An Experimental Comparison of Self-Adaptive Differential Evolution Algorithms to Induce Oblique Decision Trees by Rafael Rivera-López, Efrén Mezura-Montes, Juana Canul-Reich, Marco-Antonio Cruz-Chávez

“…This study addresses the challenge of generating accurate and compact oblique decision trees using self-adaptive differential evolution algorithms. Although traditional decision tree induction methods create explainable models, they often fail to achieve optimal classification accuracy. …”
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Green logistics optimization for coal supply in a power plant connecting maritime port by Mingsheng Qiu, Liping Gao, Zhouyong Lin, Minghong Zheng, Qingzhong Lin

“…This paper introduces an integrated optimization model grounded in green logistics and employs an Improved Particle Swarm Optimization (IPSO) algorithm to efficiently manage the coal supply chain from unloading at ports to loading into generators. …”
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Comparative Analysis of Time Series Prediction Algorithms on Multiple Network Function Data of NWDAF by Dasheng Chen, Qi Song, Yinbin Zhang, Ling Li, Zhiming Yang

“…The dataset is carefully anonymized to ensure maximum realism and comprehensiveness, while safeguarding sensitive information. It encompasses eight categories of network functions, with data collected at five-minute intervals. …”
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Hierarchical hub location model and hybrid algorithm for integration of urban and rural public transport by Wei Zhong, Zhicai Juan, Fang Zong, Huishuang Su

“…Based on the connection associated with central towns and the capacity constraints of hubs and to achieve the minimum total cost, this article proposes a mixed-integer programming model that employs a genetic and tabu search hybrid optimization algorithm to validate and analyze, which used the urban and rural public transport data from a specified area of Shandong province in China. …”
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Estimation and Assessment of Partial Shading Patterns in Large PV Farms Using ANN Algorithm by Efendi S Wirateruna, Mochamad Ashari, Dedet Candra Riawan

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Benchmarking a Novel Particle Swarm Optimization Dynamic Model Versus HOMER in Optimally Sizing Grid-Integrated Hybrid PV–Hydrogen Energy Systems by Ayatte I. Atteya, Dallia Ali

“…In this paper, a novel Particle Swarm Optimization (PSO) dynamic system model is developed by integrating a PSO algorithm with a precise dynamic hybrid PV-H₂ energy system model that is developed to accurately simulate the hybrid system by considering the dynamic behaviour of its individual system components. …”
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Integration of Machine Learning and Wavelet Algorithms for Processing Probing Signals: An Example of Oil Wells by Zukhra Abdiakhmetova, Zhanerke Temirbekova

“…Furthermore, machine learning models, including standard regression algorithms and neural networks, are leveraged to enhance signal interpretation and predictive capabilities. …”
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Maize Leaf Area Index Estimation Based on Machine Learning Algorithm and Computer Vision by Wanna Fu, Zhen Chen, Qian Cheng, Yafeng Li, Weiguang Zhai, Fan Ding, Xiaohui Kuang, Deshan Chen, Fuyi Duan

“…Traditional LAI measurement methods are often destructive and labor-intensive, while techniques relying solely on spectral data suffer from limitations such as spectral saturation. …”
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Optimizing Urban Air Mobility: A Ground-Connected Approach to Select Optimal eVTOL Takeoff and Landing Sites for Short-Distance Intercity Travel by Yantao Wang, Jiashuai Li, Yujie Yuan, Chun Sing Lai

“…Employing an enhanced iteration of the <italic>K</italic>-means algorithm, this phase undertakes the clustering of optimal takeoff and landing locations, thereby discerning their spatial distribution to effectively alleviate ground traffic congestion while aligning with eVTOL vertical port requirements and airspace regulatory mandates. …”
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Traffic environment perception algorithm based on multi-task feature fusion and orthogonal attention by Zhengfeng LI, Mingen ZHONG, Yihong ZHANG, Kang FAN, Zhiying DENG, Jiawei TAN

“…Both these metrics surpass those of existing mainstream multi-task perception algorithms with comparable parameter scales. Furthermore, the lane line segmentation accuracy, measured by IoU, reached a sub-optimal value of 27.2%. …”
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A dereverberation beamforming algorithm for noise source localization in anechoic and semi-reverberant environments by R. Singh, A. Mimani, R. Kumar

“…Additionally, the CLEAN-SC deconvolution applied to the DBF maps of the mixer-grinder significantly improved the source resolution. • DBF algorithm is presented based on CCM filtering using an optimized Hanning window to significantly improve the localization accuracy of noise sources within λ/20 uncertainty. • Trial-and-error method is used to determine the optimal Hanning window width, which depends on frequency, noise source and surroundings. • It was necessary to implement DBF even in an anechoic chamber to accurately localize noise sources generated by engineering applications.…”
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Private Stochastic Optimization with Large Worst-Case Lipschitz Parameter by Andrew Lowy, Meisam Razaviyayn

“…To do so, we develop novel algorithmic and stability-based proof techniques, which we believe will be useful for future work in obtaining optimal excess risk. …”
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A clustering algorithm based on grids for core data and adjacency relationships for edge data by Honglei He

“…To address these issues, existing research primarily focuses on three directions: (1) optimizing the adaptive selection of grid parameters, which struggles to handle variations in cluster density; (2) improving grid division methods (e.g., multi-granularity or dynamic grids), which have limited effectiveness on complex-shaped data; and (3) integrating other clustering techniques, which enhances accuracy but increases algorithmic complexity. …”
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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

“…Lastly, to reduce the training difficulty, a progressive strategy combining self-supervised and fully supervised learning is employed to optimize the entire model. We compared SAMFA with five state-of-the-art image segmentation algorithms on a labeled public dataset, and the experimental results demonstrate that SAMFA performs the best. …”
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A Comparison of Machine Learning Algorithms for Predicting Alzheimer’s Disease Using Neuropsychological Data by Zakaria Mokadem, Mohamed Djerioui, Bilal Attallah, Youcef Brik

“…Hyperparameter tuning was performed to optimize model performance. The results indicate that ensemble learning models, particularly Gradient Boosting (GB) and Random Forest (RF), exhibited superior accuracy compared to other algorithms. …”
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Big Data and AI Algorithms for Sustainable Development Goals: A Topic Modeling Analysis by Prema Nedungadi, Simi Surendran, Kai-Yu Tang, Raghu Raman

“…In energy, AI-driven solutions optimize forecasting, grid management, and renewable integration, while in industry, they bolster infrastructure resilience through innovations like predictive maintenance and automated quality control within Industry 4.0 frameworks. …”
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Stress management with HRV following AI, semantic ontology, genetic algorithm and tree explainer by Ayan Chatterjee, Michael A. Riegler, K. Ganesh, Pål Halvorsen

“…Various techniques like feature selection and dimensionality reduction are explored to improve classification accuracy while minimizing bias. Different machine learning (ML) algorithms, including traditional and ensemble methods, are employed for analyzing both imbalanced and balanced HRV datasets. …”
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Mobile-YOLO: A Lightweight Object Detection Algorithm for Four Categories of Aquatic Organisms by Hanyu Jiang, Jing Zhao, Fuyu Ma, Yan Yang, Ruiwen Yi

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Construction and Application of Carbon Emissions Estimation Model for China Based on Gradient Boosting Algorithm by Dongjie Guan, Yitong Shi, Lilei Zhou, Xusen Zhu, Demei Zhao, Guochuan Peng, Xiujuan He

“…Key variables were selected through correlation analysis, missing values were interpolated using K-means clustering, and model parameters were systematically optimized via grid search and cross-validation. Among the algorithms tested, LightGBM achieved the best performance (R² = 0.992, RMSE = 0.297), demonstrating both robustness and efficiency. …”
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An Improved KF-RBF Based Estimation Algorithm for Coverage Control with Unknown Density Function by Lei Zuo, Maode Yan, Yaoren Guo, Wenrui Ma

“…A cost function, depending on the density function and a certain metric, is provided to evaluate the performance of coverage network. Then, while considering the sampling noise, a novel estimation algorithm is developed to approximate the density function based on the Kalman filter (KF) and the Radial Basis Function (RBF) neural network. …”
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