FedCLCC: A personalized federated learning algorithm for edge cloud collaboration based on contrastive learning and conditional computing by Kangning Yin, Xinhui Ji, Yan Wang, Zhiguo Wang

“…Federated learning (FL) is a distributed machine learning paradigm for edge cloud computing. FL can facilitate data-driven decision-making in tactical scenarios, effectively addressing both data volume and infrastructure challenges in edge environments. …”
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A multimodal dataset for robotic peg extraction based on Bioin-Tacto sensor modulesMendeley Data by Viral Galayia, Ruslan Masinjila, Soheil Khatibi, Thiago Eustaquio Alves de Oliveira, Xianta Jiang, Vinicius Prado da Fonseca

“…The dataset can be used to pre-train a reinforcement machine learning model to perform peg-in-hole tasks and to study how pretraining affects a manipulator's ability to infer tactile signals and improve the success rates of the manipulator.…”
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Channel Mixer Layer: Multimodal Fusion Toward Machine Reasoning for Spatiotemporal Predictive Learning of Ionospheric Total Electron Content by Peng Liu, Tatsuhiro Yokoyama, Takuya Sori, Mamoru Yamamoto

“…Thanks to the development of machine learning for video prediction, spatiotemporal predictive models are applied on the future TEC map prediction based on the graphic features of past frames. …”
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Using Depth Cameras for Recognition and Segmentation of Hand Gestures by Khalid Twarish Alhamazani, Jalawi Alshudukhi, Talal Saad Alharbi, Saud Aljaloud, Zelalem Meraf

“…That is why, in the academy, this problem has been addressed using machine learning techniques. The experiments carried out have shown very encouraging results indicating that the choice of this type of architecture allows obtaining an excellent efficiency of parameters and prediction times. …”
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Measuring China’s Policy Stringency on Climate Change for 1954–2022 by Bo Li, Enxian Fu, Shuhao Yang, Jiaying Lin, Wei Zhang, Jian Zhang, Yaling Lu, Jiantong Wang, Hongqiang Jiang

“…This paper employs an integrated framework comprising lexicon, text analysis, machine learning, and large-language model applied to multi-source data to quantify China’s policy stringency on climate change (PSCC) from 1954 to 2022. …”
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Improving the Forecast Accuracy of PM_2.5 Using SETAR-Tree Method: Case Study in Jakarta, Indonesia by Dinda Ayu Safira, Heri Kuswanto, Muhammad Ahsan

“…PM_2.5 exhibits significant nonlinear fluctuations; thus, this study employed two machine learning approaches: self-exciting threshold autoregressive tree (SETAR-Tree) and long short-term memory (LSTM). …”
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Spectral enhancement of PlanetScope using Sentinel-2 images to estimate soybean yield and seed composition by Supria Sarkar, Vasit Sagan, Sourav Bhadra, Felix B. Fritschi

“…The study also focuses on using the additional spectral bands and different statistical machine learning models to estimate seed traits, e.g., protein, oil, sucrose, starch, ash, fiber, and yield. …”
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Optimized Prediction of Weapon Effectiveness in BVR Air Combat Scenarios Using Enhanced Regression Models by Andre R. Kuroswiski, Annie S. Wu, Angelo Passaro

“…In our evaluations, Polynomial Regression (PR) with higher interaction degrees outperforms more complex machine learning models in prediction accuracy and computational efficiency. …”
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Land use/cover change simulation research: A system literature review based on bibliometric analyses by Meili Chen, Narimah Samat, Mohammad Javad Maghsoodi Tilaki, Liyuan Duan

“…Future research should focus on establishing high spatiotemporal resolution big data systems, multi-scenario theoretical frameworks, multi-scale multi-level spatiotemporal dynamic models, and AI and machine learning. Enhancing the coupling of models improves understanding and prediction of complex LUCC processes, supporting global environmental research, resource management, and policymaking. …”
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Power flow analysis and volt/var control strategy of the active distribution network based on data-driven method by Chen Hui, Zhu Weiping, Liu Liguo, Shi Mingming, Xie Wenqiang, Zhang Chenyu

“…Firstly, the CatBoost machine learning model for the distribution network power flow analysis is proposed, and the nonlinear mapping relationship between the distribution network state and power flow results is described from the data-driven perspective. …”
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Orthogonal Wavelet Transform-Based Gaussian Mixture Model for Bearing Fault Diagnosis by Weipeng Li, Yan Cao, Lijuan Li, Siyu Hou

“…The Gaussian mixture model (GMM) is an unsupervised clustering machine learning algorithm. This procedure involves the combination of multiple probability distributions to describe different sample spaces. …”
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An overlapping sliding window and combined features based emotion recognition system for EEG signals by Shruti Garg, Rahul Kumar Patro, Soumyajit Behera, Neha Prerna Tigga, Ranjita Pandey

“….; selecting a suitable machine learning/deep learning model; improving the accuracy of the applied model.…”
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Trends and drivers of dissolved organic carbon in major Arctic rivers by Mingxin Song, Jue Huang, Desong Zhao, Yulei Mu

“…After comparing multiple empirical and machine learning models, the random forest (RF) model with best performance was selected. …”
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An annotated heterogeneous ultrasound database by Yuezhe Yang, Yonglin Chen, Xingbo Dong, Junning Zhang, Chihui Long, Zhe Jin, Yong Dai

“…However, many databases are created using a single device type and collection site, limiting the generalizability of machine learning models. Therefore, we have collected a large, publicly accessible ultrasound challenge database that is intended to significantly enhance the performance of AI-assisted ultrasound diagnosis. …”
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Sequence-variable attention temporal convolutional network for volcanic lithology identification based on well logs by Hanlin Feng, Zitong Zhang, Chunlei Zhang, Chengcheng Zhong, Qiaoyu Ma

“…., to construct a lithology identification model using SVA-TCN. Compared with machine learning and deep learning methods, the SVA-TCN demonstrates a remarkable accuracy of 99.00%, surpassing the accuracy of the comparison methods by 0.37–17.69%. …”
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Artificial intelligence in kidney transplantation: a 30-year bibliometric analysis of research trends, innovations, and future directions by Ying Jia He, Pin Lin Liu, Tao Wei, Tao Liu, Yi Fei Li, Jing Yang, Wen Xing Fan

“…Key research themes include AI-driven advancements in donor matching, deep learning for post-transplant monitoring, and machine learning algorithms for personalized immunosuppressive therapies. …”
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Automated Global Classification of Surface Layer Stratification Using High‐Resolution Sea Surface Roughness Measurements by Satellite Synthetic Aperture Radar by Justin E. Stopa, Chen Wang, Doug Vandemark, Ralph Foster, Alexis Mouche, Bertrand Chapron

“…These boundaries are identified by the characteristic boundary layer coherent structures that form in these regimes and modulate the surface roughness imaged by the radar. An automated machine learning algorithm identifies the coherent structures impressed on the images. …”
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Extending TextAE for annotation of non-contiguous entities by Jake Lever, Russ Altman, Jin-Dong Kim

“…Therefore, experts cannot even visualize non-contiguous entities, let alone annotate them to build valuable datasets for machine learning methods. To combat this problem and as part of the BLAH6 hackathon, we extended the TextAE platform to allow visualization and annotation of non-contiguous entities. …”
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A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME by Ahmed M. Salih, Zahra Raisi‐Estabragh, Ilaria Boscolo Galazzo, Petia Radeva, Steffen E. Petersen, Karim Lekadir, Gloria Menegaz

“…eXplainable artificial intelligence (XAI) methods have emerged to convert the black box of machine learning (ML) models into a more digestible form. …”
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Nuclear Fusion Pattern Recognition by Ensemble Learning by G. Farias, E. Fabregas, I. Martínez, J. Vega, S. Dormido-Canto, H. Vargas

“…It is impossible to do a complete analysis of this data manually, and it is essential to automate this process. That is why machine learning models have been used to this end in previous years. …”
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