Control Method in Coordinated Balance with the Human Body for Lower-Limb Exoskeleton Rehabilitation Robots by Li Qin, Zhanyi Xing, Jianghao Wang, Guangtong Lu, Houzhao Ji

“…To address the sparse rewards resulting from the above design, we introduce a stepwise training method that adjusts the reward function to control the model’s training direction and exploration difficulty. …”
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Mathematical Methods Applied to the Problem of Dairy Cow Replacements: A Scoping Review by Osvaldo Palma, Lluis M. Plà-Aragonés, Alejandro Mac Cawley, Víctor M. Albornoz

“…Dynamic programming is the most commonly used optimization technique, present in 58% of the studies, followed by stochastic simulation in 40%, and deterministic simulation in 8%. Machine learning methods or hybrid approaches are applied in only 5% of the cases. …”
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Advanced Methods Used for the Position and Speed Control of the Electrical Drives with PMSM Motors by SPOIALA Viorica

“…This article presents modern methods such as machine learning, neural networks for controlling the operation of the PMSM motor, which are simulated in a 3D crane system control.…”
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Document Layout Error Rate (DLER) metric to evaluate image segmentation methods by Ari Vesalainen, Mikko Tolonen, Laura Ruotsalainen

“…Most existing DLA solutions have focused on evaluating the accuracy of these methods, often neglecting to study the practical consequences of method selection. …”
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Evaluating Pretrained Deep Learning Models for Image Classification Against Individual and Ensemble Adversarial Attacks by Mafizur Rahman, Prosenjit Roy, Sherri S. Frizell, Lijun Qian

“…The robustness of Deep Neural Networks (DNNs) against adversarial attacks is an important topic in the area of deep learning. To fully investigate the robustness of DNNs, this study examines four frequently used white box adversarial attack techniques, namely, the Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), Basic Iterative Method (BIM), DeepFool, and their effects on DNN models for the image classification task. …”
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The Role of Digital Technologies in Distance Learning by O. A. Gironkina, N. A. Medvedeva, E. E. Sokolova

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TEACHING BUSINESS CORRESPONDENCE: BRIDGING TRADITIONAL METHODS AND ARTIFICIAL INTELLIGENCE TO ENHANCE LEARNING EFFICIENCY AND PROFESSIONAL COMMUNICATION SKILLS by Monica-Ariana SIM, Anamaria Mirabela POP, Rodica BOGDAN

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A Cross-Regional Load Forecasting Method Based on a Pseudo-Distributed Federated Learning Strategy by Jinsong Deng, Shaotang Cai, Weinong Wu, Rong Jiang, Hongyu Deng, Jinhua Ma, Yonghang Luo

“…Experiments demonstrated that this strategy exhibited exceptional generalization ability across various deep learning models, federated learning methods, and datasets.…”
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Failure Rate Prediction of a Power Transformer: A Decomposition-Based Bayesian Deep Learning Method by Weixin Zhang, Changzheng Shao, Wei Huang, Bo Hu, Jiahao Yan, Kaigui Xie, Maosen Cao, Zhengze Wei

“…This paper proposes a decomposition-based Bayesian deep learning (BDL) method to predict the failure rate of power transformers. …”
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Accurate segmentation of localized fuel cladding chemical interaction layers in SEM micrographs with deep learning method by Liang Zhao, Yachun Wang, Fei Xu

“…A deep learning based end-to-end method proved its capability to enable rapid and accurate segmentation of FCCI in as-collected SEM micrographs, making it highly suitable for real-time applications to automate data analysis. …”
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Retracted: Fault Diagnosis Method for UHVDC Transmission Based on Deep Learning under Cloud-Edge Architecture by Journal of Electrical and Computer Engineering

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Using Machine Learning Method for Variable Star Classification Using the TESS Sectors 1–57 Data by Li-Heng Wang, Kai Li, Xiang Gao, Ya-Ni Guo, Guo-You Sun

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A Novel Transformer-Based Self-Supervised Learning Method to Enhance Photoplethysmogram Signal Artifact Detection by Thanh-Dung Le, Clara Macabiau, Kevin Albert, Philippe Jouvet, Rita Noumeir

“…Recent research has revealed that traditional machine learning methods, such as semi-supervised label propagation and K-nearest neighbors, outperform Transformer-based models in artifact detection from photoplethysmogram (PPG) signals, mainly when data is limited. …”
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Laparoscopic Suture Gestures Recognition via Machine Learning: A Method for Validation of Kinematic Features Selection by Juan M. Herrera-Lopez, Alvaro Galan-Cuenca, Antonio J. Reina, Isabel Garcia-Morales, Victor F. Munoz

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Development of a novel deep learning method that transforms tabular input variables into images for the prediction of SLD by Gabriel Cubillos, Javier Perez-Valenzuela, Herman Aguirre, Luz Martínez, Lorena Castro, Gabriel Mezzano, Claudio A. Perez

“…We report the development of a novel Deep Learning (DL) method for the prediction of SLD, which consists of transforming the input variables from tabular data into images, with the goal of using the pattern recognition power of DL models to reach the best prediction performance. …”
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A Novel Assembly Process Knowledge Graph Inference Method Integrating Logical Rules and Embedded Learning by Peilin Shao, Zhicheng Huang, Yongqiang Wan, Lihong Qiao, Xinzheng Xu, Chao Chen, Zhujia Li, Nabil Anwer, Yifan Qie

“…Therefore, a KG inference method integrating logical rules and embedded learning is proposed in this paper for KG inference tasks in KG construction. …”
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PairReg: A method for enhancing the learning of molecular structure representation in equivariant graph neural networks. by Zhen Ren, Yu Liu, Sen Zhang

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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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Soft sensor modeling method for Pichia pastoris fermentation process based on substructure domain transfer learning by Bo Wang, Jun Wei, Le Zhang, Hui Jiang, Cheng Jin, Shaowen Huang

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Environmental assessment of soluble solids contents and pH of orange using hyperspectral method and machine learning by Mansour Rasekh, Sina Ardabili, Amir Mosavi

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