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  • Open Access

    ARTICLE

    A Deep Learning Framework for Mass-Forming Chronic Pancreatitis and Pancreatic Ductal Adenocarcinoma Classification Based on Magnetic Resonance Imaging

    Luda Chen1, Kuangzhu Bao2, Ying Chen2, Jingang Hao2,*, Jianfeng He1,3,*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 409-427, 2024, DOI:10.32604/cmc.2024.048507

    Abstract Pancreatic diseases, including mass-forming chronic pancreatitis (MFCP) and pancreatic ductal adenocarcinoma (PDAC), present with similar imaging features, leading to diagnostic complexities. Deep Learning (DL) methods have been shown to perform well on diagnostic tasks. Existing DL pancreatic lesion diagnosis studies based on Magnetic Resonance Imaging (MRI) utilize the prior information to guide models to focus on the lesion region. However, over-reliance on prior information may ignore the background information that is helpful for diagnosis. This study verifies the diagnostic significance of the background information using a clinical dataset. Consequently, the Prior Difference Guidance Network (PDGNet) is proposed, merging decoupled lesion… More >

  • Open Access

    ARTICLE

    Leveraging User-Generated Comments and Fused BiLSTM Models to Detect and Predict Issues with Mobile Apps

    Wael M. S. Yafooz*, Abdullah Alsaeedi

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 735-759, 2024, DOI:10.32604/cmc.2024.048270

    Abstract In the last decade, technical advancements and faster Internet speeds have also led to an increasing number of mobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobile apps. The use of these apps eases our daily lives, and all customers who need any type of service can access it easily, comfortably, and efficiently through mobile apps. Particularly, Saudi Arabia greatly depends on digital services to assist people and visitors. Such mobile devices are used in organizing daily work schedules and services, particularly during two large occasions, Umrah and Hajj. However, pilgrims… More >

  • Open Access

    ARTICLE

    Predicting Rock Burst in Underground Engineering Leveraging a Novel Metaheuristic-Based LightGBM Model

    Kai Wang1, Biao He2,*, Pijush Samui3, Jian Zhou4

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 229-253, 2024, DOI:10.32604/cmes.2024.047569

    Abstract Rock bursts represent a formidable challenge in underground engineering, posing substantial risks to both infrastructure and human safety. These sudden and violent failures of rock masses are characterized by the rapid release of accumulated stress within the rock, leading to severe seismic events and structural damage. Therefore, the development of reliable prediction models for rock bursts is paramount to mitigating these hazards. This study aims to propose a tree-based model—a Light Gradient Boosting Machine (LightGBM)—to predict the intensity of rock bursts in underground engineering. 322 actual rock burst cases are collected to constitute an exhaustive rock burst dataset, which serves… More >

  • Open Access

    ARTICLE

    Mechanical and Biological Properties of Chitosan Nanocomposite Films: Effects of POSS nanoparticles

    R. VENKATESAN*, S. R. DARSON IMMANUEL JOHN, N. RAJESWARI

    Journal of Polymer Materials, Vol.36, No.3, pp. 261-273, 2019, DOI:10.32381/JPM.2019.36.03.6

    Abstract Nanocomposite films of chitosan (CH) incorporated with different wt. % of the polyoligomericsilsesquioxane (POSS) were prepared by solution casting. The thermal, mechanical, morphological and antimicrobial properties of the nanocomposites were examined. TGA analyses of the nanocomposites indicate that the filler enables the enhancement of thermal stability of chitosan. The tensile strength of the nanocomposite films is enhanced (10.9 MPa for neat chitosan to 24.0MPa for 5wt. % filled chitosan) by the addition of POSS while the elongation at break is reduced. The nanocomposite films exhibited excellent antimicrobial activity against both gram positive and gram negative bacteria. This activity increases with… More >

  • Open Access

    ARTICLE

    Study on the Weathering Performance of Polypropylene by Artificial Accelerated Aging and Natural Aging

    MINGYU HE1, AMATJAN SAWUT1, LI GUAN2, YING LI1, MAMATJAN YIMIT2

    Journal of Polymer Materials, Vol.38, No.3-4, pp. 191-203, 2021, DOI:10.32381/JPM.2021.38.3-4.2

    Abstract The weathering performance of polypropylene (PP) was studied in an artificial accelerated weathering chamber of 340 nm ultraviolet light and natural conditions in Turpan, China. This work aimed to provide a method for calculating the aging failure of PP under natural conditions by the means of artificial accelerated aging. SEM showed that large crack widths of approximately 1.25 µm and 1.63 µm appealed on the PP surface after 15 days artificial accelerated aging and 150 days natural aging, respectively. XPS data revealed that the number of C-O and C=O bonds on the PP surface were generally the same after artificial… More >

  • Open Access

    ARTICLE

    A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural Networks with Leveraging LSTM

    Navaneetha Krishnan Muthunambu1, Senthil Prabakaran2, Balasubramanian Prabhu Kavin3, Kishore Senthil Siruvangur4, Kavitha Chinnadurai1, Jehad Ali5,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3089-3127, 2024, DOI:10.32604/cmc.2023.043172

    Abstract The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the Internet. Regrettably, this development has expanded the potential targets that hackers might exploit. Without adequate safeguards, data transmitted on the internet is significantly more susceptible to unauthorized access, theft, or alteration. The identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious attacks. This research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks (RNN) integrated with… More >

  • Open Access

    CASE REPORT

    A 63-Year-Old Male with D-Transposition of the Great Arteries Who Had an Early Form of the Arterial Switch Operation

    Michael A. Rebolledo1,*, Jane S. Yao2, Jason N. Johnson1, Umar S. Boston3, Benjamin R. Waller III1

    Congenital Heart Disease, Vol.19, No.1, pp. 65-68, 2024, DOI:10.32604/chd.2024.046638

    Abstract We describe a 63-year-old male who appears to have undergone an early form of the arterial switch operation for D-transposition of the great arteries performed in the mid-1960s. We review the clinical and imaging data that support our conclusion. He had a diagnostic cardiac catheterization which demonstrated severe pulmonary hypertension responsive to epoprostenol and oxygen. Our case may represent one example of the experimental surgical work done prior to Dr. Adibe Jatene’s description of the first successful arterial switch performed in 1975. More >

  • Open Access

    REVIEW

    How aging affects bone health via the intestinal micro-environment

    HUAN HU1,2,*, YUE HUANG1, FANGZHOU LIU1, QIAN WANG1,2, YANZI YAO3,*

    BIOCELL, Vol.48, No.3, pp. 353-362, 2024, DOI:10.32604/biocell.2024.048311

    Abstract Increasing life expectancy and an aging population lead to age-related bone diseases like osteoporosis and low bone mass more prevalent. These conditions represent a common, costly and chronic burden, not only for elderly but also to society at large. Consequently, elucidating the pathophysiology and developing effective therapies for these diseases is of paramount importance. Recent advances in research have identified the gut as a novel and promising target for addressing bone disorders, giving rise to the concept of the “gut-bone axis”. An in-depth review of the latest insights into the effects of age-related physiological changes in the gastrointestinal tract on… More >

  • Open Access

    ARTICLE

    MASS TRANSFER MODELING OF HEPATIC DRUG ELIMINATION USING LOCAL VOLUME AVERAGING APPROACH

    Mohammad Izadifara,b,* , Jane Alcornc

    Frontiers in Heat and Mass Transfer, Vol.3, No.3, pp. 1-7, 2012, DOI:10.5098/hmt.v3.3.3005

    Abstract Applying local volume averaging method a mathematical model including liver porosity, tortuosity, permeability, unbound drug fraction, drugplasma diffusivity, axial/radial dispersion and hepatocellular metabolism parameters was developed for hepatic drug elimination. The model was numerically solved using implicit finite difference method to describe drug concentration gradient with time across the liver. Statistically validated by observations and other models, the model suggested axial dispersion as a significant variable in drug distribution across the liver. Sensitivity analyses revealed that lower liver porosity resulted in faster drug distribution across the liver, and bioavailability was sensitive to the interaction between unbound fraction and intrinsic clearance. More >

  • Open Access

    ARTICLE

    Transparent and Accurate COVID-19 Diagnosis: Integrating Explainable AI with Advanced Deep Learning in CT Imaging

    Mohammad Mehedi Hassan1,*, Salman A. AlQahtani2, Mabrook S. AlRakhami1, Ahmed Zohier Elhendi3

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3101-3123, 2024, DOI:10.32604/cmes.2024.047940

    Abstract In the current landscape of the COVID-19 pandemic, the utilization of deep learning in medical imaging, especially in chest computed tomography (CT) scan analysis for virus detection, has become increasingly significant. Despite its potential, deep learning’s “black box” nature has been a major impediment to its broader acceptance in clinical environments, where transparency in decision-making is imperative. To bridge this gap, our research integrates Explainable AI (XAI) techniques, specifically the Local Interpretable Model-Agnostic Explanations (LIME) method, with advanced deep learning models. This integration forms a sophisticated and transparent framework for COVID-19 identification, enhancing the capability of standard Convolutional Neural Network… More >

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