Sentiment Analysis Algorithm Based on Deep Transfer Learning for Multi-Source Data Fusion by Long Qin

“…The experiment outcomes indicate that the artificial bee colony-improved particle swarm optimization algorithm designed in the study has the minimum convergence value under single-peak and multi-peak test functions, with a standard deviation of less than 0.25 and a success rate of 100% in optimization. …”
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Design Parameter Optimization of Self-centering Pier Based on Deep Learning by Weike ZHANG, Zhengnan LIU, Xingchong CHEN, Jiawei TANG

“…A finite element structural agent model created through the deep learning method can incorporate random parameters related to geometric and material mechanical properties to improve the model’s robustness and the self-centering pier’s multi-objective optimization efficiency.…”
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A Novel Long Short-Term Memory Seq2Seq Model with Chaos-Based Optimization and Attention Mechanism for Enhanced Dam Deformation Prediction by Lei Wang, Jiajun Wang, Dawei Tong, Xiaoling Wang

“…However, the nonlinear relationships between deformation and time-varying environmental factors pose significant challenges, often limiting the accuracy of conventional and deep learning models. To address these issues, this study aimed to improve the predictive accuracy and interpretability in dam deformation modeling by proposing a novel LSTM seq2seq model that integrates a chaos-based arithmetic optimization algorithm (AOA) and an attention mechanism. …”
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Abnormal Electricity Consumption Behaviors Detection Based on Improved Deep Auto-Encoder by Nvgui LIN, Lanxiu HONG, Daoshan HUANG, Yang YI, Zhixuan LIU, Qifeng XU

“…Because the effective data characteristics are destroyed by the abnormal behaviors, the abnormal behaviors can be detected through comparing the difference between the reconstruction error and the detection threshold. To improve the feature extraction ability and the robustness of AE network, the sparse restrictions and the noise coding are introduced into the auto-encoder, and the hyper-parameters of AE network are optimized through the particle swarm optimization algorithm to improve the learning efficiency and generalization ability. …”
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A Distribution Network Expansion Project Classification Model Based on Data Augmentation and Dimensionality Reduction Method by Xin ZHOU, Jingxing LIN, Zhiwei XIE, Zheng ZHANG, Ruduo LIANG, Zuhong OU

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Virtual Cluster Partitioning Method of Active Distribution Networks Using Quantum Particle Swarm Optimization and Sector Search by Wei Liu, Yifan Tong, Xinran Xing, Xi Chen, Bo Hu, Qian Sun, Yufei Wang, Huaxin Li, Huidong Guo

“…This model simplifies the search for node locations and improves the algorithm's convergence speed. …”
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Gradual Optimization of University Course Scheduling Problem Using Genetic Algorithm and Dynamic Programming by Xu Han, Dian Wang

“…To improve the computational efficiency and solution quality, a hybrid method combining a genetic algorithm and dynamic programming, named POGA-DP, was designed. …”
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YOLO-APDM: Improved YOLOv8 for Road Target Detection in Infrared Images by Song Ling, Xianggong Hong, Yongchao Liu

“…Replacing YOLOv8’s C2f module with C2f-DCNv3 increases the network’s ability to focus on the target region while lowering the amount of model parameters. The MSCA mechanism is added after the backbone’s SPPF module to improve the model’s detection performance by directing the network’s detection resources to the major road target detection zone. …”
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An Improved Crop Disease Identification Method Based on Lightweight Convolutional Neural Network by Tingzhong Wang, Honghao Xu, Yudong Hai, Yutian Cui, Ziyuan Chen

“…Finally, it saves the loss and accuracy data during the training process and evaluates the accuracy of the model. In order to improve the training learning rate, Adam optimizer combining momentum algorithm and RMSprop algorithm is used to dynamically adjust the learning rate; the combination of the two algorithms makes the loss function converge to the lowest point faster. …”
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Microgrid Load Forecasting Based on Improved Long Short-Term Memory Network by Qiyue Huang, Yuqing Zheng, Yuxuan Xu

“…In this paper, a load-forecasting algorithm for microgrid based on improved long short-term memory neural network (LSTM) is proposed. …”
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Novel validity indices for dynamic clustering and an Improved Dynamic Fuzzy C-Means by Ramiro Saltos, Ignacio Carvajal, Fernando Crespo, Richard Weber

“…To illustrate the application of these novel indices, we introduce an improved version of the dynamic fuzzy c-means algorithm (I-DFCM) which offers enhanced computational stability for handling dynamic data. …”
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Evaluating the Efficiency of Gray Wolf Optimization and Colonial Competition Algorithm in Load Balancing of Distributed Systems by Asghar Molaei-Yeznabad, Amirhossein Dehghani

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A Method for Predicting Coal-Mine Methane Outburst Volumes and Detecting Anomalies Based on a Fusion Model of Second-Order Decomposition and ETO-TSMixer by Qiangyu Zheng, Cunmiao Li, Bo Yang, Zhenguo Yan, Zhixin Qin

“…Variational mode decomposition (VMD) parameters are optimized via a novel exponential triangular optimization (ETO) algorithm to extract multi-scale features. …”
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An Accurate Book Spine Detection Network Based on Improved Oriented R-CNN by Haibo Ma, Chaobo Wang, Ang Li, Aide Xu, Dong Han

“…This allows for a more accurate computation of anchor box aspect ratios that are better aligned with the book spine dataset, enhancing the model’s training performance. We conducted comparison experiments between the proposed model and other state-of-the-art models on the book spine dataset, and the results demonstrate that the proposed approach reaches an mAP of 90.22%, which outperforms the baseline algorithm by 4.47 percentage points. …”
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Tea Disease Detection Method Based on Improved YOLOv8 in Complex Background by Junchen Ai, Yadong Li, Shengxiang Gao, Rongsheng Hu, Wengang Che

“…In order to solve the problems such as mutual occlusion of leaves, light disturbance, and small lesion area under complex background, YOLO-SSM, a tea disease detection model, was proposed in this paper. The model introduces the SSPDConv convolution module in the backbone of YOLOv8 to enhance the global information perception of the model under complex backgrounds; a new ESPPFCSPC module is proposed to replace the original spatial pyramid pool SPPF module, which optimizes the multi-scale feature expression; and the MPDIoU loss function is introduced to optimize the problem that the original CIoU is insensitive to the change of target size, and the positioning ability of small targets is improved. …”
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A Judging Scheme for Large-Scale Innovative Class Competitions Based on Z-Score Pro Computational Model and BP Neural Network Model by Zhuoting Yu, Hongzhong Deng, Shuaiwen Tang

“…By optimizing the distribution of submissions across experts, our model significantly mitigates inconsistencies arising from diverse scoring tendencies. …”
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Research on shale TOC prediction method based on improved BP neural network by Chaorong Wu, Kaixing Huang, Zhengtao Sun, Yizhen Li, Yong Li, Yuexiang Hao, Zhengxing Sun, Ziqi Wang

“…This paper studies a method for predicting shale TOC content using a BP neural network optimized by an improved cuckoo search algorithm. First, for the Longmaxi Formation shale, through logging sensitivity analysis, seven logging parameters sensitive to TOC content were determined: DEN, AC, RT, U, K, GR, and CNL. …”
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YOLO-SRSA: An Improved YOLOv7 Network for the Abnormal Detection of Power Equipment by Wan Zou, Yiping Jiang, Wenlong Liao, Songhai Fan, Yueping Yang, Jin Hou, Hao Tang

“…This paper proposes a YOLO-SRSA-based anomaly detection algorithm. For data enhancement, geometric and color transformations and rain-fog simulations are applied to preprocess the dataset, improving the model’s robustness in outdoor complex weather. …”
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A Novel Fruit Fly Optimization Algorithm with Evolution Strategy for Magnetotelluric Data Inversion by Bin Yin, Jie Yang, Yue Li

“…After the benchmark function test, the improved algorithm has better optimization ability. …”
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Framework for truck–RPAS hybrid models in last-mile delivery by Armin Mahmoodi, Leila Hashemi, Jeremy Laliberte

“…To balance operating cost, service time, regulatory risk, and energy usage, a novel multi-objective mixed-integer linear programming model is developed. High-quality Pareto-optimal solutions are produced by the non-dominated sorting genetic algorithm II, which methodically manages trade-offs between the conflicting goals. …”
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