Indoor Positioning System based on SSA-ELM Neural Network for Visible Light by JIA Kejun, NIU Zhen, YU Kai, ZHANG Zhicong, PENG Duo, CAO Minghua

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Monocular vision based on the YOLOv7 and coordinate transformation for vehicles precise positioning by Jingzhao Li, Huashun Li, Xiaobo Zhang, Qing Shi

“…The coordinate position of the beacon on the image is obtained through the YOLOv7 model based on transfer learning. …”
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A prediction method for anti-cancer drug combinations synergy based on graph attention network by QIN Weiqi;BAO Xin;CHEN Xiao;QIU Jianlong;WANG Donglin

“…Evaluated on a public dataset, MFGSynergy outperforms Deep DDS, DeepSynergy, and six machine learning methods, achieving receiver operating characteri-stic area under the curve(ROC AUC), area under the precision-recall curve(PR AUC), accuracy(ACC), precision(PREC), true positive rate(TPR), and F1scores of 0.94, 0.94, 0.86, 0.87, 0.86, and 0.86, respectively, in five-fold cross-validation. …”
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AutoLDT: a lightweight spatio-temporal decoupling transformer framework with AutoML method for time series classification by Peng Wang, Ke Wang, Yafei Song, Xiaodan Wang

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Adaptive clustering federated learning via similarity acceleration by Suxia ZHU, Binke GU, Guanglu SUN

“…In order to solve the problem of model performance degradation caused by data heterogeneity in the federated learning process, it is necessary to consider personalizing in the federated model.A new adaptively clustering federated learning (ACFL) algorithm via similarity acceleration was proposed, achieving adaptive acceleration clustering based on geometric properties of local updates and the positive feedback mechanism during clients federated training.By dividing clients into different task clusters, clients with similar data distribution in the same cluster was cooperated to improve the performance of federated model.It did not need to determine the number of clusters in advance and iteratively divide the clients, so as to avoid the problems of high computational cost and slow convergence speed in the existing clustering federation methods while ensuring the performance of models.The effectiveness of ACFL was verified by using deep convolutional neural networks on commonly used datasets.The results show that the performance of ACFL is comparable to the clustered federated learning (CFL) algorithm, it is better than the traditional iterative federated cluster algorithm (IFCA), and has faster convergence speed.…”
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An online escape room as an icebreaker in interdisciplinary and international health care professions education: A pilot mixed-methods study of “UKA Escape” by Lemos, Martin, Kouchev, Veronique, Bell, Laura

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Designing, Implementation, and Evaluation of Game-based Education in Pediatric Nursing Care Course by Fatemeh Ebrahimpour, Manijeh Nourian, Maryam Varzeshnejad

“…Introduction: Game-based education is a new method of teaching and learning in nursing education that can effectively enable teacher and learner to achieve educational goals. …”
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An Application Programming Interface (API) Sensitive Data Identification Method Based on the Federated Large Language Model by Jianping Wu, Lifeng Chen, Siyuan Fang, Chunming Wu

“…The traditional methods for identifying sensitive data in APIs mainly encompass rule-based and machine learning-based approaches. …”
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Research on a Panoramic Image Stitching Method for Images of Corn Ears, Based on Video Streaming by Yi Huangfu, Hongming Chen, Zhonghao Huang, Wenfeng Li, Jie Shi, Linlin Yang

“…<b>Methods:</b> A summary of corn ear panoramic stitching methods under complex backgrounds is presented as follows: 1. a perceptual hash algorithm and histogram equalization were used to extract video frames; 2. the U-Net image segmentation model based on transfer learning was used to predict corn labels; 3. a mask preprocessing algorithm was designed; 4. a corn ear splicing positioning algorithm was designed; 5. an algorithm for irregular surface expansion was designed; 6. an image stitching method based on template matching was adopted to assemble the video frames. …”
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The dynamic case-based learning model: conducive to enhancing the practical learning effectiveness of dermatology for medical students by Yidan Li, Li Lei, Ling Jiang, Ping Mao, Chuhan Fu, Jing Chen, Qinghai Zeng

“…Methods This study proposes the dynamic case-based learning (DCBL) model, which was presented through typical dynamic cases. …”
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Prognostic correlation analysis of colorectal cancer patients based on monocyte to lymphocyte ratio and folate receptor-positive circulating tumor cells and construction of a machi... by Siying Pan, Chi Lu, Chi Lu, Hongda Lu, Hongda Lu, Hongfeng Zhang

“…PurposeTo evaluate the prognostic value of the monocyte to lymphocyte ratio (MLR) and folate receptor-positive circulating tumor cells (FR+CTCs) in patients with colorectal cancer (CRC) and to develop predictive model for post-treatment survival using machine learning (ML) algorithms.MethodsWe retrospectively analyzed 67 CRC patients treated with radical surgery or chemoradiotherapy at The Central Hospital of Wuhan from January 2020 to December 2022. …”
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Explainable machine learning prediction of internet addiction among Chinese primary and middle school children and adolescents: a longitudinal study based on positive youth develop... by Jiahe Liu, Lang Chen, Yuxin Chen, Jingsong Luo, Kexin Yu, Linlin Fan, Chan Yong, Huiyu He, Simei Liao, Zongyuan Ge, Lihua Jiang, Lihua Jiang

“…Our study aimed to examine the risk factors associated with IA among Chinese children and adolescents and leverage explainable machine learning (ML) algorithms to predict IA status at the time of assessment, based on Young’s Internet Addiction Test.MethodsThe longitudinal data consisting of 8,824 schoolchildren from the Chengdu Positive Child Development (CPCD) survey were analyzed, where 33.3% of participants were identified with IA (Age: 10.97 ± 2.31, Male: 51.73%). …”
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Game-Based Learning: Alternative Approaches to Teaching and Learning Strategies in Health Sciences Education by Zijing Hu

“…The findings of this study revealed that game-based learning positively promoted students’ learning experiences and their curriculum outcomes. …”
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Comparing non-machine learning vs. machine learning methods for Ki67 scoring in gastrointestinal neuroendocrine tumors by Nazanin Mola, Hrafn Weishaupt, Valentin Krasontovitsch, Erlend Hodneland, Sabine Leh

“…Our findings highlight the enhanced accuracy of ML-based image analysis in detecting the correct tumor cells, outperforming traditional methods.…”
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The effects of attention to climate change on carbon, fossil energy and clean energy markets: Based on causal network learning algorithms by Wenwen Liu, Miaomiao Tang, Peng Zhao

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Pedestrian detection method in rail transit scenes based on fusion of 3D point clouds and images by HE Jia

“…To address these issues, this paper proposed a pedestrian detection method based on the fusion of 3D point clouds and images in rail transit scenes. …”
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Thermal Error Prediction in High-Power Grinding Motorized Spindles for Computer Numerical Control Machining Based on Data-Driven Methods by Quanhui Wu, Yafeng Li, Zhengfu Lin, Baisong Pan, Dawei Gu, Hailin Luo

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Position-context additive transformer-based model for classifying text data on social media by M. M. Abd-Elaziz, Nora El-Rashidy, Ahmed Abou Elfetouh, Hazem M. El-Bakry

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Working mode detection method based on bidirectional LSTM for pipe jacking inertial automatic guidance system by Yutong Zu, Lu Wang, Yuanbiao Hu, Gansheng Yang, Boning He, Zheng Zhou

“…However, the pipe-jacking velocity is so slow (1–2 m/h) that the traditional threshold-based zero-velocity detection method cannot accurately detect the zero-velocity interval (ZVI). …”
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Electroencephalogram-Based Emotion Classification Using Machine Learning and Deep Learning Techniques by Gst Ayu Vida Mastrika Giri, Made Leo Radhitya

“…SVM, K-NN, and MLP have accuracy scores of 0.98, 0.95, and 0.97. Deep learning methods CNN, LSTM, and GRU had 0.98, 0.82, and 0.97 accuracy. …”
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