Kasidit Kokkhunthod¹, Khomdet Phapatanaburi², Wongsathon Pathonsuwan¹, Talit Jumphoo¹, Patikorn Anchuen³, Porntip Nimkuntod⁴, Monthippa Uthansakul¹, Peerapong Uthansakul^1,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1775-1794, 2024, DOI:10.32604/cmc.2024.049276

Abstract Monitoring blood pressure is a critical aspect of safeguarding an individual’s health, as early detection of abnormal blood pressure levels facilitates timely medical intervention, ultimately leading to a reduction in mortality rates associated with cardiovascular diseases. Consequently, the development of a robust and continuous blood pressure monitoring system holds paramount significance. In the context of this research paper, we introduce an innovative deep learning regression model that harnesses phonocardiogram (PCG) data to achieve precise blood pressure estimation. Our novel approach incorporates a convolutional neural network (CNN)-based regression model, which not only enhances its adaptability to… More >

Awais Ahmad¹, Sohail Jabbar^1,*, Sheeraz Akram¹, Anand Paul², Umar Raza³, Nuha Mohammed Alshuqayran¹

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3129-3150, 2024, DOI:10.32604/cmc.2024.045385

Abstract This paper presents an innovative approach to enhance the querying capability of ChatGPT, a conversational artificial intelligence model, by incorporating voice-based interaction and a convolutional neural network (CNN)-based impaired vision detection model. The proposed system aims to improve user experience and accessibility by allowing users to interact with ChatGPT using voice commands. Additionally, a CNN-based model is employed to detect impairments in user vision, enabling the system to adapt its responses and provide appropriate assistance. This research tackles head-on the challenges of user experience and inclusivity in artificial intelligence (AI). It underscores our commitment to… More >

Huanhua Liu, Wei Wang^*, Hanyu Liu, Shuheng Yi, Yonghao Yu, Xunwen Yao

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 459-472, 2024, DOI:10.32604/cmes.2023.029084

Abstract Deep Convolutional Neural Networks (CNNs) have achieved high accuracy in image classification tasks, however, most existing models are trained on high-quality images that are not subject to image degradation. In practice, images are often affected by various types of degradation which can significantly impact the performance of CNNs. In this work, we investigate the influence of image degradation on three typical image classification CNNs and propose a Degradation Type Adaptive Image Classification Model (DTA-ICM) to improve the existing CNNs’ classification accuracy on degraded images. The proposed DTA-ICM comprises two key components: a Degradation Type Predictor… More >

Hyeon Park¹, SeoYeon Kim², Seok Min Ko¹, TaeGuen Kim^2,*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1891-1909, 2023, DOI:10.32604/cmc.2023.039464

Abstract The rapid growth of modern vehicles with advanced technologies requires strong security to ensure customer safety. One key system that needs protection is the passive key entry system (PKES). To prevent attacks aimed at defeating the PKES, we propose a novel radio frequency (RF) fingerprinting method. Our method extracts the cepstral coefficient feature as a fingerprint of a radio frequency signal. This feature is then analyzed using a convolutional neural network (CNN) for device identification. In evaluation, we conducted experiments to determine the effectiveness of different cepstral coefficient features and the convolutional neural network-based model. More >

Abdulwahab Ali Almazroi^1,*, Mohammed A. Alqarni², Nida Aslam³, Rizwan Ali Shah⁴

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2153-2174, 2023, DOI:10.32604/iasc.2023.039732

Abstract Nowadays days, the chief grounds of automobile accidents are driver fatigue and distractions. With the development of computer vision technology, a cutting-edge system has the potential to spot driver distractions or sleepiness and alert them, reducing accidents. This paper presents a novel approach to detecting driver tiredness based on eye and mouth movements and object identification that causes a distraction while operating a motor vehicle. Employing the facial landmarks that the camera picks up and sends to classify using a Convolutional Neural Network (CNN) any changes by focusing on the eyes and mouth zone, precision… More >

Alaknanda S. Patil^1,*, G. Sundari¹, Arun Kumar Sivaraman²

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1707-1721, 2023, DOI:10.32604/csse.2023.034270

Abstract The intact data transmission to the authentic user is becoming crucial at every moment in the current era. Steganography; is a technique for concealing the hidden message in any cover media such as image, video; and audio to increase the protection of data. The resilience and imperceptibility are improved by choosing an appropriate embedding position. This paper gives a novel system to immerse the secret information in different videos with different methods. An audio and video steganography with novel amalgamations are implemented to immerse the confidential auditory information and the authentic user’s face image. A… More >

Sang-Hyun Lee^*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4245-4260, 2022, DOI:10.32604/cmc.2022.026012

Abstract X-ray inspection equipment is divided into small baggage inspection equipment and large cargo inspection equipment. In the case of inspection using X-ray scanning equipment, it is possible to identify the contents of goods, unauthorized transport, or hidden goods in real-time by-passing cargo through X-rays without opening it. In this paper, we propose a system for detecting dangerous objects in X-ray images using the Cascade Region-based Convolutional Neural Network (Cascade R-CNN) model, and the data used for learning consists of dangerous goods, storage media, firearms, and knives. In addition, to minimize the overfitting problem caused by… More >

Hyun Yoo¹, Kyungyong Chung^2,*

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 1-13, 2022, DOI:10.32604/iasc.2022.024890

Abstract Human pose estimation has been a major concern in the field of computer vision. The existing method for recognizing human motion based on two-dimensional (2D) images showed a low recognition rate owing to motion depth, interference between objects, and overlapping problems. A convolutional neural network (CNN) based algorithm recently showed improved results in the field of human skeleton detection. In this study, we have combined human skeleton detection and deep neural network (DNN) to classify the motion of the human body. We used the visual geometry group network (VGGNet) CNN for human skeleton detection, and More >

Jin Su Kim¹, Min-Gu Kim¹, Jae Myung Kim^1,2, Sung Bum Pan^1,2,*

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2997-3007, 2022, DOI:10.32604/cmc.2022.026572

Abstract As fifth generation technology standard (5G) technology develops, the possibility of being exposed to the risk of cyber-attacks that exploits vulnerabilities in the 5G environment is increasing. The existing personal recognition method used for granting permission is a password-based method, which causes security problems. Therefore, personal recognition studies using bio-signals are being conducted as a method to access control to devices. Among bio-signal, surface electromyogram (sEMG) can solve the existing personal recognition problem that was unable to the modification of registered information owing to the characteristic changes in its signal according to the performed operation.… More >

Mohd Anul Haq, Mohd Abdul Rahim Khan^*, Talal AL-Harbi

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1769-1788, 2022, DOI:10.32604/cmc.2022.018708

Abstract Intrusion Detection System (IDS) plays a crucial role in detecting and identifying the DoS and DDoS type of attacks on IoT devices. However, anomaly-based techniques do not provide acceptable accuracy for efficacious intrusion detection. Also, we found many difficulty levels when applying IDS to IoT devices for identifying attempted attacks. Given this background, we designed a solution to detect intrusions using the Convolutional Neural Network (CNN) for Enhanced Data rates for GSM Evolution (EDGE) Computing. We created two separate categories to handle the attack and non-attack events in the system. The findings of this study… More >