Arvind Mukundan^1,2, Riya Karmakar¹, Devansh Gupta³, Hsiang-Chen Wang^1,4,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.069646 - 10 November 2025

Abstract Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0. Manual inspection of products on assembly lines remains inefficient, prone to errors and lacks consistency, emphasizing the need for a reliable and automated inspection system. Leveraging both object detection and image segmentation approaches, this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning (DL) models. Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images… More >

Irfan Abbas, Robertas Damaševičius^*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 483-502, 2025, DOI:10.32604/cmc.2025.066578 - 29 August 2025

Abstract Forests are vital ecosystems that play a crucial role in sustaining life on Earth and supporting human well-being. Traditional forest mapping and monitoring methods are often costly and limited in scope, necessitating the adoption of advanced, automated approaches for improved forest conservation and management. This study explores the application of deep learning-based object detection techniques for individual tree detection in RGB satellite imagery. A dataset of 3157 images was collected and divided into training (2528), validation (495), and testing (134) sets. To enhance model robustness and generalization, data augmentation was applied to the training part… More >

E. M. B. M. Karunathilake^1,#, Thanh Tuan Thai^1,2,3,#, Sheikh Mansoor¹, Anh Tuan Le^3,4, Faheem Shehzad Baloch^1,5, Yong Suk Chung^1,*, Dong-Wook Kim^6,*

Phyton-International Journal of Experimental Botany, Vol.94, No.7, pp. 2215-2228, 2025, DOI:10.32604/phyton.2025.067439 - 31 July 2025

Abstract Accurate and timely estimation of above-ground biomass is crucial for understanding crop growth dynamics, optimizing agricultural input management, and assessing productivity in sustainable farming practices. However, conventional biomass assessments are destructive and resource-intensive. In contrast, remote sensing techniques, particularly those utilizing low-altitude unmanned aerial vehicles, provide a non-destructive approach to collect imagery data on plant canopy features, including spectral reflectance and structural details at any stage of the crop life cycle. This study explores the potential visible-light-derived vegetative indices to improve biomass prediction during the flowering period of buckwheat (Fagopyrum tataricum). Red, green, and blue (RGB)… More >

Ching-Lung Fan^1,*, Yu-Jen Chung², Shan-Min Yen^1,3

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4627-4653, 2025, DOI:10.32604/cmc.2025.066856 - 30 July 2025

Abstract Efficient banana crop detection is crucial for precision agriculture; however, traditional remote sensing methods often lack the spatial resolution required for accurate identification. This study utilizes low-altitude Unmanned Aerial Vehicle (UAV) images and deep learning-based object detection models to enhance banana plant detection. A comparative analysis of Faster Region-Based Convolutional Neural Network (Faster R-CNN), You Only Look Once Version 3 (YOLOv3), Retina Network (RetinaNet), and Single Shot MultiBox Detector (SSD) was conducted to evaluate their effectiveness. Results show that RetinaNet achieved the highest detection accuracy, with a precision of 96.67%, a recall of 71.67%, and… More >

Subrata Kumer Paul^1,2, Abu Saleh Musa Miah^3,4, Rakhi Rani Paul^1,2, Md. Ekramul Hamid², Jungpil Shin^4,*, Md Abdur Rahim⁵

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2513-2530, 2025, DOI:10.32604/cmc.2025.063563 - 03 July 2025

Abstract The Internet of Things (IoT) and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients. Recognizing Medical-Related Human Activities (MRHA) is pivotal for healthcare systems, particularly for identifying actions critical to patient well-being. However, challenges such as high computational demands, low accuracy, and limited adaptability persist in Human Motion Recognition (HMR). While some studies have integrated HMR with IoT for real-time healthcare applications, limited research has focused on recognizing MRHA as essential for effective patient monitoring. This study proposes a novel HMR method tailored for MRHA detection, leveraging multi-stage deep… More >

Ching-Lung Fan^1,*, Yu-Jen Chung²

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1925-1945, 2025, DOI:10.32604/cmc.2025.059245 - 17 February 2025

Abstract This study aims to enhance automated crop detection using high-resolution Unmanned Aerial Vehicle (UAV) imagery by integrating the Visible Atmospherically Resistant Index (VARI) with deep learning models. The primary challenge addressed is the detection of bananas interplanted with betel nuts, a scenario where traditional image processing techniques struggle due to color similarities and canopy overlap. The research explores the effectiveness of three deep learning models—Single Shot MultiBox Detector (SSD), You Only Look Once version 3 (YOLOv3), and Faster Region-Based Convolutional Neural Network (Faster RCNN)—using Red, Green, Blue (RGB) and VARI images for banana detection. Results More >

Fengju Zhang¹, Kai Zhu^2,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2353-2372, 2025, DOI:10.32604/cmc.2024.058944 - 17 February 2025

Abstract The Internet of Vehicles (IoV) has become an important direction in the field of intelligent transportation, in which vehicle positioning is a crucial part. SLAM (Simultaneous Localization and Mapping) technology plays a crucial role in vehicle localization and navigation. Traditional Simultaneous Localization and Mapping (SLAM) systems are designed for use in static environments, and they can result in poor performance in terms of accuracy and robustness when used in dynamic environments where objects are in constant movement. To address this issue, a new real-time visual SLAM system called MG-SLAM has been developed. Based on ORB-SLAM2,… More >

Murad Njoum^1,2,*, Rossilawati Sulaiman¹, Zarina Shukur¹, Faizan Qamar¹

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 183-211, 2024, DOI:10.32604/cmc.2024.050090 - 15 October 2024

Abstract Random pixel selection is one of the image steganography methods that has achieved significant success in enhancing the robustness of hidden data. This property makes it difficult for steganalysts’ powerful data extraction tools to detect the hidden data and ensures high-quality stego image generation. However, using a seed key to generate non-repeated sequential numbers takes a long time because it requires specific mathematical equations. In addition, these numbers may cluster in certain ranges. The hidden data in these clustered pixels will reduce the image quality, which steganalysis tools can detect. Therefore, this paper proposes a… More >

Awais Khan¹, Chomyong Kim², Jung-Yeon Kim², Ahsan Aziz¹, Yunyoung Nam^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1729-1755, 2024, DOI:10.32604/cmes.2024.049618 - 20 May 2024

Abstract Sleep posture surveillance is crucial for patient comfort, yet current systems face difficulties in providing comprehensive studies due to the obstruction caused by blankets. Precise posture assessment remains challenging because of the complex nature of the human body and variations in sleep patterns. Consequently, this study introduces an innovative method utilizing RGB and thermal cameras for comprehensive posture classification, thereby enhancing the analysis of body position and comfort. This method begins by capturing a dataset of sleep postures in the form of videos using RGB and thermal cameras, which depict six commonly adopted postures: supine,… More > Graphic Abstract

Rosepreet Kaur Bhogal^1,*, V. Devendran²

Computer Systems Science and Engineering, Vol.47, No.3, pp. 2759-2772, 2023, DOI:10.32604/csse.2023.034862 - 09 November 2023

Abstract Human activity recognition is a recent area of research for researchers. Activity recognition has many applications in smart homes to observe and track toddlers or oldsters for their safety, monitor indoor and outdoor activities, develop Tele immersion systems, or detect abnormal activity recognition. Three dimensions (3D) skeleton data is robust and somehow view-invariant. Due to this, it is one of the popular choices for human action recognition. This paper proposed using a transversal tree from 3D skeleton data to represent videos in a sequence. Further proposed two neural networks: convolutional neural network recurrent neural network_1… More >