Dynamic reconfiguration of the distribution systems with Load Duration Curve (LDC) model for reducing the losses and improving the voltage profile by Sana Sadeghi, Alireza Jahangiri, Ahmad Ghaderi Shamim

“…Unlike traditional methods that rely on real-time or hourly load models, this approach utilizes a load model to address the dynamic reconfiguration problem. …”
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Improved Pacific Decadal Oscillation Prediction by an Optimizing Model Combined Bidirectional Long Short-Term Memory and Multiple Modal Decomposition by Hang Yu, Junbo Lei, Pengfei Lin, Tao Zhang, Hailong Liu, Huilin Lai, Lindong Lai, Bowen Zhao, Bo Wu

“…By utilizing the WOA to effectively optimize hyperparameters, the model enhances the PDO prediction skill compared to existing deep learning PDO prediction models, improving the correlation coefficient from 0.47 to 0.68 at a 6-month lead time. …”
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A Bi-Level Capacity Optimization Method for Hybrid Energy Storage Systems Combining the IBWO and MVMD Algorithms by Qiaoqiao Xing, Shidong Li, Da Qiu, Yang Long, Qinyi Liao, Xiangjin Yin, Yunxiang Li, Kai Qian

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Research on a hybrid deep learning model based on two-stage decomposition and an improved whale optimization algorithm for air quality index prediction by Hangyu Zhou, Yongquan Yan

“…The model's hyperparameters are optimized by the Improved Whale Optimization Algorithm (IWOA), which improves search efficacy by including chaotic mapping, a nonlinear shrinkage factor, and a Levy flight strategy. …”
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Model updating method for detect and localize structural damage using generalized flexibility matrix and improved grey wolf optimizer algorithm (I-GWO) by Sina Sadraei, Majid Gholhaki, Omid Rezaifar

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Research on lightweight weed recognition algorithm based on improved YOLOv8 by Zhang Chao, Liu Bin, Li Kun

“…Meanwhile, the size and computation amount of the model are reduced to 68.2% and 62.6% of the original model, respectively, reflecting the effectiveness of the improved algorithm in this paper.…”
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Improved YOLOv8-Based Algorithm for Citrus Leaf Disease Detection by Zhengbing Zheng, Yibang Zhang, Luchao Sun

“…Experimental results show that the improved algorithm achieves an accuracy of 93.2% on the test set, representing a 1.2 percentage increase over the original model, while also reducing the parameter volume and computational complexity by 7% and 7.4% respectively. …”
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Vehicle detection and recognition algorithm based on function improvement of YOLOv3 by SONG Huajie, ZHOU Lei

“…In view of this defect, the loss function of YOLOv3 algorithm was improved, the wide-height coordinate error was modified into the form of proportion, the non-maximum suppression method of the original model was improved and the overlap threshold was changed from the fixed value to the form of attenuation function. …”
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Improved CNN-GRU algorithm application in enterprise legal consultation system by Xuejie Zhao

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Monthly precipitation prediction based on quadratic decomposition and improved parrot algorithm by Weijie Zhang, Yuming Zeng, Shubo Zhou, Libin Zhang, Haiquan Li, Zhongsheng Yao, Rusheng Zhou

“…The model first decomposed the original precipitation data using the CEEMDAN decomposition algorithm, output the modal components and residual components, and then used the topology optimization algorithm (TTAO) to optimize the VMD, and decomposed the high-dimensional sequence in the first decomposition result for the second time. …”
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Detection of Seed Potato Sprouts Based on Improved YOLOv8 Algorithm by Yufei Li, Qinghe Zhao, Zifang Zhang, Jinlong Liu, Junlong Fang

“…In this paper, a lightweight deep learning algorithm, YOLOv8_EBG, is proposed to both improve the detection performance and reduce the model parameters. …”
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Optimizing PEMFC parameter identification using improved pufferfish algorithm and CNN by Ji Li, Changiz Bastani

“…In this research, a novel approach has been proposed for enhancing the accuracy of proton exchange membrane fuel cell (PEMFC) models based on convolutional neural networks (CNNs) and an improved optimization method (called the improved pufferfish optimization algorithm). …”
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Steel Surface Defect Detection Based on Improved GCHS-YOLO Algorithm by Ruiqiang Guo, Peiyong Ji, Yapin Zhang, Jingqi Hu, Wenlong Liu, Xuejian Li, Min Li

“…Experimental results using the NEU-DET dataset—after applying noise and Gaussian filtering—show that the GCHS-YOLO algorithm improves the mean Average Precision (mAP) by 1.2%, Precision by 0.8%, mAP@0.5:0.95 by 4.4%, and Recall by 2.8%, compared to the original YOLOv8s model.…”
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Pedestrian Detection in Fisheye Images Based on Improved YOLOv8 Algorithm by ZHU Yumin, SUN Guangling, MIAO Fei

“…In view of the problems of inaccurate positioning and insufficient detection accuracy in pedestrian detection in fisheye images in existing target detection algorithms, an improved YOLOv8 algorithm for fisheye image detection is proposed. …”
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Research on Fire Smoke Detection Algorithm Based on Improved YOLOv8 by Tianxin Zhang, Fuwei Wang, Weimin Wang, Qihao Zhao, Weijun Ning, Haodong Wu

“…To address these issues, this paper proposes a fire detection algorithm based on an improved YOLOv8 model. First, to enhance the detection capabilities for large-scale fire and smoke targets, a large target detection head is added to the backbone of the YOLOv8 model. …”
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Improved sparse domain super-resolution reconstruction algorithm based on CMUT. by Zhiqing Wei, Yanping Bai, Rong Cheng, Hongping Hu, Peng Wang, Wendong Zhang, Guojun Zhang

“…We compared the proposed algorithm with previously reported algorithms in the Shepp Logan model and the model based on the CMUT background. …”
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Segmentation of Stone Slab Cracks Based on an Improved YOLOv8 Algorithm by Qitao Tian, Runshu Peng, Fuzeng Wang

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Improved Grey Wolf Algorithm: A Method for UAV Path Planning by Xingyu Zhou, Guoqing Shi, Jiandong Zhang

“…Subsequently, an Enhanced Grey Wolf Optimizer model (NI–GWO) is introduced, which optimizes the convergence coefficient using a nonlinear function and integrates the Dynamic Window Approach (DWA) algorithm into the model based on the fitness of individual wolves, enabling it to perform dynamic obstacle avoidance tasks. …”
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Forest Fire Detection Algorithm Based on Improved YOLOv11n by Kangqian Zhou, Shuihai Jiang

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RESEARCH ON DEEP NEURAL NETWORK LEARNING BASED ON IMPROVED BP ALGORITHM by HUANG Pei

“…Deep learning can make the computing model that contains a number of processing layers to learn the data that contains many levels of abstract representation.This kind of learning way in the most advanced speech recognition,visual object recognition,object detection and many other areas,such as biology,genetics and medicine brought significant improvement.Deep learning can find the complex structure of large data,and the convolution neural network as one of the important models of the depth study in the processing of voice,image,video and text,and other aspects of a new breakthrough.It is the use of BP algorithm to guide the machine how to get the error before the layer to adjust the parameters of this layer,so that these parameters are more conducive to the calculation of the model.In view of the shortcomings of traditional BP algorithm,a fast BP algorithm is proposed,which has the disadvantages of slow convergence speed and often falls into local minimum points.The improved convolutional neural network is used to validate the data set MNIST,English character recognition and medical image.The simulation results show the effectiveness of the proposed algorithm.…”
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