Multi-Level Particle System Modeling Algorithm with WRF by Julong Chen, Bin Wang, Rundong Gan, Xuepeng Mou, Shiping Yang, Ling Tan

“…Based on the multi-scale mean-shift clustering algorithm, Adaptive Kernel Density Estimation (AKDE) is introduced to map density to bandwidth, achieving adaptive adjustment of clustering bandwidth while reducing computational resources and improving cloud hierarchy. …”
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Research for WSN routing algorithm based on novel multi-level minimal dominating clustering by LUO Guang-chun, LI Jiong

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Enhanced multi-level K-means clustering and cluster head selection using a modernized pufferfish optimization algorithm for lifetime maximization in wireless sensor networks by Anjana Koyalil, Sivacoumar Rajalingam

“…To tackle these challenges, a new multi-level clustering technique with a heuristic optimization algorithm is proposed in this research work. …”
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Few-shot segmentation based on multi-level and cross-scale clustering by Shuai Yuan, Junhai Qiu, Hongxia Xu, Yan Zhang, Jiaxing Zhang

“…A novel method that consists of two modules is proposed: a multi-level fuzzy clustering guidance module and a cross-scale feature fusion module. …”
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A control-driven transition strategy for enhanced multi-level threshold image segmentation optimization by Laith Abualigah, Mohammad H. Almomani, Saleh Ali Alomari, Raed Abu Zitar, Vaclav Snasel, Kashif Saleem, Aseel Smerat, Absalom E. Ezugwu

“…This work proposes an image segmentation approach based on a multi-threshold segmentation method and the enhanced Flood Algorithm combined with the Non-Monopolize search (named Improved IFLANO). …”
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An optimized public opinion communication system in social media networks based on K-means cluster analysis by Mingchao Qi, JunQiang Zhao, Yan Feng

“…This study proposes a public opinion monitoring model that combines the K-means clustering algorithm with Particle Swarm Optimization (PSO) to enhance the accuracy and effectiveness of public opinion monitoring on social media. …”
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Mining patterns of comorbidity evolution in patients with multiple chronic conditions using unsupervised multi-level temporal Bayesian network. by Syed Hasib Akhter Faruqui, Adel Alaeddini, Carlos A Jaramillo, Jennifer S Potter, Mary Jo Pugh

“…We also use a longest path algorithm to identify the most likely sequence of comorbidities emerging from and/or leading to specific chronic conditions. …”
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Clustering-based interactive image segmentation by B. A. Zalesky

“…In the next step, the set of colors of the selected object areas and the set of colors of the selected background areas are clustered separately by one of the clustering algorithms, for example, k-means, fuzzy c-means, or the multi-level clustering algorithm proposed by the author. …”
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A deep multiple self-supervised clustering model based on autoencoder networks by Ling Zhu, Zijin Liu, Guangyu Liu

“…The proposed model effectively integrates the advantages of autoencoder and fuzzy C-Means clustering, performing multi-level clustering evaluations throughout multiple iterations of the autoencoder network training process. …”
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Unmanned Surface Vessel Cluster Path Planning Based on Deep Reinforcement Learning by Manjiang YU, Jiawei HE, Bowen XING

“…Firstly, a long and short-term memory module was introduced based on the multi-agent deep deterministic policy gradient algorithm to enhance the ability of the USVs to utilize the temporal information in path planning; secondly, a multi-level representational experience pool was designed to improve the training efficiency and data utilization and reduce the interference between different experiences; finally, stochastic network distillation was used as a curiosity mechanism to provide intrinsic rewards for the USVs to explore new regions and solve the convergence due to the sparse rewards. …”
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Research on resource allocation algorithm of centralized and distributed Q-learning in machine communication by Yunhe YU, Jun SUN

“…Under the premise of ensuring partial machine type communication device (MTCD)’s quality of service (QoS) requirements, the resource allocation problem was studied with the goal of maximizing system throughput in the massive machine type communication (mMTC) scenario.Two resource allocation algorithms based on Q-learning were proposed: centralized Q-learning algorithm (team-Q) and distributed Q-learning algorithm (dis-Q).Firstly, taking into account MTCD’s geographic location and multi-level QoS requirements, a clustering algorithm based on cosine similarity (CS) was designed.In the clustering algorithm, multi-dimensional vectors that represent MTCD and data aggregator (DA) were constructed, and MTCDs can be grouped according to the CS value between multi-dimensional vectors.Then in the MTC network, the team-Q learning algorithm and dis-Q learning algorithm were used to allocate resource blocks and power for the MTCD.In terms of throughput performance, team-Q and dis-Q algorithms have an average increase of 16% and 23% compared to the dynamic resource allocation algorithm and the greedy algorithm, respectively.In terms of complexity performance, the dis-Q algorithm is only 25% of team-Q algorithm and even below, the convergence speed is increased by nearly 40%.…”
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Colonial bacterial memetic algorithm and its application on a darts playing robot by Szilárd Kovács, Csaba Budai, János Botzheim

“…CBMA incorporates features like multi-level clustering, dynamic gene selection, hierarchical population clustering, and adaptive co-evolutionary mechanisms, enabling efficient management of task-specific parameters and optimizing solution quality while minimizing resource consumption. …”
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Research on prediction model of adolescent suicide and self-injury behavior based on machine learning algorithm by Yao Gan, Li Kuang, Xiao-Ming Xu, Ming Ai, Jing-Lan He, Wo Wang, Su Hong, Jian mei Chen, Jun Cao, Qi Zhang

“…ObjectiveTo explore the risk factors that affect adolescents’ suicidal and self-injurious behaviors and to construct a prediction model for adolescents’ suicidal and self-injurious behaviors based on machine learning algorithms.MethodsStratified cluster sampling was used to select high school students in Chongqing, yielding 3,000 valid questionnaires. …”
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Enterprise fission path optimization and dynamic capability construction based on the soft actor-critic algorithm by Hengsheng Gu

“…This indicates that the strategy can help accelerate the formation of industrial clusters. Therefore, the SAC algorithm-based enterprise fission path optimization strategy constructed in this study can bring lasting competitive advantages to enterprises.…”
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An Efficient Density-Based Local Outlier Detection Approach for Scattered Data by Shubin Su, Limin Xiao, Li Ruan, Fei Gu, Shupan Li, Zhaokai Wang, Rongbin Xu

“…And then, we propose a safe non-outlier objects elimination approach named as rough clustering based on multi-level queries (RCMLQ) to preprocess the datasets to eliminate the non-outlier objects to the utmost. …”
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Weakly Semi Supervised learning based Mixture Model With Two-Level Constraints by Adama Nouboukpo, Mohand Saïd Allili

“…We propose a new weakly supervised approach for classification and clustering based on mixture models. Our approach integrates multi-level pairwise group and class constraints between samples to learn the underlying group structure of the data and propagate (scarce) initial labels to unlabelled data. …”
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Communication optimization method of digital-analog hybrid simulation system based on min-cut partition by Li Yiyuan, Mu Qing, Xue Wei

“…For the multi-level system architecture, the existing power grid division methods can not fully use the cluster computing power. …”
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An Ecosystem Approach to Balanced Territorial Development by L. A. Gamidullaeva, E. S. Grosheva

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Retracted: Content-Based E-Commerce Image Classification Research by Xiaoli Zhang

“…Aiming at the problems of insufficient classification accuracy and long classification training time in e-commerce image classification, an adaptive momentum learning rate based LBP-DBN training algorithm–AML-LBP-DBN and commodity image classification method based on image local feature multi-level clustering and image-class nearest neighbor classifier are proposed. …”
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3DLST: 3D Learnable Supertoken Transformer for LiDAR point cloud scene segmentation by Dening Lu, Linlin Xu, Jun Zhou, Kyle (Yilin) Gao, Jonathan Li

“…Since the learnable supertokens can be dynamically optimized by multi-level deep features during network learning, they are tailored to the semantic homogeneity-aware token clustering. …”
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