Syed Ijaz Ur Rahman¹, Naveed Abbas¹, Sikandar Ali², Muhammad Salman¹, Ahmed Alkhayat³, Jawad Khan^4,*, Dildar Hussain⁵, Yeong Hyeon Gu^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1199-1231, 2025, DOI:10.32604/cmes.2025.057462 - 27 January 2025

Abstract Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare system. Analysis of white blood cells (WBCs) in the blood or bone marrow microscopic slide images play a crucial part in early identification to facilitate medical experts. For Acute Lymphocytic Leukemia (ALL), the most preferred part of the blood or marrow is to be analyzed by the experts before it spreads in the whole body and the condition becomes worse. The researchers have done a lot of work in this field, to demonstrate… More >

Muhammad Armghan Latif¹, Zohaib Mushtaq^2,*, Saifur Rahman³, Saad Arif⁴, Salim Nasar Faraj Mursal³, Muhammad Irfan³, Haris Aziz⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1667-1695, 2025, DOI:10.32604/cmes.2024.056850 - 27 January 2025

Abstract Ransomware attacks pose a significant threat to critical infrastructures, demanding robust detection mechanisms. This study introduces a hybrid model that combines vision transformer (ViT) and one-dimensional convolutional neural network (1DCNN) architectures to enhance ransomware detection capabilities. Addressing common challenges in ransomware detection, particularly dataset class imbalance, the synthetic minority oversampling technique (SMOTE) is employed to generate synthetic samples for minority class, thereby improving detection accuracy. The integration of ViT and 1DCNN through feature fusion enables the model to capture both global contextual and local sequential features, resulting in comprehensive ransomware classification. Tested on the UNSW-NB15 More >

Mustafa Youldash¹, Atta Rahman^2,*, Manar Alsayed¹, Abrar Sebiany¹, Joury Alzayat¹, Noor Aljishi¹, Ghaida Alshammari¹, Mona Alqahtani¹

Intelligent Automation & Soft Computing, Vol.40, pp. 125-143, 2025, DOI:10.32604/iasc.2025.058509 - 23 January 2025

Abstract Diabetic retinopathy (DR) is a complication of diabetes that can lead to reduced vision or even blindness if left untreated. Therefore, early and accurate detection of this disease is crucial for diabetic patients to prevent vision loss. This study aims to develop a deep-learning approach for the early and precise diagnosis of DR, as manual detection can be time-consuming, costly, and prone to human error. The classification task is divided into two groups for binary classification: patients with DR (diagnoses 1–4) and those without DR (diagnosis 0). For multi-class classification, the categories are no DR,… More >

Syed Tasnimul Karim Ayon^1,#, Farhan Md. Siraj^1,#, Jia Uddin^2,*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 499-520, 2025, DOI:10.32604/cmc.2025.058361 - 03 January 2025

Abstract This study investigates the application of Learnable Memory Vision Transformers (LMViT) for detecting metal surface flaws, comparing their performance with traditional CNNs, specifically ResNet18 and ResNet50, as well as other transformer-based models including Token to Token ViT, ViT without memory, and Parallel ViT. Leveraging a widely-used steel surface defect dataset, the research applies data augmentation and t-distributed stochastic neighbor embedding (t-SNE) to enhance feature extraction and understanding. These techniques mitigated overfitting, stabilized training, and improved generalization capabilities. The LMViT model achieved a test accuracy of 97.22%, significantly outperforming ResNet18 (88.89%) and ResNet50 (88.90%), as well… More >

Shuqiao Liu¹, Zhao Zhang^2,*, Hongyan Zhou¹, Xuebo Chen¹

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 929-948, 2025, DOI:10.32604/cmc.2024.057319 - 03 January 2025

Abstract This study examines the effectiveness of artificial intelligence techniques in generating high-quality environmental data for species introductory site selection systems. Combining Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis data with Variation Autoencoder (VAE) and Generative Adversarial Network (GAN) the network framework model (SAE-GAN), is proposed for environmental data reconstruction. The model combines two popular generative models, GAN and VAE, to generate features conditional on categorical data embedding after SWOT Analysis. The model is capable of generating features that resemble real feature distributions and adding sample factors to more accurately track individual sample data. Reconstructed data is… More >

Adnan Ahmad, Zhao Li^*, Irfan Tariq, Zhengran He

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 729-749, 2025, DOI:10.32604/cmc.2024.055768 - 03 January 2025

Abstract Micro-expressions (ME) recognition is a complex task that requires advanced techniques to extract informative features from facial expressions. Numerous deep neural networks (DNNs) with convolutional structures have been proposed. However, unlike DNNs, shallow convolutional neural networks often outperform deeper models in mitigating overfitting, particularly with small datasets. Still, many of these methods rely on a single feature for recognition, resulting in an insufficient ability to extract highly effective features. To address this limitation, in this paper, an Improved Dual-stream Shallow Convolutional Neural Network based on an Extreme Gradient Boosting Algorithm (IDSSCNN-XgBoost) is introduced for ME… More >

Nishit Patil, Shubhalaxmi Joshi^*

Computer Systems Science and Engineering, Vol.49, pp. 49-77, 2025, DOI:10.32604/csse.2024.057702 - 03 January 2025

Abstract Intrusion detection (ID) is a cyber security practice that encompasses the process of monitoring network activities to identify unauthorized or malicious actions. This includes problems like the difficulties of existing intrusion detection models to identify emerging attacks, generating many false alarms, and their inability and difficulty to adapt themselves with time when it comes to threats, hence to overcome all those existing challenges in this research develop a Prairie Araneida optimization based fused Convolutional Neural Network model (PAO-CNN) for intrusion detection. The fused CNN (Convolutional Neural Netowrk) is a remarkable development since it combines statistical… More >

Pongsakon Promsawat¹, Weerapan Sae-dan^2,*, Marisa Kaewsuwan³, Weerawat Sudsutad³, Aphirak Aphithana³

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 579-607, 2025, DOI:10.32604/cmes.2024.057774 - 17 December 2024

Abstract The ability to accurately predict urban traffic flows is crucial for optimising city operations. Consequently, various methods for forecasting urban traffic have been developed, focusing on analysing historical data to understand complex mobility patterns. Deep learning techniques, such as graph neural networks (GNNs), are popular for their ability to capture spatio-temporal dependencies. However, these models often become overly complex due to the large number of hyper-parameters involved. In this study, we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks (DMST-GNODE), a framework based on ordinary differential equations (ODEs) that autonomously discovers effective spatial-temporal… More >

Diana Abi-Nader¹, Hassan Harb², Ali Jaber¹, Ali Mansour³, Christophe Osswald³, Nour Mostafa^2,*, Chamseddine Zaki²

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 279-298, 2025, DOI:10.32604/cmes.2024.056816 - 17 December 2024

Abstract Security and safety remain paramount concerns for both governments and individuals worldwide. In today’s context, the frequency of crimes and terrorist attacks is alarmingly increasing, becoming increasingly intolerable to society. Consequently, there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces, thereby preventing potential attacks or violent incidents. Recent advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection, particularly in identifying firearms. This paper introduces a novel automatic firearm detection surveillance system, utilizing a one-stage detection… More >

Jianyong Wang¹, Mingliang Gao¹, Qilei Li², Hyunbum Kim³, Gwanggil Jeon^3,*

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3561-3582, 2024, DOI:10.32604/cmc.2024.058637 - 19 December 2024

Abstract Quantifying the number of individuals in images or videos to estimate crowd density is a challenging yet crucial task with significant implications for fields such as urban planning and public safety. Crowd counting has attracted considerable attention in the field of computer vision, leading to the development of numerous advanced models and methodologies. These approaches vary in terms of supervision techniques, network architectures, and model complexity. Currently, most crowd counting methods rely on fully supervised learning, which has proven to be effective. However, this approach presents challenges in real-world scenarios, where labeled data and ground-truth… More >