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  • Open Access

    REVIEW

    Understanding cell-extracellular matrix interactions for topology-guided tissue regeneration

    AAYUSHI RANDHAWA1,2, SAYAN DEB DUTTA1, KEYA GANGULY1, TEJAL V. PATIL1,2, RACHMI LUTHFIKASARI1, KI-TAEK LIM1,2,*

    BIOCELL, Vol.47, No.4, pp. 789-808, 2023, DOI:10.32604/biocell.2023.026217

    Abstract Tissues are made up of cells and the extracellular matrix (ECM) which surrounds them. These cells and tissues are actively adaptable to enduring significant stress that occurs in daily life. This astonishing mechanical stress develops due to the interaction between the live cells and the non-living ECM. Cells in the matrix microenvironment can sense the signals and forces produced and initiate a signaling cascade that plays a crucial role in the body’s normal functioning and influences various properties of the native cells, including growth, proliferation, and differentiation. However, the matrix’s characteristic features also impact the repair and regeneration of the… More >

  • Open Access

    ARTICLE

    An Improved Deep Structure for Accurately Brain Tumor Recognition

    Mohamed Maher Ata1, Reem N. Yousef2, Faten Khalid Karim3,*, Doaa Sami Khafaga3

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1597-1616, 2023, DOI:10.32604/csse.2023.034375

    Abstract Brain neoplasms are recognized with a biopsy, which is not commonly done before decisive brain surgery. By using Convolutional Neural Networks (CNNs) and textural features, the process of diagnosing brain tumors by radiologists would be a noninvasive procedure. This paper proposes a features fusion model that can distinguish between no tumor and brain tumor types via a novel deep learning structure. The proposed model extracts Gray Level Co-occurrence Matrix (GLCM) textural features from MRI brain tumor images. Moreover, a deep neural network (DNN) model has been proposed to select the most salient features from the GLCM. Moreover, it manipulates the… More >

  • Open Access

    ARTICLE

    Liver Tumor Decision Support System on Human Magnetic Resonance Images: A Comparative Study

    Hiam Alquran1,2, Yazan Al-Issa3, Mohammed Alslatie4, Isam Abu-Qasmieh1, Amin Alqudah3, Wan Azani Mustafa5,7,*, Yasmin Mohd Yacob6,7

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1653-1671, 2023, DOI:10.32604/csse.2023.033861

    Abstract Liver cancer is the second leading cause of cancer death worldwide. Early tumor detection may help identify suitable treatment and increase the survival rate. Medical imaging is a non-invasive tool that can help uncover abnormalities in human organs. Magnetic Resonance Imaging (MRI), in particular, uses magnetic fields and radio waves to differentiate internal human organs tissue. However, the interpretation of medical images requires the subjective expertise of a radiologist and oncologist. Thus, building an automated diagnosis computer-based system can help specialists reduce incorrect diagnoses. This paper proposes a hybrid automated system to compare the performance of 3D features and 2D… More >

  • Open Access

    ARTICLE

    A Numerical Investigation Based on Exponential Collocation Method for Nonlinear SITR Model of COVID-19

    Mohammad Aslefallah1, Şuayip Yüzbaşi2, Saeid Abbasbandy1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1687-1706, 2023, DOI:10.32604/cmes.2023.025647

    Abstract In this work, the exponential approximation is used for the numerical simulation of a nonlinear SITR model as a system of differential equations that shows the dynamics of the new coronavirus (COVID-19). The SITR mathematical model is divided into four classes using fractal parameters for COVID-19 dynamics, namely, susceptible (S), infected (I), treatment (T), and recovered (R). The main idea of the presented method is based on the matrix representations of the exponential functions and their derivatives using collocation points. To indicate the usefulness of this method, we employ it in some cases. For error analysis of the method, the… More > Graphic Abstract

    A Numerical Investigation Based on Exponential Collocation Method for Nonlinear SITR Model of COVID-19

  • Open Access

    ARTICLE

    Detecting Double JPEG Compressed Color Images via an Improved Approach

    Xiaojie Zhao1, Xiankui Meng1, Ruyong Ren2, Shaozhang Niu2,*, Zhenguang Gao3

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1765-1781, 2023, DOI:10.32604/cmc.2023.029552

    Abstract Detecting double Joint Photographic Experts Group (JPEG) compression for color images is vital in the field of image forensics. In previous researches, there have been various approaches to detecting double JPEG compression with different quantization matrices. However, the detection of double JPEG color images with the same quantization matrix is still a challenging task. An effective detection approach to extract features is proposed in this paper by combining traditional analysis with Convolutional Neural Networks (CNN). On the one hand, the number of nonzero pixels and the sum of pixel values of color space conversion error are provided with 12-dimensional features… More >

  • Open Access

    ARTICLE

    A Derivative Matrix-Based Covert Communication Method in Blockchain

    Xiang Zhang1, Xiaona Zhang2,4,*, Xiaorui Zhang3,5,6, Wei Sun6,7, Ruohan Meng8, Xingming Sun1

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 225-239, 2023, DOI:10.32604/csse.2023.034915

    Abstract The data in the blockchain cannot be tampered with and the users are anonymous, which enables the blockchain to be a natural carrier for covert communication. However, the existing methods of covert communication in blockchain suffer from the predefined channel structure, the capacity of a single transaction is not high, and the fixed transaction behaviors will lower the concealment of the communication channel. Therefore, this paper proposes a derivation matrix-based covert communication method in blockchain. It uses dual-key to derive two types of blockchain addresses and then constructs an address matrix by dividing addresses into multiple layers to make full… More >

  • Open Access

    ARTICLE

    2D Minimum Compliance Topology Optimization Based on a Region Partitioning Strategy

    Chong Wang1, Tongxing Zuo1,2, Haitao Han1,2, Qianglong Wang1,2, Han Zhang1, Zhenyu Liu1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 655-683, 2023, DOI:10.32604/cmes.2023.025153

    Abstract This paper presents an extended sequential element rejection and admission (SERA) topology optimization method with a region partitioning strategy. Based on the partitioning of a design domain into solid regions and weak regions, the proposed optimization method sequentially implements finite element analysis (FEA) in these regions. After standard FEA in the solid regions, the boundary displacement of the weak regions is constrained using the numerical solution of the solid regions as Dirichlet boundary conditions. This treatment can alleviate the negative effect of the material interpolation model of the topology optimization method in the weak regions, such as the condition number… More > Graphic Abstract

    2D Minimum Compliance Topology Optimization Based on a Region Partitioning Strategy

  • Open Access

    ARTICLE

    Vibration-Based Fault Diagnosis Study on a Hydraulic Brake System Using Fuzzy Logic with Histogram Features

    Alamelu Manghai T Marimuthu1, Jegadeeshwaran Rakkiyannan2,*, Lakshmipathi Jakkamputi1, Sugumaran Vaithiyanathan1, Sakthivel Gnanasekaran2

    Structural Durability & Health Monitoring, Vol.16, No.4, pp. 383-396, 2022, DOI:10.32604/sdhm.2022.011396

    Abstract The requirement of fault diagnosis in the field of automobiles is growing higher day by day. The reliability of human resources for the fault diagnosis is uncertain. Brakes are one of the major critical components in automobiles that require closer and active observation. This research work demonstrates a fault diagnosis technique for monitoring the hydraulic brake system using vibration analysis. Vibration signals of a rotating element contain dynamic information about its health condition. Hence, the vibration signals were used for the brake fault diagnosis study. The study was carried out on a brake fault diagnosis experimental setup. The vibration signals… More >

  • Open Access

    ARTICLE

    An Improved Pairing-Free Ciphertext Policy Framework for IoT

    M. Amirthavalli*, S. Chithra, R. Yugha

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 3079-3095, 2023, DOI:10.32604/csse.2023.032486

    Abstract Internet of Things (IoT) enables devices to get connected to the internet. Once they are connected, they behave as smart devices thereby releasing sensitive data periodically. There is a necessity to preserve the confidentiality and integrity of this data during transmission in public communication channels and also permitting only legitimate users to access their data A key challenge of smart networks is to establish a secure end-to-end data communication architecture by addressing the security vulnerabilities of data users and smart devices. The objective of this research work is to create a framework encompassing Ciphertext policy Attribute-based Encryption scheme using block… More >

  • Open Access

    ARTICLE

    Multi-label Emotion Classification of COVID–19 Tweets with Deep Learning and Topic Modelling

    K. Anuratha1,*, M. Parvathy2

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 3005-3021, 2023, DOI:10.32604/csse.2023.031553

    Abstract The COVID-19 pandemic has become one of the severe diseases in recent years. As it majorly affects the common livelihood of people across the universe, it is essential for administrators and healthcare professionals to be aware of the views of the community so as to monitor the severity of the spread of the outbreak. The public opinions are been shared enormously in microblogging media like twitter and is considered as one of the popular sources to collect public opinions in any topic like politics, sports, entertainment etc., This work presents a combination of Intensity Based Emotion Classification Convolution Neural Network… More >

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