Fine classification of crops based on an inductive transfer learning method with compact polarimetric SAR images by Xianyu Guo, Junjun Yin, Jian Yang

“…A stable and robust inductive transfer learning method, i.e. the Transfer Bagging-based Ensemble Learning (TBEL) algorithm, is proposed. …”
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A Machine Learning-Based Method for Developing the Chinese Symptom Checklist-11 (CSCL-11) by Xuanyi Cai, Yunan Zhang, Meng Su, Fan Chang, Lei Quan, Yixing Liu, Bei Wang

“…This study aimed to utilize a condensed subset of items from the Chinese SCL-90 to identify individuals at high risk for psychological disorders based on machine learning methods, forming a concise and efficient preliminary psychopathological screening instrument for the Chinese general population. …”
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A security data detection and management method in digital library network based on deep learning by Diyin Zhu, Yihang Wei, Jiali Cai, Jingwen Wang, Zhongshan Chen

“…In response to the problems of low detection accuracy and long detection time in traditional network security methods, we propose a digital library network security data detection and management method based on bidirectional gated recurrent unit and temporal convolutional network detector (BiGRU-TCNDetector) using the powerful capabilities of deep learning technology. …”
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Research on named entity recognition method in cybersecurity based on soft prompt tuning and reinforcement learning by TIAN Zeshu, LIU Chunyu, ZHANG Yunting, ZHANG Jiayu, MENG Chao, ZHANG Hongli

“…To address the problem of poor recognition accuracy in named entity recognition methods based on large language models in the cybersecurity domain, a novel cybersecurity named entity recognition method that combined soft prompt tuning and reinforcement learning was proposed. …”
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An interpretable machine learning method for fault diagnosis of oil-immersed transformers based on edge inference by Dong Hu, Yong Yang, Hao Dai, Chao Tang, Jufang Xie

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A fault diagnosis method for rolling bearings in open-set domain adaptation with adversarial learning by Tongfei Lei, Feng Pan, Jiabei Hu, Xu He, Bing Li

“…In light of this, a domain adaptation method with adversarial learning is designed for open-set fault diagnosis. …”
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A New Receiver Design for Biomedical Magnetic Induction Tomography with a Deep Learning Method by Hofmann Anna, Schledewitz Tatiana, Rueter Dirk, Sauer Andreas

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Research on Concrete Crack Damage Assessment Method Based on Pseudo-Label Semi-Supervised Learning by Ming Xie, Zhangdong Wang, Li’e Yin

“…To address the inefficiency of traditional concrete crack detection methods and the heavy reliance of supervised learning on extensive labeled data, in this study, an intelligent assessment method of concrete damage based on pseudo-label semi-supervised learning and fractal geometry theory is proposed to solve two core tasks: one is binary classification of pixel-level cracks, and the other is multi-category assessment of damage state based on crack morphology. …”
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An adaptive coverage method for dynamic wireless sensor network deployment using deep reinforcement learning by Peng Zhou, Mingqi Kan, Wei Chen, Yingchao Wang, Bingyu Cao

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Prediction of the SYM‐H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification by Yasser Abduallah, Khalid A. Alobaid, Jason T. L. Wang, Haimin Wang, Vania K. Jordanova, Vasyl Yurchyshyn, Huseyin Cavus, Ju Jing

“…The results also show that SYMHnet generally performs better than related machine learning methods. For example, SYMHnet achieves a forecast skill score (FSS) of 0.343 compared to the FSS of 0.074 of a recent gradient boosting machine (GBM) method when predicting SYM‐H indices (1 hr in advance) in a large storm (SYM‐H = −393 nT) using 5‐min resolution data. …”
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Detection method of slight bruises of apples based on hyperspectral imaging and RELIEF-extreme learning machine by ZHANG Meng, LI Guanghui

“…Then, based on full wavebands and characteristic wavebands, an extreme learning machine (ELM) model was built, as comparison with support vector machine (SVM) and K- mean algorithm. …”
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Transmission line fault identification method based on weighted federated learning and multimodal residual network by Jian Wang, Fengkai Lin, Dongliang Nan, Qi Zhao, Han Xue, Jinxin Ouyang

“…Therefore, this paper proposed a transmission line fault identification method based on weighted federated learning and multi-modal residual network. …”
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Research on knowledge concept extraction method based on few-shot learning and chain-of-thought prompting by SHE Linlin, XIONG Longyang, LU Xuesong

“…In view of the above challenges, a method based on few-shot learning and chain-of-thought prompting for knowledge concept extraction was proposed, utilizing open-source large language models. …”
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Lessons Learned About the Education Needs of Kidney Transplant Recipients: A Mixed-Method Study by Michelle L. Gabriel, Lucy Chen, Sharon Lee, Jenny Accettura, Emily E. Campbell, Melanie Dissanayake, Teresa J. Valenzano-Hacker, Michelle Nash, Lindita Rapi, Niki Dacouris, Weiqiu Yuan, Tiffany Thai

“…Design: Our multidisciplinary team conducted a mixed-method study to better understand the learning needs of patients; what is important to them in the early postoperative period and a year after transplant. …”
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Study on multi-satellite cooperative spectrum cognitive method integrating Stackelberg game and federated learning by Xiaojin DING, Yehui XU, Wen BAO, Gengxin ZHANG

“…To solve the problem of the weak spectrum-cognitive ability caused by monitoring angle, direction resolution, limited processing ability and peak power for a low-earth-orbit (LEO) satellite, a multi-satellite cooperative spectrum cognitive method integrating Stackelberg game and federated learning was proposed.Firstly, considering the available computing resource, cognitive performance, processing and transmission delay of each spectrum cognitive satellite, a cooperative-satellite selection and computing-resource allocation algorithm was built for multiple spectrum-cognitive tasks.Secondly, based on the selected satellites and the allocated computing resources, a low-complexity multi-satellite cooperative spectrum cognitive strategy was further designed, which could automatically sense the spectrum holes, and detect interference as well as identify the modulation mode.Simulation results demonstrate that compared to the single-node cognitive method, the designed multi-satellite cooperative spectrum cognitive strategy can obtain a better cognitive performance.Moreover, comparing with the existing model, the model utilized in the designed strategy can effectively achieve 96.69% and 93.32% lower number of parameters and required floating point operations per second, whilst maintaining the performance.…”
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An Efficient Deep Learning Method for Typhoon Track Prediction Based on Spatiotemporal Similarity Feature Mining by Kaiwen Lixia, Mingyue Lu, Yifei Lu, Hui Liu, Ping Li

“…The joint method bridges the gap in deep learning models’ ability to process spatial information and the shortcomings of spatiotemporal similarity feature mining models in predicting future data. …”
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A New Hybrid Reinforcement Learning with Artificial Potential Field Method for UAV Target Search by Fang Jin, Zhihao Ye, Mengxue Li, Han Xiao, Weiliang Zeng, Long Wen

“…The experimental results demonstrate that the proposed method significantly outperforms standalone RL algorithms in terms of target search efficiency and obstacle avoidance performance. …”
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Simulation-Driven End-to-End Deep Learning Method for White-Light Interference Topography Reconstruction by Xuan Qi, Yudong Lian, Yulei Wang, Zhiwei Lu

“…These results confirm the method’s superior accuracy, speed, and reliability for industrial metrology applications.…”
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Credit card fraud Detection using Feature select method and improved machine learning algorithm by Mohammed AL-Hammadi

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A review of deep learning-based few sample fault diagnosis method for rotating machinery by Ke WU, Jun WU, Qiming SHU, Weiming SHEN, Wenbin SONG

“…Currently, according to different technical principles, existing few-sample fault diagnosis methods can be classified into five categories: meta-learning, transfer learning, domain generalization, data augmentation, and self-supervised learning. …”
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