Study of Imaginative Play in Children Using Single-Valued Refined Neutrosophic Sets

Vasantha W. B.; Ilanthenral Kandasamy; Florentin Smarandache; Vinayak Devvrat; Shivam Ghildiyal

doi:10.3390/sym12030402

Study of Imaginative Play in Children Using Single-Valued Refined Neutrosophic Sets

Symmetry ◽

10.3390/sym12030402 ◽

2020 ◽

Vol 12 (3) ◽

pp. 402

Author(s):

Vasantha W. B. ◽

Ilanthenral Kandasamy ◽

Florentin Smarandache ◽

Vinayak Devvrat ◽

Shivam Ghildiyal

Keyword(s):

Real World ◽

Pretend Play ◽

Machine Learning Algorithms ◽

Neutrosophic Set ◽

Membership Functions ◽

Neutrosophic Sets ◽

Real World Application ◽

Mental Abilities ◽

Imaginative Play ◽

Real World Problems

This paper introduces Single Valued Refined Neutrosophic Set (SVRNS) which is a generalized version of the neutrosophic set. It consists of six membership functions based on imaginary and indeterminate aspect and hence, is more sensitive to real-world problems. Membership functions defined as complex (imaginary), a falsity tending towards complex and truth tending towards complex are used to handle the imaginary concept in addition to existing memberships in the Single Valued Neutrosophic Set (SVNS). Several properties of this set were also discussed. The study of imaginative pretend play of children in the age group from 1 to 10 years was taken for analysis using SVRNS, since it is a field which has an ample number of imaginary aspects involved. SVRNS will be more apt in representing these data when compared to other neutrosophic sets. Machine learning algorithms such as K-means, parallel axes coordinate, etc., were applied and visualized for a real-world application concerned with child psychology. The proposed algorithms help in analysing the mental abilities of a child on the basis of imaginative play. These algorithms aid in establishing a correlation between several determinants of imaginative play and a child’s mental abilities, and thus help in drawing logical conclusions based on it. A brief comparison of the several algorithms used is also provided.

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Empirical Comparison of Various Discretization Procedures

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001498000567 ◽

1998 ◽

Vol 12 (07) ◽

pp. 1017-1032 ◽

Cited By ~ 10

Author(s):

Petr Berka ◽

Ivan Bruha

Keyword(s):

Machine Learning ◽

Real World ◽

Learning Algorithm ◽

Machine Learning Algorithms ◽

The Other ◽

Machine Learning Algorithm ◽

Empirical Comparison ◽

Numerical Attributes ◽

Real World Problems ◽

Discretization Procedure

The genuine symbolic machine learning (ML) algorithms are capable of processing symbolic, categorial data only. However, real-world problems, e.g. in medicine or finance, involve both symbolic and numerical attributes. Therefore, there is an important issue of ML to discretize (categorize) numerical attributes. There exist quite a few discretization procedures in the ML field. This paper describes two newer algorithms for categorization (discretization) of numerical attributes. The first one is implemented in the KEX (Knowledge EXplorer) as its preprocessing procedure. Its idea is to discretize the numerical attributes in such a way that the resulting categorization corresponds to KEX knowledge acquisition algorithm. Since the categorization for KEX is done "off-line" before using the KEX machine learning algorithm, it can be used as a preprocessing step for other machine learning algorithms, too. The other discretization procedure is implemented in CN4, a large extension of the well-known CN2 machine learning algorithm. The range of numerical attributes is divided into intervals that may form a complex generated by the algorithm as a part of the class description. Experimental results show a comparison of performance of KEX and CN4 on some well-known ML databases. To make the comparison more exhibitory, we also used the discretization procedure of the MLC++ library. Other ML algorithms such as ID3 and C4.5 were run under our experiments, too. Then, the results are compared and discussed.

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A note on different types of product of neutrosophic graphs

Complex & Intelligent Systems ◽

10.1007/s40747-020-00238-0 ◽

2021 ◽

Author(s):

Kartick Mohanta ◽

Arindam Dey ◽

Anita Pal

Keyword(s):

Decision Making ◽

Fuzzy Set ◽

Real World ◽

Real Life ◽

Neutrosophic Set ◽

Total Degree ◽

Decision Making Problem ◽

Different Types ◽

Neutrosophic Graph ◽

Real World Problems

AbstractFuzzy set and neutrosophic set are two efficient tools to handle the uncertainties and vagueness of any real-world problems. Neutrosophic set is more capable than fuzzy set to deal the uncertainties of a real-life problem. This research paper introduces some new concept of single-valued neutrosophic graph (SVNG). We have also presented some different operations on SVNG such as rejection, symmetric difference, maximal product, and residue product with appropriate examples, and some of their important theorems are also described. Then, we have described the concept of total degree of a neutrosophic graph with some interesting examples. We have also presented an efficient approach to solve a decision-making problem using SVNG.

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Road Accidents Analysis Using Comparative Study & Application of Machine Learning Algorithms

WSEAS TRANSACTIONS ON COMPUTER RESEARCH ◽

10.37394/232018.2021.9.9 ◽

2021 ◽

Vol 9 ◽

pp. 78-86

Author(s):

Arnav Saini ◽

Nipun Gauba ◽

Hardik Chawla ◽

Jabir Ali

Keyword(s):

Machine Learning ◽

Real World ◽

Autonomous Vehicles ◽

Human Life ◽

Analysis Data ◽

Road Accident ◽

Machine Learning Algorithms ◽

Road Accidents ◽

Time Period ◽

Real World Problems

Model predictive contrTraffic Collisions are one of the major sources of deaths, injuries & property damage every year. Road accidents are one of the most difficult real world problems to tackle with, due to its high order of unpredictability. The persistence as well as existence of this problem may be prevalent to a different degree for each & every place. The consequences of this may result in loss of human life & capital. To avoid this, every place needs to tackle the problem with a customized approach depending on the causes that are responsible for the accidents. Even in today's world, where the mass operation of autonomous vehicles is still grim or out of sight, the possibility of predicting a road accident before it takes place, is practically impossible. The only idea or approach that can help to decrease the number of road accidents, is to analyze the reasons that lead to these accidents. The concepts of Data Analysis, Data Visualization & Machine Learning help to tackle real world problems, by exploring & deriving valuable insights, which in turn help in taking measures to solve the targeted problem & drive business growth. In this research study, the dataset pertaining to road mishaps that occurred in UK over time period 2005 - 2015 will be analyzed using these concepts. The defined approach can help the concerned authorities & respective government, to take every possible step & amendment, & hence mitigate the identified causes & scenarios that lead to road accidents.

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Cross Entropy Measures of Bipolar and Interval Bipolar Neutrosophic Sets and Their Application for Multi-Attribute Decision Making

10.20944/preprints201801.0065.v1 ◽

2018 ◽

Cited By ~ 1

Author(s):

Surapati Pramanik ◽

Partha Pratim Dey ◽

Florentin Smarandache ◽

Jun Ye

Keyword(s):

Decision Making ◽

Fuzzy Set ◽

Cross Entropy ◽

Neutrosophic Set ◽

Membership Functions ◽

Neutrosophic Sets ◽

Entropy Measures ◽

Multi Attribute Decision Making ◽

Important Extension ◽

The Ideal

Bipolar neutrosophic set is an important extension of bipolar fuzzy set. This set is a hybridization of bipolar fuzzy set and neutrosophic set. Every element of a bipolar neutrosophic set consists of three independent positive membership functions and three independent negative membership functions. In this paper, we develop cross entropy measures of bipolar neutrosophic sets and prove its properties. We also define cross entropy measures of interval bipolar neutrosophic sets and prove its properties. Thereafter, we develop two novel multi-attribute decision making methods based on the proposed cross entropy measures. In the decision making framework, we calculate the weighted cross entropy measures between each alternative and the ideal alternative to rank the alternatives and choose the best one. We solve two illustrative examples of multi-attribute decision making problems and compare the obtained result with the results of other existing methods to show the applicability and effectiveness of the developed method. In the end, the main conclusion and future scope of research are summarized.

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p-Optimality-Based Multiobjective Root System Growth Algorithms for Multiobjective Applications

Mathematical Problems in Engineering ◽

10.1155/2019/7561398 ◽

2019 ◽

Vol 2019 ◽

pp. 1-25

Author(s):

Rui Liu ◽

Hanning Chen ◽

Lina Song ◽

Man Ding

Keyword(s):

Root System ◽

Real World ◽

Power Flow ◽

Optimal Power Flow ◽

Optimality Criterion ◽

Optimization Problems ◽

Real World Application ◽

Optimal Power ◽

Convergence Of Solution ◽

Real World Problems

In this paper, a multiobjective root system growth algorithm-based p-optimality (p-MORSGA) is proposed. The proposed p-MORSGA extended original root system growth algorithm with multiobjective nondomination strategy. To enhance its effect of convergence of solution groups, the p-optimality criterion is employed to determine the solutions of last nondominated front into the next generation group. In the evolution process, global general (GG), concerning the margin information and population density, is selected as the suitable optimality criterion of evaluating the performance of solutions. Application of the new p-MORSGA on several multiobjective benchmark functions shows a marked improvement in performance over the modified classical MOEAs with such criterion. Finally, the proposed p-MORSGA is applied to solve two real-world problems, multiobjective portfolio optimization problems (MOPOPs) and multiobjective optimal power flow (OPF) problems. The experimental results demonstrate that p-MORSGA is extremely effective for real-world application problems.

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The Pentagonal Fuzzy Number:Its Different Representations, Properties, Ranking, Defuzzification and Application in Game Problems

Symmetry ◽

10.3390/sym11020248 ◽

2019 ◽

Vol 11 (2) ◽

pp. 248 ◽

Cited By ~ 10

Author(s):

Avishek Chakraborty ◽

Sankar Prasad Mondal ◽

Shariful Alam ◽

Ali Ahmadian ◽

Norazak Senu ◽

...

Keyword(s):

Real World ◽

Level Sets ◽

Fuzzy Numbers ◽

Evaluation Process ◽

Game Problem ◽

Ranking Method ◽

Membership Functions ◽

Linear And Nonlinear ◽

Interval Valued ◽

Real World Problems

In this paper, different measures of interval-valued pentagonal fuzzy numbers (IVPFN) associated with assorted membership functions (MF) were explored, considering significant exposure of multifarious interval-valued fuzzy numbers in neoteric studies.Also, the idea of MF is generalized somewhat to nonlinear membership functions for viewing the symmetries and asymmetries of the pentagonal fuzzy structures. Accordingly,the construction of level sets, for each case of linear and nonlinear MF was also carried out. Besides, defuzzification was undertaken using three methods and a ranking method, which were also the main features of this framework.The developed intellects were implemented in a game problem by taking the parameters as PFNs, ultimately resulting in a new direction for modeling real world problems and to comprehend the uncertainty of the parameters more precisely in the evaluation process.

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Neutrosophic Environment for Traffic Control Management

10.54216/ijns.090104 ◽

2020 ◽

pp. 47-53

Author(s):

admin admin ◽

◽

Said Broumi ◽

...

Keyword(s):

Flow Control ◽

Traffic Flow ◽

Real World ◽

Traffic Control ◽

Vital Role ◽

Neutrosophic Sets ◽

The Past ◽

Decision Making Problem ◽

Control Management ◽

Real World Problems

Neutrosophic along with its environment development over the past decades. Neutrosophic environment is apply to various applications in logic,statstics,albebra, neural networks and several other fields. Neutrosophic sets has been presented to handle the indeterminacy in real-world decision-making problem. Real world problems have some kind of uncertainty in nature and one of the influential problem in environment. Neutrosophic environment results are apply to a new dimension in traffic control. Neutrosophic is the vital role on traffic flow control . It is deal with membership , non membership and also indeterminacy of the data as well. The advantage of the neutrosophic environment is to find the optimized result of the system choosing the best alternative.In this paper, traffic flow control is analyzed under neutrosophic environment using MATLAB.

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Comparative Study of Meta-heuristics Optimization Algorithm using Benchmark Function

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v7i3.pp1643-1650 ◽

2017 ◽

Vol 7 (3) ◽

pp. 1643 ◽

Cited By ~ 2

Author(s):

I. Ismail ◽

A. Hanif Halim

Keyword(s):

Optimization Algorithm ◽

Real World ◽

Computation Time ◽

Global Optimum ◽

Performance Comparison ◽

Difficulty Level ◽

Problem Type ◽

Benchmark Function ◽

Real World Application ◽

Real World Problems

Meta-heuristics optimization is becoming a popular tool for solving numerous problems in real-world application due to the ability to overcome many shortcomings in traditional optimization. Despite of the good performance, there is limitation in some algorithms that deteriorates by certain degree of problem type. Therefore it is necessary to compare the performance of these algorithms with certain problem type. This paper compares 7 meta-heuristics optimization with 11 benchmark functions that exhibits certain difficulties and can be assumed as a simulation relevant to the real-world problems. The tested benchmark function has different type of problem such as modality, separability, discontinuity and surface effects with steep-drop global optimum, bowl- and plateau-typed function. Some of the proposed function has the combination of these problems, which might increase the difficulty level of search towards global optimum. The performance comparison includes computation time and convergence of global optimum.

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