Availability and uncertainty-aware optimal placement of capacitors and DSTATCOM in distribution network using improved exponential distribution optimizer by Abdulaziz Alanazi, Mohana Alanazi, Zulfiqar Ali Memon, Ahmed Bilal Awan, Mohamed Deriche

“…The decision variables include the installation location and the capacity of compensators, which are defined by a novel meta-heuristic algorithm termed the improved exponential distribution optimizer (IEDO). …”
Get full text

Deep recurrent neural network with fractional addax optimization algorithm for influenza virus host prediction by Shweta Ashish Koparde, Sonali Kothari, Sharad Adsure, Kapil Netaji Vhatkar, Vinod V. Kimbahune

“…. • Addresses the data imbalance and improves model generalization, the oversampling technique is applied for data augmentation.The prediction model employs a Deep Recurrent Neural Network (DRNN) optimized by Fractional Addax Optimization 34 Algorithm (FAOA), a hybrid of Addax Optimization Algorithm (AOA) and Fractional Concept (FC), designed to perform 35 influenza virus host prediction. …”
Get full text

AI-Based Classification Model for Low-Energy Buildings: Promoting Sustainable Economic Development of Smart Cities With Spherical Fuzzy Decision Algorithm by Lin Yang

“…These models classify buildings based on energy consumption patterns, predicting energy needs and identifying areas for improvement. …”
Get full text

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.…”
Get full text

Hybridization of metaheuristic algorithms for resource scheduling in distributed robotic control system by P. Anand Raj, M. Rajakumaran, S. Palani Murugan, S. Senthilkumar

Get full text

Improving the Integrity of a Voting Process with Biometric Authentication and Data Encryption by Walter M. Molina, Lino R. Mac Kay, Daniel Subauste

Get full text

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. …”
Get full text

Load balancing method of service cluster based on mean-variance by Xiaoan BAO, Xue WEI, Lei CHEN, Guoheng HU, Na ZHANG

“…When a large number of concurrent requests are allocated,the load scheduling mechanism is to achieve the load balancing of nodes in the network by minimizing the response time and maximizing the utilization ratio of nodes.In the load balancing algorithm based on genetic algorithm,the fitness function is designed to have an important influence on the load balancing efficiency.A service cluster load balancing method based on mean-variance was proposed to optimize the fitness function.The investment portfolio selection model mean-variance was used to minimize the response time,which was used to get the weight of each server's resource utilization,so as to obtain the optimal allocation combination.This method improves the accuracy and efficiency of the fitness function.Compared with other models in different service environment,the simulation results show that the load balancing algorithm makes the service cluster get a better balance performance in terms of node utilization and response time.…”
Get full text

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. …”
Get full text

Human Activity Recognition Using Graph Structures and Deep Neural Networks by Abed Al Raoof K. Bsoul

“…To address this, we applied the Firefly Optimization Algorithm to fine-tune the hyperparameters of both the graph-based model and a CNN baseline for comparison. …”
Get full text

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. …”
Get full text

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. …”
Get full text

An Improved Particle Swarm Optimization for Solving Bilevel Multiobjective Programming Problem by Tao Zhang, Tiesong Hu, Yue Zheng, Xuning Guo

“…An improved particle swarm optimization (PSO) algorithm is proposed for solving bilevel multiobjective programming problem (BLMPP). …”
Get full text

An efficient metaheuristic optimization algorithm for optimal power extraction from PV systems under various weather and load-changing conditions by Md.Al Imran Fahim, Md.Salah Uddin Yusuf, Monira Islam, Munshi Jawad Ibne Azad

“…To address these challenges, a new algorithm called horse herd optimization (HHO) has been applied to the maximum power point tracking (MPPT) controller. …”
Get full text

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. …”
Get full text

Modeling of Cloud-Edge Collaborated Electricity Market Considering Flexible Ramping Products Provided by VPPs by PENG Chaoyi, CHEN Wenzhe, XU Suyue, LI Jianshe, ZHOU Huafeng, GU Huijie, NIE Yongquan, SUN Haishun

“…The distributed optimization model is iteratively solved using the analytical target cascading (ATC) method, and heuristic constraints are introduced to improve the convergence of the algorithm. …”
Get full text

PM2.5 Concentration Prediction Based on Markov Blanke Feature Selection and Hybrid Kernel Support Vector Regression Optimized by Particle Swarm Optimization by Lian-Hua Zhang, Ze-Hong Deng, Wen-Bo Wang

“…Abstract This study employed air quality and meteorological data as research materials and extracted the optimal feature subset by using the approximate Markov blanket-based normal maximum relevance minimum redundancy (nMRMR) algorithm to serve as the input data of the prediction model. …”
Get full text

Modeling of cross line operation of urban rail transit trains based on passenger travel time by LUO Qin, LIN Bin, GU Mengqi, YANG Liang, ZHANG Xiaochun

“…The model is solved by genetic algorithm. The scheme of metro Line Ⅰ of city S crossing into Line Ⅱ is used as an example to verify the feasibility of the model, and the optimal train density under different train load rates is obtained. …”
Get full text

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. …”
Get full text

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. …”
Get full text