Shiyan Zhang^1,*, Ruohan Xu², Zhangbo Xu³, Cenhua Yu¹, Yuyang Jiang¹, Yuting Zhao⁴

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2287-2308, 2024, DOI:10.32604/cmc.2023.046550

Abstract The 6th generation mobile networks (6G) network is a kind of multi-network interconnection and multi-scenario coexistence network, where multiple network domains break the original fixed boundaries to form connections and convergence. In this paper, with the optimization objective of maximizing network utility while ensuring flows performance-centric weighted fairness, this paper designs a reinforcement learning-based cloud-edge autonomous multi-domain data center network architecture that achieves single-domain autonomy and multi-domain collaboration. Due to the conflict between the utility of different flows, the bandwidth fairness allocation problem for various types of flows is formulated by considering different defined reward functions. Regarding the tradeoff between… More >

Tae Woo Kim¹, Yi Pan², Jong Hyuk Park^1,*

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1035-1049, 2022, DOI:10.32604/cmc.2022.015546

Abstract In the current era, anyone can freely access the Internet thanks to the development of information and communication technology. The cloud is attracting attention due to its ability to meet continuous user demands for resources. Additionally, Cloud is effective for systems with large data flow such as the Internet of Things (IoT) systems and Smart Cities. Nonetheless, the use of traditional networking technology in the cloud causes network traffic overload and network security problems. Therefore, the cloud requires efficient networking technology to solve the existing challenges. In this paper, we propose one-time password-based software-defined cloud architecture for secure dynamic routing… More >

Manoj Sindhwani¹, Charanjeet Singh^1,*, Rajeshwar Singh²

Computer Systems Science and Engineering, Vol.40, No.2, pp. 455-467, 2022, DOI:10.32604/csse.2022.018498

Abstract With the growth of Vehicular Ad-hoc Networks, many services delivery is gaining more attention from the intelligent transportation system. However, mobility characteristics of vehicular networks cause frequent disconnection of routes, especially during the delivery of data. In both developed and developing countries, a lot of time is consumed due to traffic congestion. This has significant negative consequences, including driver stress due to increased time demand, decreased productivity for various personalized and commercial vehicles, and increased emissions of hazardous gases especially air polluting gases are impacting public health in highly populated areas. Clustering is one of the most powerful strategies for… More >

Nancy Abbas El-Hefnawy^1,*, Osama Abdel Raouf², Heba Askr³

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1349-1362, 2022, DOI:10.32604/cmc.2022.017787

Abstract Time and space complexity is the most critical problem of the current routing optimization algorithms for Software Defined Networking (SDN). To overcome this complexity, researchers use meta-heuristic techniques inside the routing optimization algorithms in the OpenFlow (OF) based large scale SDNs. This paper proposes a hybrid meta-heuristic algorithm to optimize the dynamic routing problem for the large scale SDNs. Due to the dynamic nature of SDNs, the proposed algorithm uses a mutation operator to overcome the memory-based problem of the ant colony algorithm. Besides, it uses the box-covering method and the k-means clustering method to divide the SDN network to… More >

G. Madhu^1,*, A. Govardhan², B. Sunil Srinivas³, Kshira Sagar Sahoo⁴, N. Z. Jhanjhi⁵, K. S. Vardhan¹, B. Rohit⁶

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 903-919, 2021, DOI:10.32604/cmc.2021.016114

Abstract Malaria is a severe epidemic disease caused by Plasmodium falciparum. The parasite causes critical illness if persisted for longer durations and delay in precise treatment can lead to further complications. The automatic diagnostic model provides aid for medical practitioners to avail a fast and efficient diagnosis. Most of the existing work either utilizes a fully connected convolution neural network with successive pooling layers which causes loss of information in pixels. Further, convolutions can capture spatial invariances but, cannot capture rotational invariances. Hence to overcome these limitations, this research, develops an Imperative Dynamic routing mechanism with fully trained capsule networks for… More >

Shuqiao Liu, Junliang Li, Xiaojie Li^*

Journal on Internet of Things, Vol.2, No.4, pp. 135-144, 2020, DOI:10.32604/jiot.2020.09797

Abstract In the deep learning field, “Capsule” structure aims to overcome the shortcomings of traditional Convolutional Neural Networks (CNN) which are difficult to mine the relationship between sibling features. Capsule Net (CapsNet) is a new type of classification network structure with “Capsule” as network elements. It uses the “Squashing” algorithm as an activation function and Dynamic Routing as a network optimization method to achieve better classification performance. The main problem of the Brain Magnetic Resonance Imaging (Brain MRI) recognition algorithm is that the difference between Alzheimer’s disease (AD) image, the Mild Cognitive Impairment (MCI) image, and the normal image is not… More >

Jinyuan Li¹, Hao Li¹, Guorong Cui¹, Yan Kang^{1, *}, Yang Hu¹, Yingnan Zhou²

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 925-940, 2020, DOI:10.32604/cmc.2020.09903

Abstract With continuous urbanization, cities are undergoing a sharp expansion within the regional space. Due to the high cost, the prediction of regional traffic flow is more difficult to extend to entire urban areas. To address this challenging problem, we present a new deep learning architecture for regional epitaxial traffic flow prediction called GACNet, which predicts traffic flow of surrounding areas based on inflow and outflow information in central area. The method is data-driven, and the spatial relationship of traffic flow is characterized by dynamically transforming traffic information into images through a two-dimensional matrix. We introduce adversarial training to improve performance… More >