A data driven approach to classify descriptors based on their efficiency in translating noisy trajectories into physically-relevant information by Simone Martino, Domiziano Doria, Chiara Lionello, Matteo Becchi, Giovanni M Pavan

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Balancing Explainability and Privacy in Bank Failure Prediction: A Differentially Private Glass-Box Approach by Junyoung Byun, Jaewook Lee, Hyeongyeong Lee, Bumho Son

“…Traditional machine learning models often lack transparency, which poses challenges for stakeholders who need to understand the factors leading to predictions. …”
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Learning to rank quantum circuits for hardware-optimized performance enhancement by Gavin S. Hartnett, Aaron Barbosa, Pranav S. Mundada, Michael Hush, Michael J. Biercuk, Yuval Baum

“…We introduce and experimentally test a machine-learning-based method for ranking logically equivalent quantum circuits based on expected performance estimates derived from a training procedure conducted on real hardware. …”
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Hybrid forecasting of demand flexibility: A top-down approach for thermostatically controlled loads by Luca Massidda, Marino Marrocu

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Learning Permutation Symmetry of a Gaussian Vector with gips in R by Adam Chojecki, Paweł Morgen, Bartosz Kołodziejek

“… The study of hidden structures in data presents challenges in modern statistics and machine learning. We introduce the gips package in R, which identifies permutation subgroup symmetries in Gaussian vectors. gips serves two main purposes: Exploratory analysis in discovering hidden permutation symmetries and estimating the covariance matrix under permutation symmetry. …”
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Common Pitfalls in Psm Assessment - Case Studies and Lessons Learned by Zahra Basiri, Andrea Gritti, Leonardo Michele Carluccio

“…The increasing complexity of the process industry calls for incorporating Artificial Intelligence (AI) and machine learning, for accurate risk prediction and system effectiveness of PSM systems.…”
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CT-based AI framework leveraging multi-scale features for predicting pathological grade and Ki67 index in clear cell renal cell carcinoma: a multicenter study by Huancheng Yang, Yueyue Zhang, Fan Li, Weihao Liu, Haoyang Zeng, Haoyuan Yuan, Zixi Ye, Zexin Huang, Yangguang Yuan, Ye Xiang, Kai Wu, Hanlin Liu

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Heterogeneity Challenges of Federated Learning for Future Wireless Communication Networks by Lorena Isabel Barona López, Thomás Borja Saltos

“…Two technologies of great interest in recent years—Artificial Intelligence (AI) and massive wireless communication networks—have found a significant point of convergence through Federated Learning (FL). Federated Learning is a Machine Learning (ML) technique that enables multiple participants to collaboratively train a model while keeping their data local. …”
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Optimizing Human-Centric Warehouse Operations: A Digital Twin Approach Using Dynamic Algorithms and AI/ML by Erhan Arslan

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Network-based intrusion detection using deep learning technique by Muhammad Farhan, Hafiz Waheed ud din, Saadat Ullah, Muhammad Sajjad Hussain, Muhammad Amir Khan, Tehseen Mazhar, Umar Farooq Khattak, Ines Hilali Jaghdam

“…Most traditional Network-based Intrusion Detection Systems (NIDS) can become weak at detecting new patterns of attacks due to the use of obsolete data or traditional machine learning models. To overcome the mentioned constraints, the current research presents a new deep learning solution that combines Sequential Deep Neural Networks (DNN) and Rectified Linear Unit (ReLU) activation unit with an Extra Tree Classifier feature selection procedure. …”
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Long‐term dynamics of coastal dune landscapes and habitat diversity: Insights from a quarter century of resurveys in Castelporziano Presidential Estate by Elena Cini, Alicia Teresa Rosario Acosta, Marco Malavasi, Simona Sarmati, Silvia Del Vecchio, Daniela Ciccarelli, Flavio Marzialetti

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Efficient Edge AI for Next Generation Smart Mirror Applications by Nils Kucza, Florian Porrmann, Christian Stollenwerk, Jens Hagemeyer

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Scalable geometric learning with correlation-based functional brain networks by Kisung You, Yelim Lee, Hae-Jeong Park

“…This approach enables scalable, geometry-aware analyses and integrates seamlessly with standard machine learning techniques, including regression, dimensionality reduction, and clustering. …”
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Deep learning for property prediction of natural fiber polymer composites by Ivan P. Malashin, Dmitry Martysyuk, Vladimir Nelyub, Aleksei Borodulin, Andrei Gantimurov, Vadim Tynchenko

“…Best DNN model architecture (four hidden layers (128–64–32–16 neurons), ReLU activation, 20% dropout, a batch size of 64, and the AdamW optimizer with a learning rate of $$10^{-3}$$ ) obtained through hyperparameter optimization using Optuna, delivered the best performance (R $$^2$$ up to 0.89) and MAE reductions of 9–12% compared to gradient boosting, driven by the DNN’s ability to capture nonlinear synergies between fiber-matrix interactions, surface treatments, and processing parameters while aligning architectural complexity with multiscale material behavior.…”
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Research on Missing Data Estimation Method for UPFC Submodules Based on Bayesian Multiple Imputation and Support Vector Machines by Xiaoming Yu, Jun Wang, Ke Zhang, Zhijun Chen, Ming Tong, Sibo Sun, Jiapeng Shen, Li Zhang, Chuyang Wang

“…This study confirms the effectiveness of integrating Bayesian statistics with machine learning for power data restoration, providing a high-precision and low-complexity solution for equipment condition monitoring in complex operational environments. …”
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Improving of the Generation Accuracy Forecasting of Photovoltaic Plants Based on <i>k</i>-Means and <i>k</i>-Nearest Neighbors Algorithms by P. V. Matrenin, A. I. Khalyasmaa, V. V. Gamaley, S. A. Eroshenko, N. A. Papkova, D. A. Sekatski, Y. V. Potachits

“…In this paper, a method for adapting of forecast models to the meteorological conditions of photovoltaic stations operation based on machine learning algorithms was proposed and studied. …”
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AI-Driven Optimization of Fly Ash-Based Geopolymer Concrete for Sustainable High Strength and CO₂ Reduction: An Application of Hybrid Taguchi–Grey–ANN Approach by Muhammad Usman Siddiq, Muhammad Kashif Anwar, Faris H. Almansour, Muhammad Ahmed Qurashi, Muhammad Adeel

“…This study addresses this challenge by developing machine learning-optimized geopolymer concrete (GPC) using industrial waste fly ash as cement replacement. …”
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Fast TILs—A pipeline for efficient TILs estimation in non-small cell lung cancer by Nikita Shvetsov, Anders Sildnes, Masoud Tafavvoghi, Lill-Tove Rasmussen Busund, Stig Manfred Dalen, Kajsa Møllersen, Lars Ailo Bongo, Thomas Karsten Kilvær

“…Such a solution in computational pathology can accelerate TIL evaluation, thereby standardizing the prognostication process and facilitating personalized treatment strategies.We develop an end-to-end automated pipeline for TIL estimation in lung cancer WSIs by integrating a patch extraction approach based on hematoxylin component filtering with a machine learning-based patch classification and cell quantification method using the HoVer-Net model architecture. …”
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Modern Deep Learning Techniques for Big Medical Data Processing in Cloud by Mohammed Y. Shakor, Mustafa Ibrahim Khaleel

“…The recent advancements in Machine Learning (ML) and Deep Learning (DL) provide a new dimension in biomedical big data analysis, while the cloud computing technologies present the breakthroughs of handling massive data from hardware, software, and storage. …”
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A Review of Enhancement Techniques for Cone Beam Computed Tomography Images by Hassn Mazin Al-alaaf, Mohammed Sabah Jarjees

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