Hui Du, Zhiyuan Hu^*, Depeng Lu, Jingrui Liu

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3239-3259, 2024, DOI:10.32604/cmc.2024.046314 - 15 May 2024

Abstract Traditional clustering algorithms often struggle to produce satisfactory results when dealing with datasets with uneven density. Additionally, they incur substantial computational costs when applied to high-dimensional data due to calculating similarity matrices. To alleviate these issues, we employ the KD-Tree to partition the dataset and compute the K-nearest neighbors (KNN) density for each point, thereby avoiding the computation of similarity matrices. Moreover, we apply the rules of voting elections, treating each data point as a voter and casting a vote for the point with the highest density among its KNN. By utilizing the vote counts More >

Hanan Abdullah Mengash¹, Alaaeldin M. Hafez², Hanan A. Hosni Mahmoud^3,*

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3129-3140, 2023, DOI:10.32604/iasc.2023.030836 - 17 August 2022

Abstract Encephalitis is a brain inflammation disease. Encephalitis can yield to seizures, motor disability, or some loss of vision or hearing. Sometimes, encephalitis can be a life-threatening and proper diagnosis in an early stage is very crucial. Therefore, in this paper, we are proposing a deep learning model for computerized detection of Encephalitis from the electroencephalogram data (EEG). Also, we propose a Density-Based Clustering model to classify the distinctive waves of Encephalitis. Customary clustering models usually employ a computed single centroid virtual point to define the cluster configuration, but this single point does not contain adequate More >

Weiwu Ren^1,*, Xiaoqiang Di¹, Zhanwei Du², Jianping Zhao¹

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2057-2073, 2021, DOI:10.32604/cmc.2021.016678 - 13 April 2021

Abstract CFSFDP (Clustering by fast search and find of density peak) is a simple and crisp density clustering algorithm. It does not only have the advantages of density clustering algorithm, but also can find the peak of cluster automatically. However, the lack of adaptability makes it difficult to apply in intrusion detection. The new input cannot be updated in time to the existing profiles, and rebuilding profiles would waste a lot of time and computation. Therefore, an adaptive anomaly detection algorithm based on CFSFDP is proposed in this paper. By analyzing the influence of new input… More >