Alpine Meadow Fractional Vegetation Cover Estimation Using UAV-Aided Sentinel-2 Imagery by Kai Du, Yi Shao, Naixin Yao, Hongyan Yu, Shaozhong Ma, Xufeng Mao, Litao Wang, Jianjun Wang

“…However, due to the low and sparse vegetation in alpine meadows, it is challenging to obtain pure vegetation pixels from Sentinel-2 imagery, resulting in errors in the FVC estimation using traditional pixel dichotomy models. This study integrated Sentinel-2 imagery with unmanned aerial vehicle (UAV) data and utilized the pixel dichotomy model together with four machine learning algorithms, namely Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Deep Neural Network (DNN), to estimate FVC in an alpine meadow region. …”
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Exploring the capabilities of hyperspectral remote sensing for soil texture evaluation by Mohammad Hosseinpour-Zarnaq, Mahmoud Omid, Fereydoon Sarmadian, Hassan Ghasemi-Mobtaker, Reza Alimardani, Pouya Bohlol

“…The influence of spectral unmixing and vegetation effects on croplands significantly enhanced the prediction and mapping of soil texture using PRISMA data. Furthermore, integrating preprocessed hyperspectral data with DSM covariates resulted in improved predictive performance for sand and clay content, yielding R2 values of 0.64 and 0.51, respectively. …”
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Optimal Placement of Wind Power System Using Machine Learning by Abdul Karim, Muhammad Amir Raza, Darakhshan Ara, Muhammad Shahid, Shakir Ali Soomro

“…Hence, this study, proposed a plan for the installation of wind turbines in Doha Qatar, and forecasted the future temperature and wind speed for the optimal placement of large-scale wind turbines using the Pythons algorithms namely, Long Short-Term Memory (LSTM), Prophet (PT), Support Vector Regression (SVR), Linear Regression (LR), Seasonal Autoregressive Integrated Moving Average with External Factors (SARIMAX), and K-Nearest Neighbors (KNN). …”
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Efficient Argan Tree Deforestation Detection Using Sentinel-2 Time Series and Machine Learning by Younes Karmoude, Soufiane Idbraim, Souad Saidi, Antoine Masse, Manuel Arbelo

“…This study monitors changes in an argan forest near Agadir, Morocco, from 2017 to 2023 using Sentinel-2 satellite imagery and advanced image processing algorithms. Various machine learning models were evaluated for argan tree detection, with LightGBM achieving the highest accuracy when trained on a dataset integrating spectral bands, temporal features, and vegetation indices information. …”
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Leveraging citizen science to classify and track benthic habitat states: An unsupervised UMAP-HDBSCAN pipeline applied to the global reef life survey dataset by Clément Violet, Aurélien Boyé, Stanislas Dubois, Graham J. Edgar, Elizabeth S. Oh, Rick D. Stuart-Smith, Martin P. Marzloff

“…This study also showcases the potential of integrating the UMAP-HDBSCAN pipeline with Shapley values for clustering noisy ecological data from citizen science initiatives.…”
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Parametric optimization of the slot waveguide characteristics using a machine-learning approach by Yadvendra Singh, Suraj Jena, Harish Subbaraman

“…This specific property of the slot waveguide provides interaction between active material and electric field, which led to many interesting applications, such as optical amplification, optical switching, and optical detection in integrated photonics. In the present work, we combine machine learning (ML) algorithms and finite element simulation to predict the power confinement ( $$\hbox {P}_{conf}$$ ) and mode effective index ( $$\hbox {n}_{eff}$$ ) of slot waveguides with respect to geometric parameters such as gap, slab width, and slab height. …”
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Deep learning methods for clinical workflow phase-based prediction of procedure duration: a benchmark study by Emanuele Frassini, Teddy S. Vijfvinkel, Rick M. Butler, Maarten van der Elst, Benno H. W. Hendriks, John J. van den Dobbelsteen

“…We employed only the clinical phases derived from video analysis as input to the algorithms. Our results show that InceptionTime and LSTM-FCN yielded the most accurate predictions. …”
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An improved performance model for artificial intelligence-based diabetes prediction by Ugwu Hillary Okwudili, Oparaku Ogbonna Ukachukwu, V. C. Chijindu, Michael Okechukwu Ezea, Buhari Ishaq

“…XB demonstrates remarkable predictive performance, bolstered by its built-in cross-validation and parallel processing; nevertheless, it remains sensitive to outliers, highlighting the importance of thorough data processing. By integrating these algorithms into an ensemble framework, this study effectively mitigated their individual limitations, leading to a more accurate and improved reliable prediction model. …”
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Architecture-aware minimization (A2M): how to find flat minima in neural architecture search by Matteo Gambella, Fabrizio Pittorino, Manuel Roveri

“…In particular, we unveil the detailed geometrical structure of the architecture search landscape by uncovering the absence of barriers between well-performing architectures, finding that highly accurate architectures cluster together in flat regions, while suboptimal architectures instead remain isolated, showing higher values of the barriers. Building on these insights, we propose architecture-aware minimization (A ^2 M), a novel analytically derived algorithmic framework that explicitly biases, for the first time, the gradient of differentiable NAS methods towards flat minima in architecture space . …”
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Predictive Energy Management for Docker Containers in Cloud Computing: A Time Series Analysis Approach by Abdulmohsen Algarni, Iqrar Shah, Ali Imran Jehangiri, Mohammed Alaa Ala'Anzy, Zulfiqar Ahmad

“…These datacenters are integral to energy utilization in cloud environments, with energy consumption closely tied to resource utilization. …”
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Software and methodological support for additional professional courses: ideas, problems, monitoring by O. D. Prokhorenko, S. V. Morin, O. A. Kozyreva

“…By building prospects for theorizing and implementing ideas for improving the quality of modeling and using software and methodological support for further education courses, we can distinguish in the integrative representation of the direction and prospects of an individual’s achievements three types of solving professional and pedagogical problems theorized in the work (algorithmic, creative-stimulative and innovative-promising).…”
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Machine Learning-Driven Prediction of Brain Age for Alzheimer’s Risk: APOE4 Genotype and Gender Effects by Carter Woods, Xin Xing, Subash Khanal, Ai-Ling Lin

“…This study aimed to use machine learning (ML) algorithms to predict brain age and assess AD risk by considering the effects of the APOE4 genotype and gender. …”
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Deployment of real-time particle detection monitoring system in operating theatres for airborne contamination assessments: a methodological evaluation by Frans Stålfelt, Johan Tenghamn, Henrik Malchau, Karin Svensson Malchau

“…Future research should focus on integrating predictive algorithms and machine-learning to enhance clinical utility and drive improvements in surgical safety. …”
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Automating the Design of Scalable and Efficient IoT Architectures Using Generative Adversarial Networks and Model-Based Engineering for Industry 4.0 by William Villegas-Ch, Jaime Govea, Diego Buenano-Fernandez, Aracely Mera-Navarrete

“…Traditional approaches, such as heuristic and genetic algorithms, have proven insufficient in automating and optimizing large-scale IoT configurations, resulting in a high design and validation time cost. …”
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Data-Driven and Mechanistic Soil Modeling for Precision Fertilization Management in Cotton by Miltiadis Iatrou, Panagiotis Tziachris, Fotis Bilias, Panagiotis Kekelis, Christos Pavlakis, Aphrodite Theofilidou, Ioannis Papadopoulos, Georgios Strouthopoulos, Georgios Giannopoulos, Dimitrios Arampatzis, Evangelos Vergos, Christos Karydas, Dimitris Beslemes, Vassilis Aschonitis

“…By comparing the Mean Absolute Error (MAE) between predicted and observed cotton yield values across three ML algorithms, i.e., Random Forest (RF), XGBoost, and LightGBM, the RF model achieved the lowest error (422.6 kg/ha), outperforming XGBoost (446 kg/ha) and LightGBM (449 kg/ha). …”
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Prediction of time averaged wall shear stress distribution in coronary arteries’ bifurcation varying in morphological features via deep learning by Mohammad Hossein Sarkhosh, Mohammad Hossein Sarkhosh, Hadis Edrisnia, Mohammad Reza Raveshi, Mahkame Sharbatdar

“…To overcome this challenge, we integrate a deep learning (DL) method to improve efficiency and responsiveness. …”
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Improving chlorophyll-a estimation using Sentinel-2 data: a comparative analysis of augmented datasets by Jinmyeong Lee, Do Hyuck Kwon, Heewon Jeong, Gibeom Nam, Euiho Hwang, Jin Hwi Kim, Kyung Hwa Cho, Hyo Gyeom Kim

“…Models trained on the augmented datasets, GEND and ND-SMOGN, successfully addressed this underestimation issue for the sample with the highest Chl-a concentration. Among the six algorithms, multilayer perceptron with attention mechanism exploited the highest performance across all indicators with coefficient of determination (R2) and root mean square error (RMSE) values of 0.93 and 2.76. …”
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Advanced GIS-based Multi-Function Support System for Identifying the Best Route by Suhiar Mohammed Zeki Abd Alsammed

“…The proposed model includes several stages; Google Maps downloading, preprocessing, integrating with the database, and identifying the best route by utilizing advanced algorithms of artificial intelligence. …”
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Fine-Tuning Models for Histopathological Classification of Colorectal Cancer by Houda Saif ALGhafri, Chia S. Lim

“…To validate the models’ decision-making and improve transparency, we integrated Grad-CAM to provide visual explanations that influence classification decisions. …”
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Load Probability Density Forecasting Under FDI Attacks Based on Double-Layer LSTM Quantile Regression by Pei Zhao, Jie Zhang, Guang Ling

“…The method effectively integrates data-driven and statistical algorithms such as double-layer long short-term memory (DL-LSTM) networks, quantile regression (QR), and kernel density estimation (KDE). …”
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