Deterministic multikernel extreme learning machine with fuzzy feature extraction for pattern classification

Ahuja, Bhawna; Vishwakarma, Virendra P.

doi:10.1007/s11042-021-11097-3

Cited by 10 publications

(3 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among the patterns of the ceramic body, some of the patterns are line drafts, and the background is similar to a solid color; this part of the pattern is cut out, as shown in Figure 4 . It is conducive to the reuse of ceramic patterns, and it is also convenient for copying [ 19 , 20 ]. To extract texture patterns from images, it is necessary to use image matting technology; that is, in a relatively clean background, extract the required foreground texture images, and remove the useless background image; that is, put the required part of the image, an image processing algorithm separated from other parts [ 21 ].…”

Section: Image Registrationmentioning

confidence: 99%

A Systematic Study on the Extraction and Image Reproduction of Ceramic Sculpture Artworks

Wang

2022

International Journal of Analytical Chemistry

View full text Add to dashboard Cite

In order to solve the research on the extraction of ceramic sculpture artwork patterns, and, in the process of image reproduction, the problem of too few feature points in the object image, the author proposes an image stitching algorithm that combines SIFT and MSER algorithms. After comprehensively analyzing the principles, advantages, and disadvantages of the current main image stitching methods, in terms of feature matching, based on the K-D tree search algorithm, the improved BBF algorithm is used to improve the search efficiency of feature points. In order to remove the possible cracks in the stitching process, an improved multiband fusion algorithm is used to seamlessly stitch the registered images. The results show that the feature points detected by the one-dimensional normal distribution algorithm are on average 0.1%, 0.5%, 1.7%, 4.4%, and 9.2%. The algorithm combining SIFT and MSER to extract feature points can reach 3.6%, 4.6%, 8.4%, 15%, and 19.1%. The experimental results show that the algorithm proposed by the author can extract more image feature points to facilitate later image registration. The image blur phenomenon in the original image fusion algorithm is solved, and a complete and clear two-dimensional plane pattern is finally obtained.

show abstract

Section: Image Registrationmentioning

confidence: 99%

A Systematic Study on the Extraction and Image Reproduction of Ceramic Sculpture Artworks

Wang

2022

International Journal of Analytical Chemistry

View full text Add to dashboard Cite

show abstract

“…Given a training set {(x i , t i )}N i = 1, assuming MKELM uses K different kernel functions {g 1 (·,·), g 2 (·,·), …, g K (·,·)}, then MKELM can be expressed as: subject to constraints: where d i ∈ [0, 1] represents the weight coefficient of the ith kernel function; β ij ∈ R m is the weight vector from the jth sample to the output layer under the kth kernel function; g i (·,·) is the ith kernel function. The MKELM leverages a variety of kernel functions, including Gaussian, linear, polynomial, and Sigmoid, to enhance the model's capability [18,20]. These kernel functions are also utilized in this study to construct the MKELM model.…”

Section: The Principle Of Mkelmmentioning

confidence: 99%

“…This innovation in MKELM has offered new possibilities for handling complex data categorization tasks. Furthermore, Ahuja and Vishwakarma adopted a deterministic approach to MKELM, incorporating fuzzy feature extraction for pattern classifications [20]. Notably, their efforts aimed at resolving face recognition problems, demonstrating the advanced applications of MKELM methods.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Electrical Load Forecasting Based on Fuzzy Rough Set Feature Selection and Multi-kernel Extreme Learning Machine

Liu,

Jin,

2024

Int J Comput Intell Syst

View full text Add to dashboard Cite

As the complexity of power systems increases, accurate load forecasting becomes crucial. This paper proposes a method for short-term electrical load forecasting that integrates fuzzy rough set (FRS) theory and multi-kernel extreme learning machine (MKELM) to improve both the accuracy and reliability of load predictions. First, we introduce the FRS theory for pre-selecting features. Next, we use correlation analysis (CA) to get rid of redundant features and choose the most important ones as prediction targets. Second, we introduce a novel prediction model based on the multi-kernel extreme learning machine (MKELM), utilizing an enhanced differential evolution algorithm (DEA) to optimize the kernel function’s parameters and the model’s weights. This approach allows for effective adaptation to various feature subsets. Experimental results on actual power load data demonstrate that our approach achieves high accuracy and reliability in short-term load forecasting. Moreover, comparative evaluations reveal that the proposed method outperforms alternative prediction models on key metrics. ANOVA and multiple comparisons further validate the statistical significance and superiority of the proposed method.

show abstract

Enhanced LPQ Based Two Novel Blur Invariant Face Descriptors in Light Variations

Karanwal

Diwakar

2022

Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021)

View full text Add to dashboard Cite

Deterministic multikernel extreme learning machine with fuzzy feature extraction for pattern classification

Cited by 10 publications

References 56 publications

A Systematic Study on the Extraction and Image Reproduction of Ceramic Sculpture Artworks

A Systematic Study on the Extraction and Image Reproduction of Ceramic Sculpture Artworks

Short-Term Electrical Load Forecasting Based on Fuzzy Rough Set Feature Selection and Multi-kernel Extreme Learning Machine

Enhanced LPQ Based Two Novel Blur Invariant Face Descriptors in Light Variations

Contact Info

Product

Resources

About