Delivering data: A real-world dataset for last-mile delivery optimizationZenodo by Anna Vrani, Savvas D. Apostolidis, Athanasios Ch. Kapoutsis, Elias B. Kosmatopoulos

“…The collected matrices were processed and structured for direct use in VRP algorithms.The dataset offers substantial reuse potential by serving as a benchmark for evaluating VRP algorithms, enabling the comparison of optimization methods based on real-world logistics problems. …”
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Maximizing Energy Output of Photovoltaic Systems: Hybrid PSO-GWO-CS Optimization Approach by Hassan S. Ahmed, Ahmed J. Abid, Adel A. Obed, Ameer L. Saleh, Reheel J. Hassoon

“…This study aims to address these challenges by combining cuckoo search (CS), gray wolf optimization (GWO), and particle swarm optimization (PSO) to enhance MPPT performance. …”
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Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas by Safiah Almarri, Hur Al Safwan, Shahd Al Qisoom, Soufien Gdaim, Abdelkrim Zitouni

“…This optimized topology ensures 41–81% lower latency and 50–60% fewer hops than conventional Mesh 2D topologies. …”
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Inverse design of high-strength medium-Mn steel using a machine learning-aided genetic algorithm approach by Jin-Young Lee, Seung-Hyun Kim, Hyun-Bin Jeong, KeunWon Lee, KiSub Cho, Young-Kook Lee

“…To develop medium-Mn steels with an ultimate tensile strength (UTS) exceeding 2 GPa and excellent ductility, we created a highly accurate UTS prediction machine learning (ML) model using a boosted decision tree model and 1520 dataset of tensile properties of medium-Mn steels with micro-alloying elements. We also optimized the hyper-parameters of a genetic algorithm (GA) using the Shannon diversity index to enhance search efficiency while retaining diversity. …”
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Filling-well: An effective technique to handle incomplete well-log data for lithology classification using machine learning algorithms by Sherly Ardhya Garini, Ary Mazharuddin Shiddiqi, Widya Utama, Alif Nurdien Fitrah Insani

“…However, the ANN can suffer from overfitting and requires large datasets for optimal performance. In contrast, KNN struggled with missing-not-at-random (MNAR) data due to its reliance on the k parameter and distance metric, making it less effective in mapping missing data relationships. • Missing values in well-log data can hinder lithology classification accuracy for efficient resource exploration in the oil and gas industry. • This research aims to address the problem of missing values in well-log datasets by applying machine learning algorithms such as XGBoost, ANN, and KNN to enhance classification performance. • XGBoost demonstrated superior performance in handling extreme missing data (30 %) in well-log datasets. …”
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Optimizing role assignment for scaling innovations through AI in agricultural frameworks: An effective approach by Sonia Bisht, Ranjana, Swapnila Roy

“…The proposed approach serves as a blueprint for agricultural enterprises aiming to adopt AI technologies while ensuring optimal utilization of human and technological resources. …”
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Magnetic resonance imaging measurement of the femoral antetorsional angle: Comparison with computer tomography and clinical application in a diagnostic algorithm by Ewa Tramś, Maciej Liszka, Krzysztof Kuliński, Kamila Malesa, Marcin Tyrakowski, Jerzy Białecki, Rafał Kamiński

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Review of Demand Response-based Optimal Scheduling of Electric and Thermal Integrated Energy Systems by Jingshan MO, Guangxian YAN, Na SONG, Mingyang YUAN

“…Artificial intelligence algorithms are primarily divided into methods based on group optimization problems and machine learning algorithms. …”
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An adaptive k-means clustering algorithm based on grid and domain centroid weights for digital twins in the context of digital transformation by Wei Cai, Fei Yang, Bo Yao, Chuanxian Li, Guangyu Sun

“…The algorithm automatically determines the optimal number of clusters k and initial centroids. …”
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Dynamic Error Modeling and Predictive Compensation for Direct-Drive Turntables Based on CEEMDAN-TPE-LightGBM-APC Algorithm by Manzhi Yang, Hao Ren, Shijia Liu, Bin Feng, Juan Wei, Hongyu Ge, Bin Zhang

“…Our methodology comprises four key stages: Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-based decomposition of historical error data, development of component-specific prediction models using Tree-structured Parzen Estimator (TPE)-optimized Light Gradient Boosting Machine (LightGBM) algorithms for each Intrinsic Mode Function (IMF), integration of component predictions to generate initial values, and application of the Adaptive Prediction Correction (APC) module to produce final predictions. …”
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ADMET evaluation in drug discovery: 21. Application and industrial validation of machine learning algorithms for Caco-2 permeability prediction by Dong Wang, Jieyu Jin, Guqin Shi, Jingxiao Bao, Zheng Wang, Shimeng Li, Peichen Pan, Dan Li, Yu Kang, Tingjun Hou

“…We believe that the model developed in this study could represent a reliable tool for assessing Caco-2 permeability during early-stage drug discovery and the chemical transformation rules derived here could provide insights for optimizing Caco-2 permeability. Scientific contribution A comprehensive validation of various machine learning algorithms combined with diverse molecular representations on a large dataset for predicting Caco-2 permeability was reported. …”
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Study on risk factors of impaired fasting glucose and development of a prediction model based on Extreme Gradient Boosting algorithm by Qiyuan Cui, Jianhong Pu, Wei Li, Yun Zheng, Jiaxi Lin, Lu Liu, Peng Xue, Jinzhou Zhu, Mingqing He

“…Feature selection, parameter optimization, and model construction were performed in the training set, while the validation set was used to evaluate the predictive performance of the models. …”
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Identifying key factors influencing maize stalk lodging resistance through wind tunnel simulations with machine learning algorithms by Guanmin Huang, Ying Zhang, Shenghao Gu, Weiliang Wen, Xianju Lu, Xinyu Guo

“…Climate change has intensified maize stalk lodging, severely impacting global maize production. While numerous traits influence stalk lodging resistance, their relative importance remains unclear, hindering breeding efforts. …”
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Intraoperative Nerve Mapping and Visualization System: Optimal Neurological Function Preservation in Schwannoma Surgery by Wenjianlong Zhou, Xiaoyi Shi, Qin Xu, Xiangxiang Liu, Junshi Li, Hui Qiao, Lirui Yang, Baowang Li, Liangpeng Chen, Yuan Zhang, Xiudong Guan, Shunchang Ma, Zhongyan Wang, Linhao Yuan, Jiang Li, Tieqiang Zhang, Deling Li, Dong Huang, Zhihong Li, Wang Jia

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Crops Disease Detection, from Leaves to Field: What We Can Expect from Artificial Intelligence by Youssef Lebrini, Alicia Ayerdi Gotor

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Comparison of Innovative Strategies for the Coverage Problem: Path Planning, Search Optimization, and Applications in Underwater Robotics by Ahmed Ibrahim, Francisco F. C. Rego, Éric Busvelle

“…Conversely, MST-based approaches provide faster but fewer optimal solutions. These findings offer insights into selecting appropriate algorithms based on mission priorities, balancing efficiency and computational feasbility.…”
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Optimal nodes selection in wireless sensor and actor networks based on prioritized mutual exclusion approach by VIRENDER RANGA, Mayank Dave, Anil K. Verma

“…We have proposed two algorithms, centralizedprioritized h-out-of-k mutual exclusion algorithm (CPMEA), and distributed prioritizedh-out-of-k mutual exclusion algorithm (DPMEA) in this research paper. …”
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Development of a PYTHON library for a mathematical model of nuclear reactor kinetics by Aleksei G. Goryunov, Danil D. Kachanov, Kirill D. Kachanov

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Fast and Accurate Direct Position Estimation Using Low-Complexity Correlation and Swarm Intelligence Optimization by Yuze Duan, Zuping Tang, Jiaolong Wei, Jie Sun, Kaixian Ying

“…Furthermore, an adaptive Dung Beetle Optimization (ADBO) algorithm is developed. By leveraging insights from fitness landscape analysis, the ADBO algorithm dynamically adjusts subpopulation proportions and the convergence factor while incorporating hybrid mutation strategies for effective adaptation to various types of optimization problems. …”
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A mechanism of topology optimization for underwater acoustic sensor networks based on autonomous underwater vehicles by Ming He, Fangxin Liu, Zhuang Miao, Huan Zhou, Qiuli Chen

“…To solve this problem, a topology optimization method, also called TO-A algorithm, is developed based on topology reconfiguration. …”
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