Subjective Air Traffic Complexity Analysis Based on Weak Supervised Learning by Weining ZHANG, Weijun PAN, Changqi YANG, Xinping ZHU, Jianan YIN, Jinghan DU

“…Finally, the K-means algorithm is combined to realise preferred complexity situation level classification and evolution analysis. Taking the actual operating data of a mid-southern area sector of China as an example, the effectiveness of the proposed method is verified. …”
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Deep Learning in Power Systems: A Bibliometric Analysis and Future Trends by Seyed Mahdi Miraftabzadeh, Andrea Di Martino, Michela Longo, Dario Zaninelli

“…Advancements in deep learning enable sophisticated algorithms to learn from vast data sets, driving innovation and efficiency in power systems. …”
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META-ANALYSIS: THE IMPROVEMENT NUMERACY LITERACY THROUGH PROBLEM BASED LEARNING by Fityan Asani, Wardono Wardono, Nuriana Rachmani Dewi (Nino Adhi), Scolastika Mariani, Arief Agoestanto

“…There were 2 different studies found, both of which received an Effect Size (ES) value of 0.09 and were categorized as small, so further research related to PBL on numeracy literacy, especially at the elementary school level, considering that in this meta analysis only used 1 data with the elementary school level whose results were not significant.…”
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Genomic selection in pig breeding: comparative analysis of machine learning algorithms by Ruilin Su, Jingbo Lv, Yahui Xue, Sheng Jiang, Lei Zhou, Li Jiang, Junyan Tan, Zhencai Shen, Ping Zhong, Jianfeng Liu

“…Abstract Background The effectiveness of genomic prediction (GP) significantly influences breeding progress, and employing SNP markers to predict phenotypic values is a pivotal aspect of pig breeding. Machine learning (ML) methods are usually used to predict phenotypic values since their advantages in processing high dimensional data. …”
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An Analysis of Layer-Freezing Strategies for Enhanced Transfer Learning in YOLO Architectures by Andrzej D. Dobrzycki, Ana M. Bernardos, José R. Casar

“…It offers a practical, evidence-based approach to balanced transfer learning for object detection in scenarios with limited resources.…”
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A meta-analysis of community engaged learning and thriving in higher education by Rachel Guanlao, Jennifer Pax, Yufeng Wei, Wei Zhang

“…This meta-analysis examined the effects of CEL on academic, personal, social, and citizenship outcomes among college students.MethodsStudies were identified through PsycINFO, PsycArticles, and ERIC, and were included if they met the following criteria: peer-reviewed English-language publications from 2017 to 2024, alignment with widely accepted definitions of CEL, inclusion of a control group, and sufficient data to calculate effect sizes. …”
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Strains and stressors: an analysis of touchscreen learning in genetically diverse mouse strains. by Carolyn Graybeal, Munisa Bachu, Khyobeni Mozhui, Lisa M Saksida, Timothy J Bussey, Erica Sagalyn, Robert W Williams, Andrew Holmes

“…Finally, we examined the behavioral structure of learning via principal component analysis of the current data, plus an archival dataset, totaling 765 mice. …”
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Machine Learning Analysis of Lipid and Metabolic Profiles in Adults with Adenoid Hyperplasia by Mansur Doğan, Merve Çiftçi, Yusuf Yeşil

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Machine learning analysis of cardiovascular risk factors and their associations with hearing loss by Ali Nabavi, Farimah Safari, Ali Faramarzi, Mohammad Kashkooli, Meskerem Aleka Kebede, Tesfamariam Aklilu, Leo Anthony Celi

“…The National Health and Nutrition Examination Survey (NHANES) 2012–2018 data comprising audiometric tests and cardiovascular risk factors was utilized. …”
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Understanding EMS response times: a machine learning-based analysis by Peter Hill, Jakob Lederman, Daniel Jonsson, Peter Bolin, Veronica Vicente

“…Methods A retrospective analysis was conducted using over one million EMS missions from Stockholm, Sweden, between 2017 and 2022. …”
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Enhancing aspect-based financial sentiment analysis through contrastive learning by Viacheslav Ivanenko

“… The subject of research in the article explores the specialized application of Aspect-Based Financial Sentiment Analysis (ABFSA), focusing on the intricate and multifaceted emotional landscape of financial textual data. …”
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Advancements in Landmine Detection: Deep Learning-Based Analysis With Thermal Drones by Daniel Heuschmid, Oliver Wacker, Yannick Zimmermann, Pascal Penava, Ricardo Buettner

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Analysis of Autonomous Penetration Testing Through Reinforcement Learning and Recommender Systems by Ariadna Claudia Moreno, Aldo Hernandez-Suarez, Gabriel Sanchez-Perez, Linda Karina Toscano-Medina, Hector Perez-Meana, Jose Portillo-Portillo, Jesus Olivares-Mercado, Luis Javier García Villalba

“…This study considers these challenges, proposing a recommendation system based on a context-rich, vocabulary-aware transformer capable of processing questions related to the target environment and offering responses based on necessary pentest batteries evaluated by a Reinforcement Learning (RL) estimator. This RL component assesses optimal attack strategies based on previously learned data and dynamically explores additional attack vectors. …”
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Machine learning for grading prediction and survival analysis in high grade glioma by Xiangzhi Li, Xueqi Huang, Yi Shen, Sihui Yu, Lin Zheng, Yunxiang Cai, Yang Yang, Renyuan Zhang, Lingying Zhu, Enyu Wang

“…Abstract We developed and validated a magnetic resonance imaging (MRI)-based radiomics model for the classification of high-grade glioma (HGG) and determined the optimal machine learning (ML) approach. This retrospective analysis included 184 patients (59 grade III lesions and 125 grade IV lesions). …”
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Enhancing aspect-based financial sentiment analysis through contrastive learning by В’ячеслав Іваненко

“… The subject of research in the article explores the specialized application of Aspect-Based Financial Sentiment Analysis (ABFSA), focusing on the intricate and multifaceted emotional landscape of financial textual data. …”
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Fusing Sentinel-1 and Sentinel-2 Data With Machine Learning for Large-Scale Detection of Coastal Erosion and Accretion by Hairuo Yu

“…We employed advanced image preprocessing, robust coastline delineation using spectral indices and edge detection, and machine learning-based change analysis on multitemporal datasets spanning 2020–2022. …”
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Unveiling spatial domains from spatial multi-omics data using dual-graph regularized ensemble learning by Ying Li, Guangchang Cai, Fuqun Chen, Kepei Wen, Le Ou-Yang

“…However, data sparsity and the diversity of data distributions hinder the effective integration and analysis of spatial multi-omics data. …”
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Big data meets big wind: A scientometric review of machine learning approaches in offshore wind energy by Prangon Das, Maisha Mashiata, Gregorio Iglesias

“…The objective of this paper is to conduct a scientometric analysis of machine learning and deep learning techniques applied to offshore wind energy. …”
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Analysis of multiple faults in induction motor using machine learning techniques by Puja Pohakar, Ravi Gandhi, Surender Hans, Gulshan Sharma, Pitshou N. Bokoro

“…Through ensemble learning and feature selection, the models cope well with big data sets with enhanced fault classification accuracy and robustness against noise. …”
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Perspectives of Medical Students in Using Blended Learning: A Thematic Analysis by Almohammadi NH, Elnugomi NM, Atta Elmannan AA, Zalat MM

“…Additionally, it seeks to identify BL challenges, advantages, and disadvantages.Methods: A qualitative design using a thematic analysis was used. The study included 48 female medical students from years three through six who switched from traditional face-to-face to online during COVID-19 to mixed learning in the post-COVID-19 era.Results: Data were extracted from focus group discussions. …”
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