Capacity Controllable Location Map Free Reversible Watermarking

Huang, Yu‐Chang; Kuo, Juiming; Hsieh, Wen-Shyong

doi:10.1049/cje.2015.07.014

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…The clustering algorithm is an unsupervised learning algorithm in machine learning that classifies unlabeled data into different classes based on feature values. There are various bases for the delineation, but the common purpose is to increase the variability of the different classes between the clustering results as much as possible so that the samples within the classes are as similar as possible to each other [22][23]. The data source vector in the space as a collection of phenomena is:…”

Section: Big Data Controllable Clustering Algorithmmentioning

confidence: 99%

“…3) Compactness is a value that measures whether the sample points within a class are sufficiently compact with each other. Assuming that the recommended weights of the predicted subject and object are 𝜔 1 , 𝜔 2 , respectively, the dynamic representation is given by: CVV (𝑆 𝑖 , 𝑆 𝑗 ) = 𝑄𝑥(𝑡𝑗 𝑖 ,𝑡𝑗 𝑗 )⋅∑ 𝑚 𝑖=1 ∑ 𝑛 𝑗=1 (𝑡𝑗 𝑖 ⋅𝑡𝑗 𝑗 ⋅|𝜔 𝑖 ⋅𝑇𝐽|) 𝑄𝑛(𝑡𝑗 𝑖 ,𝑡𝑗 𝑗 )⋅𝑄𝑒(𝑡𝑗 𝑖 ,𝑡𝑗 𝑗 )⋅𝐶𝑉(𝑆 𝑖 ,𝑆 𝑗 ) (22) In Eq. ( 22), 𝑇𝐽 = {𝑡𝑗 1 , 𝑡𝑗 2 , … , 𝑡𝑗 𝑛 }is the set of clustering recommendations, and 𝑄𝑥(𝑡𝑗 𝑖 , 𝑗 𝑗 ) is the description of the correlation association.…”

Section: Portfolio Prediction Mechanismmentioning

confidence: 99%

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A Dynamic Export Product Sales Forecasting Model Based on Controllable Relevance Big Data for Cross-Border E-Commerce

Yang

2023

Applied Mathematics and Nonlinear Sciences

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To improve the prediction accuracy of export product sales, this paper constructs a dynamic export product sales prediction model based on controlled relevance big data for cross-border e-commerce to improve sales prediction’s scalability and dynamic evolution. Based on the traditional prediction model, a big data controllable clustering algorithm is used to divide the data into several macro-clusters by data dimensions to determine the number of class clusters and the location of centroids. The K-mean algorithm is used to estimate and categorize the indicators affecting the prediction online, to dig out the key factors affecting the prediction of export product sales, and to establish a dynamic prediction model. The analysis results show that the plausibility measure of the proposed model is 21.9, and the error coefficient is 5.1, which are the smaller values in the reference interval. The average prediction error ratio is 2.25%, the average confidence level is 93.05%, and the error efficiency between predicted sales and actual sales is only 0.98%. Thus, the prediction model proposed in this paper improves the prediction effect of export product sales and has high practical value.

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Section: Big Data Controllable Clustering Algorithmmentioning

confidence: 99%

Section: Portfolio Prediction Mechanismmentioning

confidence: 99%

A Dynamic Export Product Sales Forecasting Model Based on Controllable Relevance Big Data for Cross-Border E-Commerce

Yang

2023

Applied Mathematics and Nonlinear Sciences

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show abstract

“…Since the invention of the Internet, information hiding plays a very crucial role, which can be classified into steganography [ 1 , 2 ] , watermarking [ 3‐7 ] and reversible data hiding [ 8‐12 ] . As a branch of information hiding, steganography aims at concealing some secret messages into special digital mediums, such as images [ 13‐15 ] , videos [ 16 ] , and audio [ 17 , 18 ] .…”

Section: Introductionmentioning

confidence: 99%