Progress in prediction of photocatalytic CO2 reduction using machine learning approach: A mini review by Md Mohshin Ali, Md. Arif Hossen, Azrina Abd Aziz

“…Furthermore, the review outlines the commonly employed ML algorithms, presents recent progress, and identifies crucial parameters influencing PC-CO2R efficiency. …”
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SkelETT—Skeleton-to-Emotion Transfer Transformer by Pedro Victor Vieira Paiva, Josue Junior Guimaraes Ramos, Marina Gavrilova, Marco Antonio Garcia de Carvalho

“…Human skeletons, derived from depth sensors or pose estimation algorithms, offer an alternative for facial expression, including valuable spatial and temporal cues. …”
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Bioinformatics screening and clinical validation of CircRNA and related miRNA in male osteoporosis by Jiayi Li, Sijia Guo, Qingyun Sun, Ning An, Jisheng Lin, Qi Fei

“…Then, three genes, including SETD2, ATM and XPO1, were identified as hub genes with four algorithms. Ultimately, the ceRNA network, involving 4 circRNAs, 40 miRNAs, and 592 mRNAs, was obtained. …”
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Penerapan Algoritma Support Vector Machine pada Analisis Sentimen Terhadap Identitas Kependudukan Digital by Rita Ajeng Lestari, Adhitia Erfina, Wisuda Jatmiko

“…The research dataset comes from crawling Facebook user comments from February 16 to March 10, 2023, with processing using TF-IDF word weighting and Support Vector Machine algorithms. Python is the programming language chosen to collect and process research data. …”
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Construction and validation of risk prediction models for renal replacement therapy in patients with acute pancreatitis by Fei Zuo, Lei Zhong, Jie Min, Jinyu Zhang, Longping Yao

“…Using these features, four machine learning (ML) algorithms were developed. The optimal model was visualized and clarified using SHapley Additive exPlanations (SHAP) and presented as a nomogram. …”
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RETRACTED ARTICLE: A prospective diagnostic model for breast cancer utilizing machine learning to examine the molecular immune infiltrate in HSPB6 by Lizhe Wang, Yu Wang,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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Prediction of Soil Organic Carbon Content in <italic>Spartina alterniflora</italic> by Using UAV Multispectral and LiDAR Data by Jiannan He, Yongbin Zhang, Mingyue Liu, Lin Chen, Weidong Man, Hua Fang, Xiang Li, Xuan Yin, Jianping Liang, Wenke Bai, Fuping Li

“…We compared the predictive performance of these different machine learning algorithms to identify the most effective one. The results show that the following. 1) The prediction accuracy is improved by classifying the data into three types: unlodging <italic>S. alterniflora</italic> (ULSA), lodging <italic>S. alterniflora</italic> (LSA), and mudflats. 2) XGBoost outperformed RF and SVM in accurately predicting SOC content, with <italic>R</italic>²; values of 0.743 for ULSA, 0.731 for LSA, and 0.705 for mudflats; 3) In the XGBoost models constructed for ULSA, LSA, and mudflats, spectral features contributed 75.7&#x0025;, 73.1&#x0025;, and 63.1&#x0025;, respectively, with the normalized difference vegetation index emerging as the most critical spectral feature. …”
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Characterisation of cardiovascular disease (CVD) incidence and machine learning risk prediction in middle-aged and elderly populations: data from the China health and retirement lo... by Qing Huang, Zihao Jiang, Bo Shi, Jiaxu Meng, Li Shu, Fuyong Hu, Jing Mi

“…Five machine learning (ML) algorithms were employed for risk prediction. Data preprocessing included missing value imputation via random forest. …”
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Pengaruh Word Affect Intensities Terhadap Deteksi Ulasan Palsu by Raga Saputra Heri Istanto, Fitra Abdurrachman Bachtiar, Achmad Ridok

“…These features are then combined with features in previous studies and evaluated using several classification algorithms. The results showed that word affect intensities can be a factor that affects the increased accuracy of fake review detection by 2.1%. …”
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Evaluating pre-processing and deep learning methods in medical imaging: Combined effectiveness across multiple modalities by Thien B. Nguyen-Tat, Tran Quang Hung, Pham Tien Nam, Vuong M. Ngo

“…This study provides a thorough evaluation of the performance of different preprocessing methods and deep learning algorithms across commonly used medical imaging modalities. …”
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DIGITAL TOOLS FOR MATCHING QUALIFICATIONS TO THE LEVELS OF THE NATIONAL QUALIFICATIONS FRAMEWORK by Volodymyr Kovtunets, Sergiy Londar, Serhii Melnyk, Oles Kovtunets

“…The novelty of the article is that for the first time in national and international practice, it proposes an alternative/supplementary algorithmic method for determining the level of certain full and/or partial professional qualifications by the National Qualifications Framework, thus creating prerequisites for further automation of the activities of professional standards developers. …”
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Inhibition of complement system-related gene ITGB2 attenuates epithelial–mesenchymal transition and inflammation in diabetic nephropathy by Jun Peng, Wenqi Zhao, Lu Zhou, Kun Ding

“…In addition, key biomarkers were acquired by two machine learning algorithms, then immune infiltration analysis, Gene Set Enrichment Analysis (GSEA), and potential drugs screening were conducted. …”
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A new signature associated with anoikis predicts the outcome and immune infiltration in nasopharyngeal carcinoma by Yonglin Luo, Wenyang Wei, Yaxuan Huang, Jun Li, Weiling Qin, Quanxiang Hao, Jiemei Ye, Zhe Zhang, Yushan Liang, Xue Xiao, Yonglin Cai

“…Results Three differentially expressed ARGs (CDC25C, E2F1 and RBL2) with prognostic value were identified by the intersection of multiple machine learning algorithms. A risk score based on t 3-ARG feature was developed to stratify NPC patients into two distinct risk groups using the optimal model, Random Survival Forest. …”
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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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Remote sensing-based maize growth process parameters revel the maize yield: a comparison of field- and regional-scale by Minghan Cheng, Xiuliang Jin, Chenwei Nie, Kaihua Liu, Tianao Wu, Yuping Lv, Shuaibing Liu, Xun Yu, Yi Bai, Yadong Liu, Lin Meng, Xiao Jia, Yuan Liu, Lili Zhou, Fei Nan

“…However, most previous studies have relied on remote sensing data from one or a few periods for yield estimation, thus lacking a comprehensive description of entire crop growth. Furthermore, past algorithms have not considered their applicability across different observational scales (e.g., unmanned aerial vehicle (UAV)- and satellite-observed). …”
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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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In-Season Automated Mapping of Xinjiang Cotton Based on Cumulative Spectral and Phenological Characteristics by Yongsheng Huang, Yaozhong Pan, Yu Zhu, Xiufang Zhu, Xingsheng Xia, Qiong Chen, Jufang Hu, Hongyan Che, Xuechang Zheng, Lingang Wang

“…Second, using the maximum between-class variance method (OTSU) and Sauvola algorithms, a new local adaptive threshold method (Otsu&#x2013;Sauvola) was developed for the automatic determination of the classification threshold. …”
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Validation Indicator Identification and Customer Ranking in Microloans: A Study at Middle East Bank in Iran by Azadeh Ahmadi Kousha, Faegh Ahmadi, Mohammad Hossein Ranjbar, Hamidreza Kordlouie

“…Naive Bayes, Meta, Attribute Selected Classifier, and j48 algorithms were implemented and WEKA software was used to classify criteria and create patterns. …”
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An optimized approach for predicting water quality features and a performance evaluation for mapping surface water potential zones based on Discriminant Analysis (DA), Geographical... by Abhijeet Das

“…Again, this research used a strong methodology by incorporating Machine learning (ML) algorithms, such as: Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Support Vector Machine (SVM), and Linear Regression Model (LRM), were applied to forecast and confirm the quality of the water. …”
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Penerapan Decision Tree dan Random Forest dalam Deteksi Tingkat Stres Manusia Berdasarkan Kondisi Tidur by Sza Sza Amulya Larasati, Elok Nuraida Kusuma Dewi, Brahma Hanif Farhansyah, Fitra Abdurrachman Bachtiar, Fajar Pradana

“…Hyperparameter tuning is done using k-fold cross-validation, and the model is designed using the Decision Tree and Random Forest classification algorithms. The results show that five features: snoring rate, respiration rate, limb movement including eye movement, and heart rate during sleep are directly proportional to the level of stress. …”
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