Comparative assessment of standalone and hybrid deep neural networks for modeling daily pan evaporation in a semi-arid environment by Mohammed Achite, Manish Kumar, Nehal Elshaboury, Aman Srivastava, Ahmed Elbeltagi, Ali Salem

“…The present framework’s success in Algeria’s Wadi Sly basin highlights its potential for scalable adoption in irrigation scheduling and drought resilience strategies, yielding implementable steps for policymakers, addressing climate-driven water scarcity. …”
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Enabling high-throughput quantitative wood anatomy through a dedicated pipeline by Jan Van den Bulcke, Louis Verschuren, Ruben De Blaere, Simon Vansuyt, Maxime Dekegeleer, Pierre Kibleur, Olivier Pieters, Tom De Mil, Wannes Hubau, Hans Beeckman, Joris Van Acker, Francis wyffels

“…The conventional pathway for high-resolution digitization of wood surfaces and segmentation of wood features requires several manual and time consuming steps. We present a semi-automated high-throughput pipeline for sample preparation, gigapixel imaging, and analysis of the anatomy of the end-grain surfaces of discs and increment cores. …”
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Evaluating war-induced damage to agricultural land in the Gaza Strip since October 2023 using PlanetScope and SkySat imagery by He Yin, Lina Eklund, Dimah Habash, Mazin B. Qumsiyeh, Jamon Van Den Hoek

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Automated Imaging of Cataract Surgery Using Artificial Intelligence by Young Jae Kim, Sung Ha Hwang, Kwang Gi Kim, Dong Heun Nam

“…The second part of the architecture was inspired by ensemble learning, in which two ResNet-50 models were trained in parallel using fake images obtained in the previous step and unprocessed images. …”
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Breast cancer image classification by using HCNN and LeNet5 by Pramoda Patro, Shaik Honey Fathima, R. Harikishore, Aditya Kumar Sahu

“…Therefore, an effective hybrid deep learning model is introduced to improve the prediction performance and reduce the time consumption compared to the machine learning model. …”
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Flexible Job Shop Scheduling Based on Energy Consumption of Method Research by Yajie Li, Longlong Li, Xiaoying Yang, Bingfeng Zhao

“…By establishing a multi-objective optimization model aimed at minimizing the maximum completion time and energy consumption, this paper solves the flexible job-shop scheduling problem considering energy consumption (GFJSP) based on an improved deep reinforcement learning algorithm, D3QN. This paper proposes an improved MD3QN algorithm by introducing a dual experience pool, a noisy neural network, and a reward mechanism combining single-step and multi-step rewards on the basis of the D3QN algorithm, thereby further improving the algorithm’s convergence, generalization ability, and solution stability. …”
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Artificial Intelligence and Hand Hygiene Accuracy: A New Era in Infection Control for Dental Practices by Salwa A. Aldahlawi, Amr H. Almoallim, Ibtesam K. Afifi

“…Step 3 (back of fingers to opposing palm with fingers interlocked) was consistently identified as the most frequently missed step by both the AI model and the infection control auditors. …”
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SAM-Net: Spatio-Temporal Sequence Typhoon Cloud Image Prediction Net with Self-Attention Memory by Yanzhao Ren, Jinyuan Ye, Xiaochuan Wang, Fengjin Xiao, Ruijun Liu

“…Spatio-temporal sequence prediction involves learning from historical data and using the learned features to generate future images. …”
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SCGAN-enhanced surrogate modeling for FEM analysis of induction motors: A practical use case by Unai Salaberria-Flaño, Antonio Nappa, Arkaitz Artetxe, Gurutz Artetxe, Iñaki Inza

“…It also clearly demonstrates the potential of multi-fidelity learning to overcome the computational-cost barriers of complex engineering systems; notably, SCGAN's prediction step executes orders of magnitude faster than traditional FEM, paving the way for real-time deployment.…”
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Au seuil de la classe, le maître E régule différentes temporalités by Pascale Ponté, Serge Thomazet, Corinne Mérini

“…These evolutions justified a research we led for three years on the work of special need teachers in charge of the learning support (maître E in French) and its vectors of transformations. …”
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Diagnostics of the results of mastering competencies in the information environment for teaching IT students by I. A. Andrianov, A. M. Polyanskiy, S. U. Rzheutskaya, M. V. Kharina

“…Purpose of research. Diagnostics of learning results presented in a competency-based format is currently a necessary part of the educational process of universities. …”
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Taking Care of Each Other: The Transformative Education for the Rebuilding of New Orleans after Hurricane Katrina by F. Fanizza, F. Spallone

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Aircraft Trajectory Prediction With Inverted Transformer by Seokbin Yoon, Keumjin Lee

“…In a vanilla Transformer, temporal tokens containing the multivariate features are embedded for each time step, but in the Inverted Transformer, each series is embedded independently into variate tokens. …”
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National-Scale Detection of New Forest Roads in Sentinel-2 Time Series by Øivind Due Trier, Arnt-Børre Salberg

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Chaotic Vibration Prediction of a Laminated Composite Cantilever Beam by Xudong Li, Lin Sun, Xiaopei Liu, Yili Duo

“…The RNN model converts time series data into a multi-step supervised learning format and normalizes it using MinMaxScaler. …”
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Research on the robustness of the open-world test-time training model by Shu Pi, Xin Wang, Jiatian Pi

“…IntroductionGeneralizing deep learning models to unseen target domains with low latency has motivated research into test-time training/adaptation (TTT/TTA). …”
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Early Parkinson’s disease identification via hybrid feature selection from multi-feature subsets and optimized CatBoost with SMOTE by Subhashree Mohapatra, Bhanja Kishor Swain, Manohar Mishra

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Investigation of the Features Influencing the Accuracy of Wind Turbine Power Calculation at Short-Term Intervals by Pavel V. Matrenin, Dmitry A. Harlashkin, Marina V. Mazunina, Alexandra I. Khalyasmaa

“…It is discovered that using ensemble machine learning models and additional features, including the actual power from the previous time step, enhances the accuracy of the wind power calculation. …”
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Harnessing Artificial Intelligence for Wildlife Conservation by Paul Fergus, Carl Chalmers, Steven Longmore, Serge Wich

“…Conservation AI represents a significant step forward in addressing the urgent challenges of wildlife conservation, offering a scalable and adaptable solution that can be implemented globally.…”
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Edge-AI Enabled Wearable Device for Non-Invasive Type 1 Diabetes Detection Using ECG Signals by Maria Gragnaniello, Vincenzo Romano Marrazzo, Alessandro Borghese, Luca Maresca, Giovanni Breglio, Michele Riccio

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