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 >

Chaichawin Chavapradit¹, Wonnop Visessanguan², Suwan Panjanapongchai¹, Anil Kumar Anal^1,*

Journal of Renewable Materials, Vol.13, No.3, pp. 553-582, 2025, DOI:10.32604/jrm.2025.058857 - 20 March 2025

Abstract The growing population and industrialization have led to significant production in agro-industrial sectors, resulting in large amounts of agro-industrial residues often left untreated, posing potential environmental issues. Therefore, finding effective ways to utilize these bio-based residues is crucial. One promising approach is to use these low- or no-value agro-industrial wastes as raw materials for producing renewable biomaterials, including proteins and peptides. Research has extensively explored peptide extraction using plant and animal-based agro-industrial residue. Due to lower processing costs and beneficial bioactive properties, peptides derived from waste could replace synthetic peptides and those extracted from food More >

Hariharasakthisudhan Ponnarengan^1,*, Sivakumar Rajendran², Vikas Khalkar³, Gunapriya Devarajan⁴, Logesh Kamaraj⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 1-48, 2025, DOI:10.32604/cmes.2024.056605 - 17 December 2024

Abstract The purpose of this review is to explore the intersection of computational engineering and biomedical science, highlighting the transformative potential this convergence holds for innovation in healthcare and medical research. The review covers key topics such as computational modelling, bioinformatics, machine learning in medical diagnostics, and the integration of wearable technology for real-time health monitoring. Major findings indicate that computational models have significantly enhanced the understanding of complex biological systems, while machine learning algorithms have improved the accuracy of disease prediction and diagnosis. The synergy between bioinformatics and computational techniques has led to breakthroughs in More >

Zhaojie Sun¹, Linlin Xing^1,*, Longbo Zhang¹, Hongzhen Cai², Maozu Guo³

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4325-4340, 2024, DOI:10.32604/cmc.2024.053588 - 12 September 2024

Abstract Extracting valuable information from biomedical texts is one of the current research hotspots of concern to a wide range of scholars. The biomedical corpus contains numerous complex long sentences and overlapping relational triples, making most generalized domain joint modeling methods difficult to apply effectively in this field. For a complex semantic environment in biomedical texts, in this paper, we propose a novel perspective to perform joint entity and relation extraction; existing studies divide the relation triples into several steps or modules. However, the three elements in the relation triples are interdependent and inseparable, so we… More >

Rauany Cristina Lopes¹, Tamires Nossa², Wilton Rogério Lustri¹, Gabriel Lombardo^3,4,5, Maria Inés Errea^3,4, Eliane Trovatti^1,*

Journal of Renewable Materials, Vol.12, No.4, pp. 643-666, 2024, DOI:10.32604/jrm.2024.048957 - 12 June 2024

Abstract Polymers from renewable resources have been used for a long time in biomedical applications and found an irreplaceable role in some of them. Their uses have been increasing because of their attractive properties, contributing to the improvement of life quality, mainly in drug release systems and in regenerative medicine. Formulations using natural polymer, nano and microscale particles preparation, composites, blends and chemical modification strategies have been used to improve their properties for clinical application. Although many studies have been carried out with these natural polymers, the way to reach the market is long and only More >

Xiaohui Cui^1,2,#, Chao Song^1,2,#, Dongmei Li^1,2,*, Xiaolong Qu^1,2, Jiao Long^1,2, Yu Yang^1,2, Hanchao Zhang³

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3603-3618, 2024, DOI:10.32604/cmc.2024.047321 - 26 March 2024

Abstract Named Entity Recognition (NER) stands as a fundamental task within the field of biomedical text mining, aiming to extract specific types of entities such as genes, proteins, and diseases from complex biomedical texts and categorize them into predefined entity types. This process can provide basic support for the automatic construction of knowledge bases. In contrast to general texts, biomedical texts frequently contain numerous nested entities and local dependencies among these entities, presenting significant challenges to prevailing NER models. To address these issues, we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer… More >

Bhawna Goyal¹, Ayush Dogra², Dawa Chyophel Lepcha¹, Rajesh Singh³, Hemant Sharma⁴, Ahmed Alkhayyat⁵, Manob Jyoti Saikia^6,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4317-4342, 2024, DOI:10.32604/cmc.2024.047256 - 26 March 2024

Abstract Multimodal medical image fusion has attained immense popularity in recent years due to its robust technology for clinical diagnosis. It fuses multiple images into a single image to improve the quality of images by retaining significant information and aiding diagnostic practitioners in diagnosing and treating many diseases. However, recent image fusion techniques have encountered several challenges, including fusion artifacts, algorithm complexity, and high computing costs. To solve these problems, this study presents a novel medical image fusion strategy by combining the benefits of pixel significance with edge-preserving processing to achieve the best fusion performance. First,… More >

AFAF ALHARTHI^*

BIOCELL, Vol.47, No.8, pp. 1743-1755, 2023, DOI:10.32604/biocell.2023.027154 - 28 August 2023

Abstract Natural biomaterials are now frequently used to build biocarrier systems, which can carry medications and biomolecules to a target region and achieve a desired therapeutic effect. Biomaterials and polymers are of great importance in the synthesis of nanomaterials. The recent studies have tended to use these materials because they are easily obtained from natural sources such as fungi, algae, bacteria, and medicinal plants. They are also biodegradable, compatible with neighborhoods, and non-toxic. Natural biomaterials and polymers are chemically changed when they are linked by cross linking agents with other polymers to create scaffolds, matrices, composites, More >

Bhawna Goyal^1,*, Ayush Dogra², Rahul Khoond¹, Dawa Chyophel Lepcha¹, Vishal Goyal³, Steven L. Fernandes⁴

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 311-327, 2023, DOI:10.32604/cmc.2023.038398 - 08 June 2023

Abstract The synthesis of visual information from multiple medical imaging inputs to a single fused image without any loss of detail and distortion is known as multimodal medical image fusion. It improves the quality of biomedical images by preserving detailed features to advance the clinical utility of medical imaging meant for the analysis and treatment of medical disorders. This study develops a novel approach to fuse multimodal medical images utilizing anisotropic diffusion (AD) and non-subsampled contourlet transform (NSCT). First, the method employs anisotropic diffusion for decomposing input images to their base and detail layers to coarsely… More >

Amer Alsaraira^1,*, Samer Alabed¹, Eyad Hamad¹, Omar Saraereh²

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 165-177, 2023, DOI:10.32604/iasc.2023.036950 - 29 April 2023

Abstract Alzheimer’s disease (AD) is a kind of progressive dementia that is frequently accompanied by brain shrinkage. With the use of the morphological characteristics of MRI brain scans, this paper proposed a method for diagnosing moderate cognitive impairment (MCI) and AD. The anatomical features of 818 subjects were calculated using the FreeSurfer software, and the data were taken from the ADNI dataset. These features were first removed from the dataset after being preprocessed with an age correction algorithm using linear regression to estimate the effects of normal aging. With these preprocessed characteristics, the extreme learning machine More >