Effects of biochar on the chemical properties of soils and the volume of wood in a plantation of Acacia mangium Willd in the Colombian Orinoquía (highlands) by Giovanni Reyes-Moreno, Aquiles Enrique Darghan, Carlos Rivera-Moreno

“…We validated the grouping using cluster analysis algorithms. Volume in wood was used as the response, and the same soil variables were used to run a regression by partial least squares where the explanatory variables were characterized by relative importance. …”
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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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Quantitative evaluation method of stroke association based on multidimensional gait parameters by using machine learning by Cheng Wang, Cheng Wang, Cheng Wang, Zhou Long, Zhou Long, Xiang-Dong Wang, Xiang-Dong Wang, You-Qi Kong, Li-Chun Zhou, Wei-Hua Jia, Pei Li, Jing Wang, Xiao-Juan Wang, Tian Tian

“…The overall detection accuracy of the model based on KNN, SVM and Randomforest algorithms is 92.86, 92.86 and 90.00%, respectively. …”
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Personalized treatment strategies for breast adenoid cystic carcinoma: A machine learning approach by Sakhr Alshwayyat, Mahmoud Bashar Abu Al Hawa, Mustafa Alshwayyat, Tala Abdulsalam Alshwayyat, Siya sawan, Ghaith Heilat, Hanan M. Hammouri, Sara Mheid, Batool Al Shweiat, Hamdah Hanifa

“…To identify the prognostic variables, we conducted Cox regression analysis and constructed prognostic models using five Machine Learning (ML) algorithms to predict the 5-year survival. A validation method incorporating the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to validate the accuracy and reliability of ML models. …”
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O2O-PLB: A One-to-One-Based Optimizer With Priority and Load Balancing Mechanism for Resource Allocation in Fog-Cloud Environments by V. C. Bharathi, S. Syed Abuthahir, Monelli Ayyavaraiah, G. Arunkumar, Usama Abdurrahman, Sardar Asad Ali Biabani

“…Based on the experimental results, the O2O-PLB algorithm significantly outperforms the benchmark algorithms across essential performance metrics at varying task loads. …”
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A Mitochondria‐Related Signature in Diffuse Large B‐Cell Lymphoma: Prognosis, Immune and Therapeutic Features by Zhen‐Zhong Zhou, Jia‐Chen Lu, Song‐Bin Guo, Xiao‐Peng Tian, Hai‐Long Li, Hui Zhou, Wei‐Juan Huang

“…The risk model was defined using Least Absolute Shrinkage and Selection Operator (Lasso) regression algorithm, and its prognostic value was further examined in independent datasets. …”
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Preparing healthcare leaders of the digital age with an integrative artificial intelligence curriculum: a pilot study by Soo Hwan Park, Roshini Pinto-Powell, Thomas Thesen, Alexander Lindqwister, Joshua Levy, Rachael Chacko, Devina Gonzalez, Connor Bridges, Adam Schwendt, Travis Byrum, Justin Fong, Shahin Shahsavari, Saeed Hassanpour

“…This study examined the effectiveness of a pilot Digital Health Scholars (DHS) non-credit enrichment elective that paralleled the Dartmouth Geisel School of Medicine’s first-year preclinical curriculum with a focus on introducing AI algorithms and their applications in the concurrently occurring systems-blocks. …”
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A joint three-plane physics-constrained deep learning based polynomial fitting approach for MR electrical properties tomography by Kyu-Jin Jung, Thierry G. Meerbothe, Chuanjiang Cui, Mina Park, Cornelis A.T. van den Berg, Stefano Mandija, Dong-Hyun Kim

“…To estimate tissue electrical properties, various reconstruction algorithms have been proposed. However, physics-based reconstructions are prone to various artifacts such as noise amplification and boundary artifact. …”
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A Novel and Automated Approach to Detect Sea- and Land-Based Aquaculture Facilities by Maxim Veroli, Marco Martinoli, Arianna Martini, Riccardo Napolitano, Domitilla Pulcini, Nicolò Tonachella, Fabrizio Capoccioni

“…The results demonstrate that the approach proposed can identify, characterize, and geolocate sea- and land-based aquaculture structures without performing any post-processing procedure, by directly applying customized deep learning and artificial intelligence algorithms.…”
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Radiomics analysis of thoracic vertebral bone marrow microenvironment changes before bone metastasis of breast cancer based on chest CT by Hao-Nan Zhu, Yi-Fan Guo, YingMin Lin, Zhi-Chao Sun, Xi Zhu, YuanZhe Li

“…Multiple machine learning algorithms were utilized to construct various radiomics models for predicting the risk of bone metastasis, and the model with optimal performance was integrated with clinical features to develop a nomogram. …”
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The multiple uses of artificial intelligence in exercise programs: a narrative review by Alberto Canzone, Alberto Canzone, Giacomo Belmonte, Antonino Patti, Domenico Savio Salvatore Vicari, Domenico Savio Salvatore Vicari, Fabio Rapisarda, Valerio Giustino, Patrik Drid, Antonino Bianco

“…BackgroundArtificial intelligence is based on algorithms that enable machines to perform tasks and activities that generally require human intelligence, and its use offers innovative solutions in various fields. …”
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Large annotated ultrasound dataset of non-alcoholic fatty liver from Saudi hospitals for analysis and applicationsOpen Science framework or (OSF) by Fahad Alshagathrh, Mahmood Alzubaidi, Khalid Alswat, Ali Aldhebaib, Bushra Alahmadi, Meteb Alkubeyyer, Abdulaziz Alosaimi, Amani Alsadoon, Maram Alkhamash, Jens Schneider, Mowafa Househ

“…This resource supports various computer vision tasks, enabling the development of AI algorithms for accurate NAFLD diagnosis and staging. …”
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Ensembles of spectral-spatial convolutional neural network models for classifying soil types in hyperspectral images by N.A. Firsov, V.V. Podlipnov, N.A. Ivliev, D.D. Ryskova, A.V. Pirogov, A.A. Muzyka, A.R. Makarov, V.E. Lobanov, V.I. Platonov, A.N. Babichev, V.A. Monastyrskiy, V.I. Olgarenko, D.P. Nikolaev, R.V. Skidanov, A.V. Nikonorov, N.L. Kazanskiy, V.A. Soyfer

“…The paper presents a study of various approaches to the classification of soil covers based on neural network algorithms using hyperspectral remote and proximal sensing of the Earth. …”
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Desain Penilaian Risiko Privasi pada Aplikasi Seluler Melalui Model Machine Learning Berbasis Ensemble Learning dan Multiple Application Attributes by R. Ahmad Imanullah Zakariya, Kalamullah Ramli

“…The experimental results show that the application of ensemble learning with the Decision Tree (DT), K-Nearest Neighbor (KNN), and Random Forest (RF) classification algorithms provides better model performance compared to using a single classification algorithm, with an accuracy of 95.2%, a precision value of 93.2%, a F1-score of 92.4%, and a True Negative Rate (TNR) of 97.6%. …”
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The systemic oxidative stress index predicts clinical outcomes of esophageal squamous cell carcinoma receiving neoadjuvant immunochemotherapy by Jifeng Feng, Jifeng Feng, Liang Wang, Xun Yang, Qixun Chen, Qixun Chen

“…Then, a new staging that included TNM and SOSI based on RPA algorithms was produced. In terms of prognostication, the RPA model performed significantly better than TNM classification.ConclusionSOSI is a simple and useful score based on available SOS-related indices. …”
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Comparison of diet and exercise on cardiometabolic factors in young adults with overweight/obesity: multiomics analysis and gut microbiota prediction, a randomized controlled trial by Zongyu Lin, Tianze Li, Fenglian Huang, Miao Wu, Lewei Zhu, Yueqin Zhou, Ying‐An Ming, Zhijun Lu, Wei Peng, Fei Gao, Yanna Zhu

“…Additionally, we used machine learning algorithms to further predict individual responses based on baseline gut microbiota composition, with specific microbial genera guiding targeted intervention selection. …”
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Towards PErsonalised PRognosis for children with traumatic brain injury: the PEPR study protocol by Jaap Oosterlaan, Marsh Königs, Job B M van Woensel, Marc Engelen, Marjan E Steenweg, Petra J W Pouwels, Cece C Kooper, Hilgo Bruining, Arne Popma, Dennis R Buis, Maayke Hunfeld

“…In addition, the potential added value of advanced neuroimaging data and the use of machine learning algorithms in the development of prognostic models will be assessed.Methods and analysis 210 children aged 4–18 years diagnosed with mild-to-severe TBI will be prospectively recruited from a research network of Dutch hospitals. …”
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Dataset of aerial photographs acquired with UAV using a multispectral (green, red and near-infrared) camera for cherry tomato (Solanum lycopersicum var. cerasiforme) monitoringDrya... by Osiris Chávez-Martínez, Sergio Alberto Monjardin-Armenta, Jesús Gabriel Rangel-Peraza, Zuriel Dathan Mora-Felix, Antonio Jesus Sanhouse-García

“…However, this multispectral imagery dataset can also have various uses, such as creating training datasets with accurate labels or classes which can then be used to develop, train, and/or validate machine learning algorithms for image classification, object detection tasks, or change detection analysis.…”
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Retrieval of Land Surface Temperature From Passive Microwave Observations Using CatBoost-Based Adaptive Feature Selection by Yang Dai, Yingbao Yang, Xin Pan, Penghua Hu, Xiangjin Meng, Fanggang Li, Zhenwei Wang

“…Finally, the optimized feature sets were used in the CatBoost algorithm to construct the PMW-LST retrieval model. …”
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Development of robust machine learning models for predicting flexural strengths of fiber-reinforced polymeric composites by Abdulhammed K. Hamzat, Umar T. Salman, Md Shafinur Murad, Ozkan Altay, Ersin Bahceci, Eylem Asmatulu, Mete Bakir, Ramazan Asmatulu

“…This study investigates the potential of machine learning (ML) techniques to predict the flexural properties of fiber-reinforced composites accurately and efficiently. Five ML algorithms—Light gradient boosting regressor (LGBR), Extra tree regressor (ETR), Decision tree regressor (DTR), Histogram-based gradient boosting regressor (HGBR), and Adaptive boosting regressor (ABR)—were employed to predict the flexural strengths using both experimental data generated in-house and data collected from open literature. …”
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