Ang Liu^1,2, Xiu-Bo Chen^1,*, Gang Xu³, Zhuo Wang⁴, Xuefen Feng⁵, Huamin Feng⁶

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 259-277, 2023, DOI:10.32604/cmc.2023.039397 - 08 June 2023

Abstract The rapid advancement of quantum technology poses significant security risks to blockchain systems. However, quantum technology can also provide solutions for enhancing blockchain security. In this paper, we propose a quantum-enhanced blockchain scheme to achieve a high level of security against quantum computing attacks. We first discuss quantum computing attacks on classic blockchains, including attacks on hash functions, digital signatures, and consensus mechanisms. We then introduce quantum technologies, such as a quantum hash function (QHF), a quantum digital signature (QDS), and proof of authority (PoA) consensus mechanism, into our scheme to improve the security of More >

Amel Ali Alhussan¹, Fatma M. Talaat², El-Sayed M. El-kenawy³, Abdelaziz A. Abdelhamid^4,5, Abdelhameed Ibrahim⁶, Doaa Sami Khafaga^1,*, Mona Alnaggar⁷

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 499-515, 2023, DOI:10.32604/cmc.2023.039368 - 08 June 2023

Abstract In computer vision, emotion recognition using facial expression images is considered an important research issue. Deep learning advances in recent years have aided in attaining improved results in this issue. According to recent studies, multiple facial expressions may be included in facial photographs representing a particular type of emotion. It is feasible and useful to convert face photos into collections of visual words and carry out global expression recognition. The main contribution of this paper is to propose a facial expression recognition model (FERM) depending on an optimized Support Vector Machine (SVM). To test the… More >

Halah Hasan Mahmoud¹, Marwan Kadhim Mohammed Al-Shammari¹, Gehad Abdullah Amran^2,3,*, Elsayed Tag eldin^4,*, Ala R. Alareqi⁵, Nivin A. Ghamry⁶, Ehaa ALnajjar⁷, Esmail Almosharea⁸

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 573-589, 2023, DOI:10.32604/cmc.2023.039363 - 08 June 2023

Abstract Utilizing artificial intelligence (AI) to protect smart coastal cities has become a novel vision for scientific and industrial institutions. One of these AI technologies is using efficient and secure multi-environment Unmanned Vehicles (UVs) for anti-submarine attacks. This study’s contribution is the early detection of a submarine assault employing hybrid environment UVs that are controlled using swarm optimization and secure the information in between UVs using a decentralized cybersecurity strategy. The Dragonfly Algorithm is used for the orientation and clustering of the UVs in the optimization approach, and the Re-fragmentation strategy is used in the Network… More >

Hong Zhang^1,2,*, Gang Yang¹, Hailiang Yu¹, Zan Zheng¹

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1047-1063, 2023, DOI:10.32604/cmc.2023.039274 - 08 June 2023

Abstract To accurately predict traffic flow on the highways, this paper proposes a Convolutional Neural Network-Bi-directional Long Short-Term Memory-Attention Mechanism (CNN-BiLSTM-Attention) traffic flow prediction model based on Kalman-filtered data processing. Firstly, the original fluctuating data is processed by Kalman filtering, which can reduce the instability of short-term traffic flow prediction due to unexpected accidents. Then the local spatial features of the traffic data during different periods are extracted, dimensionality is reduced through a one-dimensional CNN, and the BiLSTM network is used to analyze the time series information. Finally, the Attention Mechanism assigns feature weights and performs… More >

Deye Jiang¹, Yiguang Wang^2,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 295-309, 2023, DOI:10.32604/cmc.2023.039244 - 08 June 2023

Abstract In the field of energy conversion, the increasing attention on power electronic equipment is fault detection and diagnosis. A power electronic circuit is an essential part of a power electronic system. The state of its internal components affects the performance of the system. The stability and reliability of an energy system can be improved by studying the fault diagnosis of power electronic circuits. Therefore, an algorithm based on adaptive simulated annealing particle swarm optimization (ASAPSO) was used in the present study to optimize a backpropagation (BP) neural network employed for the online fault diagnosis of… More >

Shivani Batra¹, Mohammad Zubair Khan^2,*, Gatish Priyadarshi³, Ayman Noor⁴, Talal H. Noor⁵, Namrata Sukhija⁶, Prakash Srivastava⁷

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 631-646, 2023, DOI:10.32604/cmc.2023.039241 - 08 June 2023

Abstract Plasma therapy is an extensively used treatment for critically unwell patients. For this procedure, a legitimate plasma donor who can continue to supply plasma after healing is needed. However, significant dangers are associated with supply management, such as the ambiguous provenance of plasma and the spread of infected or subpar blood into medicinal fabrication. Also, from an ideological standpoint, less powerful people may be exploited throughout the contribution process. Moreover, there is a danger to the logistics system because there are now just some plasma shippers. This research intends to investigate the blockchain-based solution for More >

Jeongha Lee¹, Hyokyung Bahn^2,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 607-629, 2023, DOI:10.32604/cmc.2023.039236 - 08 June 2023

Abstract Due to the recent trend of software intelligence in the Fourth Industrial Revolution, deep learning has become a mainstream workload for modern computer systems. Since the data size of deep learning increasingly grows, managing the limited memory capacity efficiently for deep learning workloads becomes important. In this paper, we analyze memory accesses in deep learning workloads and find out some unique characteristics differentiated from traditional workloads. First, when comparing instruction and data accesses, data access accounts for 96%–99% of total memory accesses in deep learning workloads, which is quite different from traditional workloads. Second, when… More >

Wajiha Rahim Khan¹, Tahir Mustafa Madni¹, Uzair Iqbal Janjua¹, Umer Javed², Muhammad Attique Khan³, Majed Alhaisoni⁴, Usman Tariq⁵, Jae-Hyuk Cha^6,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 647-664, 2023, DOI:10.32604/cmc.2023.039188 - 08 June 2023

Abstract Segmenting brain tumors in Magnetic Resonance Imaging (MRI) volumes is challenging due to their diffuse and irregular shapes. Recently, 2D and 3D deep neural networks have become famous for medical image segmentation because of the availability of labelled datasets. However, 3D networks can be computationally expensive and require significant training resources. This research proposes a 3D deep learning model for brain tumor segmentation that uses lightweight feature extraction modules to improve performance without compromising contextual information or accuracy. The proposed model, called Hybrid Attention-Based Residual Unet (HA-RUnet), is based on the Unet architecture and utilizes… More >

Yixin Tang^*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1195-1212, 2023, DOI:10.32604/cmc.2023.039182 - 08 June 2023

Abstract Visual illustration transformation from real-world to cartoon images is one of the famous and challenging tasks in computer vision. Image-to-image translation from real-world to cartoon domains poses issues such as a lack of paired training samples, lack of good image translation, low feature extraction from the previous domain images, and lack of high-quality image translation from the traditional generator algorithms. To solve the above-mentioned issues, paired independent model, high-quality dataset, Bayesian-based feature extractor, and an improved generator must be proposed. In this study, we propose a high-quality dataset to reduce the effect of paired training… More >

Xiuye Liu, Aihua Wu^*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 543-559, 2023, DOI:10.32604/cmc.2023.039004 - 08 June 2023

Abstract The human motion generation model can extract structural features from existing human motion capture data, and the generated data makes animated characters move. The 3D human motion capture sequences contain complex spatial-temporal structures, and the deep learning model can fully describe the potential semantic structure of human motion. To improve the authenticity of the generated human motion sequences, we propose a multi-task motion generation model that consists of a discriminator and a generator. The discriminator classifies motion sequences into different styles according to their similarity to the mean spatial-temporal templates from motion sequences of 17… More >