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Journal articles on the topic 'Hybrid clustering interference graph'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Fakhar, Abbas Naveed Ur Rehman Mohammad Irshad Zahoor Irfan Jamil. "Interference Mitigation in LTE HetNet by Resource Allocation." International Journal of Engineering works 1, no. 1 (2014): 10–14. https://doi.org/10.5281/zenodo.15743.

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To provide high date rate for indoor services and communication, femtocells and microcells are planned in LTEAdvance system but main problem is how to reduce the interference between micro and femto cells and in the middle of the femtocells. In this paper we proposed regional Average channel state (RACS) to estimate the influence of interference and then we proposed hybrid clustering based on interference graph (HCIG) to reduce interference between femtocells and microcells. Based on the Results our scheme is given to reduce the interference and improve the spectrum efficiency

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2

Zhang, Hong. "Graph Based Hybrid Clustering With Unbounded Regions." Journal of Computer Science Applications and Information Technology 2, no. 2 (2017): 1–5. http://dx.doi.org/10.15226/2474-9257/2/2/00113.

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3

Wen, Zichen, Yawen Ling, Yazhou Ren, et al. "Homophily-Related: Adaptive Hybrid Graph Filter for Multi-View Graph Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 14 (2024): 15841–49. http://dx.doi.org/10.1609/aaai.v38i14.29514.

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Recently there is a growing focus on graph data, and multi-view graph clustering has become a popular area of research interest. Most of the existing methods are only applicable to homophilous graphs, yet the extensive real-world graph data can hardly fulfill the homophily assumption, where the connected nodes tend to belong to the same class. Several studies have pointed out that the poor performance on heterophilous graphs is actually due to the fact that conventional graph neural networks (GNNs), which are essentially low-pass filters, discard information other than the low-frequency inform

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Chen, Yan, and Qin Zhou Niu. "Hybrid Clustering Algorithm Based on KNN and MCL." Applied Mechanics and Materials 610 (August 2014): 302–6. http://dx.doi.org/10.4028/www.scientific.net/amm.610.302.

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MCL is a graph clustering algorithm. With the characteristics of the MCL computational process, MCL is prone to producing small clustering and separating edge nodes from the group. A hybrid clustering based on MCL combined with KNN algorithm is proposed. Hybrid algorithm improves the quality of clustering by reclassification of elements in small clustering by using KNN classification characteristics and Clustering tables required by MCL clustering. Experiment proves the improved algorithm can enhance the quality of clustering.

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Farhan, Naureen, Safdar Rizvi, Amna Shabbir, and Imran Memon. "Clustering Approaches for Efficient Radio Resource Management in Heterogeneous Networks." VFAST Transactions on Software Engineering 9, no. 3 (2021): 68–77. http://dx.doi.org/10.21015/vtse.v9i3.641.

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5G telecommunication industry promises to manage and accomplish the massive data traffic and growing network requirement complexities in heterogeneous networks (HetNets). HetNets are K-tier networks and are expected to be seamlessly connected networks with robust services for users anywhere at any time. In near future, the significance of 5G/B5G cellular networks; in both indoor and outdoor environments will be greater than before and it would add up to an exhaustive level. However, as a result of the increased density of networks, a rise in interference within these ultra-dense networks (UDN)

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Wang, Ying, and Qi Zhu. "Joint Spectrum and Power Allocation for Femtocell Based on Graph and Game Theory." Advanced Materials Research 989-994 (July 2014): 2051–56. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2051.

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In order to mitigate the downlink interference in two-tier femtocell networks, a joint spectrum and power allocation algorithm based on graph and game theory is proposed in this paper. On the premise of ensuring the QoS (Quality of Service) of MUEs (microcell user equipments), we first adopt graph-based clustering algorithm to assign subbands to femtocells. Then, for femtocells of each cluster, their transmitting power is redistributed based on the non-cooperative game theory to further reduce the interference among them. Simulation results show that this algorithm can further improves the per

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Masoud, Mohammad Z., Yousef Jaradat, Ismael Jannoud, and Mustafa A. Al Sibahee. "A hybrid clustering routing protocol based on machine learning and graph theory for energy conservation and hole detection in wireless sensor network." International Journal of Distributed Sensor Networks 15, no. 6 (2019): 155014771985823. http://dx.doi.org/10.1177/1550147719858231.

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In this work, a new hybrid clustering routing protocol is proposed to prolong network life time through detecting holes and edges nodes. The detection process attempts to generate a connected graph without any isolated nodes or clusters that have no connection with the sink node. To this end, soft clustering/estimation maximization with graph metrics, PageRank, node degree, and local cluster coefficient, has been utilized. Holes and edges detection process is performed by the sink node to reduce energy consumption of wireless sensor network nodes. The clustering process is dynamic among sensor

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Farhan, Naureen, and Safdar Rizvi. "An Interference-Managed Hybrid Clustering Algorithm to Improve System Throughput." Sensors 22, no. 4 (2022): 1598. http://dx.doi.org/10.3390/s22041598.

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In the current smart era of 5G, cellular devices and mobile data have increased exponentially. The conventional network deployment and protocols do not fulfill the ever-increasing demand for mobile data traffic. Therefore, ultra-dense networks have widely been suggested in the recent literature. However, deploying an ultra-dense network (UDN) under macro cells leads to severe interference management challenges. Although various centralized and distributed clustering methods have been used in most research work, the issue of increased interference persists. This paper proposes a joint small cel

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Kathiria*, Preeti, and Harshal Arolkar. "Document Clustering based on Phrase and Single Term Similarity using Neo4j." International Journal of Innovative Technology and Exploring Engineering 9, no. 3 (2020): 3188–92. http://dx.doi.org/10.35940/ijitee.c9050.019320.

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Document similarity generally rely on single term similarity such as cosine similarity. To achieve better document similarity, along with single term phrase- more informative feature can be used. To find out shared phrases across the corpus the Document Index graph (DIG) representation model is used. Document representation - DIG model incrementally construct the graph and simultaneously finds the shared phrase between current document and previously inserted documents from the graph. The similarity between documents is mainly depends on the number of shared phrases and single term similarity

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10

Park, Hyuk-Gyu, Kwang-Seong Shin, and Jong-Chan Kim. "Efficient Clustering Method for Graph Images Using Two-Stage Clustering Technique." Electronics 14, no. 6 (2025): 1232. https://doi.org/10.3390/electronics14061232.

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Graphimages, which represent data structures through nodes and edges, present significant challenges for clustering due to their intricate topological properties. Traditional clustering algorithms, such as K-means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN), often struggle to effectively capture both spatial and structural relationships within graph images. To overcome these limitations, we propose a novel two-stage clustering approach that integrates conventional clustering techniques with graph-based methodologies to enhance both accuracy and efficiency. In the f

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Guo, Mao-Zu, Jun Wang, Chun-yu Wang, and Yang Liu. "A hybrid clustering and graph based algorithm for tagSNP selection." Soft Computing 13, no. 12 (2009): 1143–51. http://dx.doi.org/10.1007/s00500-009-0419-z.

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12

Yang, Xiaoqian, Zhenhong Jia, Jie Yang, and Nikola Kasabov. "Change Detection of Optical Remote Sensing Image Disturbed by Thin Cloud Using Wavelet Coefficient Substitution Algorithm." Sensors 19, no. 9 (2019): 1972. http://dx.doi.org/10.3390/s19091972.

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The detection of changes in optical remote sensing images under the interference of thin clouds is studied for the first time in this paper. First, the optical remote sensing image is subjected to thin cloud removal processing, and then the processed remote sensing image is subjected to image change detection. Based on the analysis of the characteristics of thin cloud images, a method for removing thin clouds based on wavelet coefficient substitution is proposed in this paper. Based on the change in the wavelet coefficient, the high- and low-frequency parts of the remote sensing image are repl

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Pan, Yufei, Zehui Yuan, Jiaoyu Zheng, and Xiaoyang Ma. "Recovery of Power Quality Terminal’s Harmonic Data with the Interference of Bad Data." Electronics 11, no. 11 (2022): 1694. http://dx.doi.org/10.3390/electronics11111694.

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Power quality monitoring equipment is inevitably faced with the problem of data loss and is vulnerable to the interference of noise or bad data. We propose a harmonic data recovery method that is based on graph clustering and non-negative matrix factorization (NMF) under multiple constraints. Compared with the existing harmonic data recovery methods, the proposed method can effectively recover lost data and it has a strong anti-interference ability, especially for the recovery of harmonic data with interference. In the recovery of data loss, noisy interference tests and bad data interference t

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14

Zhu, Zheng Yu, Shu Jia Dong, Chun Lei Yu, and Jie He. "A Text Hybrid Clustering Algorithm Based on HowNet Semantics." Key Engineering Materials 474-476 (April 2011): 2071–78. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.2071.

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Many existing text clustering algorithms overlook the semantic information between words and so they possess a lower accuracy of text similarity computation. A new text hybrid clustering algorithm (HCA) based on HowNet semantics has been proposed in this paper. It calculates the semantic similarity of words by using the words’ semantic concept description in HowNet and then combines it with the method of maximum weight matching of bipartite graph to calculate a semantic-based text similarity. Based on the new text similarity and by combining an improved genetic algorithm with k-medoids algorit

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15

Wang, Yifei, Yupan Wang, Zeyu Zhang, Song Yang, Kaiqi Zhao, and Jiamou Liu. "USER: Unsupervised Structural Entropy-Based Robust Graph Neural Network." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 10235–43. http://dx.doi.org/10.1609/aaai.v37i8.26219.

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Unsupervised/self-supervised graph neural networks (GNN) are susceptible to the inherent randomness in the input graph data, which adversely affects the model's performance in downstream tasks. In this paper, we propose USER, an unsupervised and robust version of GNN based on structural entropy, to alleviate the interference of graph perturbations and learn appropriate representations of nodes without label information. To mitigate the effects of undesirable perturbations, we analyze the property of intrinsic connectivity and define the intrinsic connectivity graph. We also identify the rank o

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16

Song, Woojin, and Jaesung Park. "Clustering-Based Channel Allocation Method for Mitigating Inter-WBAN Interference." Applied Sciences 12, no. 22 (2022): 11851. http://dx.doi.org/10.3390/app122211851.

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In this paper, we propose a channel assignment method that can mitigate the inter-WBAN interference when the density of WBANs is high. To achieve the goal, we group the coexisting WBANs into a set of clusters by using the Louvain algorithm and allocate different channels to the WBANs in the same cluster by using a graph coloring method. By increasing the distance between the WBANs using the same channel, our method reduces the inter-WBAN interference. As a result, compared with the conventional centralized channel allocation method, our method increases the average data rate of a WBAN more tha

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17

Qin, Yikun, Zhu Liang Yu, Chang-Dong Wang, Zhenghui Gu, and Yuanqing Li. "A Novel clustering method based on hybrid K-nearest-neighbor graph." Pattern Recognition 74 (February 2018): 1–14. http://dx.doi.org/10.1016/j.patcog.2017.09.008.

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18

Hussain, Ijaz, and Sohail Asghar. "Author Name Disambiguation by Exploiting Graph Structural Clustering and Hybrid Similarity." Arabian Journal for Science and Engineering 43, no. 12 (2018): 7421–37. http://dx.doi.org/10.1007/s13369-018-3099-0.

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19

Giovanoudi, Eleni, and Dimitrios Rafailidis. "Hybrid Attentive Graph Neural Networks for time series gene expression clustering." Expert Systems with Applications 277 (June 2025): 127136. https://doi.org/10.1016/j.eswa.2025.127136.

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20

Christophides, F., and V. Friderikos. "Iterative hybrid graph and interference aware scheduling algorithm for STDMA networks." Electronics Letters 44, no. 8 (2008): 558. http://dx.doi.org/10.1049/el:20083260.

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21

Zhang, Junchang, Chenyang Xia, Leili Hu, and Yanling Zhou. "Research on Dynamic Graph Target Tracking Method Fusing the Color Local Entropy." ITM Web of Conferences 17 (2018): 02004. http://dx.doi.org/10.1051/itmconf/20181702004.

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Focusing on the problems of target deformation, occlusion, background interference and rotation, a robust video tracking method is proposed in this paper, which is based on the superpixels and dynamic graph matching. Firstly, to make the superpixels edge fit better and structure tighter, the local gradient feature is fused into the simple linear iterative clustering (SLIC) method. Secondly, the candidate target superpixels set is generated by Graph Cuts and to obtain more accurate foreground superpixels set, the LASVM classification results are fused into the Graph Cuts energy function. Thirdl

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22

Chakraborty, Sanjay, Soharab Hossain Shaikh, Sudhindu Bikash Mandal, Ranjan Ghosh, and Amlan Chakrabarti. "A study and analysis of a discrete quantum walk-based hybrid clustering approach using d-regular bipartite graph and 1D lattice." International Journal of Quantum Information 17, no. 02 (2019): 1950016. http://dx.doi.org/10.1142/s0219749919500163.

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Traditional machine learning shares several benefits with quantum information processing field. The study of machine learning with quantum mechanics is called quantum machine learning. Data clustering is an important tool for machine learning where quantum computing plays a vital role in its inherent speed up capability. In this paper, a hybrid quantum algorithm for data clustering (quantum walk-based hybrid clustering (QWBHC)) is introduced where one-dimensional discrete time quantum walks (DTQW) play the central role to update the positions of data points according to their probability distr

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23

Ahmad, Haroon, Muhammad Sajid, Faheem Mazhar, and Muhammad Fuzail. "Mapping Unseen Connections: Graph Clustering to Expose User Interaction Patterns." Journal of Future Artificial Intelligence and Technologies 1, no. 4 (2025): 474–96. https://doi.org/10.62411/faith.3048-3719-77.

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Expanding extensive and intricate social networks has required sophisticated community detection techniques. This study presents an innovative hybrid methodology that utilizes node space similarity and local knowledge to enhance community identification. Node space similarity is defined by integrating eigenvector centrality (EC), which quantifies a node’s influence inside the network, with proximity metrics, such as closeness, to evaluate the connectivity between nodes. This enables us to identify cohorts of individuals with analogous influence and connectivity. We use local knowledge by conce

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24

Bouabdallaoui, Ibrahim, Fatima Guerouate, and Mohammed Sbihi. "Hybrid Text Embedding and Evolutionary Algorithm Approach for Topic Clustering in Online Discussion Forums." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 13 (August 27, 2024): e31448. http://dx.doi.org/10.14201/adcaij.31448.

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Leveraging discussion forums as a medium for information exchange has led to a surge in data, making topic clustering in these platforms essential for understanding user interests, preferences, and concerns. This study introduces an innovative methodology for topic clustering by combining text embedding techniques—Latent Dirichlet Allocation (LDA) and BERT—trained on a singular autoencoder. Additionally, it proposes an amalgamation of K-Means and Genetic Algorithms for clustering topics within triadic discussion forum threads. The proposed technique begins with a preprocessing stage to clean a

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25

Chen, Ya, Hongliang Yuan, Tingting Liu, and Nan Ding. "Name Disambiguation Based on Graph Convolutional Network." Scientific Programming 2021 (May 8, 2021): 1–11. http://dx.doi.org/10.1155/2021/5577692.

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Recently, massive online academic resources have provided convenience for scientific study and research. However, the author name ambiguity degrades the user experience in retrieving the literature bases. Extracting the features of papers and calculating the similarity for clustering constitute the mainstream of present name disambiguation approaches, which can be divided into two branches: clustering based on attribute features and clustering based on linkage information. They cannot however get high performance. In order to improve the efficiency of literature retrieval and provide technical

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26

Zhao, Yang, and Zhiqiang Li. "Import and Export Trade Prediction Algorithm of Belt and Road Countries Based on Hybrid RVM Model." Mathematical Problems in Engineering 2022 (March 20, 2022): 1–8. http://dx.doi.org/10.1155/2022/6467326.

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Due to the large fluctuation of import and export volume and many influencing factors, it is difficult for the general prediction algorithm to obtain more accurate prediction results. To solve this problem, a trade forecasting method based on the Particle Swarm Optimization (PSO) optimization hybrid Relevance Vector Machine (RVM) model is proposed. The method firstly finds out the indexes that affect the import and export trade and extracts the principal factors of the indexes as the input data of the model through principal component analysis. Then, based on the single-kernel RVM model traini

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27

Long, Xiangyu, Shufan Wu, Xiaofeng Wu, Yixin Huang, and Zhongcheng Mu. "A GA-SA Hybrid Planning Algorithm Combined with Improved Clustering for LEO Observation Satellite Missions." Algorithms 12, no. 11 (2019): 231. http://dx.doi.org/10.3390/a12110231.

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This paper presents a space mission planning tool, which was developed for LEO (Low Earth Orbit) observation satellites. The tool is focused on a two-phase planning strategy with clustering preprocessing and mission planning, where an improved clustering algorithm is applied, and a hybrid algorithm that combines the genetic algorithm with the simulated annealing algorithm (GA–SA) is given and discussed. Experimental simulation studies demonstrate that the GA–SA algorithm with the improved clique partition algorithm based on the graph theory model exhibits higher fitness value and better optimi

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Fatemi, Zahra, Jean Pouget-Abadie, and Elena Zheleva. "Cascade-Based Randomization for Inferring Causal Effects under Diffusion Interference." Proceedings of the International AAAI Conference on Web and Social Media 18 (May 28, 2024): 394–407. http://dx.doi.org/10.1609/icwsm.v18i1.31322.

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The presence of interference, where the outcome of an individual may depend on the treatment assignment and behavior of neighboring nodes, can lead to biased causal effect estimation. Current approaches to network experiment design focus on limiting interference through cluster-based randomization, in which clusters are identified using graph clustering, and cluster randomization dictates the node assignment to treatment and control. However, cluster-based randomization approaches perform poorly when interference propagates in cascades, whereby the response of individuals to treatment propagat

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He, Ji-Ai, Lu Jia, Lei Xu, and Wei Chen. "Throughput Maximization for Multiple D2D Group Communications Underlaying Cellular Networks." Wireless Communications and Mobile Computing 2020 (October 9, 2020): 1–10. http://dx.doi.org/10.1155/2020/8833579.

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In the scenario where multiple device-to-device (D2D) users and cellular users coexist, the large number of D2D users not only results in the shortage of spectrum resources but also brings interference to the communication of cellular users. In this paper, we establish a clustering model centered on cellular users and propose a resource allocation algorithm based on a D2D clustering model. On the basis of ensuring the throughput requirements of cellular users, this algorithm reconstructs various matrices in the graph theory through probability models, sets the priority of D2D pairs, and maximi

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30

Shi, Min, Dong Dong Yang, Yuan Zhou, Huan Zhao, and Yu Fang. "Analysis of Migraine Induced Monitoring Imaging Data by Multilayer Mixed Cluster Detection." Journal of Medical Imaging and Health Informatics 9, no. 6 (2019): 1278–83. http://dx.doi.org/10.1166/jmihi.2019.2729.

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The complex network of resting brain function was constructed by graph theory to study the difference of network topology between migraine patients and normal people. The complex network of brain function of the two groups was constructed respectively, and the average clustering coefficient, characteristic path length, small cosmopolitan, homology, median centrality and other measurement parameters of the two groups of complex networks were calculated and compared. The multi-layer hybrid ensemble clustering detection is introduced for data analysis, and the edge connectivity of consensus is op

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31

Liu, Yu, Kai Guo, Xiangdong Huang, Zhou Zhou, and Yichi Zhang. "Detecting Android Malwares with High-Efficient Hybrid Analyzing Methods." Mobile Information Systems 2018 (March 13, 2018): 1–12. http://dx.doi.org/10.1155/2018/1649703.

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In order to tackle the security issues caused by malwares of Android OS, we proposed a high-efficient hybrid-detecting scheme for Android malwares. Our scheme employed different analyzing methods (static and dynamic methods) to construct a flexible detecting scheme. In this paper, we proposed some detecting techniques such as Com+ feature based on traditional Permission and API call features to improve the performance of static detection. The collapsing issue of traditional function call graph-based malware detection was also avoided, as we adopted feature selection and clustering method to un

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32

Magaraja, Anousouya Devi, Ezhilarasie Rajapackiyam, Vaitheki Kanagaraj, et al. "A Hybrid Linear Iterative Clustering and Bayes Classification-Based GrabCut Segmentation Scheme for Dynamic Detection of Cervical Cancer." Applied Sciences 12, no. 20 (2022): 10522. http://dx.doi.org/10.3390/app122010522.

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Cervical cancer earlier detection remains indispensable for enhancing the survival rate probability among women patients worldwide. The early detection of cervical cancer is done relatively by using the Pap Smear cell Test. This method of detection is challenged by the degradation phenomenon within the image segmentation task that arises when the superpixel count is minimized. This paper introduces a Hybrid Linear Iterative Clustering and Bayes classification-based GrabCut Segmentation Technique (HLC-BC-GCST) for the dynamic detection of Cervical cancer. In this proposed HLC-BC-GCST approach,

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Kaur, Rashmeet, Amit Gupta, and Rakesh Goyal. "Analysis of Coverage Hole Problem for Detection and Restoration in Wireless Sensor Networks." Advanced Science, Engineering and Medicine 12, no. 3 (2020): 403–8. http://dx.doi.org/10.1166/asem.2020.2537.

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Wireless Sensor Network is an evolving technology which has gained massive attention in the past few years. Researchers are focusing on designing the wireless sensors more and more intelligent and efficient to make our life extremely comfortable and luxurious. Wireless Sensor Networks are used in bridge monitoring, smart agriculture, health care monitoring, landslide detection, biodiversity mapping, etc. Coverage holes are one of the key problems which occur in the Wireless Sensor Network accidentally and they cannot be neglected. The coverage holes appear in the sensing field due to poor inst

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34

Zhang, Pei, Siwei Wang, Liang Li, et al. "Let the Data Choose: Flexible and Diverse Anchor Graph Fusion for Scalable Multi-View Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (2023): 11262–69. http://dx.doi.org/10.1609/aaai.v37i9.26333.

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In the past few years, numerous multi-view graph clustering algorithms have been proposed to enhance the clustering performance by exploring information from multiple views. Despite the superior performance, the high time and space expenditures limit their scalability. Accordingly, anchor graph learning has been introduced to alleviate the computational complexity. However, existing approaches can be further improved by the following considerations: (i) Existing anchor-based methods share the same number of anchors across views. This strategy violates the diversity and flexibility of multi-vie

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Zhang, Changsheng, and Bin Zhang. "A Hybrid Artificial Bee Colony Algorithm for the Service Selection Problem." Discrete Dynamics in Nature and Society 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/835071.

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To tackle the QoS-based service selection problem, a hybrid artificial bee colony algorithm calledh-ABC is proposed, which incorporates the ant colony optimization mechanism into the artificial bee colony optimization process. In this algorithm, a skyline query process is used to filter the candidates related to each service class, which can greatly shrink the search space in case of not losing good candidates, and a flexible self-adaptive varying construct graph is designed to model the search space based on a clustering process. Then, based on this construct graph, different foraging strateg

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Liu, Yaoyao, Ping Guo, and Yi Zeng. "HA-CCP: A Hybrid Algorithm for Solving Capacitated Clustering Problem." Computational Intelligence and Neuroscience 2022 (January 21, 2022): 1–24. http://dx.doi.org/10.1155/2022/6400318.

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The capacitated clustering problem (CCP) divides the vertices of the undirected graph into several disjoint clusters so that the sum of the node weights in each cluster meets the capacity limit while maximizing the sum of the weight of the edges between nodes in the same cluster. CCP is a typical NP-hard problem with a wide range of engineering applications. In recent years, heuristic algorithms represented by greedy random adaptive search program (GRASP) and variable neighborhood search (VNS) have achieved excellent results in solving CCP. To improve the efficiency and quality of the CCP solu

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Ghasemi, Ahmad, and Seyed A. Zekavat. "Low-Cost mmWave MIMO Multi-Streaming via Bi-Clustering, Graph Coloring, and Hybrid Beamforming." IEEE Transactions on Wireless Communications 20, no. 7 (2021): 4113–27. http://dx.doi.org/10.1109/twc.2021.3056077.

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Ding, Feng, Yibing Zhao, Lie Guo, Mingheng Zhang, and Linhui Li. "Obstacle Detection in Hybrid Cross-Country Environment Based on Markov Random Field for Unmanned Ground Vehicle." Discrete Dynamics in Nature and Society 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/540968.

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In order to detect the obstacle from the large amount of 3D LIDAR data in hybrid cross-country environment for unmanned ground vehicle, a new graph approach based on Markov random field was presented. Firstly, the preprocessing method based on the maximum blurred line is applied to segment the projection of every laser scan line inx-yplane. Then, based onK-means clustering algorithm, the same properties of the line are combined. Secondly, line segment nodes are precisely positioned by using corner detection method, and the next step is to take advantage of line segment nodes to build an undire

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Sserwadda, Abubakhari, Alper Ozcan, and Yusuf Yaslan. "Topological Similarity and Centrality Driven Hybrid Deep Learning for Temporal Link Prediction." JUCS - Journal of Universal Computer Science 29, no. 5 (2023): 470–90. http://dx.doi.org/10.3897/jucs.99169.

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Several real-world phenomena, including social, communication, transportation, and biological networks, can be efficiently expressed as graphs. This enables the deployment of graph algorithms to infer information from such complex network interactions to enhance graph applications&rsquo; accuracy, including link prediction, node classification, and clustering. However, the large size and complexity of the network data limit the efficiency of the learning algorithms in making decisions from such graph datasets. To overcome these limitations, graph embedding techniques are usually adopted. H

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Xiaoping, Zhou, and Liu Yang. "MANIFOLD DISCRIMINATIVE LEARNING INSPIRED HYBRID BEAMFORMING FOR MILLIMETER-WAVE MASSIVE MIMO SYSTEMS." MANIFOLD DISCRIMINATIVE LEARNING INSPIRED HYBRID BEAMFORMING FOR MILLIMETER-WAVE MASSIVE MIMO SYSTEMS 09, no. 11 (2021): 2455–68. https://doi.org/10.47191/ijmcr/v9i11.02.

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Millimeter-wave (mmWave) massive MIMO (multiple-input multiple-output) is a promising technology as it provides significant beamforming gains and interference reduction capabilities due to the large number of antennas. However, mmWave massive MIMO is computationally demanding, as the high antenna count results in high-dimensional matrix operations when conventional MIMO processing is applied. Hybrid precoding is an effective solution for the mmWave massive MIMO systems to significantly decrease the number of radio frequency (RF) chains without an apparent sum-rate loss. In this paper, we propo

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Ma, Li, Yang Li, Suohai Fan, and Runzhu Fan. "A Hybrid Method for Image Segmentation Based on Artificial Fish Swarm Algorithm and Fuzzyc-Means Clustering." Computational and Mathematical Methods in Medicine 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/120495.

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Image segmentation plays an important role in medical image processing. Fuzzyc-means (FCM) clustering is one of the popular clustering algorithms for medical image segmentation. However, FCM has the problems of depending on initial clustering centers, falling into local optimal solution easily, and sensitivity to noise disturbance. To solve these problems, this paper proposes a hybrid artificial fish swarm algorithm (HAFSA). The proposed algorithm combines artificial fish swarm algorithm (AFSA) with FCM whose advantages of global optimization searching and parallel computing ability of AFSA ar

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Zhang, Pei Ying, Ya Jun Du, and Chang Wang. "A Hybrid Method Based on HITS for Literature Recommendation." Applied Mechanics and Materials 55-57 (May 2011): 1636–41. http://dx.doi.org/10.4028/www.scientific.net/amm.55-57.1636.

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In this paper we propose a hybrid method of literature recommendation in the academic community. First, we refer the objective recommendation based on HITS algorithm by constructing a directed graph according to the literature citation relation and then select the articles considering the authority and hub score of each article synthetically and add them to the recommendation list. This can narrow the recommendation scope and give a more authoritive recommendation. Second, the subjective recommendation is based on collaborative filtering by comparing the ratings of other similar users for the

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Sserwadda, Abubakhari, Alper Ozcan, and Yusuf Yaslan. "Topological Similarity and Centrality Driven Hybrid Deep Learning for Temporal Link Prediction." JUCS - Journal of Universal Computer Science 29, no. (5) (2023): 470–90. https://doi.org/10.3897/jucs.99169.

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Several real-world phenomena, including social, communication, transportation, and biological networks, can be efficiently expressed as graphs. This enables the deployment of graph algorithms to infer information from such complex network interactions to enhance graph applications’ accuracy, including link prediction, node classification, and clustering. However, the large size and complexity of the network data limit the efficiency of the learning algorithms in making decisions from such graph datasets. To overcome these limitations, graph embedding techniques are usually adopted. Howev

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Wang, Xiaorui, Chao Liu, and Jing Guo. "Research on Entrepreneurship Course Knowledge Recommendation System Combining Knowledge Graph and Clustering Technology." International Journal of Web-Based Learning and Teaching Technologies 20, no. 1 (2025): 1–19. https://doi.org/10.4018/ijwltt.380658.

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This research works on creating a hybrid Knowledge Recommendation System (KRS) for an Entrepreneurship Course using the Knowledge Graph (KG) and Clustering Technologies (CTs). The system aims at improving students' learning experience by providing relevant learning materials and even focusing on learner preferences. These results are already part of the student profiles and advanced learning paths modules that aim to adapt to a certain student's learning style, tastes, and level of mastery of the subject. Additionally, the system creates feedback paths to hammer the recommendation algorithm in

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Jiang, Jing, Ming Lei, and Huanhuan Hou. "Downlink Multiuser Hybrid Beamforming for MmWave Massive MIMO-NOMA System with Imperfect CSI." International Journal of Antennas and Propagation 2019 (May 13, 2019): 1–10. http://dx.doi.org/10.1155/2019/9764958.

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This paper aims to provide a comprehensive scheme with limited feedback for downlink millimeter wave (mmWave) multiuser multiple-input multiple-output (MIMO) nonorthogonal multiple access (NOMA) system. Based on the feedback of the best beam and the channel quality information (CQI) on this beam, the users are grouped into a cluster having the same or coherent best beam and the maximal CQI-difference. To further reduce the intercluster interference, only the candidate cluster can join the cluster set whose intercluster correlation with the existing clusters is lower than threshold. Based on th

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Gurudev, A. "Graph and Similarity-Based Approaches to On Campus Roommate Matching." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 2153–57. https://doi.org/10.22214/ijraset.2025.70417.

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Having the right roommate could be a game changer in a student’s university life by influencing academic achievements, health, and social activities. Roommate assignments are often carried out randomly or sometimes based on arrival time which can often result in suboptimal pairings. To address this, we suggest a machine learning solution that automates roommate assignment based on similarities shared between individuals in on-campus accommodation. The model implements similaritybased roommate matching, using K-Nearest Neighbours algorithm and a hybrid graph model of Louvain and Spectral Cluste

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Xu, Jiang, Siqian Liu, Zhikui Chen, and Yonglin Leng. "A Hybrid Imputation Method Based on Denoising Restricted Boltzmann Machine." International Journal of Grid and High Performance Computing 10, no. 2 (2018): 1–13. http://dx.doi.org/10.4018/ijghpc.2018040101.

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Data imputation is an important issue in data processing and analysis which has serious impact on the results of data mining and learning. Most of the existing algorithms are either utilizing whole data sets for imputation or only considering the correlation among records. Aiming at these problems, the article proposes a hybrid method to fill incomplete data. In order to reduce interference and computation, denoising restricted Boltzmann machine model is developed for robust feature extraction from incomplete data and clustering. Then, the article proposes partial-distance and co-occurrence ma

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Zhu, Pengyu, Youwei Li, Peidong Xu, Ping Li, Zhenbing Zhao, and Gang Li. "Secondary Operation Risk Assessment Method Integrating Graph Convolutional Networks and Semantic Embeddings." Sensors 25, no. 6 (2025): 1934. https://doi.org/10.3390/s25061934.

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In the power industry, secondary operation risk assessment is a critical step in ensuring operational safety. However, traditional assessment methods often rely on expert judgment, making it difficult to efficiently address the challenges posed by unstructured textual data and complex equipment relationships. To address this issue, this paper proposes a hybrid model that integrates graph convolutional networks (GCNs) with semantic embedding techniques. The model consists of two main components: the first constructs a domain-specific knowledge graph for the power industry and uses a GCN to extr

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Mishra, Abhishek, and Pramod Kumar Mishra. "A Randomized Scheduling Algorithm for Multiprocessor Environments Using Local Search." Parallel Processing Letters 26, no. 01 (2016): 1650002. http://dx.doi.org/10.1142/s012962641650002x.

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The LOCAL(A, B) randomized task scheduling algorithm is proposed for fully connected multiprocessors. It combines two given task scheduling algorithms (A, and B) using local neighborhood search to give a hybrid of the two given algorithms. Objective is to show that such type of hybridization can give much better performance results in terms of parallel execution times. Two task scheduling algorithms are selected: DSC (Dominant Sequence Clustering as algorithm A), and CPPS (Cluster Pair Priority Scheduling as algorithm B) and a hybrid is created (the LOCAL(DSC, CPPS) or simply the LOCAL task sc

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Alibak, Ali Hosin, Seyed Mehdi Alizadeh, Shaghayegh Davodi Monjezi, As’ad Alizadeh, Falah Alobaid, and Babak Aghel. "Developing a Hybrid Neuro-Fuzzy Method to Predict Carbon Dioxide (CO2) Permeability in Mixed Matrix Membranes Containing SAPO-34 Zeolite." Membranes 12, no. 11 (2022): 1147. http://dx.doi.org/10.3390/membranes12111147.

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This study compares the predictive performance of different classes of adaptive neuro-fuzzy inference systems (ANFIS) in predicting the permeability of carbon dioxide (CO2) in mixed matrix membrane (MMM) containing the SAPO-34 zeolite. The hybrid neuro-fuzzy technique uses the MMM chemistry, pressure, and temperature to estimate CO2 permeability. Indeed, grid partitioning (GP), fuzzy C-means (FCM), and subtractive clustering (SC) strategies are used to divide the input space of ANFIS. Statistical analyses compare the performance of these strategies, and the spider graph technique selects the b

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