Lei Gu^*, Furong Zhang, Li Ma

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5911-5928, 2023, DOI:10.32604/cmc.2023.038127

Abstract Learning unlabeled data is a significant challenge that needs to handle complicated relationships between nominal values and attributes. Increasingly, recent research on learning value relations within and between attributes has shown significant improvement in clustering and outlier detection, etc. However, typical existing work relies on learning pairwise value relations but weakens or overlooks the direct couplings between multiple attributes. This paper thus proposes two novel and flexible multi-attribute couplings-based distance (MCD) metrics, which learn the multi-attribute couplings and their strengths in nominal data based on information theories: self-information, entropy, and mutual information, for measuring both numerical and nominal distances. MCD… More >

V. Dinesh¹, S. Srinivasan², Gyanendra Prasad Joshi³, Woong Cho^4,*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 687-699, 2023, DOI:10.32604/csse.2023.035459

Abstract In a vehicular ad hoc network (VANET), a massive quantity of data needs to be transmitted on a large scale in shorter time durations. At the same time, vehicles exhibit high velocity, leading to more vehicle disconnections. Both of these characteristics result in unreliable data communication in VANET. A vehicle clustering algorithm clusters the vehicles in groups employed in VANET to enhance network scalability and connection reliability. Clustering is considered one of the possible solutions for attaining effectual interaction in VANETs. But one such difficulty was reducing the cluster number under increasing transmitting nodes. This article introduces an Evolutionary Hide… More >

Taher M. Ghazal^1,2, Muhammad Zahid Hussain³, Raed A. Said⁵, Afrozah Nadeem⁶, Mohammad Kamrul Hasan¹, Munir Ahmad⁷, Muhammad Adnan Khan^3,4,*, Muhammad Tahir Naseem³

Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 735-742, 2021, DOI:10.32604/iasc.2021.019067

Abstract Clustering is the process of grouping the data based on their similar properties. Meanwhile, it is the categorization of a set of data into similar groups (clusters), and the elements in each cluster share similarities, where the similarity between elements in the same cluster must be smaller enough to the similarity between elements of different clusters. Hence, this similarity can be considered as a distance measure. One of the most popular clustering algorithms is K-means, where distance is measured between every point of the dataset and centroids of clusters to find similar data objects and assign them to the nearest… More >

Xinliang Tang¹, Xing Sun¹, Zhenzhou Wang¹, Pingping Yu¹, Ning Cao^{2, *}, Yunfeng Xu³

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 1185-1198, 2020, DOI:10.32604/cmc.2020.010283

Abstract The appearance of pedestrians can vary greatly from image to image, and different pedestrians may look similar in a given image. Such similarities and variabilities in the appearance and clothing of individuals make the task of pedestrian re-identification very challenging. Here, a pedestrian re-identification method based on the fusion of local features and gait energy image (GEI) features is proposed. In this method, the human body is divided into four regions according to joint points. The color and texture of each region of the human body are extracted as local features, and GEI features of the pedestrian gait are also… More >