Constitutive modeling and workability characterization of pre-deformed AZ31 magnesium alloy during hot shear-compression deformation by Junsong Jin, Fangtao Chai, Jinchuan Long, Chang Gao, Shaolei Wang, Pan Zeng, Xuefeng Tang, Pan Gong, Mao Zhang, Lei Deng, Xinyun Wang

“…The deformation characteristics, flow behavior and microstructure/texture evolution mechanisms of pre-deformed AZ31 alloy were systematically investigated under varying process parameters. A genetic algorithm-optimized artificial neural network (GA-ANN) constitutive model was developed using machine learning methods, and hot processing maps were established based on this model. …”
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Predicting Student Performance and Enhancing Learning Outcomes: A Data-Driven Approach Using Educational Data Mining Techniques by Athanasios Angeioplastis, John Aliprantis, Markos Konstantakis, Alkiviadis Tsimpiris

“…This study offers insights into the effective application of data-driven approaches to improve educational outcomes and foster student success.…”
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Lung Cancer Prediction Using an Enhanced Neutrosophic Set Combined with a Machine Learning Approach by Vakeel A. Khan, Asheesh Kumar Yadav, Mohammad Arshad, Nadeem Akhtar

“…To address this issue, we propose an Enhanced Neutrosophic Set (ENS) framework integrated with machine learning algorithms to improve the prediction accuracy of lung cancer. …”
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Prediction of formation pressure in underground gas storage based on data-driven method by SUI Gulei, FU Yujiang, ZHU Hongxiang, LI Zunzhao, WANG Xiaolin

“…The experimental results show that predictive performances of three predictive models are ranked from high to low: SVR, XGBoost, LSTM, among which the predictive performance of SVR is the most stable. Introducing the proportion of gas injection-production to screen pressure monitoring wells can improve the predictive performance of the data-driven model. …”
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Determination of lithium concentration in black mass using laser-induced breakdown spectroscopy hand-held instrumentation by Elisa Galli, Mattia Massa, Alessandra Zanoletti, Silvana De Iuliis, Elza Bontempi, Laura Eleonora Depero, Vincenzo Palleschi, Laura Borgese

“…Abstract Lithium has become one of the most strategic materials in the industry, given its wide use for the realization of efficient energy storage devices and for improving the chemical and physical characteristics of advanced ceramic and glass materials. …”
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An adaptive intelligent thermal-aware routing protocol for wireless body area networks by Abdollah Rahimi, Mehdi Jafari Shahbazzadeh, Amid Khatibi

“…In the first phase, sensor nodes exchange vital network status information, including residual energy, node temperature, link reliability, and delay, to build an optimized network topology. Instead of relying solely on shortest-path routing, a multi-criteria decision-making algorithm is employed to select the most efficient paths, prioritizing those that balance energy consumption, temperature regulation, and communication stability. …”
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A novel canopy water indicator for UAV imaging to monitor winter wheat water status by Meiyan Shu, Zhenghang Ge, Yang Li, Jibo Yue, Wei Guo, Yuanyuan Fu, Ping Dong, Hongbo Qiao, Xiaohe Gu

“…However, the complexity involved in acquiring LAI data and the associated high costs limit the practical application of FMCc in crop water monitoring. …”
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The geriatric 5Ms, artificial intelligence, and Hannah Arendt’s critique: ethical reflections within contemporary gerontology by Virgílio Garcia Moreira, Andréia Pain, Ivan Aprahamian

“…The integration of AI into geriatrics has the potential to improve diagnostic accuracy, optimize therapies, and individualize interventions. …”
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Predicting Employee Turnover Using Machine Learning Techniques by Adil Benabou, Fatima Touhami, My Abdelouahed Sabri

“…Background: Employee turnover is a persistent issue in human resource management, leading to significant costs for organizations. This study aims to identify the most effective machine learning model for predicting employee attrition, thereby providing organizations with a reliable tool to anticipate turnover and implement proactive retention strategies.Objective: This study aims to address the challenge of employee attrition by applying machine learning techniques to provide predictive insights that can improve retention strategies.Methods: Nine machine learning algorithms are applied to a dataset of 1,470 employee records. …”
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Emerging trends in sustainable energy system assessments: integration of machine learning with techno-economic analysis and lifecycle assessment by Ebrahimpourboura Zahra, Mosalpuri Manish, Jonas Baltrusaitis, Dubey Pallavi, Mba Wright Mark

“…Key case studies demonstrate the transformative potential of ML in improving economic viability and environmental sustainability, highlighting its role in predicting system performance, optimizing configurations, and reducing costs and impacts. …”
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Comparative effect of traditional and collaborative watershed management approaches on flood components by Ali Nasiri Khiavi, Mehdi Vafakhah, Seyed Hamidreza Sadeghi, Changhyun Jun, Sayed M. Bateni

“…Abstract Identifying the critical areas of flood generation and determining the optimal measures for flood control and management (FCM) is one of the most important basics of watershed management. …”
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Application of machine learning for predicting the incubation period of water droplet erosion in metals by Khaled AlHammad, Mamoun Medraj, Moussa Tembely

“…Hyperparameter optimization techniques showed minimal improvement in model performance, suggesting that the transformations effectively captured the underlying relationships in the data. …”
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The unwell patient with advanced chronic liver disease: when to use each score? by Oliver Moore, Wai-See Ma, Scott Read, Jacob George, Golo Ahlenstiel

“…Incorporating artificial intelligence to personalise predictive algorithms may provide the most effective prognostication for all clinical phenotypes. …”
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FPA-based weighted average ensemble of deep learning models for classification of lung cancer using CT scan images by Liang Zhou, Achin Jain, Arun Kumar Dubey, Sunil K. Singh, Neha Gupta, Arvind Panwar, Sudhakar Kumar, Turki A. Althaqafi, Varsha Arya, Wadee Alhalabi, Brij B. Gupta

“…Abstract Cancer is among the most dangerous diseases contributing to rising global mortality rates. …”
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RuleKit2: Faster and simpler rule learning by Adam Gudyś, Cezary Maszczyk, Joanna Badura, Adam Grzelak, Marek Sikora, Łukasz Wróbel

“…Here we present its second version. New algorithms and optimized implementations of those previously included, significantly improved the computational performance of our suite, reducing the analysis time of some data sets by two orders of magnitude. …”
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A novel biochemical analysis for ApoE4 quantification in plasma and discrimination of homozygous and heterozygous APOE ε4 carriers by Andrés Rodríguez, Olga Calero, Sergio Veiga, Miriam Menacho-Román, Ignacio Arribas, Lluís Cano, Guillermo García-Ribas, Miguel Calero

“…Discussion The e4Quant assay offers a rapid, cost-effective, and highly accurate biochemical alternative to APOE genotyping, suitable for clinical and research settings, particularly in assessing AD risk and optimizing anti-amyloid therapeutic strategies. …”
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SiCRNN: A Siamese Approach for Sleep Apnea Identification via Tracheal Microphone Signals by Davide Lillini, Carlo Aironi, Lucia Migliorelli, Leonardo Gabrielli, Stefano Squartini

“…The final detection of <i>apnea</i> events is performed using an unsupervised clustering algorithm, specifically <i>k-means</i>. Multiple experimental runs were carried out to determine the optimal network configuration and the most suitable type and frequency range for the input data. …”
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Postmarketing safety evaluation of pemetrexed using FAERS and JADER databases by Luo Lv, Xiangyang Wu, Yubo Ren, Yuli Guo, Haixiong Wang, Xiaofang Li

“…Continuous pharmacovigilance is essential to optimize its clinical use and improve patient safety.…”
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Machine learning-based prediction of physical parameters in heterogeneous carbonate reservoirs using well log data by Fuyong Wang, Xianmu Hou

“…The results demonstrate that GPR achieves the highest accuracy in porosity prediction, with a coefficient of determination (R2) value of 0.7342, while RF proves to be the most accurate for permeability prediction. Despite these improvements, accurately predicting low-permeability zones in heterogeneous carbonate rocks remains a significant challenge. …”
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Efficient spatio-temporal modeling for sign language recognition using CNN and RNN architectures by Kasian Myagila, Kasian Myagila, Devotha Godfrey Nyambo, Mussa Ally Dida

“…These results show that more effort is required to improve signer independence performance, including the challenges of hand dominance by optimizing spatial features.…”
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