Determinação do coeficiente convectivo de transferência de calor de figos submetidos ao resfriamento rápido Determination of the convective heat transfer coefficient of fig fruits... by Mariangela Amendola, Saul Dussán-Sarria, Anderson A. Rabello

“…Algorithms employing the finite differences and finite elements methods were implemented for the one-dimensional and three-dimensional models. …”
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Unveiling new insights into migraine risk stratification using machine learning models of adjustable risk factors by Yu-Chen Liu, Ye-Hai Liu, Hai-Feng Pan, Wei Wang

“…Methods We developed a three-step analytical framework combining causal inference, predictive modeling, and burden projection to systematically evaluate modifiable factors associated with migraine. …”
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RE-YOLOv5: Enhancing Occluded Road Object Detection via Visual Receptive Field Improvements by Tianyu Li, Xuanrui Xiong, Yuan Zhang, Xiaolin Fan, Yushu Zhang, Haihong Huang, Dan Hu, Mengting He, Zhanjun Liu

“…This study proposes an occluded object detection algorithm, RE-YOLOv5, based on receptive field enhancement to assist with the difficult identification of occluded objects in complex road environments. …”
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Integrating Remote Sensing and AI for precision Monitoring of Soil and Vegetation Contamination by M. Spiralski, A. Miszczak, G. Siebielec, Ż. Piasecka, R. Trojnacki, P. Kwaśnik, J. Kotlarz, K. A. Kubiak-Siwinska, M. Kacprzak, S. Marciniak, K. A. Rotchimmel, K. P. Beben, J. Szymanski

“…The future of the study will be focused on the multi-temporal analyses, improving prediction accuracy and dataset and environmental risk mapping. …”
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Machine-learning derived identification of prognostic signature to forecast head and neck squamous cell carcinoma prognosis and drug response by Sha-Zhou Li, Hai-Ying Sun, Yuan Tian, Liu-Qing Zhou, Tao Zhou

“…Dasatinib and 7 medicine showed the superior sensitivity to the high-risk NHSCC, which had potential to the clinical.ConclusionsThe construction of MLDPM effectively eliminated artificial bias by utilizing 101 algorithm combinations. This model demonstrated high accuracy in predicting HNSCC outcomes and has the potential to identify novel therapeutic targets for HNSCC patients, thus offering significant advancements in personalized treatment strategies.…”
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MRI-based intratumoral and peritumoral radiomics for assessing deep myometrial invasion in patients with early-stage endometrioid adenocarcinoma by Jing Yang, Yang Liu, Xiaolong Liu, Yaoxin Wang, Xianhong Wang, Conghui Ai, Qiu Bi, Ying Zhao

“…The integrated models are of little value in increasing the prediction.…”
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Grassland management and phenology affect trait retrieval accuracy from remote sensing observations by Maksim Iakunin, Franziska Taubert, Reimund Goss, Severin Sasso, Hannes Feilhauer, Daniel Doktor

“…This study combines radiative transfer model (RTM) and machine learning algorithms to assess the efficacy of the model inversion in predicting plant functional traits under different grassland management regimes. …”
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Characterization and stratification of risk factors of stroke in people living with HIV: A theory-informed systematic review by Martins Nweke, Nombeko Mshunqane

“…Diabetes, atrial fibrillation, smoking habits, hypertension, age, and viral load demonstrated a high likelihood of association with stroke in PLWH and should be prioritized when constructing clinical prediction algorithms for HIV-related stroke. Conclusions The most important factors were hypertension and chronic kidney disease, followed by smoking, dyslipidemia, diabetes, HCV, HBV, CD4 count, use of ART, TB, and substance use (cocaine). …”
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Determining time since deposition of latent fingerprints on forensic adhesive tape using ultrafast DESI-MS and machine learning by Nora Rajs, Yinon Harush-Brosh, Ron Raisch, Ravit Yakobi Arancibia, Amani Zoabi, Guy Nevet Golan, Moshe Shpitzen, Sarena Wiesner, Michal Levin-Elad, Tommy Kaplan, Katherine Margulis

“…Data analysis using the XGBoost and SMOTE algorithms achieved a correlation of 0.54 (p-value < 1e−5) between TSD prediction and true TSD, achieving 83.3% accuracy in distinguishing between 0-4 days and 10–15 days old prints. …”
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Leaf area index-based phenotypic assessment of sweet potato varieties using UAV multispectral imagery and a hybrid retrieval approach by Philemon Tsele, Abel Ramoelo, Lucy Moleleki, Sunette Laurie, Whelma Mphela, Natasha Tshuma

“…However, the BRT performance in-comparison to KRR, captured more spatial variability of observed LAI with a better prediction accuracy across the 20 sweet potato varieties. …”
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Artificial intelligence for difficult airway assessment: a protocol for a systematic review with meta-analysis by Dan Liu, Tingting Li, Li Du, Jianqiao Zheng, Yujie Huang, Weiyi Zhang

“…Nevertheless, the diagnostic performance of AI algorithms for difficult airway assessment remains unclear due to the small sample sizes, insufficient image acquisition standards and poor predictive accuracies. …”
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A Novel Weighted Fractional GM(1,1) Model and Its Applications by Qin-Qin Shen, Quan Shi, Tian-Pei Tang, Lin-Quan Yao

“…Numerical results show that the new proposed prediction model is very efficient and has both the best fitting accuracy and the best prediction accuracy compared with the GM(1,1) and the FGM(1,1) as well as the NIPGM(1,1) prediction models.…”
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Integration of graph neural networks and transcriptomics analysis identify key pathways and gene signature for immunotherapy response and prognosis of skin melanoma by Maodong Ye, Shuai Ren, Huanjuan Luo, Xiumin Wu, Hongwei Lian, Xiangna Cai, Yingchang Ji

“…Methods GNNs models were developed to predict the response to immunotherapy and to pinpoint key pathways. …”
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Counterfactual choice and learning in a neural network centered on human lateral frontopolar cortex. by Erie D Boorman, Timothy E Behrens, Matthew F Rushworth

“…Using a three-alternative decision-making task, a Bayesian reinforcement-learning algorithm, and fMRI, we investigated the coding of counterfactual choices and prediction errors in the human brain. …”
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Analyzing key users’ behavior trends in volunteer-based networks by Nofar Piterman, Tamar Makov, Michael Fire

“…These algorithms allowed us to analyze the factors that significantly influence behavior predictions. …”
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APPLICATION OF LINEAR AND NONLINEAR EXTRAPOLATIONS IN ANALYZING THE TRENDS OF CHANGES IN FINANCIAL STABILITY by EVGENY Negashev

“…The article researches the possibilities of applying the methods of linear and nonlinear extrapolation in the predictive analysis of a company’s financial stability based on the study of its trends. …”
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Potential distribution of endemic lizards from Brazilian restingas: The present announcing the end by Hugo Andrade, Luisa Maria Diele‐Viegas Costa Silva, Carlos Frederico Duarte Rocha, Antônio Jorge Suzart Argôlo, Eduardo José dos Reis Dias

“…Here, we used an ensemble of three modeling algorithms (Bioclim, GLM, and SVM). In predicting the effects of climate change on their future distributions, we used intermediate and pessimistic socio‐economic pathway scenarios (SSP3 70 and SSP5 85, respectively) considering projections for 2081–2100. …”
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A Wi-Fi Indoor Localization Strategy Using Particle Swarm Optimization Based Artificial Neural Networks by Nan Li, Jiabin Chen, Yan Yuan, Xiaochun Tian, Yongqiang Han, Mingzhe Xia

“…Thus, with the combined strategy, we can reduce the positioning error and shorten the prediction time. We implement the proposed system on a mobile phone and the positioning results show that our algorithm can provide a higher localization accuracy and significantly improves the prediction speed.…”
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Internet of medical things and trending converged technologies: A comprehensive review on real-time applications by Shiraz Ali Wagan, Jahwan Koo, Isma Farah Siddiqui, Muhammad Attique, Dong Ryeol Shin, Nawab Muhammad Faseeh Qureshi

“…It also discusses various applied machine learning algorithms available in the healthcare industry. It compares a variety of disease predictions and the accuracy of the equipment and decision recommendations. …”
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Role of artificial intelligence in medical radiology and imaging by Mohd Arfat, T. K. Nisha, Sapna Sahu, Mohd Rashid

“…AI is crucial in analyzing large medical datasets, including imaging and clinical data, through advanced algorithms and ML techniques. In veterinary medicine, AI is key to addressing complex challenges in host–pathogen interactions, precision medicine, and predictive epidemiology. …”
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