In this paper, the notion of the interval valued neutrosophic soft sets (ivn soft sets) is defined which is a combination of an interval valued neutrosophic sets [36] and a soft sets [30]. Our ivn soft sets generalizes the concept of the soft set, fuzzy soft set, interval valued fuzzy soft set, intuitionistic fuzzy soft set, interval valued intuitionistic fuzzy soft set and neutrosophic soft set. Then, we introduce some definitions and operations on ivn soft sets sets. Some properties of ivn soft sets which are connected to operations have been established. Also, the aim of this paper is to investigate the decision making based on ivn soft sets by level soft sets. Therefore, we develop a decision making methods and then give a example to illustrate the developed approach. Keyword: Interval sets, soft sets, fuzzy sets, intuitionistic fuzzy sets, neutrosophic sets, level soft set.

/pdf/interval-valued-neutrosophic-soft-sets-and-its-decision-5crsfpy2sr.pdf

Interval-valued neutrosophic soft sets and its decision making

Reviewing ensemble classification methods in breast cancer.

The Internet today lacks an identity protocol for identifying people and organizations. As a result, service providers needed to build and maintain their own databases of user information. This solution is costly to the service providers, inefficient as much of the information is duplicated across different providers, difficult to secure as evidenced by recent large-scale personal data breaches around the world, and cumbersome to the users who need to remember different sets of credentials for different services. Furthermore, personal information could be collected for data mining, profiling and exploitation without users' knowledge or consent. The ideal solution would be self-sovereign identity, a new form of identity management that is owned and controlled entirely by each individual user. This solution would include the individual's consolidated digital identity as well as their set of verified attributes that have been cryptographically signed by various trusted issuers. The individual provides proof of identity and membership by sharing relevant parts of their identity with the service providers. Consent for access may also be revoked hence giving the individual full control over its own data. This survey critically investigates different blockchain based identity management and authentication frameworks. A summary of the state-of-the-art blockchain based identity management and authentication solutions from year 2014 to 2018 is presented. The paper concludes with the open issues, main challenges and directions highlighted for future work in this area. In a nutshell, the discovery of this new mechanism disrupted the existing identity management and authentication solutions and by providing a more promising secure platform.

/pdf/blockchain-technology-the-identity-management-and-84y55m8su8.pdf

Blockchain technology the identity management and authentication service disruptor: A survey

In this paper, the notion of the interval valued neutrosophic soft set (ivn-soft sets) is defined which is a combination of an interval valued neutrosophic set [35] and a soft set [29]. Our ivn-soft sets generalizes the concept of the soft set, fuzzy soft set, interval valued fuzzy soft set, intuitionistic fuzzy soft set, interval valued intuitionistic fuzzy soft set and neutrosophic soft set. Then, we introduce some definitions and operations on ivn-soft sets sets. Some properties of ivn-soft sets which are connected to operations have been established. Also, the aim of this paper is to investigate the decision making based on ivn-soft sets by level soft sets. Therefore, we develop a decision making methods and then give a example to illustrate the developed approach.

Soft set theory is a mathematical approach that provides solution for dealing with uncertain data. As a standard soft set, it can be represented as a Boolean-valued information system, and hence it has been used in hundreds of useful applications. Meanwhile, these applications become worthless if the Boolean information system contains missing data due to error, security or mishandling. Few researches exist that focused on handling partially incomplete soft set and none of them has high accuracy rate in prediction performance of handling missing data. It is shown that the data filling approach for incomplete soft set (DFIS) has the best performance among all previous approaches. However, in reviewing DFIS, accuracy is still its main problem. In this paper, we propose an alternative data filling approach for prediction of missing data in soft sets, namely ADFIS. The novelty of ADFIS is that, unlike the previous approach that used probability, we focus more on reliability of association among parameters in soft set. Experimental results on small, 04 UCI benchmark data and causality workbench lung cancer (LUCAP2) data shows that ADFIS performs better accuracy as compared to DFIS.

/pdf/an-alternative-data-filling-approach-for-prediction-of-3lryzsl2a9.pdf

An alternative data filling approach for prediction of missing data in soft sets (ADFIS).

Clustering process is an essential part of the image processing. Its aim to group the data according to having the same attributes or similarities of the images. Consequently, determining the number of the optimum clusters or the best (well-clustered) for the image in different color models is very crucial. This is because the cluster validation is fundamental in the process of clustering and it reflects the split between clusters. In this study, the k-means algorithm was used on three colors model: CIE Lab, RGB and HSV and the clustering process made up to k clusters. Next, the Silhouette Index (SI) is used to the cluster validation process, and this value is range between 0 to 1 and the greater value of SI illustrates the best of cluster separation. The results from several experiments show that the best cluster separation occurs when k=2 and the value of average SI is inversely proportional to the number of k cluster for all color model. The result shows in HSV color model the average SI decreased 14.11% from k = 2 to k = 8, 11.1% in HSV color model and 16.7% in CIE Lab color model. Comparisons are also made for the three color models and generally the best cluster separation is found within HSV, followed by the RGB and CIE Lab color models.

/pdf/silhouette-index-for-determining-optimal-k-means-clustering-3mob8qwhgp.pdf

Silhouette index for determining optimal k-means clustering on images in different color models

In this paper, we present an extended technique of decision making by implementing column reduction with reduction based on calculated maximal support objects. Using a Boolean valued information system, certain rows or objects can be defined as ultimate maximum support object, ultimate minimum support object and zero significance parameter. One can then reduce a table by eliminating the defined row or objects in what has been defined as hybrid reduction. As part of our paper, we have managed to show that our proposed model of hybrid reduction yielded a better data size reduction whilst still maintaining consistent results.

Hybrid reduction in soft set decision making

Solving Incomplete Datasets in Soft Set Using Supported Sets and Aggregate Values

This paper presents the applicability of soft set theory for discovering a decision attribute in information systems. It is based on the notion of a mapping inclusion in soft set theory. The proposed technique is implemented with example test case and one UCI benchmark data; US Census 1990 dataset. The results from test case show that the selected decision attribute is equivalent to that under rough set theory.

Soft set approach for selecting decision attribute in data clustering

The theory of soft set proposed by Molodtsov [2]in 1999 is a new method for handling uncertain data and can be redefined as a Boolean-valued information system. The soft set theory has been applied to data analysis and decision support systems based on large datasets. Using retrieved datasets, we have calculated the supported values and then determine the even parity bits. Using the parity bit, the problem of missing values from the retrieved datasets can be solved.

Mohd Isa Awang

Papers

Silhouette index for determining optimal k-means clustering on images in different color models

Hybrid reduction in soft set decision making

Solving Incomplete Datasets in Soft Set Using Supported Sets and Aggregate Values

Soft set approach for selecting decision attribute in data clustering

Solving Incomplete Datasets in Soft Set Using Parity Bits of Supported Sets