Design of Block-Scrambling-Based privacy protection mechanism in healthcare using fusion of transfer learning models with Hippopotamus optimization algorithm by Ghada Moh. Samir Elhessewi, Mohammed Yahya Alzahrani, Mohammad Alamgeer, Abdulbasit A. Darem, Da’ad Albalawneh, Mohammed Alqahtani, Mutasim Al Sadig, Sultan Alanazi

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Artificial intelligence-driven cybersecurity: enhancing malicious domain detection using attention-based deep learning model with optimization algorithms by Fatimah Alhayan, Asma Alshuhail, Ahmed Omer Ahmed Ismail, Othman Alrusaini, Sultan Alahmari, Abdulsamad Ebrahim Yahya, Monir Abdullah, Samah Al Zanin

“…This manuscript presents an Enhance Malicious Domain Detection Using an Attention-Based Deep Learning Model with Optimization Algorithms (EMDD-ADLMOA) technique. …”
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Sensitivity Analysis for Dynamic Parameters of High–Speed Train Based on Multimodal–Optimization Improved Kriging Model and Distance Correlation by Jie JIANG, Xufeng YANG, Guofu DING

“…To improve the accuracy of the approximation model, a novel multimodal optimization algorithm is introduced to globally optimize the hyperparameters of these Kriging models. …”
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An Improved Optimal Cloud Entropy Extension Cloud Model for the Risk Assessment of Soft Rock Tunnels in Fault Fracture Zones by Shuangqing Ma, Yongli Xie, Junling Qiu, Jinxing Lai, Hao Sun

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Optimizing PID control for multi-model adaptive high-speed rail platform door systems with an improved metaheuristic approach by Dong Zhan, Ai-Qing Tian, Shao-Quan Ni

“…This study delves into the optimization of PID control parameters for Multi-model Adaptive High-speed Rail Platform Door Control Systems (MMAHSR-PDCS) using the Individual-Based Model Dynamic Multi-Swarm Snow Goose Algorithm (IBM-Dy-SGA). …”
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Improved SOM algorithm for damage characterization based on visual sensing by Hongtao Zhu, Shuyun Guo

“…Additionally, employing stochastic gradient descent as an optimization algorithm enhances the model training efficiency. …”
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Underwater Object Detection Algorithm Based on an Improved YOLOv8 by Fubin Zhang, Weiye Cao, Jian Gao, Shubing Liu, Chenyang Li, Kun Song, Hongwei Wang

“…This paper proposes an underwater object detection algorithm based on an improved YOLOv8 model. First, the introduction of CIB building blocks into the backbone network, along with the optimization of the C2f structure and the incorporation of large-kernel depthwise convolutions, effectively enhances the model’s receptive field. …”
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Research on the evolution of college online public opinion risk based on improved Grey Wolf Optimizer combined with LSTM model. by Chao Cao, Ziyu Li, Lingzhi Li, Fanglu Luo

“…This research proposes a public opinion crisis prediction model that applies the Grey Wolf Optimizer (GWO) algorithm combined with long short-term memory (LSTM) and implements it to analyze a trending topic on Sina Weibo to validate its prediction accuracy. …”
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Multi-objective optimization and parameter sensitivity study on microreactor nuclear power systems by Ersheng You, Yiyi Li, Jianjun Xu, Dianchuan Xing, Haochun Zhang

“…A set of comprehensive calculation models suitable for multi-objective optimization of system performance were established from three aspects, including thermal cycle calculation, heat exchanger thermal balance and component weight estimation. …”
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Predicting CO₂ Emissions with Advanced Deep Learning Models and a Hybrid Greylag Goose Optimization Algorithm by Amel Ali Alhussan, Marwa Metwally, S. K. Towfek

“…In this paper, we propose a general framework that combines advanced deep learning models (such as GRU, Bidirectional GRU (BIGRU), Stacked GRU, and Attention-based BIGRU) with a novel hybridized optimization algorithm, GGBERO, which is a combination of Greylag Goose Optimization (GGO) and Al-Biruni Earth Radius (BER). …”
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Research on Fire Detection of Cotton Picker Based on Improved Algorithm by Zhai Shi, Fangwei Wu, Changjie Han, Dongdong Song

“…Therefore, in this study, we designed an improved algorithm for multi-sensor data fusion; built a cotton picker fire detection system by using infrared temperature sensors, CO sensors, and the upper computer; and proposed a BP neural network model based on improved mutation operator hybrid gray wolf optimizer and particle swarm optimization (MGWO-PSO) algorithm based on the BP neural network model. …”
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Advanced internet of things enhanced activity recognition for disability people using deep learning model with nature-inspired optimization algorithms by Mohammed Maray

“…The EARDP-DLMNOA model mainly relies on improving the activity recognition model using advanced optimization algorithms. …”
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Multi-Agent Communication for Dynamic Job-Shop Scheduling: A Robust Single-Machine Scheduling Model With Genetic Algorithm Optimization by Hong-Phuc Nguyen, Quang-Hieu Ngo, van-Can Nguyen

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Parameter extraction of PV models under varying meteorological conditions using a modified electric eel foraging optimization algorithm by Hadeer Khalifa, Mohamed Ebeed, Gaber Magdy, Sherif A. Khaleel, Mohamed I. Shehata, Moataz M. Salah, Francisco Jurado, Hossam Hassan Ali

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An application of Arctic puffin optimization algorithm of a production model for selling price and green level dependent demand with interval uncertainty by Hachen Ali, Md. Al-Amin Khan, Ali Akbar Shaikh, Adel Fahad Alrasheedi, Seyedali Mirjalili

“…To assess the accuracy and reliability of the proposed model, the Arctic Puffin Optimization (APO) algorithm is employed to analyze and solve a specific numerical illustration. …”
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Solving Integer Ambiguity Based on an Improved Ant Lion Algorithm by Wuzheng Guo, Yuanfa Ji, Xiyan Sun, Xizi Jia

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Assignment Technology Based on Improved Great Wall Construction Algorithm by Xianjun Zeng, Yao Wei, Yang Yu, Hanjie Hu, Qixiang Tang, Ning Hu

“…This paper presents an autonomous multi-UAV cooperative task allocation method based on an improved Great Wall Construction Algorithm. A model integrating battlefield environmental factors, 3D terrain data, and threat assessments is developed for optimized task allocation and trajectory planning. …”
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Nighttime Vehicle Detection Algorithm Based on Improved YOLOv7 by Fan Zhang

“…Ablation experiments verify the synergistic optimization effect and efficiency of each module. Furthermore, a comparison with other state-of-the-art algorithms like SSD and DETR confirms the superiority of our approach. …”
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Multi-step Prediction of Monthly Sediment Concentration Based on WPT-ARO-DBN/WPT-EPO-DBN Model by GAO Xuemei, CUI Dongwen

“…Accurate multi-step sediment concentration prediction is of significance for regional soil erosion control,flood control and disaster reduction.To improve the multi-step prediction accuracy of sediment concentration and the prediction performance of the deep belief network (DBN),this paper proposes a multi-step prediction model of monthly sediment concentration by combining the artificial rabbit optimization (ARO) algorithm,eagle habitat optimization (EPO) algorithm,and DBN based on wavelet packet transform (WPT).The model is validated using time series data of monthly sediment concentration from Longtan Station in Yunnan Province.Firstly,WPT is employed to decompose the time series data of the monthly sediment concentration of the case in three layers,and eight more regular subsequence components are obtained.Secondly,the principles of ARO and EPO algorithms are introduced,and hyperparameters such as the neuron number in the hidden layer of DBN are optimized by ARO and EPO.Meanwhile,WPT-ARO-DBN and WPT-EPO-DBN prediction models are built,and WPT-PSO (particle swarm optimization)-DBN and WPT-DBN are constructed for comparative analysis.Finally,four models are adopted to predict each subsequence component,and the predicted values are superimposed to obtain the multi-step prediction results of the final monthly sediment concentration.The results are as follows.① WPT-ARO-DBN and WPT-EPO-DBN models have satisfactory prediction effects on the monthly sediment concentration of the case from one step ahead to four steps ahead.This yields sound prediction results for five steps ahead.The prediction effect for six steps ahead and seven steps ahead is average,and the prediction accuracy for eight steps ahead is poor and cannot meet the prediction accuracy requirements.② The multi-step prediction performance of WPT-ARO-DBN and WPT-EPO-DBN models is superior to WPT-PSO-DBN models and far superior to WPT-DBN models,with higher prediction accuracy,better generalization ability,and larger prediction step size.③ ARO and EPO can effectively optimize DBN hyperparameters,improve DBN prediction performance,and have better optimization effects than PSO.Additionally,WPT-ARO-DBN and WPT-EPO-DBN models can give full play to the advantages of WPT,new swarm intelligence algorithms and the DBN network and improve the multi-step prediction accuracy of monthly sediment concentration,and the prediction accuracy decreases with the increasing prediction steps.…”
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Acoustic Identification Method of Partial Discharge in GIS Based on Improved MFCC and DBO-RF by Xueqiong Zhu, Chengbo Hu, Jinggang Yang, Ziquan Liu, Zhen Wang, Zheng Liu, Yiming Zang

“…To accurately identify partial discharge in GIS, this paper proposes an acoustic identification method based on improved mel frequency cepstral coefficients (MFCC) and dung beetle algorithm optimized random forest (DBO-RF) based on the ultrasonic detection method. …”
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