Ali Batouche¹, Souham Meshoul^2,*, Hadil Shaiba³, Mohamed Batouche^2,*

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 >

Zijian Sun^1,2, Yaqian Li^3,4,*, Haoran Liu^1,2, Haibin Li^3,4, Wenming Zhang^3,4

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 >

Xinyi Song¹, Haixiang Zhang^1,*, 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 >

Yawar Abbas¹, Aisha Ahmed Alarfaj², Ebtisam Abdullah Alabdulqader³, Asaad Algarni⁴, Ahmad Jalal^1,5, Hui Liu^6,*

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 >

Hoon Ko¹, Marek R. Ogiela^2,*

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 >

Mohd Asif Hajam¹, Tasleem Arif¹, Akib Mohi Ud Din Khanday², Mudasir Ahmad Wani^3,*, Muhammad Asim^3,*

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 >

Tanvir Fatima Naik Bukht¹, Abdulwahab Alazeb², Naif Al Mudawi², Bayan Alabdullah³, Khaled Alnowaiser⁴, Ahmad Jalal¹, Hui Liu^5,*

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 >

So-Yeon Lee¹, Jihoon Park¹, Dae-Young Kim^2,*

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 >

Yuhang Chen¹, Mahdi Bahaghighat^2,*, Aghil Esmaeili Kelishomi³, Jingyi Du^1,*

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 >

Bareen Shamsaldeen Tahir¹, Zainab Salih Ageed², Sheren Sadiq Hasan³, Subhi R. M. Zeebaree^4,*

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 >