Hyperspectral data of understory elements in boreal forests: In situ and laboratory measurementsMendeley DataMendeley Data by Audrey Mercier, Susanna Karlqvist, Aarne Hovi, Miina Rautiainen

“…These data support the analysis of vegetation characteristics, training of classification algorithms and improvement of forest radiative transfer models, and could be used to evaluate the potential of hyperspectral data to discriminate the understory elements of boreal forest.…”
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Advanced Deep Learning Based Predictive Maintenance of DC Microgrids: Correlative Analysis by M. Y. Arafat, M. J. Hossain, Li Li

“…This study can contribute to the emerging technology for predictive maintenance of MGs and provide significant insights into the modeling and performance of RNN architectures for improving fault detection in MG systems. …”
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Unsupervised Class Generation to Expand Semantic Segmentation Datasets by Javier Montalvo, Álvaro García-Martín, Pablo Carballeira, Juan C. SanMiguel

“…Our approach aims to improve the performance of unsupervised domain adaptation methods by introducing novel samples into the training data without modifications to the underlying algorithms. …”
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Sex hormones correlate with heart rate variability in healthy women and this correlation is conserved in women with well-controlled type 2 diabetes mellitus. by Adriana Robles-Cabrera, Claudia Lerma, Silvia Ruiz-Velasco Acosta, Iván Pérez-Díaz, Ruben Fossion

“…However, in regression models adjusted for age and glucose levels, only estradiol was found to be significant, and should be considered an important variable related to cardiovascular and autonomic balance in T2DM women and may provide crucial information to improve cardiovascular risk algorithms.…”
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Approach to knowledge management and the development of a multi-agent knowledge representation and processing system by E. I. Zaytsev, E. V. Nurmatova

“…Methods for distribution of intelligent software agents on the MKRPS nodes are proposed along with algorithms for optimizing the logical structure of the distributed knowledge base (DKB) to improve the performance of the MKRPS in terms of volume, cost and time criteria.Conclusions. …”
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Securing fruit trees future: AI-driven early warning and predictive systems for abiotic stress in changing climate by Muhammad Ahtasham Mushtaq, Muhammad Ateeq, Muhammad Ikram, Shariq Mahmood Alam, Muhammad Mohsin Kaleem, Muhammad Atiq Ashraf, Muhammad Asim, Khalid F. Almutairi, Mahmoud F. Seleiman, Fareeha Shireen

“…AI integrated approaches such as stress prediction, irrigation optimization, and image-based phenotyping have enhanced agriculture, while machine learning models like Random Forest and Gradient Boosting improve stress management. …”
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Optimization Scheduling of Multiple Heterogeneous Energy Sources by Ying Zhao, Zhiwen Yu, Xiaobin Wang, Jianlin Tang, Xiaoming Lin, Fan Zhang, Bin Qian

“…The study summarizes the mainstream mathematical modeling and optimization algorithms, intelligent optimization techniques, and real-time data processing technologies. …”
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Hole Cleaning during Drilling Oil and Gas Wells: A Review for Hole-Cleaning Chemistry and Engineering Parameters by Mohammed Al-Rubaii, Mohammed Al-Shargabi, Dhafer Al-Shehri

“…The development of hole-cleaning models is important for use as hole-cleaning indicators to ensure optimized drilling efficiency. …”
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Architectural Gaps in Generative AI: Quantifying Cognitive Risks for Safety Applications by He Wen, Pingfan Hu

“…<b>Methods</b>: This study investigates whether architectural design elements in Transformer-based generative models contribute to a measurable divergence from human reasoning. …”
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Clays Are Not Created Equal: How Clay Mineral Type Affects Soil Parameterization by P. Lehmann, B. Leshchinsky, S. Gupta, B. B. Mirus, S. Bickel, N. Lu, D. Or

“…Clay mineral‐informed pedotransfer functions and machine learning algorithms trained with datasets including different clay types and soil structure formation processes improve SHMP representation regionally with broad implications for hydrological and geomechanical Earth surface processes.…”
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Advancement of artificial intelligence based treatment strategy in type 2 diabetes: A critical update by Aniruddha Sen, Palani Selvam Mohanraj, Vijaya Laxmi, Sumel Ashique, Rajalakshimi Vasudevan, Afaf Aldahish, Anupriya Velu, Arani Das, Iman Ehsan, Anas Islam, Sabina Yasmin, Mohammad Yousuf Ansari

“…At the same time, the rapidly increasing role of AI in diabetes care is woven into the story, mainly targeting how insulin therapy can be modified and personalized through algorithms and predictive modelling. It leaves a deep review of their pre-existing synergies, which helps understand how collaborative opportunities will unlock the future of T2DM care. …”
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Artificial Intelligence in the Organization of Nursing Care: A Scoping Review by João Ventura-Silva, Maria Manuela Martins, Letícia de Lima Trindade, Ana da Conceição Alves Faria, Soraia Pereira, Samuel Spiegelberg Zuge, Olga Maria Pimenta Lopes Ribeiro

“…Conclusions: AI tools such as automated alert systems, predictive algorithms, and decision support transform nursing by increasing efficiency, accuracy, and patient-centered care, improving communication, reducing errors, and enabling earlier interventions with safer and more efficient quality care.…”
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Fighting Evaluation Inflation: Concentrated Datasets for Grammatical Error Correction Task by Vladimir Starchenko, Darya Kharlamova, Elizaveta Klykova, Anastasia Shavrina, Aleksey Starchenko, Olga Vinogradova, Olga Lyashevskaya

“…Conclusion: The lower scores registered on concentrated datasets confirm that they provide a way for future improvement of GEC models. The dataset can be used for further studies focusing on distant-context-sensitive GEC. …”
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Clustering and classification of early knee osteoarthritis using machine-learning analysis of step-up and down test kinematics in recreational table tennis players by Ui-jae Hwang, Kyu Sung Chung, Sung-min Ha

“…Louvain clustering identified four distinct kinematic profiles with varying proportions of EOA (ranging from 41.2% to 70.7%). Supervised learning models achieved high performance in classifying EOA status, with Random Forest, gradient boosting, and decision tree algorithms achieving 100% classification accuracy (AUC = 1.000) on the test dataset. …”
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TAE Predict: An Ensemble Methodology for Multivariate Time Series Forecasting of Climate Variables in the Context of Climate Change by Juan Frausto Solís, Erick Estrada-Patiño, Mirna Ponce Flores, Juan Paulo Sánchez-Hernández, Guadalupe Castilla-Valdez, Javier González-Barbosa

“…Additionally, data remediation techniques improve data set quality. The ensemble combines Long Short-Term Memory neural networks, Random Forest regression, and Support Vector Machines, optimizing their contributions using heuristic algorithms such as Particle Swarm Optimization. …”
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Integrating Handcrafted Features with Machine Learning for Hate Speech Detection in Albanian Social Media by Fetahi Endrit, Hamiti Mentor, Susuri Arsim, Zenuni Xhemal, Ajdari Jaumin

“…To address this issue, this research aims to conduct extensive experiments using machine learning models and handcrafted feature extraction in the low-resource language Albanian. …”
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Bridge Digital Twin for Practical Bridge Operation and Maintenance by Integrating GIS and BIM by Yan Gao, Guanyu Xiong, Ziyu Hu, Chengzhang Chai, Haijiang Li

“…Additionally, it features an adaptive data structure incorporating JSON-based bridge defect information modeling and triple-based roadmap graphs to streamline data management and decision-making. …”
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Future Era of Accountants under the Impact of AI by Guan Ran

“…As AI reshapes the industry, accountants must develop expertise in data analytics, predictive modeling, and cybersecurity to remain competitive. …”
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Operational factors influencing quality control in ore milling: a systematic review by Eliana de Jesus Lopes, Guilherme Graciano dos Santos, Adriano Ricardo Almeida Alexandre

“…The identified studies explore advanced techniques such as algorithms and mathematical modeling to optimize the grinding process and improve product quality. …”
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DeepBase: A Deep Learning-based Daily Baseflow Dataset across the United States by Parnian Ghaneei, Hamid Moradkhani

“…Abstract High quality baseflow data is important for advancing water resources modeling and management, as it captures the critical role of groundwater and delayed sources in contributing to streamflow. …”
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