Optimization research on UAV semantic communication system based on SVD-MADRL by Yibo Yang, Yahe Tan, Liu Liu

“…The study optimizes the flight trajectory and power of multiple unmanned aerial vehicles with the deep deterministic policy gradient algorithm and constructs a multi-unmanned aerial vehicle semantic communication optimization model based on singular value decomposition and multi-agent deep reinforcement learning. …”
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Time-dependent robust reliability optimization of structure based on genetic interval affine response surface by GUAN Yi, SHENG GuoJun

“…Finally, combined with structural with structural performance degradation behaviour and robust reliability optimization method, the time dependent robust reliability optimization model for structure was established and structural optimization design was carried out. …”
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Design and Optimization of Brushless Hybrid Wound Rotor Vernier Motor for Variable Speed Applications by Kapeel Kumar, Qasim Ali, Sheeraz Ahmed, Muhammad Ilyas, Hanlin Zhan, Mudassir Raza Siddiqi

“…To find out the optimal shape of the magnets, the Genetic algorithm optimization technique is used. …”
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Advances to IoT security using a GRU-CNN deep learning model trained on SUCMO algorithm by Amit Sagu, Nasib Singh Gill, Preeti Gulia, Noha Alduaiji, Piyush Kumar Shukla, Mohd Asif Shah

“…The SUCMO algorithm fine-tunes the deep learning model’s hyperparameters to improve classification accuracy. …”
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Optimizing Aircraft Routes in Dynamic Conditions Utilizing Multi-Criteria Parameters by Oleh Sydorenko, Nataliia Lysa, Liubomyr Sikora, Roman Martsyshyn, Yuliya Miyushkovych

“…In addition, optimal flight paths were modeled using the improved algorithms, which allow for increasing the efficiency of decision-making in the field of air traffic control. …”
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Method for EEG signal recognition based on multi-domain feature fusion and optimization of multi-kernel extreme learning machine by Shan Guan, Tingrui Dong, Long-kun Cong

“…Abstract In response to the current issues of one-sided effective feature extraction and low classification accuracy in multi-class motor imagery recognition, this study proposes an Electroencephalogram (EEG) signal recognition method based on multi-domain feature fusion and optimized multi-kernel extreme learning machine. Firstly, the EEG signals are preprocessed using the Improved Comprehensive Ensemble Empirical Mode Decomposition (ICEEMD) algorithm combined with the Pearson correlation coefficient to eliminate noise and interference. …”
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Assessment of soil classification based on cone penetration test data for Kaifeng area using optimized support vector machine by Hanliang Bian, Zhongxun Sun, Jiahan Bian, Zhaowei Qu, Jianwei Zhang, Xiangchun Xu

“…Notably, the Thermal Exchange Optimization (TEO) algorithm resulted in the most significant improvement, increasing the accuracy of the original SVM model by 10% and exceeding the standard by 4.3%. …”
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Reservoir water level prediction using combined CEEMDAN-FE and RUN-SVM-RBFNN machine learning algorithms by Lan-ting Zhou, Guan-lin Long, Can-can Hu, Kai Zhang

“…This study proposed a method for reservoir water level prediction based on CEEMDAN-FE and RUN-SVM-RBFNN algorithms. By integrating the adaptive complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method and fuzzy entropy (FE) with the new and highly efficient Runge–Kuta optimizer (RUN), adaptive parameter optimization for the support vector machine (SVM) and radial basis function neural network (RBFNN) algorithms was achieved. …”
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The impact of cultural factors on digital marketing strategies with Machine learning and honey bee Algorithm (HBA) by Muhammad Khan, Masood Ahmad, Rakhmonov Dilshodjon Alidjonovich, Kalonov Mukhiddin Bakhritdinovich, Kurbanbekova Mohichehra Turobjonovna, Imomov Jamshidxon Odilovich

“…This paper analyses the impact of cultural factors on digital marketing strategies in Pakistan. Improvement of machine learning (ML) techniques combined with the Honey Bee Algorithm (HBA) has been incorporated for better solutions. …”
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A novel time difference of arrival localization algorithm using a neural network ensemble model by Zhenkai Zhang, Feng Jiang, Boyuan Li, Bing Zhang

“…The simulation results show that the proposed algorithm is efficient in improving the generalization ability and localization precision of the neural network ensemble model.…”
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Improving Modularity Regularization Techniques for Estimating Structures in Complex Network by Samaa F. Ibraheem, Basad Al-Sarray

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SAMFA: A Flame Segmentation Algorithm for Infrared and Visible Aerial Images in the Same Scene by Jianye Yuan, Min Yang, Haofei Wang, Xinwang Ding, Song Li, Wei Gong

“…In response to the issues of poor flame segmentation performance in the current fire images and the large number of learnable parameters in large models, we propose an improved large model algorithm, SAMFA (Segmentation Anything Model, Fire, Adapter). …”
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Multi-Objective Evolution and Swarm-Integrated Optimization of Manufacturing Processes in Simulation-Based Environments by Panagiotis D. Paraschos, Georgios Papadopoulos, Dimitrios E. Koulouriotis

“…To this end, the proposed approach couples the JaamSim-based digital twins with evolutionary and swarm-based algorithms to carry out the multi-objective optimization under varying conditions. …”
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A Novel, Self-Adaptive, Multiclass Priority Algorithm with VM Clustering for Efficient Cloud Resource Allocation by Hicham Ben Alla, Said Ben Alla, Abdellah Ezzati, Abdellah Touhafi

“…The simulation results and analysis demonstrate that the proposed algorithm effectively optimizes makespan and energy consumption. …”
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Evolutionary Cost Analysis and Computational Intelligence for Energy Efficiency in Internet of Things-Enabled Smart Cities: Multi-Sensor Data Fusion and Resilience to Link and Devi... by Khalid A. Darabkh, Muna Al-Akhras

“…Thorough simulations and comparative analysis reveal the protocol’s superior performance across key performance metrics, namely, network lifespan, energy consumption, throughput, and average delay. When compared to the most recent and relevant protocols, including the Particle Swarm Optimization-based energy-efficient clustering protocol (PSO-EEC), linearly decreasing inertia weight PSO (LDIWPSO), Optimized Fuzzy Clustering Algorithm (OFCA), and Novel PSO-based Protocol (NPSOP), our approach achieves very promising results. …”
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Improving Offshore Wind Speed Forecasting with a CRGWAA-Enhanced Adaptive Neuro-Fuzzy Inference System by Yingjie Liu, Fahui Miao

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Optimized Adaptive Fuzzy Synergetic Controller for Suspended Cable-Driven Parallel Robots by Yasser Hatim Alwan, Ahmed A. Oglah, Muayad Sadik Croock

“…The parameters of the controller are optimized using the Dragonfly Algorithm, a metaheuristic technique known for its simplicity and fast convergence. …”
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Precise Rail Transit Sleeper Positioning Technology Based on Improved YOLOv8n Model by YAN Xiaoxia, GUO Jianqin, ZHAI Huchao, LIU Yutao, LI Junxin, WANG Liangxian

“…Through optimization of loss function and structural constraints on sleeper counting, a sleeper object detection model YOLOv8n_SC is proposed based on the improved YOLOv8n model. …”
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Optimizing Curriculum for Students: A Machine Learning Approach to Time Management Analysis by Jianmin Dong

“…Additionally, Tasmanian Devil Optimization (TDO) and Equilibrium Slime Mould Algorithm (ESMA) are integrated to enhance the accuracy of both XGBC and HGBC models. …”
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Toward a linear-ramp QAOA protocol: evidence of a scaling advantage in solving some combinatorial optimization problems by J. A. Montañez-Barrera, Kristel Michielsen

“…Abstract The quantum approximate optimization algorithm (QAOA) is a promising algorithm for solving combinatorial optimization problems (COPs), with performance governed by variational parameters $${\{{\gamma }_{i},{\beta }_{i}\}}_{i = 0}^{p-1}$$ { γ i , β i } i = 0 p − 1 . …”
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