Three-dimensional reconstruction and structural surface identification of high steep slopes based on UAV close-range photogrammetry by Linfeng WANG, Hui JIANG, Ning TANG, Xiaoming HUANG, Guojin TAN

“…An adaptive KNN algorithm is introduced to enhance the coplanarity detection passing rate in point clouds, while optimal planar equations are fitted using the least squares method. …”
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An Industrial Internet Security Assessment Model Based on a Selectable Confidence Rule Base by Qingqing Yang, Shiming Li, Yuhe Wang, Guoxing Li, Yanbin Yuan

“…Then, in combination with the Selection covariance matrix adaptive evolution strategy (S-CMA-ES) algorithm, a parameter optimization method for the BRB-s model is designed, which expands the selective constraints on expert knowledge. …”
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Tabu Search for Multi-Objective Hub Location Planning: A Case Study of the Northern Rail Line, Thailand by Nguyen Dang Nha, Pham Van Nam, Chanta Sunarin, Sangsawang Ornurai

“…This approach allows the model to handle large-scale problems within a reasonable time frame while generating Pareto fronts. The results demonstrate the effectiveness of multi-objective optimization in supporting rail hub location planning.…”
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Design of 3D Environment Combining Digital Image Processing Technology and Convolutional Neural Network by Xiaofei Lu, Shouwang Li

“…To enhance 3D reconstruction accuracy, this study proposes a digital image processing technology that combines binocular camera calibration, stereo correction, and a convolutional neural network (CNN) algorithm for optimization and improvement. By employing the refined stereo-matching algorithm, a 3D reconstruction model was developed to augment 3D environment design and reconstruction accuracy while optimizing the 3D reconstruction effect. …”
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Task Allocation and Path Planning Method for Unmanned Underwater Vehicles by Feng Liu, Wei Xu, Zhiwen Feng, Changdong Yu, Xiao Liang, Qun Su, Jian Gao

“…Experimental results demonstrate that the proposed task allocation mechanism and improved path planning algorithm exhibit significant advantages in task completion rate and path optimization efficiency.…”
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PCMINN: A GPU-Accelerated Conditional Mutual Information-Based Feature Selection Method by Nikolaos Papaioannou, Georgios Myllis, Alkiviadis Tsimpiris, Stamatis Aggelopoulos, Vasiliki Vrana

“…Conditional Mutual Information Nearest-Neighbor (CMINN) is an algorithm developed to address this challenge by using Conditional Mutual Information (CMI) to assess the relevance of individual features to the target variable, while identifying redundancy among similar features. …”
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Komparasi Kinerja Algoritma Blocking Pada Proses Indexing Untuk Deteksi Duplikasi by M. Miftakul Amin, Yevi Dwitayanti

“…While the Soundex and Cosine Similarity algorithms provide the most optimal results, which are 8 candidate record pairs. …”
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The Role of Artificial Intelligence (AI) in the Future of Forestry Sector Logistics by Leonel J. R. Nunes

“…Results: The results demonstrated that optimization algorithms reduced transportation costs and carbon emissions. …”
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Efficient collision-avoidance navigation strategy for autonomous surface vehicles in unstructured and constricted marine environments by Wenlong Meng, Rongdian Ku, Yanbo Pu, Xiaoxiao Shi, Ya Gong

“…Current path planning algorithms designed for unstructured environments, particularly those characterized by narrow spaces, often face difficulties in efficiently exploring the target area while producing high-quality paths. …”
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Effect of Voltage Dependent Load Model on Placement and Sizing of Distributed Generator in Large Scale Distribution System by Gopisetti Manikanta, Ashish Mani, Hemender Pal Singh, Devendra Kumar Chaturvedi

“…In addition to AQiEA, four other algorithms (Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Grey Wolf Optimization (GWO), and Ecogeography-based Optimization (EBO) with Classification based on Multiple Association Rules (CMAR)) have also been employed for comparison. …”
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Deployment and Operation of Battery Swapping Stations for Electric Two-Wheelers Based on Machine Learning by Yu Feng, Xiaochun Lu

“…This study developed a data-driven optimization model based on machine learning algorithms using Beijing’s battery swapping stations and point of interest (POI) dataset. …”
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RFID-Based Real-Time Salt Concentration Monitoring with Adaptive EKF by Renhai Feng, Xinyi Lin

“…The concentration dynamics are modeled as a hidden Markov process and tracked using an adaptive extended Kalman filter (AEKF). The AEKF algorithm avoids computationally expensive inversion of complex observation equations while automatically adjusting noise covariance matrices via innovation sequence. …”
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Graph attention networks based multi-agent path finding via temporal-spatial information aggregation. by Qingling Zhang, Peng Wang, Cui Ni, Xianchang Liu

“…An effective Multi-Agent Path Finding (MAPF) algorithm must efficiently plan paths for multiple agents while adhering to constraints, ensuring safe navigation from start to goal. …”
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Short-term results of treatment of non-epithelial gastro-intestinal tumors complicated by bleeding by S. G. Shapovalyants, A. I. Mikhalev, Ye. D. Fedorov, R. V. Plakhov, L. M. Mikhaleva, Z. V. Galkova, Ye. V. Ivanova, A. E. Sergeyenko

“…According to suggested diagnostic algorithm detailed characteristic of patients with bleeding tumor was given, the optimal method of surgical intervention was chosen. …”
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MAARS: Multiagent Actor–Critic Approach for Resource Allocation and Network Slicing in Multiaccess Edge Computing by Ducsun Lim, Inwhee Joe

“…This paper presents a novel algorithm to address resource allocation and network-slicing challenges in multiaccess edge computing (MEC) networks. …”
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Detection Model for Cotton Picker Fire Recognition Based on Lightweight Improved YOLOv11 by Zhai Shi, Fangwei Wu, Changjie Han, Dongdong Song, Yi Wu

“…The improved detection algorithm maintains high accuracy while achieving faster inference speed and fewer model parameters. …”
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OR-FCOS: an enhanced fully convolutional one-stage approach for growth stage identification of Oudemansiella raphanipes by Runze Fang, Huamao Huang, Nuoyan Guo, Haichuan Wei, Shiyi Wang, Haiying Hu, Ming Liu

“…Channel pruning further reduces the decoder’s parameters, decreasing model size and computational requirements while maintaining precision. The enhanced algorithm achieved a mean average precision (mAP) of 89.4% ( $$\hbox {mAP}_{50}$$ ) and 78.3% ( $$\hbox {mAP}_{50:95}$$ ), while the number of model parameters was reduced to 9.9 M, the model size was reduced to 40.1 MB, and the number of floating point operations per second (FLOPs) was reduced to 31.2 G. …”
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New Heuristic Local Search Method for University Course Timetabling by Mohammad Sadegh Shiri, Mostafa Khorramizadeh, Vahid Ahmadi

“…In the second phase, several new neighbourhood functions are used to improve the quality of computed feasible solutions. While the fitness function of the first phase is based on the violation of hard constraints, the fitness function of the second phase is based on the penalty of the feasible solution.Findings: The numerical results indicate that the required computing time increases with the size of instances, and the algorithm tends to converge towards the optimal solution after a few minutes.Originality/Value: The presented algorithm enables us to deal with extensive university course timetabling problems in practice. …”
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Rail surface identification and adhesion control based on dynamic adhesion characteristics by Wen Liu, Hongfeng Qi, Jingchun Huang, Yiyuan Chen, Sheng He, Haoxiang Feng

“…Therefore, to achieve stable tracking of the optimal adhesion state while adopting the feedback dynamic adhesion characteristic model, this study utilizes a machine learning algorithm to perform classification training on discrete points of the simulated adhesion characteristic model under various rail conditions. …”
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PP-QADMM: A Dual-Driven Perturbation and Quantized ADMM for Privacy Preserving and Communication-Efficient Federated Learning by Anis Elgabli

“…We provide a rigorous theoretical proof of convergence, showing that PP-QADMM converges to the optimal solution for convex problems while achieving a convergence rate comparable to standard ADMM, but with significantly lower communication and energy costs, and robust privacy protection. …”
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