Mudasir Dilawar^*, Muhammad Shahbaz

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5091-5114, 2025, DOI:10.32604/cmc.2025.064090 - 19 May 2025

Abstract In the era of Industry 4.0, condition monitoring has emerged as an effective solution for process industries to optimize their operational efficiency. Condition monitoring helps minimize unplanned downtime, extending equipment lifespan, reducing maintenance costs, and improving production quality and safety. This research focuses on utilizing Bayesian search-based machine learning and deep learning approaches for the condition monitoring of industrial equipment. The study aims to enhance predictive maintenance for industrial equipment by forecasting vibration values based on domain-specific feature engineering. Early prediction of vibration enables proactive interventions to minimize downtime and extend the lifespan of critical… More >

Trong Hieu Luu¹, Phan Nguyen Ky Phuc², Quang Hieu Ngo^1,*, Thanh Tam Nguyen³, Huu Cuong Nguyen¹

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5643-5656, 2025, DOI:10.32604/cmc.2025.064007 - 19 May 2025

Abstract This study presents a drone-based aerial imaging method for automated rice seedling detection and counting in paddy fields. Utilizing a drone equipped with a high-resolution camera, images are captured 14 days post-sowing at a consistent altitude of six meters, employing autonomous flight for uniform data acquisition. The approach effectively addresses the distinct growth patterns of both single and clustered rice seedlings at this early stage. The methodology follows a two-step process: first, the GoogleNet deep learning network identifies the location and center points of rice plants. Then, the U-Net deep learning network performs classification and… More >

Veena Dillshad¹, Muhammad Attique Khan^2,*, Muhammad Nazir¹, Jawad Ahmad², Dina Abdulaziz AlHammadi³, Taha Houda², Hee-Chan Cho⁴, Byoungchol Chang^5,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5299-5321, 2025, DOI:10.32604/cmc.2025.063851 - 19 May 2025

Abstract Globally, skin cancer is a prevalent form of malignancy, and its early and accurate diagnosis is critical for patient survival. Clinical evaluation of skin lesions is essential, but several challenges, such as long waiting times and subjective interpretations, make this task difficult. The recent advancement of deep learning in healthcare has shown much success in diagnosing and classifying skin cancer and has assisted dermatologists in clinics. Deep learning improves the speed and precision of skin cancer diagnosis, leading to earlier prediction and treatment. In this work, we proposed a novel deep architecture for skin cancer… More >

Ahmet Emre Ergün, Aytuğ Onan^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4855-4877, 2025, DOI:10.32604/cmc.2025.063826 - 19 May 2025

Abstract Large Language Models (LLMs) have significantly advanced human-computer interaction by improving natural language understanding and generation. However, their vulnerability to adversarial prompts–carefully designed inputs that manipulate model outputs–presents substantial challenges. This paper introduces a classification-based approach to detect adversarial prompts by utilizing both prompt features and prompt response features. Eleven machine learning models were evaluated based on key metrics such as accuracy, precision, recall, and F1-score. The results show that the Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) cascade model delivers the best performance, especially when using prompt features, achieving an accuracy of over 97% in… More >

Ali Mohammad Alqudah¹, Zahra Moussavi^1,2,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3753-3841, 2025, DOI:10.32604/cmc.2025.063643 - 19 May 2025

Abstract This review presents a comprehensive technical analysis of deep learning (DL) methodologies in biomedical signal processing, focusing on architectural innovations, experimental validation, and evaluation frameworks. We systematically evaluate key deep learning architectures including convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformer-based models, and hybrid systems across critical tasks such as arrhythmia classification, seizure detection, and anomaly segmentation. The study dissects preprocessing techniques (e.g., wavelet denoising, spectral normalization) and feature extraction strategies (time-frequency analysis, attention mechanisms), demonstrating their impact on model accuracy, noise robustness, and computational efficiency. Experimental results underscore the superiority of deep learning… More >

Van-Ha Hoang¹, Jong Weon Lee¹, Chun-Su Park^2,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4097-4116, 2025, DOI:10.32604/cmc.2025.063468 - 19 May 2025

Abstract Fire can cause significant damage to the environment, economy, and human lives. If fire can be detected early, the damage can be minimized. Advances in technology, particularly in computer vision powered by deep learning, have enabled automated fire detection in images and videos. Several deep learning models have been developed for object detection, including applications in fire and smoke detection. This study focuses on optimizing the training hyperparameters of YOLOv8 and YOLOv10 models using Bayesian Tuning (BT). Experimental results on the large-scale D-Fire dataset demonstrate that this approach enhances detection performance. Specifically, the proposed approach… More >

Taher Alzahrani^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4575-4606, 2025, DOI:10.32604/cmc.2025.063448 - 19 May 2025

Abstract The rapid evolution of malware presents a critical cybersecurity challenge, rendering traditional signature-based detection methods ineffective against novel variants. This growing threat affects individuals, organizations, and governments, highlighting the urgent need for robust malware detection mechanisms. Conventional machine learning-based approaches rely on static and dynamic malware analysis and often struggle to detect previously unseen threats due to their dependency on predefined signatures. Although machine learning algorithms (MLAs) offer promising detection capabilities, their reliance on extensive feature engineering limits real-time applicability. Deep learning techniques mitigate this issue by automating feature extraction but may introduce computational overhead,… More >

He Su, Jianwei Gao, Kang Kong^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4193-4213, 2025, DOI:10.32604/cmc.2025.063355 - 19 May 2025

Abstract Accurate and robust navigation in complex surgical environments is crucial for bronchoscopic surgeries. This study purposes a bronchoscopic lumen feature matching network (BLFM-Net) based on deep learning to address the challenges of image noise, anatomical complexity, and the stringent real-time requirements. The BLFM-Net enhances bronchoscopic image processing by integrating several functional modules. The FFA-Net preprocessing module mitigates image fogging and improves visual clarity for subsequent processing. The feature extraction module derives multi-dimensional features, such as centroids, area, and shape descriptors, from dehazed images. The Faster R-CNN Object detection module detects bronchial regions of interest and… More >

Dang Hung Tran, Van Tinh Nguyen^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4679-4698, 2025, DOI:10.32604/cmc.2025.063228 - 19 May 2025

Abstract It is known that long non-coding RNAs (lncRNAs) play vital roles in biological processes and contribute to the progression, development, and treatment of various diseases. Obviously, understanding associations between diseases and lncRNAs significantly enhances our ability to interpret disease mechanisms. Nevertheless, the process of determining lncRNA-disease associations is costly, labor-intensive, and time-consuming. Hence, it is expected to foster computational strategies to uncover lncRNA-disease relationships for further verification to save time and resources. In this study, a collaborative filtering and graph attention network-based LncRNA-Disease Association (CFGANLDA) method was nominated to expose potential lncRNA-disease associations. First, it… More >

Peicheng Shi^1,*, Li Yang¹, Xinlong Dong¹, Heng Qi², Aixi Yang³

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3877-3917, 2025, DOI:10.32604/cmc.2025.063205 - 19 May 2025

Abstract As the number and complexity of sensors in autonomous vehicles continue to rise, multimodal fusion-based object detection algorithms are increasingly being used to detect 3D environmental information, significantly advancing the development of perception technology in autonomous driving. To further promote the development of fusion algorithms and improve detection performance, this paper discusses the advantages and recent advancements of multimodal fusion-based object detection algorithms. Starting from single-modal sensor detection, the paper provides a detailed overview of typical sensors used in autonomous driving and introduces object detection methods based on images and point clouds. For image-based detection… More >