Sarmad Dheyaa Azeez¹, Muhammad Ilyas^2,*, Saadaldeen Rashid Ahmed^3,4

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.074930 - 09 April 2026

Abstract The rapid evolution of 5G-enabled Software Defined Networks (SDNs) has transformed modern communication systems by enabling ultra-low latency, massive connectivity, and high throughput. However, the increased complexity of traffic flows and the rise of sophisticated cyber-attacks such as Distributed Denial of Service (DDoS), Botnets, Fake Base Stations, and Zero-Day exploits have made intrusion detection a critical challenge. Traditional Intrusion Detection System (IDS) approaches often suffer from poor gen-eralization, high false positives, and lack of interpretability, making them unsuitable for dynamic 5G environments. This paper presents a novel Graph Neural Network (GNN) with Multi-Head Attention (MHA)… More >

Novsheena Rasool^1,*, Javaid Iqbal Bhat¹, Najib Ben Aoun^2,3, Abdullah Alharthi⁴, Niyaz Ahmad Wani⁵, Vikram Chopra⁶, Muhammad Shahid Anwar^7,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 885-909, 2024, DOI:10.32604/cmc.2024.055900 - 15 October 2024

Abstract Gliomas are aggressive brain tumors known for their heterogeneity, unclear borders, and diverse locations on Magnetic Resonance Imaging (MRI) scans. These factors present significant challenges for MRI-based segmentation, a crucial step for effective treatment planning and monitoring of glioma progression. This study proposes a novel deep learning framework, ResNet Multi-Head Attention U-Net (ResMHA-Net), to address these challenges and enhance glioma segmentation accuracy. ResMHA-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention mechanisms. This powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture… More >

Meijing Li^*, Runqing Huang, Xianxian Qi

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2283-2299, 2024, DOI:10.32604/cmc.2024.053630 - 15 August 2024

Abstract Chinese Clinical Named Entity Recognition (CNER) is a crucial step in extracting medical information and is of great significance in promoting medical informatization. However, CNER poses challenges due to the specificity of clinical terminology, the complexity of Chinese text semantics, and the uncertainty of Chinese entity boundaries. To address these issues, we propose an improved CNER model, which is based on multi-feature fusion and multi-scale local context enhancement. The model simultaneously fuses multi-feature representations of pinyin, radical, Part of Speech (POS), word boundary with BERT deep contextual representations to enhance the semantic representation of text… More >

Hina Naz¹, Zuping Zhang^1,*, Mohammed Al-Habib¹, Fuad A. Awwad², Emad A. A. Ismail², Zaid Ali Khan³

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2673-2696, 2024, DOI:10.32604/cmc.2024.049186 - 15 May 2024

Abstract Cardiovascular disease is the leading cause of death globally. This disease causes loss of heart muscles and is also responsible for the death of heart cells, sometimes damaging their functionality. A person’s life may depend on receiving timely assistance as soon as possible. Thus, minimizing the death ratio can be achieved by early detection of heart attack (HA) symptoms. In the United States alone, an estimated 610,000 people die from heart attacks each year, accounting for one in every four fatalities. However, by identifying and reporting heart attack symptoms early on, it is possible to… More >

Bao Rong Chang¹, Hsiu-Fen Tsai^2,*, Han-Lin Chou¹

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 107-134, 2023, DOI:10.32604/cmes.2023.024018 - 05 January 2023

Abstract Our previous work has introduced the newly generated program using the code transformation model GPT-2, verifying the generated programming codes through simhash (SH) and longest common subsequence (LCS) algorithms. However, the entire code transformation process has encountered a time-consuming problem. Therefore, the objective of this study is to speed up the code transformation process significantly. This paper has proposed deep learning approaches for modifying SH using a variational simhash (VSH) algorithm and replacing LCS with a piecewise longest common subsequence (PLCS) algorithm to faster the verification process in the test phase. Besides the code transformation More > Graphic Abstract

Bao Rong Chang^1,*, Hsiu-Fen Tsai², Po-Wen Su¹

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.1, pp. 253-277, 2021, DOI:10.32604/cmes.2021.015673 - 24 August 2021

Abstract This paper introduces a novel transform method to produce the newly generated programs through code transform model called the second generation of Generative Pre-trained Transformer (GPT-2) reasonably, improving the program execution performance significantly. Besides, a theoretical estimation in statistics has given the minimum number of generated programs as required, which guarantees to find the best one within them. The proposed approach can help the voice assistant machine resolve the problem of inefficient execution of application code. In addition to GPT-2, this study develops the variational Simhash algorithm to check the code similarity between sample program More >

Jiaohua Qin¹, Yusi Cao¹, Xuyu Xiang^{1, *}, Yun Tan¹, Lingyun Xiang², Jianjun Zhang³

CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 389-399, 2020, DOI:10.32604/cmc.2020.07819 - 30 March 2020

Abstract With the massive growth of images data and the rise of cloud computing that can provide cheap storage space and convenient access, more and more users store data in cloud server. However, how to quickly query the expected data with privacy-preserving is still a challenging in the encryption image data retrieval. Towards this goal, this paper proposes a ciphertext image retrieval method based on SimHash in cloud computing. Firstly, we extract local feature of images, and then cluster the features by K-means. Based on it, the visual word codebook is introduced to represent feature information More >