Enhancing the prediction of vitamin D deficiency levels using an integrated approach of deep learning and evolutionary computing by Ahmed Alzahrani, Muhammad Zubair Asghar

“…To improve the models effectiveness and guarantee the optimal choice of the features and hyper-parameters, we incorporate evolutionary computing methods, particularly genetic algorithms (GA). …”
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THEORETICAL AND METHODOLOGICAL PRINCIPLES OF APPLICATION OF AGENT-ORIENTED APPROACH TO MODELING PROCESSES OF LOCAL HROMADAS by Yevgen Kotukh, Maryna Riabokin

“…The article also outlines a multi-step algorithm for modeling decision-making and coordination processes within the decentralized budget structure. …”
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Deep learning-based artificial intelligence for assisting diagnosis, assessment and treatment in soft tissue sarcomas by Ruiling Xu, Jinxin Tang, Chenbei Li, Hua Wang, Lan Li, Yu He, Chao Tu, Zhihong Li

“…It is worth noting that high-quality data with accurate annotations, as well as optimized algorithmic performance are pivotal in the clinical application of DL in STSs.…”
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Advance path loss model for LoRaWAN coverage estimation by Van Lic Tran, Tran Dang Khoa Phan, Tran Thi Minh Hanh, Vu Van Thanh, Thai Van Tien, Thai Vu Hien, Tran Anh Kiet

“…This paper demonstrates that, using multiple data points around each gateway allows for the creation of accurate coverage heat maps by using a proposed LoRaWAN coverage estimation algorithm. This method effectively identifies areas with varying signal strength across Da Nang City, highlighting where additional gateways are needed to improve network reliability. …”
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Training Large Models on Heterogeneous and Geo-Distributed Resource with Constricted Networks by Zan Zong, Minkun Guo, Mingshu Zhai, Yinan Tang, Jianjiang Li, Jidong Zhai

“…To achieve this goal, we formulate the model partitioning problem among heterogeneous hardware and introduce a hierarchical searching algorithm to solve the optimization problem. …”
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Dark Ship Detection via Optical and SAR Collaboration: An Improved Multi-Feature Association Method Between Remote Sensing Images and AIS Data by Fan Li, Kun Yu, Chao Yuan, Yichen Tian, Guang Yang, Kai Yin, Youguang Li

“…Subsequently, an advanced JVC global optimization algorithm is employed to ensure high-precision association in dense scenes. …”
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Application of KTA-KELM in Fault Diagnosis of Rolling Bearing by Zhuo Wang, Wenjun Zhao, Tao Ma, Zhijun Li, Bo Qin

“…Then，the Kernel Target Alignment（KTA） parameters of maximum KTA value Ai and the kernel parameter σi are initialized, and the different kernel parameter values are adjusted by judging the distance between the kernel matrix and the ideal target matrix，so as to obtain the minimum corresponding maximum kernel arrangement value when the kernel matrix distance is obtained，and the kernel parameter at this time is optimal. Finally，the high-dimensional feature vector set of the above rolling bearing is used as input to learn the KTA-KELM algorithm, the state recognition model of rolling bearing is built based on KTA-KELM algorithm. …”
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Joint beamforming design for distributed IRS assisted millimeter wave MU-MISO system by Zhongjie LI, Jiyuan XIONG, Wei GAO, Jinying WEI

“…A joint beamforming design scheme for distributed intelligent reflecting surface (IRS) assisted multi-user communication was proposed to solve the reliable communication problem in the downlink of millimeter-wave multi user-multiple input single output (MU-MISO) system in non line of sight scenarios.Considering the power and constant-mode constraints, the active beamforming of the base station and passive beamforming of multiple IRS were modeled as a joint non-convex optimization problem with the objective of user weighting and sum rate maximization.A closed-form fractional programming technique was used to decouple this joint optimization problem into an easily solvable equivalent problem.A non-convex block coordinate descent algorithm was used to alternately optimize the active beamforming and passive beamforming matrices according to the prox-linear rule and the distributed successive convex approximation rule.The convergence proof and complexity analysis of the proposed algorithm were also given.The simulation results demonstrate that the algorithm can converge fast, and can effectively improve the system transmission rate with reduced complexity compared with the two baseline algorithms.…”
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Rescheduling of Multi-Scenario and Multi-Objective Dynamic Changes of Ship Group Construction by ZHANG Aoyuan, HU Xiaofeng, ZHANG Yahui

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GNSS Precipitable Water Vapor Prediction for Hong Kong Based on ICEEMDAN-SE-LSTM-ARIMA Hybrid Model by Jie Zhao, Xu Lin, Zhengdao Yuan, Nage Du, Xiaolong Cai, Cong Yang, Jun Zhao, Yashi Xu, Lunwei Zhao

“…This process significantly reduces modeling complexity and improves computational efficiency. …”
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Bilevel Optimization Framework for Multiregional Integrated Energy Systems Considering 6G Network Slicing and Battery Energy Storage Capacity Sharing by Kun Cui, Ming Chi, Yong Zhao, Zhi-Wei Liu

“…The proposed line search-based global Levenberg–Marquardt algorithm addresses the limitations of existing algorithms with necessary and innovative improvements to tackle the challenge of global convergence in nonsmooth optimization problems. …”
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Development of a manipulator kinematic model using Abb Robot Studio by Артем Бронніков, Марія Бендеберя

“…Conclusions: a model and software module were developed for the adaptive execution of the production process as part of the virtual model of the ABB IRB 1200 robot, which allows to increase the efficiency of the roduction process, reduce downtime, and improve product quality. …”
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Strategic Traffic Management in Mixed Traffic Road Networks: A Methodological Approach Integrating Game Theory, Bilevel Optimization, and C-ITS by Areti Kotsi, Ioannis Politis, Evangelos Mitsakis

“…The methodology includes defining a model to achieve optimal mixed equilibria, designing an algorithm for multiclass traffic assignment, formulating strategic games to analyze player interactions, and establishing key performance indicators to evaluate network efficiency and effectiveness. …”
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Construction and analysis of yarn tension model in the process of electromagnetic weft insertion by Zhang Chengjun, Wang Yi, Liu Pengwei, Li Chengyuan, Xu Chuqiao, Zuo Xiaoyan, Zhou Xiangyang

“…When constructing the yarn tension model, the Levenberg–Marquardt algorithm in ORIGIN software is used for data fitting. …”
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AlgaeClass_Net: Optimizing Few-Shot Marine Microalgae Classification With Multi-Scale Feature Enhancement Network by Dan Liu, Guihong Yuan, Huachao Tan, Yanbo Jiang, Hai Bi, Yuan Cheng

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A statistical study on factors influencing piezoelectric properties of upside-down composites towards machine learning-driven development for recycling by Sivagnana Sundaram Anandakrishnan, Suhas Yadav, Mohadeseh Tabeshfar, Mikko Nelo, Jani Peräntie, Yang Bai

“…The approach lays down a foundation for scaling up the optimization of the recycled materials by providing training and/or testing datasets for possible machine learning algorithms via potential high-throughput manufacturing routes.…”
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Economic Dispatch of Fully Renewable Energy System Considering Uncertainty of both Energy Source and Load by Fan YU, Honghai NIU, Bing LI, Yang ZHAO, Pei CHEN, Xiaochen GUAN, Yu YANG

“…Considering the uncertainty of both energy source and load, a day-ahead economic dispatch model based on two-stage robust optimization was built and solved by column constraint generation (C & CG) algorithm. …”
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Exploring dynamical whole-brain models in high-dimensional parameter spaces. by Kevin J Wischnewski, Florian Jarre, Simon B Eickhoff, Oleksandr V Popovych

“…To enable the parameter optimizations for the high-dimensional model fitting to empirical data, we applied two dedicated mathematical optimization algorithms (Bayesian Optimization, Covariance Matrix Adaptation Evolution Strategy). …”
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On the Performance of Downlink Channel Estimation‐Based Cell‐Free Massive MIMO Systems Under Correlated Rician Fading Channels by Jiarui Li, Yao Zhang, Fengjiao Gan, Na Liu, Longxiang Yang, Hongbo Zhu

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Parameter sensitivity analysis for diesel spray penetration prediction based on GA-BP neural network by Yifei Zhang, Gengxin Zhang, Dawei Wu, Qian Wang, Ebrahim Nadimi, Penghua Shi, Hongming Xu

“…The GA-BP neural network was selected for its ability to optimize neural network weights and thresholds, thereby improving model convergence and avoiding local minima, which are common challenges in complex, non-linear problems such as spray prediction. …”
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