Amrita Mundh, Vicky L. Ng, Simon C. Ling, Robert H.J. Bandsma, Binita M. Kamath

“…These data indicate the need to re-evaluate standard treatment algorithms for pediatric AIH in terms of steroid dosing and potential nonsteroid alternatives.…”
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The association of origin and environmental conditions with performance in professional IRONMAN triathletes by Beat Knechtle, Mabliny Thuany, David Valero, Elias Villiger, Pantelis T. Nikolaidis, Marilia S. Andrade, Ivan Cuk, Thomas Rosemann, Katja Weiss

“…Three different ML models were built and evaluated, based on three algorithms, in order of growing complexity and predictive power: Decision Tree Regressor, Random Forest Regressor, and XG Boost Regressor. …”
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Implementasi Algoritme Morus V2 untuk Pengamanan Data Pada Perangkat Bluetooth Low Energy by Diah Ratih Destyorini, Ari Kusyanti, Reza Andria Siregar

“…The speed of this algorithm can reach 0.69 CPB, faster than other algorithms. …”
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Low-cost phone-based LiDAR scanning technology provides sub-centimeter accuracy when measuring the main dimensions of motor-manual tree felling cuts by Stelian Alexandru Borz, Andrea Rosario Proto

“…Short-range LiDAR technology integrated in affordable mobile platforms has already been proved to produce reliable estimates on objects located in a limited space, and point cloud processing algorithms have been developed to compare two instances of the same object, potentially enabling the quantification of tree-level wood loss. …”
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Relationship between stress hyperglycemia ratio and progression of non target coronary lesions: a retrospective cohort study by Shiqi Liu, Ziyang Wu, Gaoliang Yan, Yong Qiao, Yuhan Qin, Dong Wang, Chengchun Tang

“…Logistic regression models, restricted cubic spline analysis, and machine learning algorithms (LightGBM, decision tree, and XGBoost) were utilized to analyse the relationship of stress hyperglycemia ratio and non target lesion progression. …”
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Automating airborne pollen classification: Identifying and interpreting hard samples for classifiers by Manuel Milling, Simon D.N. Rampp, Andreas Triantafyllopoulos, Maria P. Plaza, Jens O. Brunner, Claudia Traidl-Hoffmann, Björn W. Schuller, Athanasios Damialis

“…To shed some light on this issue, we conducted a sample-level difficulty analysis based on the likelihood for one of the largest automatically-generated datasets of pollen grains on microscopy images and investigated the reason for which certain airborne samples and specific pollen taxa pose particular problems to deep learning algorithms. It is here concluded that the main challenges lie in A) the (partly) co-occurring of multiple pollen grains in a single image, B) the occlusion of specific markers through the 2D capturing of microscopy images, and C) for some taxa, a general lack of salient, unique features. …”
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ϵ-Confidence Approximately Correct (ϵ-CoAC) Learnability and Hyperparameter Selection in Linear Regression Modeling by Soosan Beheshti, Mahdi Shamsi

“…Linear regression modeling is an important category of learning algorithms. The practical uncertainty of the label samples in the training data set has a major effect in the generalization ability of the learned model. …”
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Improvement of organizational and methodological issues of analytical control of natural gas quality in Testing laboratory by A. F. Battalov, R. T. Saetova, Ya. V. Denisova

“…This article proposes a method for calculating the frequency of gas sampling using automated algorithms taking into account data on changes in the composition and properties of gas streams for the reporting period. …”
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Machine learning prediction of obesity-associated gut microbiota: identifying Bifidobacterium pseudocatenulatum as a potential therapeutic target by Hao Wu, Yuan Li, Yuxuan Jiang, Xinran Li, Shenglan Wang, Changle Zhao, Changle Zhao, Ximiao Yang, Baocheng Chang, Juhong Yang, Juhong Yang, Jianjun Qiao, Jianjun Qiao

“…Utilizing machine learning (ML) algorithms to predict obesity-associated gut microbiota and validating their efficacy with specific bacterial strains could significantly enhance obesity management strategies.MethodsWe leveraged gut microbiome data from 1,563 healthy individuals and 2,043 overweight patients sourced from the GMrepo database. …”
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RETRACTED ARTICLE: A prospective diagnostic model for breast cancer utilizing machine learning to examine the molecular immune infiltrate in HSPB6 by Lizhe Wang, Yu Wang, Yueyang Li, Li Zhou, Sihan Liu, Yongyi Cao, Yuzhi Li, Shenting Liu, Jiahui Du, Jin Wang, Ting Zhu

“…Methods The toolkit analyses involve techniques such as differential gene expression analysis, Gene Set Enrichment Analysis (GSEA), Weighted Co-Expression Network Analysis (WGCNA), and Machine Learning algorithms. Furthermore, in vitro cell experiments have demonstrated the impact of HSPB6 on cell migration, proliferation, and apoptosis. …”
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Oura Ring as a Tool for Ovulation Detection: Validation Analysis by Nina Thigpen, Shyamal Patel, Xi Zhang

“…In each subgroup, we compared the algorithm’s performance with the traditional calendar method, which estimates the ovulation date based on an individual’s last period start date and average menstrual cycle length. …”
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Direct targeting of risk factors significantly increases the detection of liver cirrhosis in primary care: a cross-sectional diagnostic study utilising transient elastography by Guruprasad P Aithal, Indra Neil Guha, Stephen D Ryder, Emilie A Wilkes, David J Harman, Martin W James, Matthew Jelpke, Dominic S Ottey, Timothy R Card

“…Importantly, 71/98 (72.4%) patients with elevated liver stiffness had normal liver enzymes and would be missed by traditional investigation algorithms. We identified 11 new patients with definite cirrhosis, representing a 140% increase in the number of diagnosed cases in this population.Conclusions A non-invasive liver investigation algorithm based in a community setting is feasible to implement. …”
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Experimental study and model prediction of the influence of different factors on the mechanical properties of saline clay by Hui Cheng, Lingkai Zhang, Chong Shi, Pei Pei Fan

“…The boundary point of the 2% salt content divides the effect of salt ions from promoting free water flow to blocking seepage channels, with the proportion of micropores being the primary influencing factor. (4) Employing statistical theory and machine learning algorithms, dry density, water content, and salinity are used to predict mechanical index values. …”
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Disproportionality analysis of upadacitinib-related adverse events in inflammatory bowel disease using the FDA adverse event reporting system by Shiyi Wang, Xiaojian Wang, Jing Ding, Xudong Zhang, Hongmei Zhu, Yihong Fan, Changbo Sun

“…This study evaluates upadacitinib-related adverse events (AEs) utilizing data from the US Food and Drug Administration Adverse Event Reporting System (FAERS).MethodsWe employed disproportionality analyses, including the proportional reporting ratio (PRR), reporting odds ratio (ROR), Bayesian confidence propagation neural network (BCPNN), and empirical Bayesian geometric mean (EBGM) algorithms to identify signals of upadacitinib-associated AEs for treating inflammatory bowel disease (IBD).ResultsFrom a total of 7,037,004 adverse event reports sourced from the FAERS database, 37,822 identified upadacitinib as the primary suspect drug in adverse drug events (ADEs), including 1,917 reports specifically related to the treatment of inflammatory bowel disease (IBD). …”
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Multimodal marvels of deep learning in medical diagnosis using image, speech, and text: A comprehensive review of COVID-19 detection by Md Shofiqul Islam, Khondokar Fida Hasan, Hasibul Hossain Shajeeb, Humayan Kabir Rana, Md. Saifur Rahman, Md. Munirul Hasan, AKM Azad, Ibrahim Abdullah, Mohammad Ali Moni

“…We explore the architecture of deep learning models, emphasising their data-specific structures and underlying algorithms. Subsequently, we compare different deep learning strategies utilised in COVID-19 analysis, evaluating them based on methodology, data, performance, and prerequisites for future research. …”
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Correcting forest aboveground biomass biases by incorporating independent canopy height retrieval with conventional machine learning models using GEDI and ICESat-2 data by Biao Zhang, Zhichao Wang, Tiantian Ma, Zhihao Wang, Hao Li, Wenxu Ji, Mingyang He, Ao Jiao, Zhongke Feng

“…In contrast to advances focused on the refinement of ML algorithms, this study aims to enhance AGB estimation accuracy by integrating an additional Canopy Height (CH) information. …”
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Quantifying the Suitability of Biosignals Acquired During Surgery for Multimodal Analysis by Ennio Idrobo-Avila, Gergo Bognar, Dagmar Krefting, Thomas Penzel, Peter Kovacs, Nicolai Spicher

“…<italic>Methods:</italic> We applied widely known algorithms entitled &#x201C;signal quality indicators&#x201D; to the common biosignals in both datasets, namely electrocardiography, electroencephalography, and respiratory signals split in segments of 10 s duration. …”
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Trends and Gaps in Digital Precision Hypertension Management: Scoping Review by Namuun Clifford, Rachel Tunis, Adetimilehin Ariyo, Haoxiang Yu, Hyekyun Rhee, Kavita Radhakrishnan

“…The most commonly used digital technologies were mobile phones (33/46, 72%), blood pressure monitors (18/46, 39%), and machine learning algorithms (11/46, 24%). In total, 45% (21/46) of the studies either did not report race or ethnicity data (14/46, 30%) or partially reported this information (7/46, 15%). …”
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Klasifikasi Siswa Slow Learner untuk Mendukung Sekolah dalam Meningkatkan Pemahaman Siswa Menggunakan Algoritma Naïve Bayes by Abdul Harris Wicaksono, Ahmad Afif Supianto, Satrio Hadi Wijoyo, Didik Krisnandi, Ana Heryana

“…The study used naive bayes algorithms for classification and cross validation of 10 folds as a testing method. …”
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