Divya Arivalagan, Vignesh Ochathevan^*, Rubankumar Dhanasekaran

Congenital Heart Disease, Vol.20, No.4, pp. 477-501, 2025, DOI:10.32604/chd.2025.070372 - 18 September 2025

Abstract Background: The accurate identification of cardiac abnormalities is essential for proper diagnosis and effective treatment of cardiovascular diseases. Method: This work introduces an advanced methodology for detecting cardiac abnormalities and estimating electrocardiographic age (ECG Age) using sophisticated signal processing and deep learning techniques. This study looks at six main heart conditions found in 12-lead electrocardiogram (ECG) data. It addresses important issues like class imbalances, missing lead scenarios, and model generalizations. A modified residual neural network (ResNet) architecture was developed to enhance the detection of cardiac abnormalities. Results: The proposed ResNet demonst rated superior performance when compared with… More > Graphic Abstract

Zengyu Cai^1,*, Yuming Dai¹, Jianwei Zhang^2,3,*, Yuan Feng⁴

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3335-3350, 2025, DOI:10.32604/cmc.2025.061206 - 16 April 2025

Abstract The rapid development and widespread adoption of Internet technology have significantly increased Internet traffic, highlighting the growing importance of network security. Intrusion Detection Systems (IDS) are essential for safeguarding network integrity. To address the low accuracy of existing intrusion detection models in identifying network attacks, this paper proposes an intrusion detection method based on the fusion of Spatial Attention mechanism and Residual Neural Network (SA-ResNet). Utilizing residual connections can effectively capture local features in the data; by introducing a spatial attention mechanism, the global dependency relationships of intrusion features can be extracted, enhancing the intrusion More >

Tsung-Yu Lu¹, Mu-En Wu², Er-Hao Chen³, Yeong-Luh Ueng^4,*

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 935-952, 2022, DOI:10.32604/cmc.2022.026717 - 18 May 2022

Abstract This paper presents an off-line handwritten signature verification system based on the Siamese network, where a hybrid architecture is used. The Residual neural Network (ResNet) is used to realize a powerful feature extraction model such that Writer Independent (WI) features can be effectively learned. A single-layer Siamese Neural Network (NN) is used to realize a Writer Dependent (WD) classifier such that the storage space can be minimized. For the purpose of reducing the impact of the high intraclass variability of the signature and ensuring that the Siamese network can learn more effectively, we propose a More >

Qaisar Abbas^1,*, Mostafa E.A. Ibrahim^1,2, Shakir Khan¹, Abdul Rauf Baig¹

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1999-2007, 2022, DOI:10.32604/cmc.2022.022553 - 03 November 2021

Abstract Traffic accidents are caused by driver fatigue or distraction in many cases. To prevent accidents, several low-cost hypovigilance (hypo-V) systems were developed in the past based on a multimodal-hybrid (physiological and behavioral) feature set. Similarly in this paper, real-time driver inattention and fatigue (Hypo-Driver) detection system is proposed through multi-view cameras and biosignal sensors to extract hybrid features. The considered features are derived from non-intrusive sensors that are related to the changes in driving behavior and visual facial expressions. To get enhanced visual facial features in uncontrolled environment, three cameras are deployed on multiview points… More >

Schahrazad Soltane^*, Sameer Alsharif , Salwa M.Serag Eldin

Computer Systems Science and Engineering, Vol.40, No.2, pp. 629-644, 2022, DOI:10.32604/csse.2022.019333 - 09 September 2021

Abstract Current cancer diagnosis procedure requires expert knowledge and is time-consuming, which raises the need to build an accurate diagnosis support system for lymphoma identification and classification. Many studies have shown promising results using Machine Learning and, recently, Deep Learning to detect malignancy in cancer cells. However, the diversity and complexity of the morphological structure of lymphoma make it a challenging classification problem. In literature, many attempts were made to classify up to four simple types of lymphoma. This paper presents an approach using a reliable model capable of diagnosing seven different categories of rare and… More >