Classification algorithm for imbalance data of ECG based on PSOFS and TSK fuzzy system by Xinhui LI, Qing SHEN, Xiongtao ZHANG

“…A new classification model of electrocardiogram (ECG) signal based on particle swarm optimization feature selection (PSOFS) and TSK (Takagi-Sugeno-Kang) fuzzy system was proposed, i.e., parallel ensemble fuzzy neural network based on PSOFS and TSK (PE-PT-FN), which was used for ECG prediction.Each class sample in the training set was randomly sampled, and the samples obtained by randomly sampled were added.Then, the feature selection method PSOFS was carried out independently and parallelly.In PSOFS, particles that were random initial positions represent different feature subsets and converge to the optimal positions after many iterations.Each subset had a corresponding feature subset.Several groups of TSK fuzzy neural network (TSK-FNN) were trained by each feature subset in parallel.Medical researchers could effectively find the correlation between ECG signal data and different types of disease through the interpretability of the fuzzy system and the feature subsets by the PSOFS algorithm.Experiments prove that PE-PT-FN greatly improves the macro-R to 92.35% while retaining interpretability.…”
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Improved faster R-CNN for steel surface defect detection in industrial quality control by Yuefeng Leng, Jiazhi Liu

“…Evaluated on the NEU-DET dataset, the optimized model achieves a mean average precision (mAP) of 80.2%-yielding a 12.6% improvement over the baseline-while increasing detection speed by 40.9%. …”
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Research on the Trusted Traceability Model of Taishan Tea Products Based on Blockchain by Kangchen Liu, Pingzeng Liu, Shuaishuai Gao

“…The optimized SM2 encryption algorithm is introduced, and the execution efficiency of the encryption algorithm is improved by the concurrent processing framework, which guarantees the security and transmission speed of the data. …”
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Design of a liquid cooled battery thermal management system using neural networks, cheetah optimizer and salp swarm algorithm by Anjan Kumar, Laith Hussein Jasim, Padmanabha Vijaya, Dipak Patel, J. Gowrishankar, R. Sivaranjani, Ankur Srivastava, Mayank Kundlas, Sarbeswara Hota, Banafshe Hamidi

“…In the first phase, predictive modeling was performed using multilayer perceptron neural networks (MLPNN) optimized by three metaheuristic algorithms: cheetah optimizer (CO), grey wolf optimizer (GWO), and marine predators algorithm (MPA). …”
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Performance assessment of basalt fibre concrete under freeze-thaw cycles using hybrid long short-term memory models by Qingguo Yang, Honghu Wang, Qigui Yi, Liuyuan Zeng, Rui Xiang, Longfei Guan, Jiawei Cheng, Keling Chen, Yunhao Li

“…The results demonstrate that swarm intelligence optimization algorithms (SIOA) significantly enhance the prediction accuracy and stability of the LSTM model. …”
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Bearing fault diagnosis for high-speed train based on improved VMD and APSO-SVM by ZHANG Qingsong, ZHANG Bing, QIN Yi

“…The experimental results show that traditional SVM has better effect on the diagnosis of rolling element fault and composite type fault, but the diagnosis effect on cage fault is relatively poor. Therefore, APSO algorithm was used to optimize the core parameters of the SVM, which further improved the recognition accuracy of cage fault and realized the effective identification for the fault bearings of high-speed train.…”
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Time-dependent robust reliability optimization of structure based on genetic interval affine response surface by GUAN Yi, SHENG GuoJun

“…Finally, combined with structural with structural performance degradation behaviour and robust reliability optimization method, the time dependent robust reliability optimization model for structure was established and structural optimization design was carried out. …”
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Identification of maize kernel varieties based on interpretable ensemble algorithms by Chunguang Bi, Chunguang Bi, Xinhua Bi, Jinjing Liu, Hao Xie, Shuo Zhang, He Chen, Mohan Wang, Lei Shi, Lei Shi, Shaozhong Song

“…IntroductionMaize kernel variety identification is crucial for reducing storage losses and ensuring food security. Traditional single models show limitations in processing large-scale multimodal data.MethodsThis study constructed an interpretable ensemble learning model for maize seed variety identification through improved differential evolutionary algorithm and multimodal data fusion. …”
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Optimization of delivery routes for takeout under time-varying road networks by Jiaci Wang, Liang Huang, Yuanqiao Wen, Yamin Huang, Man Zhu

“…The proposed model is solved using a taboo search algorithm. The effectiveness of the delivery model and algorithm is validated through simulations that compare the performance of the delivery route model under different weather conditions and road obstacle coefficients. …”
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Prediction of Insulator ESDD Based on Meteorological Feature Mining and AdaBoost-MEA-ELM Model by Yaoping WANG, Te LI, Kaihua JIANG, Wenhui LI, Qiang WU, Yu WANG

“…Then the AdaBoost algorithm was applied to further improve the accuracy of the model. …”
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A highly generalized federated learning algorithm for brain tumor segmentation by Jun Wen, Xiusheng Li, Xin Ye, Xiaoli Li, Hang Mao

“…To address these issues, this paper proposes a client-side brain tumor image segmentation model utilizing Virtual Adversarial Training (VAT) integrated into a 3D U-Net to improve model performance under conditions of limited datasets, effectively addressing data scarcity and imbalance within the federated learning environment by optimizing the use of brain tumor image data held by each client. …”
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Optimizing Class Imbalance in Facial Expression Recognition Using Dynamic Intra-Class Clustering by Qingdu Li, Keting Fu, Jian Liu, Yishan Li, Qinze Ren, Kang Xu, Junxiu Fu, Na Liu, Ye Yuan

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Design of optimized single-switch capacitor cell balancing for EV battery management by Anjan N. Padmasali, Savitha G. Kini, Arun P. Parameswaran

“…However, there are possible modifications to them that may lead to algorithms with improved results. Two algorithms are explored for a four-series connected Li-ion cell battery system. …”
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Nonlinear Optimization and Adaptive Heuristics for Solving Irregular Object Packing Problems by János D. Pintér, Ignacio Castillo, Frank J. Kampas

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Enhancing Machine Learning Models Through PCA, SMOTE-ENN, and Stochastic Weighted Averaging by Youngjin Han, Inwhee Joe

“…By integrating Principal Component Analysis (PCA)<i>,</i> hyperparameter optimization, and resampling methods, as well as combining Edited Nearest Neighbors (<i>ENN</i>) with the Synthetic Minority Oversampling Technique (SMOTE), the model significantly improves predictive accuracy and model generalization. …”
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Design and Optimization of Brushless Hybrid Wound Rotor Vernier Motor for Variable Speed Applications by Kapeel Kumar, Qasim Ali, Sheeraz Ahmed, Muhammad Ilyas, Hanlin Zhan, Mudassir Raza Siddiqi

“…To find out the optimal shape of the magnets, the Genetic algorithm optimization technique is used. …”
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Development and evaluation of a machine learning model for post-surgical acute kidney injury in active infective endocarditis by XinPei Liu, SanXi Ai, RuiMing Yu, ChaoJi Zhang, Qi Miao

“…Machine learning models enable early prediction of post-surgical AKI, facilitating targeted perioperative optimization and risk stratification in this distinct patient group.…”
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Mapping and interpretability of aftershock hazards using hybrid machine learning algorithms by Bo Liu, Haijia Wen, Mingrui Di, Junhao Huang, Mingyong Liao, Jingyuan Yu, Yutao Xiang

“…By employing the stacking algorithm to optimize and combine XGBoost and LightGBM models, the proposed model significantly improves the prediction performance. …”
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A multi-modal feature combination mechanism for identification of harmonic load in distribution networks based on artificial intelligence models by Renzeng Yang, Shuang Peng, Gang Yao

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Optimization research on UAV semantic communication system based on SVD-MADRL by Yibo Yang, Yahe Tan, Liu Liu

“…The study optimizes the flight trajectory and power of multiple unmanned aerial vehicles with the deep deterministic policy gradient algorithm and constructs a multi-unmanned aerial vehicle semantic communication optimization model based on singular value decomposition and multi-agent deep reinforcement learning. …”
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