Recent Advances in WSN-Based Indoor Localization: A Systematic Review of Emerging Technologies, Methods, Challenges, and Trends by Nur Ahmad

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Machine learning analysis of the relationships between traumatic childbirth experience with positive and negative fertility motivations in Iran in a community-based sample by Mahdieh Arian, Talat Khadivzadeh, Mahla Shafeei, Sedigheh Abdollahpour

“…Considering the importance of fertility growth and strengthening positive fertility motivations in …, this community-based study was conducted to investigate the relationship between traumatic childbirth history and positive and negative fertility motivations. …”
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Enhancing reading skills in at-risk students: the combined effects of smartphone format, cardiac coherence, positive feedback, and interest-based personalization by Peddy Caliari

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Research and Optimization of White Blood Cell Classification Methods Based on Deep Learning and Fourier Ptychographic Microscopy by Mingjing Li, Junshuai Wang, Shu Fang, Le Yang, Xinyang Liu, Haijiao Yun, Xiaoli Wang, Qingyu Du, Ziqing Han

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Classification of NSCLC subtypes using lung microbiome from resected tissue based on machine learning methods by Pragya Kashyap, Kalbhavi Vadhi Raj, Jyoti Sharma, Naveen Dutt, Pankaj Yadav

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Predicting cadmium enrichment in crops/vegetables and identifying the effects of soil factors based on transfer learning methods by Rui Chen, Zean Liu, Jingyan Yang, Tiantian Ma, Aihong Guo, Rongguang Shi

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A Multi-Task Causal Knowledge Fault Diagnosis Method for PMSM-ITSF Based on Meta-Learning by Ping Lan, Liguo Yao, Yao Lu, Taihua Zhang

“…A multi-task causal knowledge fault diagnosis method for inter-turn short circuits of permanent magnet synchronous motors based on meta-learning is proposed. …”
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A fast prediction method of fatigue life for crane structure based on Stacking ensemble learning model by Jincheng Zhao, Qing Dong, Gening Xu, Hongjuan Li, Haiting Lu, Weishan Zhuang

“…Meanwhile, the fast prediction method of fatigue life of crane metal structures based on the Stacking ensemble learning model is proposed. …”
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A Malware Classification Method Based on Knowledge Distillation and Feature Fusion by Xin Guan, Guodong Zhang

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A deep learning-based hybrid method for PM2.5 prediction in central and western China by Zuhan Liu, Zihai Fang, Yuanhao Hu

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Study on the Compressive Strength Predicting of Steel Fiber Reinforced Concrete Based on an Interpretable Deep Learning Method by Huiming Wang, Jie Lin, Shengpin Guo

“…In order to predict the compressive strength of SFRC efficiently and accurately, this study proposes a deep learning-based prediction model, trained and tested on a large set of experimental data. …”
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deep-Sep: a deep learning-based method for fast and accurate prediction of selenoprotein genes in bacteria by Yao Xiao, Yan Zhang

“…We have developed a deep learning-based algorithm to predict selenoprotein genes in bacterial genomic sequences, which demonstrates superior performance compared to currently available methods. …”
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Research on Fracture Energy Prediction and Size Effect of Concrete Based on Deep Learning with SHAP Interpretability Method by Huiming Wang, Weiqi Zhang, Jie Lin, Shengpin Guo

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Malicious code within model detection method based on model similarity by Degang WANG, Yi SUN, Chuanxin ZHOU, Qi GAO, Fan YANG

“…The privacy of user data in federated learning is mainly protected by exchanging model parameters instead of source data.However, federated learning still encounters many security challenges.Extensive research has been conducted to enhance model privacy and detect malicious model attacks.Nevertheless, the issue of risk-spreading through malicious code propagation during the frequent exchange of model data in the federated learning process has received limited attention.To address this issue, a method for detecting malicious code within models, based on model similarity, was proposed.By analyzing the iterative process of local and global models in federated learning, a model distance calculation method was introduced to quantify the similarity between models.Subsequently, the presence of a model carrying malicious code is detected based on the similarity between client models.Experimental results demonstrate the effectiveness of the proposed detection method.For a 178MB model containing 0.375MB embedded malicious code in a training set that is independent and identically distributed, the detection method achieves a true rate of 82.9% and a false positive rate of 1.8%.With 0.75MB of malicious code embedded in the model, the detection method achieves a true rate of 96.6% and a false positive rate of 0.38%.In the case of a non-independent and non-identically distributed training set, the accuracy of the detection method improves as the rate of malicious code embedding and the number of federated learning training rounds increase.Even when the malicious code is encrypted, the accuracy of the proposed detection method still achieves over 90%.In a multi-attacker scenario, the detection method maintains an accuracy of approximately 90% regardless of whether the number of attackers is known or unknown.…”
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Vision-Based Localization Method for Picking Points in Tea-Harvesting Robots by Jingwen Yang, Xin Li, Xin Wang, Leiyang Fu, Shaowen Li

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The Positive Impact of Teams Games Tournament Learning Model Assisted with Video Media on Students’ Mathematics Learning Outcomes by Ida Ayu Kadek Sri Puspita Dewi, Ni Wayan Arini

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Project-Based Problem Learning: Improving Problem-Solving Skills in Higher Education Engineering Students by Muhammad Husin, Usmeldi Usmeldi, Universidad Estatal de Padang, Indonesia, Wakhinuddin Simatupang, Fadhilah Fadhilah, Yeka Hendriyani

“… In engineering education, fostering problem-solving skills is essential for students to meet industry demands. Project-Based Problem Learning (PBPL) is a promising approach to enhancing these skills. …”
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Antiviral therapy can effectively suppress irAEs in HBV positive hepatocellular carcinoma treated with ICIs: validation based on multi machine learning by Shuxian Pan, Zibing Wang

“…BackgroundImmune checkpoint inhibitors have proven efficacy against hepatitis B-virus positive hepatocellular. However, Immunotherapy-related adverse reactions are still a major challenge faced by tumor immunotherapy, so it is urgent to establish new methods to effectively predict immunotherapy-related adverse reactions.ObjectiveMulti-machine learning model were constructed to screen the risk factors for irAEs in ICIs for the treatment of HBV-related hepatocellular and build a prediction model for the occurrence of clinical IRAEs.MethodsData from 274 hepatitis B virus positive tumor patients who received PD-1 or/and CTLA4 inhibitor treatment and had immune cell detection results were collected from Henan Cancer Hospital for retrospective analysis. …”
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3D Human Motion Capture Method Based on Computer Vision by A. D. Obukhov, D. L. Dedov, E. O. Surkova, I. L. Korobova

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Developing Pre-Service Teachers’ Pedagogical Content Knowledge: Lessons from a Science Methods Class by Dalila Dragnić-Cindrić, Janice L. Anderson

“…One such instructional approach with proven positive science learning outcomes is project-based learning. …”
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