Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (159)
  • Open Access

    ARTICLE

    A Privacy-Preserving System Design for Digital Presence Protection

    Eric Yocam1, Ahmad Alomari2, Amjad Gawanmeh3,*, Wathiq Mansoor3

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3091-3110, 2023, DOI:10.32604/cmc.2023.032826

    Abstract A person’s privacy has become a growing concern, given the nature of an expansive reliance on real-time video activities with video capture, stream, and storage. This paper presents an innovative system design based on a privacy-preserving model. The proposed system design is implemented by employing an enhanced capability that overcomes today’s single parameter-based access control protection mechanism for digital privacy preservation. The enhanced capability combines multiple access control parameters: facial expression, resource, environment, location, and time. The proposed system design demonstrated that a person’s facial expressions combined with a set of access control rules can achieve a person’s privacy-preserving preferences.… More >

  • Open Access

    ARTICLE

    A Low-Power 12-Bit SAR ADC for Analog Convolutional Kernel of Mixed-Signal CNN Accelerator

    Jungyeon Lee1, Malik Summair Asghar1,2, HyungWon Kim1,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4357-4375, 2023, DOI:10.32604/cmc.2023.031372

    Abstract As deep learning techniques such as Convolutional Neural Networks (CNNs) are widely adopted, the complexity of CNNs is rapidly increasing due to the growing demand for CNN accelerator system-on-chip (SoC). Although conventional CNN accelerators can reduce the computational time of learning and inference tasks, they tend to occupy large chip areas due to many multiply-and-accumulate (MAC) operators when implemented in complex digital circuits, incurring excessive power consumption. To overcome these drawbacks, this work implements an analog convolutional filter consisting of an analog multiply-and-accumulate arithmetic circuit along with an analog-to-digital converter (ADC). This paper introduces the architecture of an analog convolutional… More >

  • Open Access

    ARTICLE

    Recognition of Handwritten Words from Digital Writing Pad Using MMU-SNet

    V. Jayanthi*, S. Thenmalar

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3551-3564, 2023, DOI:10.32604/iasc.2023.036599

    Abstract In this paper, Modified Multi-scale Segmentation Network (MMU-SNet) method is proposed for Tamil text recognition. Handwritten texts from digital writing pad notes are used for text recognition. Handwritten words recognition for texts written from digital writing pad through text file conversion are challenging due to stylus pressure, writing on glass frictionless surfaces, and being less skilled in short writing, alphabet size, style, carved symbols, and orientation angle variations. Stylus pressure on the pad changes the words in the Tamil language alphabet because the Tamil alphabets have a smaller number of lines, angles, curves, and bends. The small change in dots,… More >

  • Open Access

    ARTICLE

    Hyperparameter Tuned Deep Hybrid Denoising Autoencoder Breast Cancer Classification on Digital Mammograms

    Manar Ahmed Hamza*

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2879-2895, 2023, DOI:10.32604/iasc.2023.034719

    Abstract Breast Cancer (BC) is considered the most commonly scrutinized cancer in women worldwide, affecting one in eight women in a lifetime. Mammography screening becomes one such standard method that is helpful in identifying suspicious masses’ malignancy of BC at an initial level. However, the prior identification of masses in mammograms was still challenging for extremely dense and dense breast categories and needs an effective and automatic mechanisms for helping radiotherapists in diagnosis. Deep learning (DL) techniques were broadly utilized for medical imaging applications, particularly breast mass classification. The advancements in the DL field paved the way for highly intellectual and… More >

  • Open Access

    ARTICLE

    An Improved Steganographic Scheme Using the Contour Principle to Ensure the Privacy of Medical Data on Digital Images

    R. Bala Krishnan1, D. Yuvaraj2, P. Suthanthira Devi3, Varghese S. Chooralil4, N. Rajesh Kumar1, B. Karthikeyan5, G. Manikandan5,*

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1563-1576, 2023, DOI:10.32604/csse.2023.035307

    Abstract With the improvement of current online communication schemes, it is now possible to successfully distribute and transport secured digital Content via the communication channel at a faster transmission rate. Traditional steganography and cryptography concepts are used to achieve the goal of concealing secret Content on a media and encrypting it before transmission. Both of the techniques mentioned above aid in the confidentiality of feature content. The proposed approach concerns secret content embodiment in selected pixels on digital image layers such as Red, Green, and Blue. The private Content originated from a medical client and was forwarded to a medical practitioner… More >

  • Open Access

    ARTICLE

    Classification of Multi-view Digital Mammogram Images Using SMO-WkNN

    P. Malathi1,*, G. Charlyn Pushpa Latha2

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1741-1758, 2023, DOI:10.32604/csse.2023.035185

    Abstract Breast cancer (BCa) is a leading cause of death in the female population across the globe. Approximately 2.3 million new BCa cases are recorded globally in females, overtaking lung cancer as the most prevalent form of cancer to be diagnosed. However, the mortality rates for cervical and BCa are significantly higher in developing nations than in developed countries. Early diagnosis is the only option to minimize the risks of BCa. Deep learning (DL)-based models have performed well in image processing in recent years, particularly convolutional neural network (CNN). Hence, this research proposes a DL-based CNN model to diagnose BCa from… More >

  • Open Access

    REVIEW

    Modeling Methods of 3D Model in Digital Twins

    Ruijun Liu1, Haisheng Li1,*, Zhihan Lv2

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 985-1022, 2023, DOI:10.32604/cmes.2023.023154

    Abstract To understand the current application and development of 3D modeling in Digital Twins (DTs), abundant literatures on DTs and 3D modeling are investigated by means of literature review. The transition process from 3D modeling to DTs modeling is analyzed, as well as the current application of DTs modeling in various industries. The application of 3D DTs modeling in the fields of smart manufacturing, smart ecology, smart transportation, and smart buildings in smart cities is analyzed in detail, and the current limitations are summarized. It is found that the 3D modeling technology in DTs has broad prospects for development and has… More >

  • Open Access

    ARTICLE

    Digital Twin-Based Automated Fault Diagnosis in Industrial IoT Applications

    Samah Alshathri1, Ezz El-Din Hemdan2, Walid El-Shafai3,4,*, Amged Sayed5,6

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 183-196, 2023, DOI:10.32604/cmc.2023.034048

    Abstract In recent years, Digital Twin (DT) has gained significant interest from academia and industry due to the advanced in information technology, communication systems, Artificial Intelligence (AI), Cloud Computing (CC), and Industrial Internet of Things (IIoT). The main concept of the DT is to provide a comprehensive tangible, and operational explanation of any element, asset, or system. However, it is an extremely dynamic taxonomy developing in complexity during the life cycle that produces a massive amount of engendered data and information. Likewise, with the development of AI, digital twins can be redefined and could be a crucial approach to aid the… More >

  • Open Access

    ARTICLE

    Accelerating Falcon Post-Quantum Digital Signature Algorithm on Graphic Processing Units

    Seog Chung Seo1, Sang Woo An2, Dooho Choi3,*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1963-1980, 2023, DOI:10.32604/cmc.2023.033910

    Abstract Since 2016, the National Institute of Standards and Technology (NIST) has been performing a competition to standardize post-quantum cryptography (PQC). Although Falcon has been selected in the competition as one of the standard PQC algorithms because of its advantages in short key and signature sizes, its performance overhead is larger than that of other lattice-based cryptosystems. This study presents multiple methodologies to accelerate the performance of Falcon using graphics processing units (GPUs) for server-side use. Direct GPU porting significantly degrades performance because the Falcon reference codes require recursive functions in its sampling process. Thus, an iterative sampling approach for efficient… More >

  • Open Access

    ARTICLE

    Intelligent Digital Envelope for Distributed Cloud-Based Big Data Security

    S. Prince Chelladurai1,*, T. Rajagopalan2

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 951-960, 2023, DOI:10.32604/csse.2023.034262

    Abstract Cloud computing offers numerous web-based services. The adoption of many Cloud applications has been hindered by concerns about data security and privacy. Cloud service providers’ access to private information raises more security issues. In addition, Cloud computing is incompatible with several industries, including finance and government. Public-key cryptography is frequently cited as a significant advancement in cryptography. In contrast, the Digital Envelope that will be used combines symmetric and asymmetric methods to secure sensitive data. This study aims to design a Digital Envelope for distributed Cloud-based large data security using public-key cryptography. Through strategic design, the hybrid Envelope model adequately… More >

Displaying 31-40 on page 4 of 159. Per Page