Modified Golomb-Rice Algorithm for Color Image Compression

Sharma, Mr. Rahul

doi:10.17762/ijnpme.v2i01.13

Cited by 7 publications

(5 citation statements)

References 6 publications

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“…The second problem is future link prediction; it says that if you have been given a graph at time t0, your task is to predict the edges which will be added at time t1. Future link prediction can be further classified into two categories: periodic, where the link is expected on time, and the other is non-periodic, where the link is predicted based on a particular snapshot of the network [15]. The link prediction problem is very complex in itself.…”

Section: Gt0(ve): Topology At Time T=t0 Gt1(v'e'): Topology At Time T=t1mentioning

confidence: 99%

Forecasting the Missing Links in Heterogeneous Social Networks

Gupta,

Matta,

Pant

2023

IJRITCC

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Social network analysis has gained attention from several researchers in the past time because of its wide application in capturing social interactions. One of the aims of social network analysis is to recover missing links between the users which may exist in the future but have not yet appeared due to incomplete data. The prediction of hidden or missing links in criminal networks is also a significant problem. The collection of criminal data from these networks appears to be incomplete and inconsistent which is reflected in the structure in the form of missing nodes and links. Many machine learning algorithms are applied for this detection using supervised techniques. But, supervised machine learning algorithms require large datasets for training the link prediction model for achieving optimum results. In this research, we have used a Facebook dataset to solve the problem of link prediction in a network. The two machine learning classifiers applied are LogisticRegression and K-Nearest Neighbour where KNN has higher accuracy than LR. In this article, we have proposed an algorithm Graph Sample Aggregator with Low Reciprocity, (GraphSALR), for the generation of node embeddings in larger graphs which use node feature information.

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Section: Gt0(ve): Topology At Time T=t0 Gt1(v'e'): Topology At Time T=t1mentioning

confidence: 99%

Forecasting the Missing Links in Heterogeneous Social Networks

Gupta,

Matta,

Pant

2023

IJRITCC

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“…Friedman and Schuster [20] enhanced the Data Mining-Decision Tree algorithm for better privacy and data accuracy. Simi, Nayaki, and Elayidom [21] addressed business and research-oriented fields, discussing the use of k-anonymization to protect privacy.…”

Section: Meyerson and Williamsmentioning

confidence: 99%

Advancing Data Privacy: A Novel K-Anonymity Algorithm with Dissimilarity Tree-Based Clustering and Minimal Information Loss

Patil,

Wang

2023

IJRITCC

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Anonymization serves as a crucial privacy protection technique employed across various technology domains, including cloud storage, machine learning, data mining and big data to safeguard sensitive information from unauthorized third-party access. As the significance and volume of data grow exponentially, comprehensive data protection against all threats is of utmost importance. The main objective of this paper is to provide a brief summary of techniques for data anonymization and differential privacy.A new k-anonymity method, which deviates from conventional k-anonymity approaches, is proposed by us to address privacy protection concerns. Our paper presents a new algorithm designed to achieve k-anonymity through more efficient clustering. The processing of data by most clustering algorithms requires substantial computation. However, by identifying initial centers that align with the data structure, a superior cluster arrangement can be obtained.Our study presents a Dissimilarity Tree-based strategy for selecting optimal starting centroids and generating more accurate clusters with reduced computing time and Normalised Certainty Penalty (NCP). This method also has the added benefit of reducing the Normalised Certainty Penalty (NCP). When compared to other methods, the graphical performance analysis shows that this one reduces the amount of overall information lost in the dataset being anonymized by around 20% on average. In addition, the method that we have designed is capable of properly handling both numerical and category characteristics.

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“…A solid-state room temperature terahertz source is developed using standard microelectronics fabrication process steps [ 32 ]. The details of the design and the development of the source are reported elsewhere [ [14] , [15] , [16] , [17] , [18] ]. The position of the source with respect to the detector is varied for getting the optimum penetration depth.…”

Section: Model Descriptionmentioning

confidence: 99%

“…Here they have developed a room temperature terahertz source for the study and reported elsewhere [ [14] , [15] , [16] , [17] , [18] ]. The radiation coming out from the waveguide embedded THz-solid-state source is incident on the human respiratory tract and THz thermographs are generated and an image analyzer is placed for analyzing the output.…”

Section: Introductionmentioning

confidence: 99%