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  • Open Access

    ARTICLE

    A Novel Approach to Enhanced Cancelable Multi-Biometrics Personal Identification Based on Incremental Deep Learning

    Ali Batouche1, Souham Meshoul2,*, Hadil Shaiba3, Mohamed Batouche2,*

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1727-1752, 2025, DOI:10.32604/cmc.2025.063227 - 16 April 2025

    Abstract The field of biometric identification has seen significant advancements over the years, with research focusing on enhancing the accuracy and security of these systems. One of the key developments is the integration of deep learning techniques in biometric systems. However, despite these advancements, certain challenges persist. One of the most significant challenges is scalability over growing complexity. Traditional methods either require maintaining and securing a growing database, introducing serious security challenges, or relying on retraining the entire model when new data is introduced—a process that can be computationally expensive and complex. This challenge underscores the… More >

  • Open Access

    ARTICLE

    End-to-End Audio Pattern Recognition Network for Overcoming Feature Limitations in Human-Machine Interaction

    Zijian Sun1,2, Yaqian Li3,4,*, Haoran Liu1,2, Haibin Li3,4, Wenming Zhang3,4

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3187-3210, 2025, DOI:10.32604/cmc.2025.061920 - 16 April 2025

    Abstract In recent years, audio pattern recognition has emerged as a key area of research, driven by its applications in human-computer interaction, robotics, and healthcare. Traditional methods, which rely heavily on handcrafted features such as Mel filters, often suffer from information loss and limited feature representation capabilities. To address these limitations, this study proposes an innovative end-to-end audio pattern recognition framework that directly processes raw audio signals, preserving original information and extracting effective classification features. The proposed framework utilizes a dual-branch architecture: a global refinement module that retains channel and temporal details and a multi-scale embedding… More >

  • Open Access

    ARTICLE

    Token Masked Pose Transformers Are Efficient Learners

    Xinyi Song1, Haixiang Zhang1,*, Shaohua Li2

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2735-2750, 2025, DOI:10.32604/cmc.2025.059006 - 16 April 2025

    Abstract In recent years, Transformer has achieved remarkable results in the field of computer vision, with its built-in attention layers effectively modeling global dependencies in images by transforming image features into token forms. However, Transformers often face high computational costs when processing large-scale image data, which limits their feasibility in real-time applications. To address this issue, we propose Token Masked Pose Transformers (TMPose), constructing an efficient Transformer network for pose estimation. This network applies semantic-level masking to tokens and employs three different masking strategies to optimize model performance, aiming to reduce computational complexity. Experimental results show More >

  • Open Access

    ARTICLE

    Drone-Based Public Surveillance Using 3D Point Clouds and Neuro-Fuzzy Classifier

    Yawar Abbas1, Aisha Ahmed Alarfaj2, Ebtisam Abdullah Alabdulqader3, Asaad Algarni4, Ahmad Jalal1,5, Hui Liu6,*

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4759-4776, 2025, DOI:10.32604/cmc.2025.059224 - 06 March 2025

    Abstract Human Activity Recognition (HAR) in drone-captured videos has become popular because of the interest in various fields such as video surveillance, sports analysis, and human-robot interaction. However, recognizing actions from such videos poses the following challenges: variations of human motion, the complexity of backdrops, motion blurs, occlusions, and restricted camera angles. This research presents a human activity recognition system to address these challenges by working with drones’ red-green-blue (RGB) videos. The first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while… More >

  • Open Access

    ARTICLE

    Security Strategy of Digital Medical Contents Based on Blockchain in Generative AI Model

    Hoon Ko1, Marek R. Ogiela2,*

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 259-278, 2025, DOI:10.32604/cmc.2024.057257 - 03 January 2025

    Abstract This study presents an innovative approach to enhancing the security of visual medical data in the generative AI environment through the integration of blockchain technology. By combining the strengths of blockchain and generative AI, the research team aimed to address the timely challenge of safeguarding visual medical content. The participating researchers conducted a comprehensive analysis, examining the vulnerabilities of medical AI services, personal information protection issues, and overall security weaknesses. This multifaceted exploration led to an in-depth evaluation of the model’s performance and security. Notably, the correlation between accuracy, detection rate, and error rate was… More >

  • Open Access

    REVIEW

    AI-Driven Pattern Recognition in Medicinal Plants: A Comprehensive Review and Comparative Analysis

    Mohd Asif Hajam1, Tasleem Arif1, Akib Mohi Ud Din Khanday2, Mudasir Ahmad Wani3,*, Muhammad Asim3,*

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2077-2131, 2024, DOI:10.32604/cmc.2024.057136 - 18 November 2024

    Abstract The pharmaceutical industry increasingly values medicinal plants due to their perceived safety and cost-effectiveness compared to modern drugs. Throughout the extensive history of medicinal plant usage, various plant parts, including flowers, leaves, and roots, have been acknowledged for their healing properties and employed in plant identification. Leaf images, however, stand out as the preferred and easily accessible source of information. Manual plant identification by plant taxonomists is intricate, time-consuming, and prone to errors, relying heavily on human perception. Artificial intelligence (AI) techniques offer a solution by automating plant recognition processes. This study thoroughly examines cutting-edge… More >

  • Open Access

    ARTICLE

    Robust Human Interaction Recognition Using Extended Kalman Filter

    Tanvir Fatima Naik Bukht1, Abdulwahab Alazeb2, Naif Al Mudawi2, Bayan Alabdullah3, Khaled Alnowaiser4, Ahmad Jalal1, Hui Liu5,*

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2987-3002, 2024, DOI:10.32604/cmc.2024.053547 - 18 November 2024

    Abstract In the field of computer vision and pattern recognition, knowledge based on images of human activity has gained popularity as a research topic. Activity recognition is the process of determining human behavior based on an image. We implemented an Extended Kalman filter to create an activity recognition system here. The proposed method applies an HSI color transformation in its initial stages to improve the clarity of the frame of the image. To minimize noise, we use Gaussian filters. Extraction of silhouette using the statistical method. We use Binary Robust Invariant Scalable Keypoints (BRISK) and SIFT More >

  • Open Access

    ARTICLE

    Context Awareness by Noise-Pattern Analysis of a Smart Factory

    So-Yeon Lee1, Jihoon Park1, Dae-Young Kim2,*

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1497-1514, 2023, DOI:10.32604/cmc.2023.034914 - 30 August 2023

    Abstract Recently, to build a smart factory, research has been conducted to perform fault diagnosis and defect detection based on vibration and noise signals generated when a mechanical system is driven using deep-learning technology, a field of artificial intelligence. Most of the related studies apply various audio-feature extraction techniques to one-dimensional raw data to extract sound-specific features and then classify the sound by using the derived spectral image as a training dataset. However, compared to numerical raw data, learning based on image data has the disadvantage that creating a training dataset is very time-consuming. Therefore, we… More >

  • Open Access

    ARTICLE

    Radon CLF: A Novel Approach for Skew Detection Using Radon Transform

    Yuhang Chen1, Mahdi Bahaghighat2,*, Aghil Esmaeili Kelishomi3, Jingyi Du1,*

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 675-697, 2023, DOI:10.32604/csse.2023.038234 - 26 May 2023

    Abstract In the digital world, a wide range of handwritten and printed documents should be converted to digital format using a variety of tools, including mobile phones and scanners. Unfortunately, this is not an optimal procedure, and the entire document image might be degraded. Imperfect conversion effects due to noise, motion blur, and skew distortion can lead to significant impact on the accuracy and effectiveness of document image segmentation and analysis in Optical Character Recognition (OCR) systems. In Document Image Analysis Systems (DIAS), skew estimation of images is a crucial step. In this paper, a novel,… More >

  • Open Access

    ARTICLE

    Modified Wild Horse Optimization with Deep Learning Enabled Symmetric Human Activity Recognition Model

    Bareen Shamsaldeen Tahir1, Zainab Salih Ageed2, Sheren Sadiq Hasan3, Subhi R. M. Zeebaree4,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4009-4024, 2023, DOI:10.32604/cmc.2023.037433 - 31 March 2023

    Abstract Traditional indoor human activity recognition (HAR) is a time-series data classification problem and needs feature extraction. Presently, considerable attention has been given to the domain of HAR due to the enormous amount of its real-time uses in real-time applications, namely surveillance by authorities, biometric user identification, and health monitoring of older people. The extensive usage of the Internet of Things (IoT) and wearable sensor devices has made the topic of HAR a vital subject in ubiquitous and mobile computing. The more commonly utilized inference and problem-solving technique in the HAR system have recently been deep… More >

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