AI for tribology: Present and future by Nian Yin, Pufan Yang, Songkai Liu, Shuaihang Pan, Zhinan Zhang

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Investigation of the application of an automated monitoring system for detecting transmission cable deterioration in Nigeria: A case study of transmission cable lines between Offa... by C.S. Omoniabipi, R. Agbadede, K.C. Emmanuel, O.J. Adewuni, I. Allison

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Soil Texture Mapping in the Permafrost Region: A Case Study on the Eastern Qinghai–Tibet Plateau by Wangping Li, Yadong Liu, Xiaodong Wu, Lin Zhao, Tonghua Wu, Guojie Hu, Defu Zou, Yongping Qiao, Xiaoying Fan, Xiaoxian Wang

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Direct Discrimination and Growth Estimation of Foodborne Bacteria in Raw Meat Using Electronic Nose by Wellington Belarmino Gonçalves, Wanderson Sirley Reis Teixeira, Aryele Nunes da Cruz Encide Sampaio, Otávio Augusto Martins, Evelyn Perez Cervantes, Mateus de Souza Ribeiro Mioni, Jonas Gruber, Juliano Gonçalves Pereira

“…To estimate the growth rates of the microorganisms, the distance between clusters was calculated and provided a pattern for the three bacteria, with the slowest-, moderate-, and fastest-growing being EC, SA, and PF, respectively. …”
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CINNAMON: A hybrid approach to change point detection and parameter estimation in single-particle tracking data by Jakub Malinowski, Marcin Kostrzewa, Michał Balcerek, Weronika Tomczuk, Janusz Szwabiński

“…Change point detection has become an important part of the analysis of the single-particle tracking (SPT) data, as it allows one to identify moments, in which the motion patterns of observed particles undergo significant changes. …”
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Lightweight ECG signal classification via linear law-based feature extraction by Péter Pósfay, Marcell T Kurbucz, Péter Kovács, Antal Jakovác

“…The method identifies linear laws that capture shared patterns within a reference class, enabling compact and verifiable representations of time series data. …”
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Intelligent visibility forecasting at airports: a systematic review by Décio Alves, Margarida Belo-Pereira, Fábio Mendonça, Fernando Morgado-Dias

“…This review synthesizes findings on data-driven solutions, including machine learning and deep learning, that harness large datasets to reveal hidden patterns, offering better performance and adaptability. …”
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Determination of Geochemical Background and Baseline and Research on Geochemical Zoning in the Desert and Sandy Areas of China by Weiji Wen, Fan Yang, Shuyun Xie, Chengwen Wang, Yuntao Song, Yuepeng Zhang, Weihang Zhou

“…This research will greatly enhance our ability to interpret the geochemical distribution and evolutionary patterns of desert elements in China, and it has important scientific significance and practical value for desert research.…”
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High-speed encrypted traffic classification by using payload features by Xinge Yan, Liukun He, Yifan Xu, Jiuxin Cao, Liangmin Wang, Guyang Xie

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Optimizing ensemble learning for satellite-based multi-hazard monitoring and susceptibility assessment of landslides, land subsidence, floods, and wildfires by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Farman Ali, Biswajeet Pradhan, Soo-Mi Choi

“…Past studies have relied mainly on traditional machine learning models, but these models do not perform well for complex spatial patterns. …”
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Improving rainfall forecasting using deep learning data fusing model approach for observed and climate change data by Farhan Amir Fardush Sham, Ahmed El-Shafie, Wan Zurina Binti Wan Jaafar, S. Adarsh, Mohsen Sherif, Ali Najah Ahmed

“…Traditional forecasting methods, such as linear regression, autoregressive models, and time-series analysis, are limited in their ability to capture the intricate and dynamic nature of rainfall patterns. To address these shortcomings, this study utilizes a fusion of observed rainfall data and climate change projections to improve the precision of rainfall predictions over daily, 3-day, and weekly intervals. …”
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Deep Learning and Recurrence Information Analysis for the Automatic Detection of Obstructive Sleep Apnea by Daniele Padovano, Arturo Martinez-Rodrigo, José M. Pastor, José J. Rieta, Raul Alcaraz

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Credit Scoring Prediction Using Deep Learning Models in the Financial Sector by Xi Shi, Dingfen Tang, Yike Yu

“…Existing approaches often struggle with integrating structured numerical records and unstructured user behavior signals, limiting their ability to capture meaningful temporal and non-linear patterns. In the swiftly transforming domain of computational science, the incorporation of sophisticated machine learning algorithms has emerged as a critical driver in addressing these challenges. …”
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Advancing seasonal prediction of tropical cyclone activity with a hybrid AI-physics climate model by Gan Zhang, Megha Rao, Janni Yuval, Ming Zhao

“…Machine learning (ML) models are successful with weather forecasting and have shown progress in climate simulations, yet leveraging them for useful climate predictions needs exploration. …”
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Enhancing Network Security: A Study on Classification Models for Intrusion Detection Systems by Abeer Abd Alhameed Mahmood, Azhar A. Hadi, Wasan Hashim Al-Masoody

“…This study leverages AI methods to develop nine classification models using supervised machine learning classifiers. The author has implemented several machine learning models, including bagging, multi-layer perceptron, logistic regression, extreme gradient boosting, and random forest. …”
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Hybrid Intelligence approach to study post-processing impact on the mechanical performance of notched additively manufactured AlSi10Mg by Erfan Maleki, Sara Bagherifard, Okan Unal, Mario Guagliano

“…AI identified patterns and developed predictive models that provided deeper insights into how process parameters affect material properties and performance. …”
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Quality Metrics of Automated Machinery in Potato Plant Cultivation for Breeding and Seed Production by N. V. Sazonov, M. A. Mosyakov, V. S. Teterin, N. S. Panferov, M. M. Godyaeva, M. S. Trunov

“…(Research purpose) The research aims to determine the patterns of changes in the quality indicators of the machinery employed in cultivating potato plants. …”
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Botnet detection in internet of things using stacked ensemble learning model by Mudasir Ali, Muhammad Faheem Mushtaq, Urooj Akram, Daniel Gavilanes Aray, Manuel Masias Vergara, Hanen Karamti, Imran Ashraf

“…The KSDRM model successfully captures the complex patterns and traits of botnet attacks and obtains 99.99% training accuracy. …”
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Detection and Mitigation of Distributed Denial of Service Attacks Using Ensemble Learning and Honeypots in a Novel SDN-UAV Network Architecture by Mohamed Amine Ould Rabah, Hamza Drid, Yasmine Medjadba, Mohamed Rahouti

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Open-Loop Wavefront Reconstruction with Pyramidal Sensors Using Convolutional Neural Networks by Saúl Pérez-Fernández, Alejandro Buendía-Roca, Carlos González-Gutiérrez, Francisco García-Riesgo, Javier Rodríguez-Rodríguez, Santiago Iglesias-Alvarez, Julia Fernández-Díaz, Francisco Javier Iglesias-Rodríguez

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