Jingfa Ma, Hu Liu^*, Lingxiao Chen

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 443-469, 2024, DOI:10.32604/cmc.2024.056209 - 15 October 2024

Abstract Demand-responsive transportation (DRT) is a flexible passenger service designed to enhance road efficiency, reduce peak-hour traffic, and boost passenger satisfaction. However, existing optimization methods for initial passenger requests fall short in addressing real-time passenger needs. Consequently, there is a need to develop real-time DRT route optimization methods that integrate both initial and real-time requests. This paper presents a two-stage, multi-objective optimization model for DRT vehicle scheduling. The first stage involves an initial scheduling model aimed at minimizing vehicle configuration, and operational, and CO₂ emission costs while ensuring passenger satisfaction. The second stage develops a real-time scheduling… More >

Menghan Zhang^1,2, Fangfang Shan^1,2,*, Mengyao Liu^1,2, Zhenyu Wang^1,2

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1565-1580, 2024, DOI:10.32604/cmc.2024.056008 - 15 October 2024

Abstract With the emergence and development of social networks, people can stay in touch with friends, family, and colleagues more quickly and conveniently, regardless of their location. This ubiquitous digital internet environment has also led to large-scale disclosure of personal privacy. Due to the complexity and subtlety of sensitive information, traditional sensitive information identification technologies cannot thoroughly address the characteristics of each piece of data, thus weakening the deep connections between text and images. In this context, this paper adopts the CLIP model as a modality discriminator. By using comparative learning between sensitive image descriptions and… More >

Hongjun Zhang^1,2, Zeyu Zhang³, Yilong Ruan⁴, Hao Ye^5,6, Peng Li^1,*, Desheng Shi¹

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1409-1432, 2024, DOI:10.32604/cmc.2024.055406 - 15 October 2024

Abstract The scale and complexity of big data are growing continuously, posing severe challenges to traditional data processing methods, especially in the field of clustering analysis. To address this issue, this paper introduces a new method named Big Data Tensor Multi-Cluster Distributed Incremental Update (BDTMCDIncreUpdate), which combines distributed computing, storage technology, and incremental update techniques to provide an efficient and effective means for clustering analysis. Firstly, the original dataset is divided into multiple sub-blocks, and distributed computing resources are utilized to process the sub-blocks in parallel, enhancing efficiency. Then, initial clustering is performed on each sub-block… More >

Shijie Yang¹, Jiateng Yuan¹, Zhipeng Zhang¹, Zhibo Chen^1,2, Hanchao Zhang⁴, Xiaohui Cui^1,2,3,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1529-1563, 2024, DOI:10.32604/cmc.2024.055402 - 15 October 2024

Abstract In recent years, sensor technology has been widely used in the defense and control of sensitive areas in cities, or in various scenarios such as early warning of forest fires, monitoring of forest pests and diseases, and protection of endangered animals. Deploying sensors to collect data and then utilizing unmanned aerial vehicle (UAV) to collect the data stored in the sensors has replaced traditional manual data collection as the dominant method. The current strategies for efficient data collection in above scenarios are still imperfect, and the low quality of the collected data and the excessive… More >

Xionglve Li, Chengyu Wang, Yifan Yang, Changsheng Hou, Bingnan Hou, Zhiping Cai^*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 53-78, 2024, DOI:10.32604/cmc.2024.055186 - 15 October 2024

Abstract The global Internet is a complex network of interconnected autonomous systems (ASes). Understanding Internet inter-domain path information is crucial for understanding, managing, and improving the Internet. The path information can also help protect user privacy and security. However, due to the complicated and heterogeneous structure of the Internet, path information is not publicly available. Obtaining path information is challenging due to the limited measurement probes and collectors. Therefore, inferring Internet inter-domain paths from the limited data is a supplementary approach to measure Internet inter-domain paths. The purpose of this survey is to provide an overview… More >

Zhi Wang¹, Jinming Cao¹, Zhonglei Liu¹, Yuhong Zhao^1,2,3,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 213-228, 2024, DOI:10.32604/cmc.2024.055169 - 15 October 2024

Abstract Recent years, the polarization response of ferroelectrics has been entirely studied. However, it is found that the polarization may disappear gradually with the continually applied of electric field. In this paper, taking K_0.48Na_0.52NbO₃(KNN) as an example, it was demonstrated that the residual polarization began to decrease when the electric field frequency increased to a certain extent using a phase-field methods. The results showed that the content of out-of-plane domains increased first and then decreased with the increase of applied electric field frequency, the maximum polarization disappeared at high frequencies, and the hysteresis loop became elliptical. In More >

Qingkun Chen, Qinmu Wu^*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1301-1318, 2024, DOI:10.32604/cmc.2024.054799 - 15 October 2024

Abstract Vehicular Ad-hoc Networks (VANETs) make it easy to transfer information between vehicles, and this feature is utilized to enable collaborative decision-making between vehicles to enhance the safety, economy, and entertainment of vehicle operation. The high mobility of vehicles leads to a time-varying topology between vehicles, which makes inter-vehicle information transfer challenging in terms of delay control and ensuring the stability of collaborative decision-making among vehicles. The clustering algorithm is a method aimed at improving the efficiency of VANET communication. Currently, most of the research based on this method focuses on maintaining the stability of vehicle… More >

Anitha Ramachandran, Sendhil Kumar K.S.^*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 827-842, 2024, DOI:10.32604/cmc.2024.054768 - 15 October 2024

Abstract Research on panicle detection is one of the most important aspects of paddy phenotypic analysis. A phenotyping method that uses unmanned aerial vehicles can be an excellent alternative to field-based methods. Nevertheless, it entails many other challenges, including different illuminations, panicle sizes, shape distortions, partial occlusions, and complex backgrounds. Object detection algorithms are directly affected by these factors. This work proposes a model for detecting panicles called Border Sensitive Knowledge Distillation (BSKD). It is designed to prioritize the preservation of knowledge in border areas through the use of feature distillation. Our feature-based knowledge distillation method More >

Yujian Wang¹, Jianxun Zhang^1,*, Renhao Sun²

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 533-548, 2024, DOI:10.32604/cmc.2024.054644 - 15 October 2024

Abstract The effectiveness of facial expression recognition (FER) algorithms hinges on the model’s quality and the availability of a substantial amount of labeled expression data. However, labeling large datasets demands significant human, time, and financial resources. Although active learning methods have mitigated the dependency on extensive labeled data, a cold-start problem persists in small to medium-sized expression recognition datasets. This issue arises because the initial labeled data often fails to represent the full spectrum of facial expression characteristics. This paper introduces an active learning approach that integrates uncertainty estimation, aiming to improve the precision of facial… More >

Ke Li^1,*, Xiaofeng Wang^1,2,*, Hu Wang¹

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1391-1407, 2024, DOI:10.32604/cmc.2024.054484 - 15 October 2024

Abstract In the realm of data privacy protection, federated learning aims to collaboratively train a global model. However, heterogeneous data between clients presents challenges, often resulting in slow convergence and inadequate accuracy of the global model. Utilizing shared feature representations alongside customized classifiers for individual clients emerges as a promising personalized solution. Nonetheless, previous research has frequently neglected the integration of global knowledge into local representation learning and the synergy between global and local classifiers, thereby limiting model performance. To tackle these issues, this study proposes a hierarchical optimization method for federated learning with feature alignment… More >