Calibration Transfer of Soil Total Carbon and Total Nitrogen between Two Different Types of Soils Based on Visible-Near-Infrared Reflectance Spectroscopy by Xue-Ying Li, Yan Liu, Mei-Rong Lv, Yan Zou, Ping-Ping Fan

“…The RMSEP decreased from 2.42 to approximately 0.04 for TN and from 15.74 to approximately 0.4 for TC. The WMPDS-S/B algorithm had advantages in selecting fewer known samples and obtaining better prediction results. …”
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Assessing cyber risks in construction projects: A machine learning-centric approach by Dongchi Yao, Borja García de Soto

“…This approach comprises three components: (1) For risk prediction, a simulated dataset is generated using Monte Carlo simulations, which is utilized for model training. …”
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Interpretable machine learning model of effective mass in perovskite oxides with cross-scale features by Changjiao Li, Zhengtao Huang, Hua Hao, Zhonghui Shen, Guanghui Zhao, Ben Xu, Hanxing Liu

“…The interpretability of machine learning reveals associations between input features and predicted physical properties in models, which are essential for discovering new materials. …”
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Enhancing decision-making on detractor-causing failures: an approach combining data mining and machine learning by Yuri A. V. da Silva, Geraldo Cardoso de Oliveira Neto, Gustavo Lima, Sidnei A. de Araújo, Rodrigo Neri Bueno da Silva, Francisco Elanio Bezerra, Marlene Amorim

“…In this context, this study proposes a methodology that combines data mining (DM) and machine learning (ML) to analyze and predict the root causes of detractors using transactional data from the big data repository of a major sports retail company. …”
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Demystifying multiple sclerosis diagnosis using interpretable and understandable artificial intelligence by Chadaga Krishnaraj, Khanna Varada Vivek, Prabhu Srikanth, Sampathila Niranjana, Chadaga Rajagopala, Palkar Anisha

“…Hence, supervised machine learning (ML) algorithms and several hyperparameter tuning techniques, including Bayesian optimization, have been utilized in this study to predict MS in patients. …”
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Optimizing solar energy utilization in facilities using machine learning-based scheduling techniques: A case study by Hussam J. Khasawneh, Waseem M. Al-Khatib, Zaid A. Ghazal, Ahmad M. Al-Hadi, Zaid M. Arabiyat, Osama Habahbeh

“…Our approach overcomes these limitations by employing ML algorithms to accurately predict solar generation patterns, enabling more efficient scheduling of electrical appliances. …”
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Strategies and Challenges in Detecting XSS Vulnerabilities Using an Innovative Cookie Collector by Germán Rodríguez-Galán, Eduardo Benavides-Astudillo, Daniel Nuñez-Agurto, Pablo Puente-Ponce, Sonia Cárdenas-Delgado, Mauricio Loachamín-Valencia

“…Machine learning models were developed to classify suspicious web domains and predict their vulnerability to XSS attacks. Additionally, clustering algorithms enabled user segmentation based on cookie data, identification of behavioral patterns, enhanced personalized web recommendations, and browsing experience optimization. …”
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Strong parameter hierarchy in the interstellar phosphorus chemical network by Marina Fernández-Ruz, Marina Fernández-Ruz, Izaskun Jiménez-Serra, Mario Castro, Mario Castro, Marta Ruiz-Bermejo, Jacobo Aguirre, Jacobo Aguirre

“…The simplified model retains its predictive accuracy, offering deeper insights into the mechanisms driving phosphorus chemistry in the interstellar medium. …”
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The Impact of AI-Driven Application Programming Interfaces (APIs) on Educational Information Management by David Pérez-Jorge, Miriam Catalina González-Afonso, Anthea Gara Santos-Álvarez, Zeus Plasencia-Carballo, Carmen de los Ángeles Perdomo-López

“…The findings highlight five main benefits: data interoperability, personalized learning, automated feedback, real-time student monitoring, and predictive performance analytics. All studies addressed personalization, 74.1% focused on platform integration, and 37% examined automated feedback. …”
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Maglev Derived Systems: An Interoperable Freight Vehicle Application Focused on Minimal Modifications to the Rail Infrastructure and Vehicles by Jesus Felez, Miguel A. Vaquero-Serrano, William Z. Liu, Carlos Casanueva, Michael Schultz-Wildelau, Gerard Coquery, Pietro Proietti

“…Target speed profiles were precomputed using dynamic programming, while a model predictive control algorithm determined the optimal train state and control trajectories. …”
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Mapping the landscape of AI and ML in vaccine innovation: A bibliometric study by Jirui Niu, Ruotian Deng, Zipu Dong, Xue Yang, Zhaohui Xing, Yin Yu, Jian Kang

“…By leveraging data-driven insights and predictive modeling, AI can streamline processes such as antigen discovery, clinical trial design, and risk assessment, thereby enabling faster responses to public health emergencies. …”
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Study of model construction of fuel production from waste plastic pyrolysis based on machine learning by CHEN Sihan, YUAN Zhilong, WANG Ye, SUN Yifei*

“…The Gradient Boosting Regression (GBR) algorithm has the best fitting performance for predicting oil yield (R^2=0.91, RMSE=7.78), while the adaptive boosting algorithm (AdaBoost) has the best fitting performance for predicting gas yield (R^2=0.83, RMSE=6.42), enabling accurate prediction of reaction conditions. …”
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Harnessing Novel Data‐Driven Techniques for Precise Rainfall–Runoff Modeling by Saad Sh. Sammen, Reza Mohammadpour, Karam AlSafadi, Ali Mokhtar, Shamsuddin Shahid

“…Although the GMDH predicts runoff with higher accuracy, ELM provides reliable performance in simulating both low and high values. …”
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PFML: Self-Supervised Learning of Time-Series Data Without Representation Collapse by Einari Vaaras, Manu Airaksinen, Okko Rasanen

“…This paper introduces a novel SSL algorithm for time-series data called Prediction of Functionals from Masked Latents (PFML). …”
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Deep learning-based technique for investigating the behavior of MEMS systems with multiwalled carbon nanotubes and electrically actuated microbeams by Muhammad Amir, Jamshaid Ul Rahman, Ali Hasan Ali, Ali Raza, Zaid Ameen Abduljabbar, Husam A. Neamah

“…Numerical simulations and graphical demonstrations are presented to verify the accuracy and efficiency of the algorithm. • The study develops a novel DNN-based model to solve non-linear systems in MEMS, particularly for oscillators with MWCNTs. • Deep learning optimizers are applied to improve the accuracy and efficiency of predicting MEMS behavior. • Numerical simulations confirm the effectiveness of the proposed methodology.…”
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Impact of bridging the gap between Artificial Intelligence and nanomedicine in healthcare by Divyam Mishra, Bhavishya Chaturvedi, Vishal Soni, Dhairya Valecha, Megha Goel, Jamilur R. Ansari

“…Furthermore, this study investigates the application of AI in predicting nanomedicine interactions with biological systems, aiming to establish AI-enabled platforms for personalized nanomedicine therapies. …”
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Diurnal distribution of phytoplankton in large shallow lakes based on time series clustering by Yanhong Chen, Haibin Cai, Yiqing Gong, Kun Lu, Jingqiao Mao, Weiyu Chen, Kang Wang, Huan Gao, Mingming Tian

“…However, short-term changes in phytoplankton distributions are often overlooked, leading to underestimations in predictions and difficulties in lake management. Considering that potential information from abundant automatic monitoring datasets has not been fully explored, we developed an automated recognition method to identify diurnal variations in phytoplankton via time series clustering. …”
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Combination of Remote Sensing and Artificial Intelligence in Fruit Growing: Progress, Challenges, and Potential Applications by Danielle Elis Garcia Furuya, Édson Luis Bolfe, Taya Cristo Parreiras, Jayme Garcia Arnal Barbedo, Thiago Teixeira Santos, Luciano Gebler

“…With the advancement of technologies, mapping fruits using remote sensing and machine learning (ML) and deep learning (DL) techniques has become an essential tool to optimize production, monitor crop health, and predict harvests with greater accuracy. This study was developed in four main stages. …”
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Artificial intelligence-driven modeling of biodiesel production from fats, oils, and grease (FOG) with process optimization via particle swarm optimization by Badril Azhar, Muhammad Ikhsan Taipabu, Cries Avian, Karthickeyan Viswanathan, Wei Wu, Raymond Lau

“…A ML model evaluation, using various algorithms, identify XGBoost, Extra Trees, Gradient Boosting, LGBM, and Random Forest demonstrate the best performer for predicting process parameters, achieving an R2 value of nearly to 1. …”
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Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validation by Guiling Wu, Guiling Wu, Sihui Wu, Sihui Wu, Tian Xiong, Tian Xiong, Tian Xiong, You Yao, You Yao, Yu Qiu, Yu Qiu, Yu Qiu, Liheng Meng, Cuihong Chen, Xi Yang, Xi Yang, Xi Yang, Xinghuan Liang, Yingfen Qin

“…Immune dysregulation was observed in MAFLD, with TNFRSF1A and SERPINB2 strongly linked to immune regulation.ConclusionThe sensitivity and accuracy in diagnosing and predicting T2DM-associated MAFLD can be greatly improved using SERPINB2 and TNFRSF1A. …”
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