Análise de conglomerados em curvas de aprendizado para formação de agrupamentos homogêneos de trabalhadores Cluster analysis of learning curves for grouping workers with homogeneou... by Renato Eduardo Stroieke, Flavio Sanson Fogliatto, Michel Jose Anzanello

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SUPERVISI BERBASIS DATA: MENINGKATKAN HASIL PEMBELAJARAN MELALUI ANALISIS MODUL AJAR PMM DI TK NEGERI PEMBINA KABUPATEN KAPUAS HULU by Margaretha Corry, Marinus Waruwu, Halida Halida, Eni Eni

“…A case study approach was used in qualitative research, which included in-depth interviews, direct observations, and document analysis. Thematic analysis techniques were employed to analyze the collected data to detect developing patterns and themes. …”
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Elucidating the Prognostic and Therapeutic Implications of Insulin Resistance Genes in Breast Cancer: A Machine Learning-Powered Analysis by Lengyun Wei, Dashuai Li, Hongjin Chen, Yajing Pu, Qun Wang, Jintao Li, Meng Zhou, Chenfeng Liu, Pengpeng Long

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Machine learning prediction and explainability analysis of high strength glass powder concrete using SHAP PDP and ICE by Muhammad Sarmad Mahmood, Tariq Ali, Inamullah Inam, Muhammad Zeeshan Qureshi, Syed Salman Ahmad Zaidi, Muwaffaq Alqurashi, Hawreen Ahmed, Muhammad Adnan, Abdul Hakim Hotak

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Analysis of shared pathogenic mechanisms and drug targets in myocardial infarction and gastric cancer based on transcriptomics and machine learning by Junyang Ma, Junyang Ma, Shufu Hou, Xinxin Gu, Peng Guo, Jiankang Zhu

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Optimizing Air Quality Monitoring: Comparative Analysis of Linear Regression and Machine Learning in Low-Cost Sensor Calibration by Runcheng Fang, Scott Collingwood, Yue Zhang, Joseph B. Stanford, Christina Porucznik, Darrah Sleeth

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Investigating combined hypoxia and stemness indices for prognostic transcripts in gastric cancer: Machine learning and network analysis approaches by Sharareh Mahmoudian-Hamedani, Maryam Lotfi-Shahreza, Parvaneh Nikpour

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Burnout protective patterns among oncology nurses: a cross-sectional study using machine learning analysis by Ana Rocha, Cristina Costeira, Raul Barbosa, Florbela Gonçalves, Miguel Castelo-Branco, Joaquim Viana, Margarida Gaudêncio, Filipa Ventura

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Effects of sudden stratospheric warmings on the global ionospheric total electron content using a machine learning analysis by G. Ma, K. Hocke, K. Hocke

“…Here we analyse major SSW influences on diurnal/semidiurnal variations of the global ionosphere with global maps of total electron content (TEC) from 1998 to 2022. We use machine learning (ML) with neural networks to establish the TEC (ML-TEC) model related to the solar/geomagnetic activities and seasonal changes from the long-term global TEC data. …”
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Identification of potential biomarkers for 2022 Mpox virus infection: a transcriptomic network analysis and machine learning approach by Joy Prokash Debnath, Kabir Hossen, Sabrina Bintay Sayed, Md. Sayeam Khandaker, Preonath Chondrow Dev, Saifuddin Sarker, Tanvir Hossain

“…Subsequently, gene expression network analyses pinpoint the key DEGs, followed by their candidate drug assessment using the Drug SIGnatures DataBase (DSigDB) and validation by multiple machine learning algorithms. …”
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Optimization method of investment package based on Markowitz portfolio theory by A. D. Baydalin

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Leveraging compact convolutional transformers for enhanced COVID-19 detection in chest X-rays: a grad-CAM visualization approach by Aravinda C. V, Sudeepa K. B, S. Pradeep, P. Suraksha, Meng Lin

Subjects: “…deep learning…”
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Compound dataset and custom code for deep generative multi-target compound design by Thomas Blaschke, Jürgen Bajorath

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Entropy, Irreversibility, and Time-Series Deep Learning of Kinematic and Kinetic Data for Gait Classification in Children with Cerebral Palsy, Idiopathic Toe Walking, and Hereditar... by Alfonso de Gorostegui, Massimiliano Zanin, Juan-Andrés Martín-Gonzalo, Javier López-López, David Gómez-Andrés, Damien Kiernan, Estrella Rausell

“…While previous studies have explored the use of statistical physics principles for the analysis of impaired gait patterns, gaps remain in integrating both kinematic and kinetic information or benchmarking these approaches against Deep Learning models. …”
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An Intelligent Smart Dynamic Feature Analysis Based Approach by Utilizing Deep Learning to Improve the Breast Cancer Detection by Seong-O Shim, Lal Hussain, Eesa Alsolami, Monagi H. Alkinani

“…Motivated by the limitations, the present study comes up with an innovative automated solution for the identification of breast cancer using deep learning analysis of mammograms. Moving away from the traditional approaches with inherent pre-processing and feature extraction constraints, this research focuses on a two-pronged improvement strategy: improved mammogram quality and highly optimized deep learning architecture. …”
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Machine Learning–Based Analysis of Lifestyle Risk Factors for Atherosclerotic Cardiovascular Disease: Retrospective Case-Control Study by Hye-Jin Kim, Heeji Choi, Hyo-Jung Ahn, Seung-Ho Shin, Chulho Kim, Sang-Hwa Lee, Jong-Hee Sohn, Jae-Jun Lee

“…Shapley additive explanations were used to identify the models’ important variables in assessing high ASCVD risks. In sensitivity analysis, we additionally performed binary LR analysis, in which the ML model’s results were consistent with the conventional statistical model. …”
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Evolution of deep learning tooth segmentation from CT/CBCT images: a systematic review and meta-analysis by Wai Ying Kot, Sum Yin Au Yeung, Yin Yan Leung, Pui Hang Leung, Wei-fa Yang

“…This systematic review and meta-analysis aims to evaluate the evolution and performance of deep learning in tooth segmentation. …”
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Unveiling the ultimate advantage: a meta-analysis of 3D visualization and problem-based learning in orthopedic education by Ting Li, Yidan Hu, Ruohong Song, Hangyu Liu, Xin Gao, Fei Wang, Chengwei Xiao, Xilin Liu

“…Relevant studies were selected based on established eligibility criteria, with clinical data carefully extracted for analysis. Eighteen randomized controlled trials involving a total of 1,077 participants were included in the analysis. …”
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Machine learning-assisted multi-dimensional transcriptomic analysis of cytoskeleton-related molecules and their relationship with prognosis in hepatocellular carcinoma by Yuxuan Li, Mingbo Cao, Xiaorui Su, Gaoyuan Yang, Yupeng Ren, Zhiwei He, Zheng Shi, Ziyi Hu, Guirong Liang, Qi Zhang, Zhicheng Yao, Meihai Deng

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New Methods for Pathogen Risk Assessment: Machine Learning in the Analysis of Toxicity Spectrum of <i>Albifimbria verrucaria</i> by V. T. Tkachenko, M. V. Fedorov, V. V. Fedorova, A. V. Pozdeev, E. B. Kormanovskaya, A. S. Klimova, P. V. Gunina

“…This makes the search for new approaches to assess their danger and control highly relevant.Purpose of the study is to study the toxicological profile of mycotoxins produced by the pathogenic fungus Albifimbria verrucaria and to determine their level of danger using chemoinformatics and machine learning.Study base sources. Analysis of scientific literature available through open Russian and English-language Internet resources.Method. …”
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