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46 results for “Sparse”
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Research ArticleOpen accessEvery word in the titleFake News Detection Using Weighted Fine-Tuned BERT and Sparse Recurrent Neural NetworkAsha Kathigi, Mukta Pujar, A M S Akshatha et al.Fake news refers to misinformation or false reports shared in the form of images, articles, or videos, disguised as real news to manipulate people’s opinions. Recently, fake news and rumors have spread extensively and rapidly around the world. This has led to the production and propagation of inaccurate news articles. Therefore, it is necessary to restrict the spread of fake … …Journal of Computer Science, Vol. 21, Issue 12 · 23 Jan 2026 · 2,087 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleMultiresolution Laplacian Sparse Coding Technique for Image ClassificationIntidhar Jemel, Ridha Ejbali, Mourad ZaiedSparse coding is a set of techniques used for learning a collection of over-complete bases to represent data efficiently. This technique has been used in different domain such as feature quantization and image classification. Despite its capacity of modeling, it could not represent similarity of the image coding which cause a poor performance in locality. The cause of this limitation … …Journal of Computer Science, Vol. 14, Issue 8 · 23 May 2018 · 7,920 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleSparse Representation Tracking with Auxiliary Adaptive Appearance ModelsJunqiu Wang, Chao Yang, Zhichao Gan, Yasushi YagiWe propose an effective tracking algorithm based on sparse representation and auxiliary adaptive appearance modeling. Based on a sparse representation, l1 minimization can follow targets in challenging situations. Unfortunately, tracking approaches based on l1 minimization are likely to be inefficient because they measure using dense coefficient distributions. The number of target candidates can be very large when the state space … …American Journal of Engineering and Applied Sciences, Vol. 10, Issue 1 · 14 Jan 2017 · 7,676 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleRegularization Method for Solving Denoising and Inpainting Task Using Stacked Sparse Denoising AutoencodersPavel Vyacheslavovich Skribtsov, Sergey Olegovich SurikovThis article offers a regularization method for training stacked sparse denoising autoencoders aimed at designing model description of objects used for image denoising and inpainting. The offered regularization method allows increasing the generalizing ability of model description, which results in greater stability of denoising methods using it with regard to variation of the noise type. This makes the offered method … …American Journal of Applied Sciences, Vol. 13, Issue 1 · 13 Jan 2016 · 7,609 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleSome P-RAM Algorithms for Sparse Linear SystemsRakesh Kumar Katare, N.S. ChaudhariPRAM algorithms for Symmetric Gaussian elimination is presented. We showed actual testing operations that will be performed during Symmetric Gaussian elimination, which caused symbolic factorization to occur for sparse linear systems. The array pattern of processing elements (PE) in row major order for the specialized sparse matrix in formulated. We showed that the access function in2+jn+k contains topological properties. We … …Journal of Computer Science, Vol. 3, Issue 12 · 31 Dec 2007 · 6,971 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleParallel Approaches for Intervals Analysis of Variable Statistics in Large and Sparse Linear Equations with RHS RangesPeerayuth CharnsethikulThis study proposes an algorithm capable of working in parallel for solving large and sparse linear equations under given right hand side (RHS) ranges. A comparative study to the direct linear programming method is reported theoretically, computationally and discussed. Moreover, the approach can be adapted for the system under domain decompositions structure leading to a better efficiency experimentally. …American Journal of Applied Sciences, Vol. 4, Issue 5 · 31 May 2007 · 6,823 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleSparse Sliced Inverse Quantile RegressionAli Alkenani, Tahir R. DikheelThe current paper proposes the sliced inverse quantile regression method (SIQR). In addition to the latter this study proposes both the sparse sliced inverse quantile regression method with Lasso (LSIQR) and Adaptive Lasso (ALSIQR) penalties. This article introduces a comprehensive study of SIQR and sparse SIQR. The simulation and real data analysis have been employed to check the performance of … …Journal of Mathematics and Statistics, Vol. 12, Issue 3 · 10 Sep 2016 · 7,131 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleSparse Partial Optimal Transport via Quadratic RegularizationKhang Tran, Khoa Nguyen, Anh Nguyen et al.Partial Optimal Transport (POT) has recently emerged as a central tool in various Machine Learning (ML) applications. It lifts the stringent assumption of the conventional Optimal Transport (OT) that input measures are of equal masses, which is often not guaranteed in real-world datasets, and thus offers greater flexibility by permitting transport between unbalanced input measures. Nevertheless, existing major solvers for … …Journal of Computer Science, Vol. 21, Issue 7 · 17 Jul 2025 · 3,905 accessesView PDFCite
Review ArticleOpen accessIn the keywordsDenoising of Underwater Images with Regulated AutoencodersShreya Priyadarshini Roy, N Maheswari, M Sivagami, Angelin Beulah SIn the domain of underwater image processing, the images obtained in underwater lighting conditions are of very poor in quality and contain distortions due to the scattering of lights and color absorption of images in water. This makes it difficult to analyze these images for scientific, commercial and military purposes. Therefore, there is a need for efficient techniques that can … …Journal of Computer Science, Vol. 20, Issue 6 · 4 Apr 2024 · 5,571 accessesView PDFCite
Research ArticleOpen accessIn the keywordsEscalating SVM-Efficiency by Sequential QP LP and Slack Variable AnalysisAmit Kumar Kundu, Rezaul Karim, Ali Ahmed AveSupport Vector Machine (SVM) is a highly attractive algorithm among many machine learning models due to its generalization power and classification performance based on sound mathematical formulation being convex that offers global minimum. However, despite being sparse, its high classification cost from kernel execution with Support Vectors (SVs) reduces the user's interest when there are hard computational constraints in the … …Journal of Computer Science, Vol. 19, Issue 10 · 6 Sep 2023 · 5,913 accessesView PDFCite