Amrutanshu Panigrahi¹, Abhilash Pati¹, Bibhuprasad Sahu², Ashis Kumar Pati³, Subrata Chowdhury⁴, Khursheed Aurangzeb^5,*, Nadeem Javaid⁶, Sheraz Aslam^7,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1633-1657, 2025, DOI:10.32604/cmc.2025.063562 - 09 June 2025

Abstract The integration of IoT and Deep Learning (DL) has significantly advanced real-time health monitoring and predictive maintenance in prognostic and health management (PHM). Electrocardiograms (ECGs) are widely used for cardiovascular disease (CVD) diagnosis, but fluctuating signal patterns make classification challenging. Computer-assisted automated diagnostic tools that enhance ECG signal categorization using sophisticated algorithms and machine learning are helping healthcare practitioners manage greater patient populations. With this motivation, the study proposes a DL framework leveraging the PTB-XL ECG dataset to improve CVD diagnosis. Deep Transfer Learning (DTL) techniques extract features, followed by feature fusion to eliminate redundancy… More >

Vidivelli Soundararajan^*, Manikandan Ramachandran^*, Srivatsan Vinodh Kumar

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5259-5277, 2025, DOI:10.32604/cmc.2025.063059 - 19 May 2025

Abstract Many applications, including security systems, medical diagnostics, and human-computer interfaces, depend on eye gaze recognition. However, due to factors including individual variations, occlusions, and shifting illumination conditions, real-world scenarios continue to provide difficulties for accurate and consistent eye gaze recognition. This work is aimed at investigating the potential benefits of employing transfer learning to improve eye gaze detection ability and efficiency. Transfer learning is the process of fine-tuning pre-trained models on smaller, domain-specific datasets after they have been trained on larger datasets. We study several transfer learning algorithms and evaluate their effectiveness on eye gaze… More >

Manjit Singh, Sunil Kumar Singla^*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3003-3029, 2025, DOI:10.32604/cmes.2025.060863 - 03 March 2025

Abstract Deep Learning-based systems for Finger vein recognition have gained rising attention in recent years due to improved efficiency and enhanced security. The performance of existing CNN-based methods is limited by the puny generalization of learned features and deficiency of the finger vein image training data. Considering the concerns of existing methods, in this work, a simplified deep transfer learning-based framework for finger-vein recognition is developed using an EfficientNet model of deep learning with a self-attention mechanism. Data augmentation using various geometrical methods is employed to address the problem of training data shortage required for a… More > Graphic Abstract

Farhad Mortezapour Shiri^*, Thinagaran Perumal, Norwati Mustapha, Raihani Mohamed

Journal on Artificial Intelligence, Vol.6, pp. 301-360, 2024, DOI:10.32604/jai.2024.054314 - 20 November 2024

Abstract Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and artificial intelligence (AI), outperforming traditional ML methods, especially in handling unstructured and large datasets. Its impact spans across various domains, including speech recognition, healthcare, autonomous vehicles, cybersecurity, predictive analytics, and more. However, the complexity and dynamic nature of real-world problems present challenges in designing effective deep learning models. Consequently, several deep learning models have been developed to address different problems and applications. In this article, we conduct a comprehensive survey of various deep learning models, including Convolutional Neural Network (CNN), Recurrent… More >

Ahmad Alzu’bi^1,*, Amjad Albashayreh², Abdelrahman Abuarqoub³, Mai A. M. Alfawair⁴

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3785-3802, 2024, DOI:10.32604/cmc.2024.052599 - 12 September 2024

Abstract In the era of the Internet of Things (IoT), the proliferation of connected devices has raised security concerns, increasing the risk of intrusions into diverse systems. Despite the convenience and efficiency offered by IoT technology, the growing number of IoT devices escalates the likelihood of attacks, emphasizing the need for robust security tools to automatically detect and explain threats. This paper introduces a deep learning methodology for detecting and classifying distributed denial of service (DDoS) attacks, addressing a significant security concern within IoT environments. An effective procedure of deep transfer learning is applied to utilize More >

M. Jayasree¹, K. A. Sunitha^2,*, A. Brindha¹, Punna Rajasekhar³, G. Aravamuthan³, G. Joselin Retnakumar¹

Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 745-764, 2024, DOI:10.32604/iasc.2024.052983 - 06 September 2024

Abstract Identifying faces in non-frontal poses presents a significant challenge for face recognition (FR) systems. In this study, we delved into the impact of yaw pose variations on these systems and devised a robust method for detecting faces across a wide range of angles from 0° to ±90°. We initially selected the most suitable feature vector size by integrating the Dlib, FaceNet (Inception-v2), and “Support Vector Machines (SVM)” + “K-nearest neighbors (KNN)” algorithms. To train and evaluate this feature vector, we used two datasets: the “Labeled Faces in the Wild (LFW)” benchmark data and the “Robust… More >

Wufei Wu¹, Javad Hassannataj Joloudari^2,3,4, Senthil Kumar Jagatheesaperumal⁵, Kandala N. V. P. S. Rajesh⁶, Silvia Gaftandzhieva^7,*, Sadiq Hussain⁸, Rahimullah Rabih⁹, Najibullah Haqjoo¹⁰, Mobeen Nazar¹¹, Hamed Vahdat-Nejad⁹, Rositsa Doneva¹²

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2785-2813, 2024, DOI:10.32604/cmc.2024.053037 - 15 August 2024

Abstract The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles (IoV) technology. The functional advantages of IoV include online communication services, accident prevention, cost reduction, and enhanced traffic regularity. Despite these benefits, IoV technology is susceptible to cyber-attacks, which can exploit vulnerabilities in the vehicle network, leading to perturbations, disturbances, non-recognition of traffic signs, accidents, and vehicle immobilization. This paper reviews the state-of-the-art achievements and developments in applying Deep Transfer Learning (DTL) models for Intrusion Detection Systems in the Internet of Vehicles (IDS-IoV) based on anomaly… More >

Roseline Oluwaseun Ogundokun^1,2, Joseph Bamidele Awotunde³, Hakeem Babalola Akande⁴, Cheng-Chi Lee^5,6,*, Agbotiname Lucky Imoize^7,8

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 139-161, 2024, DOI:10.32604/cmc.2024.052153 - 18 July 2024

Abstract Mobile technology is developing significantly. Mobile phone technologies have been integrated into the healthcare industry to help medical practitioners. Typically, computer vision models focus on image detection and classification issues. MobileNetV2 is a computer vision model that performs well on mobile devices, but it requires cloud services to process biometric image information and provide predictions to users. This leads to increased latency. Processing biometrics image datasets on mobile devices will make the prediction faster, but mobiles are resource-restricted devices in terms of storage, power, and computational speed. Hence, a model that is small in size,… More >

Qixin Lan¹, Binqiang Chen^1,*, Bin Yao¹, Wangpeng He²

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2825-2844, 2024, DOI:10.32604/cmes.2023.030378 - 15 December 2023

Abstract The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the tool will generate significant noise and vibration, negatively impacting the accuracy of the forming and the surface integrity of the workpiece. Hence, during the cutting process, it is imperative to continually monitor the tool wear state and promptly replace any heavily worn tools to guarantee the quality of the cutting. The conventional tool wear monitoring models, which are based on machine learning, are specifically built for the intended cutting conditions. However, these models require retraining when… More >

C. S. S. Anupama¹, Saud Yonbawi², G. Jose Moses³, E. Laxmi Lydia⁴, Seifedine Kadry^5,6,7, Jungeun Kim^8,*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2079-2095, 2023, DOI:10.32604/csse.2023.038322 - 28 July 2023

Abstract Skin cancer is one of the most dangerous cancer. Because of the high melanoma death rate, skin cancer is divided into non-melanoma and melanoma. The dermatologist finds it difficult to identify skin cancer from dermoscopy images of skin lesions. Sometimes, pathology and biopsy examinations are required for cancer diagnosis. Earlier studies have formulated computer-based systems for detecting skin cancer from skin lesion images. With recent advancements in hardware and software technologies, deep learning (DL) has developed as a potential technique for feature learning. Therefore, this study develops a new sand cat swarm optimization with a… More >