Unsupervised detection of high-frequency oscillations in intracranial electroencephalogram: promoting a valuable automated diagnostic tool for epilepsy by Wenjing Chen, Tongzhou Kang, Md Belal Bin Heyat, Jamal E. Fatima, Yuanning Xu, Dakun Lai

“…The CVAE model is trained for dimensionality reduction and feature reconstruction, followed by clustering of the reconstructed maps using the K-means algorithm for automated HFOs detection.ResultsEvaluation of the proposed unsupervised method on clinical iEEG data demonstrates its superior performance compared to traditional supervised models. …”
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Grouping control of electric vehicles based on improved golden eagle optimization for peaking by Yang Yu, Yuhang Huo, Shixuan Gao, Qian Wu, Mai Liu, Xiao Chen, Xiaoming Zheng, Xinlei Cai

“…Next, using the k-means algorithm, EVs are dynamically divided into priority charging groups, backup groups, and priority discharging groups based on SOC differences. …”
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Localization of Capsule Endoscope in Alimentary Tract by Computer-Aided Analysis of Endoscopic Images by Ruiyao Zhang, Boyuan Peng, Yiyang Liu, Xinkai Liu, Jie Huang, Kohei Suzuki, Yuki Nakajima, Daiki Nemoto, Kazutomo Togashi, Xin Zhu

“…At first, a classifier based on a Swin Transformer was proposed to classify each frame of the capsule endoscopy videos into images of the stomach, small intestine, and large intestine, respectively. Then, a K-means algorithm was used to correct outliers in the classification results. …”
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Innovative Landslide Susceptibility Mapping Portrayed by CA-AQD and K-Means Clustering Algorithms by Mao Yimin, Li Yican, Deborah Simon Mwakapesa, Wang Genglong, Yaser Ahangari Nanehkaran, Muhammad Asim Khan, Zhang Maosheng

“…The CA-AQD algorithm was adopted to split all grids in the study area into many groups with more similar characteristic values, which also owed to efficiently quantifying the uncertain (rainfall) value by using AQD. The K-means algorithm divides these groups into five susceptibility classes according to the values of landslide density in each group. …”
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Novi pristup u upravljanju hidroelektranama zasnovan na neuronskim mrežama by Radmila Koleva, Darko Babunski, Emil Zaev, Atanasko Tuneski, Laze Trajkovski

“…Artificial Neural Networks (ANN) as a technique used in the HPP control systems have advantages in getting a stable and faster response but the complexity of the structure behind the neural networks (NN), meaning algorithms, number of hidden layers, training function, activation function can complicate and destabilize the process. …”
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Long‐Term Monitoring of the Annual Irrigated Cropland Extent in Fragmented and Heterogeneous Arid Landscapes Using Machine Learning and Landsat Imagery by Meibao Tan, Youhua Ran, Min Feng, Guotao Dong, Deyan Du, Gaofeng Zhu, Yanyun Nian, Xin Li

“…This framework combines the k‐means algorithm with a semiautomatically trained random forest classifier for initial classification and employs the Bayesian Updating of Land Cover algorithm for subsequent postprocessing. …”
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Connectivity-Enhanced 3D Deployment Algorithm for Multiple UAVs in Space–Air–Ground Integrated Network by Shaoxiong Guo, Li Zhou, Shijie Liang, Kuo Cao, Zhiqun Song

“…Firstly, the horizontal positions of the UAVs at a fixed height are initialized using the k-means algorithm. Subsequently, the connections between the UAVs are established based on constraint conditions, and a fairness connection strategy is employed to establish connections between the UAVs and GUs. …”
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Reliability evaluation of dynamic face recognition systems based on improved Fuzzy Dynamic Bayesian Network by Zhiqiang Liu, Wenbo Zhu, Hongzhou Zhang, Shengjin Wang, Lu Fang, Weijun Hong, Hua Shao, Guopeng Wang

“…Subsequently, we infer to solve the fuzzy reliability state probabilities of the six systems with Netica and get two most important factors with the improved fuzzy C-means algorithm. We verify the model by comparing the evaluation results with actual achievements of these systems. …”
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Power System Reliability Assessment with Quantification of Demand Response Uncertainty Based on Advanced Sigmoid Cloud Model by Bo Hu, Yue Sun, Wei Huang, Changzheng Shao, Tao Niu, Xin Cheng, Kaigui Xie

“…Finally, a reliability assessment framework for a composite power system is developed considering the uncertain price-based DR model and a k-means algorithm is used to accelerate the assessment process. …”
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K-Means Based Bee Colony Optimization for Clustering in Heterogeneous Sensor Network by Prince Modey, Gaddafi Abdul-Salaam, Emmanuel Freeman, Patrick Acheampong, William Leslie Brown-Acquaye, Israel Edem Agbehadji, Richard C. Millham

“…This study proposes a Bee Colony Optimization that synergistically combines K-mean algorithms (referred to as K-BCO) for efficient clustering in heterogeneous sensor networks. …”
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Intelligent Gamification Mechanics Using Fuzzy-AHP and K-Means to Provide Matched Partner Reference by Fitri Marisa, Sharifah Sakinah Syed Ahmad, Nasreen Kausar, Sajida Kousar, Dragan Pamucar, Nasr Al Din Ide

“…Second, design an intelligent system formula using the Fuzzy-Analytical Hierarchy Process (Fuzzy-AHP) and K-Means algorithms to obtain partner reference recommendation patterns and segmentation of similarity of interests between partners. …”
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SAFE-CAST: secure AI-federated enumeration for clustering-based automated surveillance and trust in machine-to-machine communication by Yusuf Kursat Tuncel, Kasım Öztoprak

“…SAFE-CAST integrates several innovative components: (1) a federated learning approach using Lloyd’s K-means algorithm for secure clustering, (2) a quality diversity optimization algorithm (QDOA) for secure channel selection, (3) a dynamic trust management system utilizing blockchain technology, and (4) an adaptive multi-agent reinforcement learning for context-aware transmission scheme (AMARLCAT) to minimize latency and improve scalability. …”
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Bias-aware degradation models for reinforced concrete bridges based on XAI by Francesca Marsili, Filippo Landi, Rade Hajdin, Sylvia Kessler

“…The analysis comprises four steps: (1) cluster analysis of damage transition times using the k-means algorithm to identify damage patterns with similar damage evolution rates (fast, normal, slow, corresponding to bridge components with a fragile, normal, and robust deterioration behavior); (2) Random Forest classification to predict the cluster based on bridge inventory data; (3) SHAP analysis to explain the predictions of the Random Forest classifier; (4) application of the gamma process to the grouped damage transition times to assess damage evolution. …”
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Application of K-means supported by clustered systems in big data association rule mining by Lihua Liu

“…To this end, the study introduced a parallel frequent itemset mining technique, FiDoop DP, which used the MapReduce programming paradigm for data partitioning on Hadoop clusters and integrates an improved k-means++ algorithm for data preprocessing to provide better data processing results. …”
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Outlier Detection in VPN Authentication Logs for Corporate Computer Networks Access using CRISP-DM by Nilo Legowo, Wilyu Mahendra Bad

“…By utilizing the K-Means algorithm for data clustering and K-NN for measuring inter-point distances, the study identifies outliers that warrant further investigation by the company. …”
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Optimizing UAV Location for Deployment in Cell-Free Massive MIMO Networks Using a Soft Actor-Critic Reinforcement Learning by Okello Kenneth, Elijah Mwangi, Dominic Bernard Onyango Konditi

“…We employ a hybrid user association mechanism that combines gradient descent and an enhanced K-means algorithm for efficient user-to-UAV cluster assignment. …”
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Management of Energy Consumption in Wireless Sensor Networks by Aziz Hanifi, Mohammad Reza Taghva, Robab Haghi, Kamran Feizi

“…In the second, clustering is done by use of k-means algorithm. In the last phase, it is presented a multi criteria decision-making approach for the selection of cluster heads. …”
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Photovoltaic Module Fault Detection Technology Based on Remote Sensing Technology and Deeplabv3+ Model by Xiaowei Xu, Mingxian Liu, Yongjie Nie, Ke Wang, Wenhua Xu

“…The statistical test results showed that the improved K-means algorithm was significantly better than the traditional K-means in clustering accuracy, and its average error was only 0.008, which was much lower than the 0.035 of the traditional K-means. …”
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Profit-Efficient Elastic Allocation of Cloud Resources Using Two-Stage Adaptive Workload Prediction by Lei Li, Xue Gao

“…To avoid the cost associated with manual labeling of the training data, the first-order gradient feature is used with the <i>k</i>-means algorithm to cluster and label the original training data set automatically into uphill and downhill training data sets. …”
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Exploring endometriosis before surgical treatment: unraveling pain, sexual function and quality of life patterns by Juliana Olavo Pereira, Jaime Kulak Junior

“…Descriptive and frequency analysis were employed. Using the K-means algorithm, cluster analysis was performed to group participants based on response similarities. …”
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