Steganographic model to conceal the secret data in audio files utilizing a fourfold paradigm: Interpolation, multi-layering, optimized sample space, and smoothing by Daffa Tristan Firdaus, Ntivuguruzwa Jean De La Croix, Tohari Ahmad, Didacienne Mukanyiligira, Louis Sibomana

“…To address these limitations, this study offers valuable insights to guide researchers in developing high-performing audio steganography models. The proposed method seeks to improve stego audio quality by implementing a smoothing-based technique and optimizing the sample space through linear interpolation, followed by a multi-layering process. …”
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A novel approach for efficient resource allocation in 6G V2V networks using neighbor-aware greedy algorithm and sweep line model by Spandana Mande, Nandhakumar Ramachandran

“…The resource distribution strategy for V2V users participating in the exchange of information is based on the greedy method. The recommended algorithm determines which resource is optimal for each V2V user by taking a multi-level approach. …”
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Metaheuristic Prediction Models for Kerf Deviation in Nd-YAG Laser Cutting of AlZnMgCu1.5 Alloy by Arulvalavan Tamilarasan, Devaraj Rajamani

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Enhanced recurrent attention-deep Q learning with optimal node constrains and effective penalty based model for data transmission scheduling on wireless sensor networks by D.R. Anita Sofia Liz, Yesubai Rubavathi C

“…This technique performs dynamic scheduling of data transmission tasks considering energy consumption and network interference by combining a penalty-based model, optimal node limitations, and recurrent attention techniques. …”
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Minimum Revenue Guarantee and Toll Revenue Cap Optimization for Ppp Highways: Pareto Optimal State Approach by Yuanqing Wang, Zihua Li, Yanan Gao

“…Therefore, the model has an effect on improving the fairness of the risk sharing measures, reducing the financial burden of the investors especially the government, and increasing the investment attraction of the private sectors.…”
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A Novel Black Widow Optimization Algorithm Based on Lagrange Interpolation Operator for ResNet18 by Peiyang Wei, Can Hu, Jingyi Hu, Zhibin Li, Wen Qin, Jianhong Gan, Tinghui Chen, Hongping Shu, Mingsheng Shang

“…In an attempt to surmount this challenge, evolutionary computation algorithms can automatically adjust the hyper-parameter learning rate to improve training efficiency and model performance. …”
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Tailoring orthotropic mechanical and thermal properties in nonlinear contact, vibration and thermal problems via kriging-based optimization by Yicong Zhou, Jinyan Duan, Guoxin Mao, Jun Hong

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Multi-Objective Optimization Design of Ship Propulsion Shafting Based on Response Surface Methodology and Genetic Algorithm by ZHANG Cong, SHU Bingnan, ZHANG Jiangtao, JIN Yong

“…In order to reduce the power loss of the transmission equipment, enhance the transmission efficiency of the propulsion shafting, and improve the vibration performance of the shafting, a multi-objective optimization design of a ship shafting experimental platform is performed based on the response surface model and genetic algorithm. …”
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Prediction of Soil Liquefaction Using a Multi-Algorithm Technique: Stacking Ensemble Techniques and Bayesian Optimization by Long Tsang, Mahdi Akbari, Pouyan Fakharian

“…A Bayesian optimization method is also used to improve the accuracy of the predictions of soil liquefaction by adjusting the hyperparameters of these four classification algorithms. …”
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Efficient identification of photovoltaic cell parameters via Bayesian neural network-artificial ecosystem optimization algorithm by Bo Yang, Ruyi Zheng, Yucun Qian, Boxiao Liang, Jingbo Wang

“…Furthermore, the AEO algorithm is utilized to identify unknown parameters in the single-diode model (SDM), double-diode model (DDM), and three-diode model (TDM). …”
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Vehicle Attitude Control of Magnetorheological Semi-Active Suspension Based on Multi-Objective Intelligent Optimization Algorithm by Kailiang Han, Yiming Hu, Dequan Zeng, Yinquan Yu, Lei Xiao, Jinwen Yang, Weidong Liu, Letian Gao

“…Then, the optimization design model of the joint vehicle controller and vehicle dynamics is established to design the optimization fitness function oriented to the body attitude control performance, and the attitude optimal controller is calculated with the help of multi-objective intelligent optimization algorithm. …”
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AGV Scheduling and Energy Consumption Optimization in Automated Container Terminals Based on Variable Neighborhood Search Algorithm by Ning Zhao, Rongao Li, Xiaoming Yang

“…The research results show that the model and variable neighborhood search algorithm proposed in this paper have a significant effect on reducing the total energy consumption of AGVs and show good stability and practical application potential.…”
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Optimization of urban green space in Wuhan based on machine learning algorithm from the perspective of healthy city by Xuechun Zhou, Xiaofei Zou, Wenzuixiong Xiong

“…Adopting a healthy city development perspective, the research aims to assess the impact of green space optimization on urban health, economic performance, and social structure.MethodsA multivariable model was constructed using random forest and Support Vector Machine (SVM) algorithms to evaluate the influence of key indicators on urban green space. …”
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Optimal Dynamics Control in Trajectory Tracking of Industrial Robots Based on Adaptive Gaussian Pseudo-Spectral Algorithm by Jing Zhang, Xiaokai Zhu, Te Chen, Guowei Dou

“…The results show that compared with the control variable parameterization method and the traditional Gaussian pseudo-spectral method, the proposed dynamic optimal control method based on an adaptive Gaussian point reconstruction algorithm can effectively improve the solving time and efficiency of constrained optimization problems, thereby further enhancing the dynamic optimization control and trajectory tracking effect of industrial robots.…”
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Using crafted features and polar bear optimization algorithm for short-term electric load forecast system by Mansi Bhatnagar, Gregor Rozinaj, Radoslav Vargic

“…Optimized hyperparameters with PBO effectively decreased RMSE, MAE, and MAPE and improved the model prediction, showcasing the capability of the PBO in hyperparameter tuning for STLF. …”
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SABO-ELM model for remaining life prediction of lithium-ion batteries under multiple health factors by Jiabo LI, Zhonglin SUN, Di TIAN, Zhixuan WANG

“…The SABO algorithm optimizes the weights and bias thresholds of the ELM model, which effectively reduces the risk of local optima and improves its predictive performance and stability. …”
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Empowering Retail Through Advanced Consumer Product Recognition Using Aquila Optimization Algorithm With Deep Learning by Mohammed Alghamdi, Hanan Abdullah Mengash, Mohammed Aljebreen, Mohammed Maray, Abdulbasit A. Darem, Ahmed S. Salama

“…To achieve this, the ACPR-AOADL model first pre-processes the input data utilizing a Wiener filter (WF) to improve the image quality. …”
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A dual layer secure and energy-efficient model for border surveillance using sea lion inspired strategy in wireless sensor networks by Jayachandran J, Vimaladevi K

“…This paper presents a Dual Layer Sea Lion Optimization algorithm (DL-SLnOA) model, a bio-inspired, clustering and routing that focuses on energy efficiency and security to enhance performance under challenging conditions. …”
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An optimized feature selection using triangle mutation rule and restart strategy in enhanced slime mould algorithm by Ibrahim Musa Conteh, Gibril Njai, Abass Conteh, Qingguo Du

“…This paper proposes an improved feature selection method based on an improved Slime Mould Algorithm (SMA), called the Triangular Mutation Rule Restart Strategy Slime Mould Algorithm (TRSMA), to overcome some of the shortcomings of the SMA, including premature convergence, poor population diversity, and local optima entrapment. …”
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A Temporal Convolutional Network–Bidirectional Long Short-Term Memory (TCN-BiLSTM) Prediction Model for Temporal Faults in Industrial Equipment by Jinyin Bai, Wei Zhu, Shuhong Liu, Chenhao Ye, Peng Zheng, Xiangchen Wang

“…First, preprocessed industrial operation data are fed into the model, and hyperparameter optimization is conducted using the Optuna framework to improve training efficiency and generalization capability. …”
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