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  • Open Access

    ARTICLE

    AI-Driven Prioritization and Filtering of Windows Artifacts for Enhanced Digital Forensics

    Juhwan Kim, Baehoon Son, Jihyeon Yu, Joobeom Yun*

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3371-3393, 2024, DOI:10.32604/cmc.2024.057234 - 18 November 2024

    Abstract Digital forensics aims to uncover evidence of cybercrimes within compromised systems. These cybercrimes are often perpetrated through the deployment of malware, which inevitably leaves discernible traces within the compromised systems. Forensic analysts are tasked with extracting and subsequently analyzing data, termed as artifacts, from these systems to gather evidence. Therefore, forensic analysts must sift through extensive datasets to isolate pertinent evidence. However, manually identifying suspicious traces among numerous artifacts is time-consuming and labor-intensive. Previous studies addressed such inefficiencies by integrating artificial intelligence (AI) technologies into digital forensics. Despite the efforts in previous studies, artifacts were… More >

  • Open Access

    ARTICLE

    Pyramid Separable Channel Attention Network for Single Image Super-Resolution

    Congcong Ma1,3, Jiaqi Mi2, Wanlin Gao1,3, Sha Tao1,3,*

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4687-4701, 2024, DOI:10.32604/cmc.2024.055803 - 12 September 2024

    Abstract Single Image Super-Resolution (SISR) technology aims to reconstruct a clear, high-resolution image with more information from an input low-resolution image that is blurry and contains less information. This technology has significant research value and is widely used in fields such as medical imaging, satellite image processing, and security surveillance. Despite significant progress in existing research, challenges remain in reconstructing clear and complex texture details, with issues such as edge blurring and artifacts still present. The visual perception effect still needs further enhancement. Therefore, this study proposes a Pyramid Separable Channel Attention Network (PSCAN) for the… More >

  • Open Access

    ARTICLE

    Artifacts Reduction Using Multi-Scale Feature Attention Network in Compressed Medical Images

    Seonjae Kim, Dongsan Jun*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3267-3279, 2022, DOI:10.32604/cmc.2022.020651 - 27 September 2021

    Abstract Medical image compression is one of the essential technologies to facilitate real-time medical data transmission in remote healthcare applications. In general, image compression can introduce undesired coding artifacts, such as blocking artifacts and ringing effects. In this paper, we proposed a Multi-Scale Feature Attention Network (MSFAN) with two essential parts, which are multi-scale feature extraction layers and feature attention layers to efficiently remove coding artifacts of compressed medical images. Multi-scale feature extraction layers have four Feature Extraction (FE) blocks. Each FE block consists of five convolution layers and one CA block for weighted skip connection. More >

  • Open Access

    ARTICLE

    Adaptive Signal Enhancement Unit for EEG Analysis in Remote Patient Care Monitoring Systems

    Ch. Srinivas1,*, K. Chandrabhushana Rao2

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1801-1817, 2021, DOI:10.32604/cmc.2021.014981 - 05 February 2021

    Abstract In this paper we propose an efficient process of physiological artifact elimination methodology from brain waves (BW), which are also commonly known as electroencephalogram (EEG) signal. In a clinical environment during the acquisition of BW several artifacts contaminates the actual BW component. This leads to inaccurate and ambiguous diagnosis. As the statistical nature of the EEG signal is more non-stationery, adaptive filtering is the more promising method for the process of artifact elimination. In clinical conditions, the conventional adaptive techniques require many numbers of computational operations and leads to data samples overlapping and instability of… More >

  • Open Access

    ARTICLE

    Fuzzy Based Adaptive Deblocking Filters at Low-Bitrate HEVC Videos for Communication Networks

    Anudeep Gandam1,*, Jagroop Singh Sidhu2

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3045-3063, 2021, DOI:10.32604/cmc.2021.013663 - 28 December 2020

    Abstract In-loop filtering significantly helps detect and remove blocking artifacts across block boundaries in low bitrate coded High Efficiency Video Coding (HEVC) frames and improves its subjective visual quality in multimedia services over communication networks. However, on faster processing of the complex videos at a low bitrate, some visible artifacts considerably degrade the picture quality. In this paper, we proposed a four-step fuzzy based adaptive deblocking filter selection technique. The proposed method removes the quantization noise, blocking artifacts and corner outliers efficiently for HEVC coded videos even at low bit-rate. We have considered Y (luma), U… More >

  • Open Access

    ARTICLE

    Ring Artifacts Reduction in CBCT: Pixels Detection and Patch Based Correction

    Haitong Zhao1, Yi Li1, Shouhua Luo1,*

    Molecular & Cellular Biomechanics, Vol.16, No.4, pp. 265-273, 2019, DOI:10.32604/mcb.2019.07381

    Abstract The ring artifacts introduced by the defective pixels with non-linear responses in the high-resolution detector, have a great impact on subsequent processing and quantitative analysis of the reconstructed images. In this paper, a multistep method is proposed to suppress the ring artifacts of micro CT images, which firstly locates the positions of the defective pixels in the sinogram, and then corrects the corresponding value in the projections. Since the defective pixels always appear as vertical stripes in the sinogram, a horizontal curve is derived by summing the pixel values along vertical direction, thus the abrupt… More >

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