End-of-Line Quality Control Based on Mel-Frequency Spectrogram Analysis and Deep Learning by Jernej Mlinarič, Boštjan Pregelj, Gregor Dolanc

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Wear Characterization and Coefficient of Friction Prediction Using a Convolutional Neural Network Model for Chromium-Coated SnSb11Cu6 Alloy by Mihail Kolev, Vladimir Petkov, Veselin Petkov, Rositza Dimitrova, Shaban Uzun, Boyko Krastev

“…This study not only advances the understanding of chromium coatings for babbitt materials but also demonstrates the potential of machine learning in optimizing tribological performance.…”
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Consumer-Centric Rate Design for Peak Time Energy Demand Coincidence Reduction at Domestic Sector Level-A Smart Energy Service for Residential Demand Response by Swathi G, Srinivasa Varma P, Sudha Rani Donepudi, Ramash Kumar K

“…For this, customers are classified into different clusters using the Machine Learning Algorithm K-Means. The proposed rate design model has been analyzed on synthetic smart meter data of 10 houses, and it is observed that the proposed tariff shows an increase in the monthly revenue by 4.3% for the utility and a variation of -0.4% to 7% of energy charge for different customers. …”
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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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Optimizing Human-Centric Warehouse Operations: A Digital Twin Approach Using Dynamic Algorithms and AI/ML by Erhan Arslan

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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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Hybrid forecasting of demand flexibility: A top-down approach for thermostatically controlled loads by Luca Massidda, Marino Marrocu

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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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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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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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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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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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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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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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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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Medium-Term Hourly Electricity Tariff Forecasting Using Ensemble Models by Matrenin P.V., Arestova A.Yu., Antonenkov D.V.

“…This work aims to study the potential for medium-term forecasting of retail electricity tariff rates and develop a predictive machine learning model. Hourly data on the retail market tariffs of the Novosibirsk region (Siberia) for four years were collected, several machine learning models were applied, and an analysis of the input parameters for forecasting was carried out. …”
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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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High risk of political bias in black box emotion inference models by Hubert Plisiecki, Paweł Lenartowicz, Maria Flakus, Artur Pokropek

“…Abstract This paper investigates the presence of political bias in emotion inference models used for sentiment analysis (SA). Machine learning models often reflect biases in their training data, impacting the validity of their outcomes. …”
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Deep Learning for Opportunistic Rain Estimation via Satellite Microwave Links by Giovanni Scognamiglio, Andrea Rucci, Attilio Vaccaro, Elisa Adirosi, Fabiola Sapienza, Filippo Giannetti, Giacomo Bacci, Sabina Angeloni, Luca Baldini, Giacomo Roversi, Alberto Ortolani, Andrea Antonini, Samantha Melani

“…This study investigates a range of machine learning (ML) approaches, including deep learning (DL) models and traditional methods like gradient boosting machine (GBM), for estimating rainfall rates from SNR data collected by interactive satellite receivers. …”
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