Recurrence Multilinear Regression Technique for Improving Accuracy of Energy Prediction in Power Systems by Quota Alief Sias, Rahma Gantassi, Yonghoon Choi, Jeong Hwan Bae

“…This paper also creates a pre-clustering using the K-Means algorithm before the energy prediction to improve accuracy. …”
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Method of Diagnostics of Operation Modes of Individual Heat Supply Units, Allowing to Detect Pre-Emergency Situations at an Early Stage by Dvortsevoy A.I., Borush O.V., Khoreva V.A., Yakovina I.N.

“…To achieve this goal, the following tasks were solved: creating and debugging methods for diagnosing IH operating modes; using the cluster analysis method, in particular the K-means algorithm, to identify pre-emergency situations at an early stage; analyzing the relationship between outdoor air temperature data and the pressures of direct and return network water in IHU operating modes using Novosibirsk as an example. …”
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Clustering approach for crack detection in GPR data: Influence of infilling material on detectability by Mercedes Solla, Norberto Fernández, Ahmed Elseicy, Jorge Pais

“…In order to cluster the time series of the A-Scans, the usage of a K-Spectral Centroid (KSC) algorithm is proposed, though a comparison with the popular K-means algorithm is also provided. Different crack sizes and infilling materials were simulated to analyze their influence on detectability. …”
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Differentiation of natural scrub communities of the Cotoneastro-Amelanchieretum group in Central Europe. by Krzysztof Świerkosz, Kamila Reczyńska

“…Vegetation types of rocky scrubs were identified using the unsupervised K-means algorithm and detrended correspondence analysis. …”
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Agricultural Machinery Movement Trajectory Recognition Method Based on Two-Stage Joint Clustering by Shuya Zhang, Hui Liu, Xiangchen Cao, Zhijun Meng

“…The silhouette coefficient method is used to determine the optimal number of clusters k for the K-Means algorithm, thus reducing the data scale. The second stage focuses on trajectory recognition, where a list of Eps and Minpts parameters is generated based on the statistical properties of the trajectory dataset. …”
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Chess Position Evaluation Using Radial Basis Function Neural Networks by Dimitrios Kagkas, Despina Karamichailidou, Alex Alexandridis

“…The proposed approach introduces models based on the radial basis function (RBF) neural network architecture trained with the fuzzy means algorithm, in conjunction with a novel set of input features; different methods of network training are also examined and compared, involving the multilayer perceptron (MLP) and convolutional neural network (CNN) architectures and a different set of input features. …”
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Novel validity indices for dynamic clustering and an Improved Dynamic Fuzzy C-Means by Ramiro Saltos, Ignacio Carvajal, Fernando Crespo, Richard Weber

“…To illustrate the application of these novel indices, we introduce an improved version of the dynamic fuzzy c-means algorithm (I-DFCM) which offers enhanced computational stability for handling dynamic data. …”
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Implementation of Clustering and Association for Early Warning of Disasters in Bojonegoro Regency by Denny Nurdiansyah, Erna Hayati, Ika Purnamasari, Anna Apriana Hidayanti, Yuliana Fuji Rahayu

“…The methods applied included mapping, clustering using the K-means algorithm, and association rule mining with the Apriori algorithm. …”
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Calculation Method of Phenotypic Traits for Tomato Canopy in Greenhouse Based on the Extraction of Branch Skeleton by Xiaodan Ma, Qiu Jiang, Haiou Guan, Lu Wang, Xia Wu

“…First, a top-view-based acquisition platform was built to obtain the point cloud data of the tomato canopy, and the improved K-means algorithm was used to segment the three-dimensional (3D) point cloud of branches. …”
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A Lightweight Deep Learning Network with an Optimized Attention Module for Aluminum Surface Defect Detection by Yizhe Li, Yidong Xie, Hu He

“…Furthermore, we employed the genetic K-means algorithm to optimize prior region selection, and a lightweight Ghost model to reduce network complexity by 14.3%, demonstrating the superior performance of the Ghost model in terms of loss function optimization during training and validation as well as in terms of detection accuracy, speed, and stability. …”
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Multi-Depot Pickup and Delivery Problem with Resource Sharing by Yong Wang, Lingyu Ran, Xiangyang Guan, Yajie Zou

“…Second, a two-stage hybrid algorithm composed of a k-means clustering algorithm, a Clark-Wright (CW) algorithm, and a nondominated sorting genetic algorithm II (NSGA-II) is designed. The k-means algorithm is adopted in the first stage to reallocate customers to logistics facilities according to the Manhattan distance between them, by which the computational complexity of solving the MDPDPRS is reduced. …”
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Railway Foreign Object Intrusion Detection Using UAV Images and YOLO-UAT by Yang Yang, Zhanhao Liu, Junming Chen, Haiming Gao, Tao Wang

“…Concurrently, the K-means++ algorithm is introduced to cluster the a priori frames, which improves the accuracy and convergence speed of the a priori frame clustering. …”
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Enhancement method of series hybrid ship energy efficiency for speed and energy collaborative optimization by Yupeng Yuan, Xiaoyu Wang, Liang Tong, Chengqing Yuan, Boyang Shen, Teng Long

“…By analysing the data collected from an actual ship, the operating conditions of the navigation environment were identified using the k‐means algorithm, and the total distance travelled by the ship was divided into several sub‐trajectories. …”
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Freight Time and Cost Optimization in Complex Logistics Networks by Egemen Sert, Leila Hedayatifar, Rachel A. Rigg, Amir Akhavan, Olha Buchel, Dominic Elias Saadi, Aabir Abubaker Kar, Alfredo J. Morales, Yaneer Bar-Yam

“…In addition, using customer logistics and the k-means algorithm, we propose additional warehouse locations. …”
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The Relationship Between Horizontal Jumping and Sprinting Ability Across Sexes in Young Active Adults by Adam Kleeberger, Dana Agar-Newman, Ming-Chang Tsai, Marc Klimstra

“…Data were separated by sex and then grouped as fast, average, and slow using the K-Means algorithm in three conditions (acceleration, max speed, and combined). …”
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Evaluating regional sustainable energy potential through hierarchical clustering and machine learning by Selen Avcı Azkeskin, Zerrin Aladağ

“…Membership degrees from the fuzzy clustering phase are then integrated into a new dataset, which undergoes crisp clustering using the K-Means algorithm. This approach provides both granular and definitive cluster structures, enabling a robust analysis of regional energy characteristics. …”
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Influence of geomagnetic disturbances on myocardial infarctions in women and men from Brazil by Luiz Felipe C. Rezende, Eurico R. De Paula, Marcio T. A. H. Muella, Severino L. G. Dutra, Reinaldo R. Rosa, Paulo H. N. Saldiva, Jean P. H. B. Ometto

“…Using the unsupervised K-Means algorithm, the results indicate that the group associated with disturbed geomagnetic conditions has a higher incidence of MIs in women. …”
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A Novel Color Difference-Based Method for Palette Extraction and Evaluation Using Images of Birds by Melike Bektas Kosesoy, Seckin Yilmaz

“…Then, Fuzzy c-means (FCM), Gaussian Mixture Model (GMM), k-means, Mean Shift, and mini batch k-means algorithms were used to extract the color palette of each bird image. …”
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Study on the fusion of improved YOLOv8 and depth camera for bunch tomato stem picking point recognition and localization by Guozhu Song, Jian Wang, Rongting Ma, Yan Shi, Yaqi Wang

“…Subsequently, the optimized K-means algorithm, utilizing K-means++ for clustering centre initialization and determining the optimal number of clusters via Silhouette coefficients, is employed to segment the fruit stalk region. …”
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Enhancing Fake Review Detection Using Linguistic Exaggeration, BERT Embeddings, and Fuzzy Logic by Mohammed Ennaouri, Ahmed Zellou

“…To classify the reviews, we introduce a personalized multicentroid K-means algorithm, which improves traditional K-means by allowing a more precise clustering of false and genuine reviews. …”
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