Go-Eun Woo, Sang-Bo Park, Gi-Tae Park, Muhammad Junaid, Hyung-Won Kim^*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3241-3257, 2025, DOI:10.32604/cmc.2025.063723 - 03 July 2025

Abstract On-device Artificial Intelligence (AI) accelerators capable of not only inference but also training neural network models are in increasing demand in the industrial AI field, where frequent retraining is crucial due to frequent production changes. Batch normalization (BN) is fundamental to training convolutional neural networks (CNNs), but its implementation in compact accelerator chips remains challenging due to computational complexity, particularly in calculating statistical parameters and gradients across mini-batches. Existing accelerator architectures either compromise the training accuracy of CNNs through approximations or require substantial computational resources, limiting their practical deployment. We present a hardware-optimized BN accelerator… More >

Chao Li^1,3,#, Quanzhi Feng^1,3,#, Caichang Ding^2,*, Zhiwei Ye^1,3

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3605-3621, 2025, DOI:10.32604/cmc.2025.063703 - 03 July 2025

Abstract The increasing adoption of unmanned aerial vehicles (UAVs) in urban low-altitude logistics systems, particularly for time-sensitive applications like parcel delivery and supply distribution, necessitates sophisticated coordination mechanisms to optimize operational efficiency. However, the limited capability of UAVs to extract state-action information in complex environments poses significant challenges to achieving effective cooperation in dynamic and uncertain scenarios. To address this, we presents an Improved Multi-Agent Hybrid Attention Critic (IMAHAC) framework that advances multi-agent deep reinforcement learning (MADRL) through two key innovations. Firstly, a Temporal Difference Error and Time-based Prioritized Experience Replay (TT-PER) mechanism that dynamically adjusts… More >

Atif Raza Zaidi¹, Tahir Abbas^1,*, Ali Daud^2,*, Omar Alghushairy³, Hussain Dawood⁴, Nadeem Sarwar⁵

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3281-3304, 2025, DOI:10.32604/cmc.2025.063646 - 03 July 2025

Abstract Safeguarding against malware requires precise machine-learning algorithms to classify harmful apps. The Drebin dataset of 15,036 samples and 215 features yielded significant and reliable results for two hybrid models, CNN + XGBoost and KNN + XGBoost. To address the class imbalance issue, SMOTE (Synthetic Minority Over-sampling Technique) was used to preprocess the dataset, creating synthetic samples of the minority class (malware) to balance the training set. XGBoost was then used to choose the most essential features for separating malware from benign programs. The models were trained and tested using 6-fold cross-validation, measuring accuracy, precision, recall,… More >

Pooja Bidwai^1,2, Shilpa Gite^1,3, Biswajeet Pradhan^4,*, Abdullah Almari⁵

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2645-2676, 2025, DOI:10.32604/cmc.2025.061018 - 03 July 2025

Abstract Diabetic Retinopathy (DR) is a critical disorder that affects the retina due to the constant rise in diabetics and remains the major cause of blindness across the world. Early detection and timely treatment are essential to mitigate the effects of DR, such as retinal damage and vision impairment. Several conventional approaches have been proposed to detect DR early and accurately, but they are limited by data imbalance, interpretability, overfitting, convergence time, and other issues. To address these drawbacks and improve DR detection accurately, a distributed Explainable Convolutional Neural network-enabled Light Gradient Boosting Machine (DE-ExLNN) is… More >

Shivani Sood¹, Harjeet Singh^2,*, Surbhi Bhatia Khan^3,4,5,*, Ahlam Almusharraf⁶

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2751-2787, 2025, DOI:10.32604/cmc.2025.060185 - 03 July 2025

Abstract Wheat fungal infections pose a danger to the grain quality and crop productivity. Thus, prompt and precise diagnosis is essential for efficient crop management. This study used the WFD2020 image dataset, which is available to everyone, to look into how deep learning models could be used to find powdery mildew, leaf rust, and yellow rust, which are three common fungal diseases in Punjab, India. We changed a few hyperparameters to test TensorFlow-based models, such as SSD and Faster R-CNN with ResNet50, ResNet101, and ResNet152 as backbones. Faster R-CNN with ResNet50 achieved a mean average precision More >

Da Li¹, Yuqing Tian¹, Chengxin Weng^2,3, Fuyou Liang^1,4,5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2867-2882, 2025, DOI:10.32604/cmes.2025.066444 - 30 June 2025

Abstract Veno-arterial extracorporeal membrane oxygenation (VA-ECMO) is a life support intervention for patients with refractory cardiogenic shock or severe cardiopulmonary failure. However, the choice of cannulation strategy remains contentious, partly due to insufficient understanding of hemodynamic characteristics associated with the site of arterial cannulation. In this study, a geometrical multiscale model was built to offer a mathematical tool for addressing the issue. The outflow cannula of ECMO was inserted into the ascending aorta in the case of central cannulation, whereas it was inserted into the right subclavian artery (RSA) or the left iliac artery (LIA) in… More >

Tawfeeq Shawly^1,2, Ahmed A. Alsheikhy^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3033-3064, 2025, DOI:10.32604/cmes.2025.065264 - 30 June 2025

Abstract Epilepsy is a long-term neurological condition marked by recurrent seizures, which result from abnormal electrical activity in the brain that disrupts its normal functioning. Traditional methods for detecting epilepsy through machine learning typically utilize discrete-time models, which inadequately represent the continuous dynamics of electroencephalogram (EEG) signals. To overcome this limitation, we introduce an innovative approach that employs Neural Ordinary Differential Equations (NODEs) to model EEG signals as continuous-time systems. This allows for effective management of irregular sampling and intricate temporal patterns. In contrast to conventional techniques, such as Convolutional Neural Networks (CNNs) and Recurrent Neural… More >

Mohammed Alshahrani¹, Mohammed Al-Jabbar^1,*, Ebrahim Mohammed Senan^2,3, Fatima Ali Amer jid Almahri⁴, Sultan Ahmed Almalki¹, Eman A. Alshari^3,5

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3639-3675, 2025, DOI:10.32604/cmes.2025.064668 - 30 June 2025

Abstract Multiple Sclerosis (MS) poses significant health risks. Patients may face neurodegeneration, mobility issues, cognitive decline, and a reduced quality of life. Manual diagnosis by neurologists is prone to limitations, making AI-based classification crucial for early detection. Therefore, automated classification using Artificial Intelligence (AI) techniques has a crucial role in addressing the limitations of manual classification and preventing the development of MS to advanced stages. This study developed hybrid systems integrating XGBoost (eXtreme Gradient Boosting) with multi-CNN (Convolutional Neural Networks) features based on Ant Colony Optimization (ACO) and Maximum Entropy Score-based Selection (MESbS) algorithms for early… More >

Abu Saleh Musa Miah¹, Md Mahbubur Rahman Tusher², Md. Moazzem Hossain², Md Mamun Hossain², Md Abdur Rahim³, Md Ekramul Hamid⁴, Md. Saiful Islam⁴, Jungpil Shin^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2573-2614, 2025, DOI:10.32604/cmes.2025.064168 - 30 June 2025

Abstract Advanced artificial intelligence technologies such as ChatGPT and other large language models (LLMs) have significantly impacted fields such as education and research in recent years. ChatGPT benefits students and educators by providing personalized feedback, facilitating interactive learning, and introducing innovative teaching methods. While many researchers have studied ChatGPT across various subject domains, few analyses have focused on the engineering domain, particularly in addressing the risks of academic dishonesty and potential declines in critical thinking skills. To address this gap, this study explores both the opportunities and limitations of ChatGPT in engineering contexts through a two-part… More >

Smita Khairnar^1,2, Shilpa Gite^1,3,*, Biswajeet Pradhan^4,*, Sudeep D. Thepade^2,5, Abdullah Alamri⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3677-3707, 2025, DOI:10.32604/cmes.2025.058855 - 30 June 2025

Abstract Face liveness detection is essential for securing biometric authentication systems against spoofing attacks, including printed photos, replay videos, and 3D masks. This study systematically evaluates pre-trained CNN models— DenseNet201, VGG16, InceptionV3, ResNet50, VGG19, MobileNetV2, Xception, and InceptionResNetV2—leveraging transfer learning and fine-tuning to enhance liveness detection performance. The models were trained and tested on NUAA and Replay-Attack datasets, with cross-dataset generalization validated on SiW-MV2 to assess real-world adaptability. Performance was evaluated using accuracy, precision, recall, FAR, FRR, HTER, and specialized spoof detection metrics (APCER, NPCER, ACER). Fine-tuning significantly improved detection accuracy, with DenseNet201 achieving the highest… More > Graphic Abstract