Mathematical model of on-demand route formation for public transport based on individual passenger requests in low-density population area by Svetlana S. Titova, Andrey V. Ostroukh

“…A mathematical model was developed that accounts for the specifics of populated areas with low population density, including uneven distribution of demand, large distances between populated areas, and limited financial resources. Various route optimization algorithms were investigated, and the most suitable method was selected for solving the stated problem. …”
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Enhanced NDVI prediction accuracy in complex geographic regions by integrating machine learning and climate data—a case study of Southwest basin by Zehui Zhou, Jiaxin Jin, Bin Yong, Weidong Huang, Lei Yu, Peiqi Yang, Dianchen Sun

“…To address these limitations, this study developed an NDVI time-series prediction optimization model, LSKRX, which integrates multiple machine learning algorithms with local geographic and climatic data. …”
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A systematic review of deep learning applications in database query execution by Bogdan Milicevic, Zoran Babovic

“…We categorize these approaches into three groups based on how such models are applied: improving performance of index structures and consequently data manipulation algorithms, query optimization tasks, and externally controlling query optimizers through parameter tuning. …”
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Multi-strategy fusion binary SHO guided by Pearson correlation coefficient for feature selection with cancer gene expression data by Yu-Cai Wang, Hao-Ming Song, Jie-Sheng Wang, Xin-Ru Ma, Yu-Wei Song, Yu-Liang Qi

“…Firstly, the CEC-2022 test functions were used to test the performance of the multi-strategy fusion SHO, from which the best variant TanASSHO was selected, and then compared with other nine swarm intelligent optimization algorithms. Performance tests of various algorithm variants on 18 UCI datasets show that V1PTASSHO is the most effective binary version. …”
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A Metaheuristic Framework for Cost-Effective Renewable Energy Planning: Integrating Green Bonds and Fiscal Incentives by Juan D. Saldarriaga-Loaiza, Johnatan M. Rodríguez-Serna, Jesús M. López-Lezama, Nicolás Muñoz-Galeano, Sergio D. Saldarriaga-Zuluaga

“…To do this, we use three optimization techniques to identify solutions that lower electricity generation costs: Teaching Learning, Harmony Search, and the Shuffled Frog Leaping Algorithm. …”
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Сontrol of Intelligent Transport System in Minsk by D. V. Kapskiy, D. V. Navoy, P. A. Pegin

“…The paper considers algorithms for searching a maximum traffic volume of road vehicles in a traffic light cycle with a distributed intensity pulse and optimization of shifts under coordinated traffic flow control. …”
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New method for landslide susceptibility evaluation in alpine valley regions that considers the suitability of InSAR monitoring and introduces deformation rate grading by Dingyi Zhou, Zhifang Zhao, Wenfei Xi, Xin Zhao, Jiangqin Chao

“…Taking the Dongchuan district, the most typical high mountain valley area in southwest China, as the research object, the SAR is quantitatively simulated and analyzed in this study using the improved R-index shadow layover method. …”
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Fractional-Order Swarming Intelligence Heuristics for Nonlinear Sliding-Mode Control System Design in Fuel Cell Hybrid Electric Vehicles by Nabeeha Qayyum, Laiq Khan, Mudasir Wahab, Sidra Mumtaz, Naghmash Ali, Babar Sattar Khan

“…This framework integrates moth flame optimization (MFO) with the gravitational search algorithm (GSA) and Fractal Heritage Evolution, implemented through three spiral-based variants: MFOGSAPSO-A (Archimedean), MFOGSAPSO-H (Hyperbolic), and MFOGSAPSO-L (Logarithmic). …”
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Advanced Machine Learning Methodology for Earthquake Magnitude Forecasting Using Comprehensive Seismic Data by Subhieh El-Salhi, Bashar Igried, Sari Awwad

“…Feature selection was performed using Genetic Algorithm, Particle Swarm Optimization, and Simulated Annealing, while ten machine learning models were implemented — ranging from Linear Regression and Decision Trees to Gradient Boosting, XGBoost, LightGBM, and Long Short-Term Memory (LSTM) networks. …”
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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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Enhancing fever of unknown origin diagnosis: machine learning approaches to predict metagenomic next-generation sequencing positivity by Zhi Gao, Zhi Gao, Zhi Gao, Yongfang Jiang, Yongfang Jiang, Yongfang Jiang, Mengxuan Chen, Mengxuan Chen, Mengxuan Chen, Weihang Wang, Weihang Wang, Weihang Wang, Qiyao Liu, Qiyao Liu, Qiyao Liu, Jing Ma, Jing Ma, Jing Ma

“…Using the SHAP method, the five most important factors for predicting mNGS-positive results were albumin, procalcitonin, blood culture, disease type, and sample type.ConclusionThe validated LightGBM-based predictive model could have practical clinical value in enhancing the application of mNGS in the etiological diagnosis of FUO, representing a powerful tool to optimize the timing of mNGS.…”
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NLP for computational insights into nutritional impacts on colorectal cancer care by Shengnan Gong, Xiaohong Jin, Yujie Guo, Jie Yu

“…Colorectal cancer (CRC) is one of the most prominent cancers globally, with its incidence rising among younger adults due to improved screening practices. …”
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Q8S: Emulation of Heterogeneous Kubernetes Clusters Using QEMU by Jonathan Decker, Vincent Florens Hasse, Julian Kunkel

“…However, as the majority of Kubernetes clusters operate on homogeneous hardware, most scheduling algorithms are also only developed for homogeneous systems. …”
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A Novel Model for Noninvasive Haemoglobin Detection Based on Visibility Network and Clustering Network for Multi-Wavelength PPG Signals by Lei Liu, Ziyi Wang, Xiaohan Zhang, Yan Zhuang, Yongbo Liang

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Encoding-Based Machine Learning Approach for Health Status Classification and Remote Monitoring of Cardiac Patients by Sohaib R. Awad, Faris S. Alghareb

“…In short, this study aims to explore how ML algorithms can enhance diagnostic accuracy, improve real-time monitoring, and optimize treatment outcomes. …”
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Corrosion rate prediction for long-distance submarine pipelines based on MWIWOA-SVM by Zhengshan LUO, Haipeng LYU, Jihao LUO

“…MethodsTo address these issues, Multi-Way Improved Whale Optimization Algorithm (MWIWOA) was proposed to optimize the SVM-based prediction model for the internal corrosion rate of long-distance submarine pipelines. …”
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Multidisciplinary Collaborative Reliability Analysis of the Gear Reducer based on Inverse Reliability Strategy by Wang Liangliang, Peng Jinshuan, Shao Yiming

“…The genetic algorithms- based collaborative optimization（ GA- CO） is one of the improved forms of CO that overcomes the difficulty of convergence given the existing of highly nonlinear consistency constraints. …”
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Maximum Power Exploitation of Photovoltaic System under Fast-Varying Solar Irradiation and Local Shading by Yi-Jui Chiu, Bi Li, Chin-Ling Chen, Shui-Yang Lien, Ding Chen, Ji-Ming Yi, Yung-Hui Shih

“…In addition, under fast-varying solar irradiation and local shading, the speed, ability, and stability of the improved MPPT system with the PF-MPPT algorithm when tracking the maximum power were 9.52, 1.32, and 1.84 times of the MPPT system with the P&O algorithm and 2.18, 1.41, and 2.00 times of the MPPT system with the particle swarm optimization algorithm, respectively.…”
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基于改进Kriging模型的主动学习可靠性分析方法 by 陈哲, 杨旭锋, 程鑫

“…Active learning Kriging（ ALK） model is able to only approximate the performance function in a narrow region around the limit state surface.Therefore,the efficiency of reliability analysis is remarkably improved.However,most of the existing strategies build the ALK model based on a so-called DACE toolbox.DACE cannot obtain the global optimal parameter of a Kriging model and the training point chosen in each iteration cannot be the optimal one.In this paper,one famous global optimization,i.e.…”
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Reinforcement Learning for Computational Guidance of Launch Vehicle Upper Stage by Shiyao Li, Yushen Yan, Hao Qiao, Xin Guan, Xinguo Li

“…This manuscript investigates the use of a reinforcement learning method for the guidance of launch vehicles and a computational guidance algorithm based on a deep neural network (DNN). Computational guidance algorithms can deal with emergencies during flight and improve the success rate of missions, and most of the current computational guidance algorithms are based on optimal control, whose calculation efficiency cannot be guaranteed. …”
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