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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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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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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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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Baseline high-resolution maps of soil nutrients in Morocco to support sustainable agriculture by Yassine Bouslihim, Abdelkrim Bouasria, Ahmed Jelloul, Lotfi Khiari, Sara Dahhani, Rachid Mrabet, Rachid Moussadek

“…This paper presents the first national reference maps of available P and exchangeable K at 250 m resolution over Morocco’s croplands using digital soil mapping with machine learning algorithms and environmental covariates. Unlike previous efforts employing traditional interpolation methods, these maps were developed using Random Forest by integrating 5,276 soil samples for P and 6,978 for K with 76 environmental covariates representing climate, topography, vegetation, and parent material. …”
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ON THE QUESTION OF MASS-ENERGY CHARACTERISTICS OF VARIOUS AVIATION SOURCES OF ELECTRICITY by A. V. Kechin

“…The article raises the issue of the synthesis of power supply systems for new generation aircraft, which is understood as: the power supply system (complex) of the aircraft, which includes the power and information structures closely integrated. Using this method, the power generation, transformation and distribution functions are assigned to the power structure, and the information structure provides work algorithms. …”
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PSO Tuned Super-Twisting Sliding Mode Controller for Trajectory Tracking Control of an Articulated Robot by Zewdalem Abebaw Ayinalem, Abrham Tadesse Kassie

“…This paper introduces a novel method that integrates SolidWorks modeling to create precise digital representations of the robot’s mechanical structure, facilitating easier development and simulation of control algorithms. …”
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Prediction and optimization of hardness in AlSi10Mg alloy produced by laser powder bed fusion using statistical and machine learning approaches by İnayet Burcu Toprak

“…In addition, Machine Learning algorithms were applied to predict hardness values. …”
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Application of statistical methods for predicting udp-flood attacks by M. V. Tumbinskaya, V. V. Volkov, B. G. Zagidullin

“…Web resources are an integral part of modern human life. Today, these resources are increasingly exposed to hacker attacks, such as the introduction of SQL operators, crosssite scripting, etc. …”
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Food Public Opinion Prevention and Control Model Based on Sentiment Analysis by Leiyang Chen, Xiangzhen Peng, Liang Dong, Zhenyu Wang, Zhidong Shen, Xiaohui Cui

“…This paper introduces the Food Public Opinion Prevention and Control (FPOPC) model driven by deep learning and personalized recommendation algorithms, rigorously tested and analyzed through experimentation. …”
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Segmentation of Spatial Crack Structures in Concrete by Deep Learning Enabling Image Based Characterization by Anna Nowacka, Katja Schladitz, Szymon Grzesiak, Matthias Pahn

“…The reconstructed tomographic images are 3d images, consisting of voxels whose gray values represent local X-ray absorption. In order to identify voxels belonging to the crack, so to segment the crack structure in the images, appropriate algorithms need to be developed. …”
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