Evaluating the strength of industrial wastesbased concrete reinforced with steel fiber using advanced machine learning by Kennedy C. Onyelowe, Viroon Kamchoom, Ahmed M. Ebid, Shadi Hanandeh, Susana Monserrat Zurita Polo, Vilma Fernanda Noboa Silva, Rodney Orlando Santillán Murillo, Rolando Fabián Zabala Vizuete, Paul Awoyera, Siva Avudaiappan

Get full text

2D data arrangement to train ANN for depression levels measurementMendeley Data by Al Fathjri Wisesa, Eny Latifah, Sutrisno, Suyatno, Tutut Chusniyah, Kukuh Setyo Pambudi, Mochamad Khoirul Rifai, Moh. Fariq Firdaus Karim, Anugerah Agung Dwi Putra

“…Using two-dimensional data, we aim for the ANNs to learn interaction patterns between these parameters, improving accuracy in depression detection.…”
Get full text

Diagnostic Accuracy of Machine Learning-Assisted MRI for Mild Cognitive Impairment in Parkinson’s Disease: A Systematic Review and Meta-Analysis by Feng Zhang, Liangqing Guo, Lin Liu, Xiaochun Han

“…We systematically searched for studies that applied machine learning algorithms to MRI data for diagnosing PD with mild cognitive impairment (PD-MCI). …”
Get full text

A comparative analysis of emotion recognition from EEG signals using temporal features and hyperparameter-tuned machine learning techniques by Rabita Hasan, Sheikh Md. Rabiul Islam

Get full text

Diagnostic Analysis of Crosswalk Safety Hazards in Pedestrian Environments: A SHAP-Enhanced Machine Learning Approach With Street-View Imagery by Caryl Anne M. Barquilla, Jeongwoo Lee

“…These results highlight the importance of combining visual and structural data for thorough risk assessment and further the use of interpretable machine learning in urban safety research. …”
Get full text

Exploring genomic feature selection: A comparative analysis of GWAS and machine learning algorithms in a large‐scale soybean dataset by Hawlader A. Al‐Mamun, Monica F. Danilevicz, Jacob I. Marsh, Cedric Gondro, David Edwards

“…By constructing predictive models based on the selected features, we ascertain the comparative prediction accuracies, thereby illuminating the strengths and limitations of these feature selection methodologies in the realm of genomic data analysis.…”
Get full text

Leveraging machine learning for integrative analysis of T-cell receptor repertoires in colorectal cancer: Insights into MAIT cell dynamics and risk assessment by Romi Goldner Kabeli, Ben Boursi, Alona Zilberberg, Sol Efroni

Get full text

Integration of single‐cell and bulk RNA‐sequencing data reveals the prognostic potential of epithelial gene markers for prostate cancer by Zhuofan Mou, Lorna W. Harries

“…By analysing single‐cell RNA‐sequencing data, we found these genes predominantly as markers in epithelial cells. …”
Get full text

Differentiation between multiple sclerosis and neuromyelitis optic spectrum disorders with multilevel fMRI features: A machine learning analysis by Xiao Liang, Qingwen Zeng, Yanyan Zhu, Yao Wang, Ting He, Lin Wu, Muhua Huang, Fuqing Zhou

Get full text

Variance-based sensitivity analysis of climate variability impact on crop yield using machine learning: A case study in Jordan by Yingqiang Xu, Abeer Albalawneh, Maysoon Al-Zoubi, Hiba Baroud

“…By leveraging public remote sensing data and advanced sensitivity analysis methods, this approach can be adapted to other data-scarce regions to support food security and sustainable agricultural management.…”
Get full text

Lymph Node Involvement Prediction Using Machine Learning: Analysis of Prostatic Nodule, Prostatic Gland, and Periprostatic Adipose Tissue (PPAT) by Eliodoro Faiella, Giulia D’amone, Raffaele Ragone, Matteo Pileri, Elva Vergantino, Bruno Beomonte Zobel, Rosario Francesco Grasso, Domiziana Santucci

“…When compared with a radiologist’s assessment, radiomics models enhance the diagnostic accuracy of lymph node staging for prostate cancer. Although data are still lacking, deep learning models may be able to further improve on this.…”
Get full text

A two phase ensembled deep learning approach of prominent gene extraction and disease risk prediction by Prajna Paramita DEBATA, Alakananda TRIPATHY, Pournamasi PARHI, Smruti Rekha DAS

“…However, available voluminous gene expression data is too much to handle for the traditional data analysis approaches. …”
Get full text

Machine learning prediction of permeability distribution in the X field Malay Basin using elastic properties by Zaky Ahmad Riyadi, John Oluwadamilola Olutoki, Maman Hermana, Abdul Halim Abdul Latif, Ida Bagus Suananda Yogi, Said Jadid A. Kadir

“…Accurate estimation of porosity and permeability distribution is vital for reservoir characterization, particularly challenging in areas with limited well data. This study introduces a new approach to permeability estimation by implementing feature selection analysis and using elastic properties extracted from simultaneous seismic inversion method. …”
Get full text

Multisensor Data Fusion for Coastal Boundary Detection by Res-U-Net Implementation Using High-Resolution UAV Imagery by Qin Wang, Nyasha J. Kavhiza, Fakhrul Islam, Ilyas Ahmad Huqqani, Mohsin Abbas, Sanjoy Barman

“…This work utilizes high-resolution UAV data to develop a deep learning framework based on a Residual U-Net architecture for shoreline semantic segmentation. …”
Get full text

Identification of novel molecular subtypes and construction of a prognostic signature via multi-omics analysis and machine learning in lung adenocarcinoma by Ke Ma, Jie Xu, Jie Xu, Congyue Wang, Congyue Wang, Xu Cao, Xu Cao, Wenjie Yu, Jingjing Xi, Xuan Zhang, Jiamin Zhan, Yang Liu, Aoyang Yu, Aoyang Yu, Shuhan Liu, Yanhua Liu, Yanhua Liu, Chong Chen, Chong Chen, Xiaoli Mai, Xiaoli Mai

“…Notably, ANLN knockdown and dnANLN expression significantly inhibited cell proliferation and migration in vitro and enhanced the efficacy of docetaxel.ConclusionA comprehensive analysis of multi-omics data redefines the molecular subtype of LUAD patients. …”
Get full text

Exploration and analysis of risk factors for coronary artery disease with type 2 diabetes based on SHAP explainable machine learning algorithm by Dandan Tang, Fengwei Liang, Xingli Gu, Yuanyuan Jin, Xuanjie Hu, Fen Liu, Yining Yang

“…A total of 25 predictive variables were ultimately identified through Lasso regression analysis. Among the seven machine learning models, the RF model demonstrated significantly superior performance and achieved the highest net benefit in the DCA. …”
Get full text

Comparative analysis of machine learning models and explainable AI for agriculture drought prediction: A case study of the Ta-pieh mountains by Lichang Xu, Shaowei Ning, Xiaoyan Xu, Shenghan Wang, Le Chen, Rujian Long, Shengyi Zhang, Yuliang Zhou, Min Zhang, Bhesh Raj Thapa

“…The models were trained on data from 2000 to 2021, with 2022 serving as an independent case study to evaluate their prediction accuracy. …”
Get full text

Optimal machine learning algorithms and UAV multispectral imagery for crop phenotypic trait estimation: a comprehensive review and meta-analysis by Adama Ndour, Gerald Blasch, João Valente, Bisrat Haile Gebrekidan, Tesfaye Shiferaw Sida

“…We systematically reviewed the current literature to catalog and assess the variety of machine learning methodologies applied to multispectral UAV data for the prediction of key phenotypic traits such as biomass, yield and nitrogen. …”
Get full text

Unveiling the role of coagulation-related genes in acute myeloid leukemia prognosis and immune microenvironment through machine learning by Liyun Ji, Yanxia Yang, Siyue Ma

“…Furthermore, intersection analysis using three machine learning methods identified MMP7 and F12 as key biomarkers. …”
Get full text

Construction and interpretation of tobacco leaf position discrimination model based on interpretable machine learning by Ranran Kou, Cong Wang, Jinxia Liu, Ran Wan, Zhe Jin, Le Zhao, Youjie Liu, Junwei Guo, Feng Li, Hongbo Wang, Song Yang, Cong Nie

“…The study proposes a novel approach that integrates machine learning with advanced interpretability techniques for both tobacco leaf position discrimination and analysis. …”
Get full text