FFL-IDS: A Fog-Enabled Federated Learning-Based Intrusion Detection System to Counter Jamming and Spoofing Attacks for the Industrial Internet of Things by Tayyab Rehman, Noshina Tariq, Farrukh Aslam Khan, Shafqat Ur Rehman

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Teaching activity in the digital environment of the university by Margarita V. Selezneva, Victoriya Yu. Aksenova

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Four-path unsupervised learning-based image defogging network by Wei LIU, Cheng CHEN, Rui JIANG, Tao LU

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Learning the factors controlling mineral dissolution in three-dimensional fracture networks: applications in geologic carbon sequestration by Aleksandra A. Pachalieva, Aleksandra A. Pachalieva, Jeffrey D. Hyman, Daniel O’Malley, Gowri Srinivasan, Hari Viswanathan

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Research on Runoff Prediction Based on EMD-FBI-ELM Model by ZHANG Yajie, CUI Dongwen

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EMSPAN: Efficient Multi-Scale Pyramid Attention Network for Object Counting Under Size Heterogeneity and Dense Scenarios by Phu Nguyen Phan Hai, Bao Bui Quoc, Trang Hoang

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Improved Honey Badger Algorithm and Its Application to K-Means Clustering by Shuhao Jiang, Huimin Gao, Yizi Lu, Haoran Song, Yong Zhang, Mengqian Wang

Subjects: “…random opposition-based learning…”
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Time Series Methods and Business Intelligent Tools for Budget Planning—Case Study by Katarzyna Grobler-Dębska, Rafał Mularczyk, Bartłomiej Gawęda, Edyta Kucharska

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Development and validation of machine learning models for MASLD: based on multiple potential screening indicators by Hao Chen, Jingjing Zhang, Xueqin Chen, Ling Luo, Wenjiao Dong, Yongjie Wang, Jiyu Zhou, Canjin Chen, Wenhao Wang, Wenbin Zhang, Zhiyi Zhang, Yongguang Cai, Danli Kong, Yuanlin Ding

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Enhancing Smart Grids for Sustainable Energy Transition and Emission Reduction with Advanced Forecasting Techniques by Farah Rania, Farou Brahim, Kouahla Zineddine and Seridi Hamid

Subjects: “…smart grid, short-term load forecasting, carbon dioxide, electrical consumption, deep learning model, harmful energy…”
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Automatic priority analysis of emergency response systems using internet of things (IoT) and machine learning (ML) by Abu S.M. Mohsin, Shadab H. Choudhury, Munyem Ahammad Muyeed

Subjects: “…Internet of things (IoT) and machine learning…”
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Large Convolution Kernel Network With Edge Self-Attention for Oriented SAR Ship Detection by Yan Feng, Yupeng Zhang, Xiangqing Zhang, Yuning Wang, Shaohui Mei

Subjects: “…Deep learning…”
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Assessing career success: the role of protean career attitude, organizational learning practices, and employability perception by Poh Kiong Tee, Devinder Kaur, Bee Lian Song, Behrooz Gharleghi

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Modern Genetic Education: How Does It Meet Students’ Requests? by T. A. Sedykh, L. A. Amirova, L. A. Gaysina, G. F. Galikeeva, A. I. Fazlutdinova, N. V. Sukhanova

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fNIRS Classification of Adults With ADHD Enhanced by Feature Selection by Minyeong Hong, Suh-Yeon Dong, Roger S. McIntyre, Soon-Kiat Chiang, Roger Ho

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EMNet: A Novel Few-Shot Image Classification Model with Enhanced Self-Correlation Attention and Multi-Branch Joint Module by Fufang Li, Weixiang Zhang, Yi Shang

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Lattice-inspired NiTi-based metamaterials with widely tunable mechanical-superelastic synergy by Zhi Zhang, Jianbao Gao, Shuaishuai Wei, Bo Song, Yonggang Yao, Xu Zheng, Yuanjie Zhang, Lei Zhang, Qiaojiao Li, Jiafeng Wu, Yusheng Shi

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Anomaly detection solutions: The dynamic loss approach in VAE for manufacturing and IoT environment by Praveen Vijai, Bagavathi Sivakumar P

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Machine learning integrated with in vitro experiments for study of drug release from PLGA nanoparticles by Yu Sun, Shuhuai Qin, Yingli Li, Naimul Hasan, Yan Vivian Li, Jiangguo Liu

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Diagnostics of lung cancer by fragmentated blood circulating cell-free DNA based on machine learning methods by Ivan O. Meshkov, Alexander P. Koturgin, Pavel V. Ershov, Liubov A. Safonova, Julia A. Remizova, Valentina V. Maksyutina, Ekaterina D. Maralova, Vasilisa A. Astafieva, Alexey A. Ivashechkin, Boris D. Ignatiev, Antonida V. Makhotenko, Ekaterina A. Snigir, Valentin V. Makarov, Vladimir S. Yudin, Anton A. Keskinov, Sergey M. Yudin, Anna S. Makarova, Veronika I. Skvortsova

Subjects: “…machine learning methods…”
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