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  • Open Access


    Deep Learning-Based Stacked Auto-Encoder with Dynamic Differential Annealed Optimization for Skin Lesion Diagnosis

    Ahmad Alassaf*

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2773-2789, 2023, DOI:10.32604/csse.2023.035899

    Abstract Intelligent diagnosis approaches with shallow architectural models play an essential role in healthcare. Deep Learning (DL) models with unsupervised learning concepts have been proposed because high-quality feature extraction and adequate labelled details significantly influence shallow models. On the other hand, skin lesion-based segregation and disintegration procedures play an essential role in earlier skin cancer detection. However, artefacts, an unclear boundary, poor contrast, and different lesion sizes make detection difficult. To address the issues in skin lesion diagnosis, this study creates the UDLS-DDOA model, an intelligent Unsupervised Deep Learning-based Stacked Auto-encoder (UDLS) optimized by Dynamic Differential… More >

  • Open Access


    High-Efficiency Video Coder in Pruned Environment Using Adaptive Quantization Parameter Selection

    Krishan Kumar1,*, Mohamed Abouhawwash2,3, Amit Kant Pandit1, Shubham Mahajan1, Mofreh A. Hogo4

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1977-1993, 2022, DOI:10.32604/cmc.2022.027850

    Abstract The high-efficiency video coder (HEVC) is one of the most advanced techniques used in growing real-time multimedia applications today. However, they require large bandwidth for transmission through bandwidth, and bandwidth varies with different video sequences/formats. This paper proposes an adaptive information-based variable quantization matrix (AI-VQM) developed for different video formats having variable energy levels. The quantization method is adapted based on video sequence using statistical analysis, improving bit budget, quality and complexity reduction. Further, to have precise control over bit rate and quality, a multi-constraint prune algorithm is proposed in the second stage of the… More >

  • Open Access


    A New Adaptive Regularization Parameter Selection Based on Expected Patch Log Likelihood

    Jianwei Zhang1, Ze Qin1, Shunfeng Wang1, *

    Journal of Cyber Security, Vol.2, No.1, pp. 25-36, 2020, DOI:10.32604/jcs.2020.06429

    Abstract Digital images have been applied to various areas such as evidence in courts. However, it always suffers from noise by criminals. This type of computer network security has become a hot issue that can’t be ignored. In this paper, we focus on noise removal so as to provide guarantees for computer network security. Firstly, we introduce a well-known denoising method called Expected Patch Log Likelihood (EPLL) with Gaussian Mixture Model as its prior. This method achieves exciting results in noise removal. However, there remain problems to be solved such as preserving the edge and meaningful… More >

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