2020
52
2
2
0
1

$4$total mean cordial labeling in subdivision graphs
https://jac.ut.ac.ir/article_78640.html
1
Let $G$ be a graph. Let $f:Vleft(Gright)rightarrow left{0,1,2,ldots,k1right}$ be a function where $kin mathbb{N}$ and $k>1$. For each edge $uv$, assign the label $fleft(uvright)=leftlceil frac{fleft(uright)+fleft(vright)}{2}rightrceil$. $f$ is called $k$total mean cordial labeling of $G$ if $leftt_{mf}left(iright)t_{mf}left(jright) right leq 1$, for all $i,jinleft{0,1,2,ldots,k1right}$, where $t_{mf}left(xright)$ denotes the total number of vertices and edges labelled with $x$, $xinleft{0,1,2,ldots,k1right}$. A graph with admit a $k$total mean cordial labeling is called $k$total mean cordial graph.
0

1
11


R
Ponraj
Department of Mathematics
Sri Parakalyani College
Alwarkurichi 627 412, India
Iran
ponrajmaths@gmail.com


S
SUBBULAKSHMI
Sri Paramakalyani College
Alwarkurichi627412, Tamilnadu, India
Iran
ssubbulakshmis@gmail.com


S
Somasundaram
Department of Mathematics
Manonmaniam sundarnar university, Abishekapatti, Tirunelveli627012,
Tamilnadu, India
Iran
somutvl@gmail.com
corona
subdivision of star
subdivision of bistar
subdivision of comb
subdivision of crown
subdivision of double comb
subdivision of ladder
1

Linear optimization constrained by fuzzy inequalities defined by MaxMin averaging operator
https://jac.ut.ac.ir/article_79080.html
1
In this paper, optimization of a linear objective function with fuzzy relational inequality constraints is investigated whereby the feasible region is formed as the intersection of two inequality fuzzy systems and textquotedblleft Fuzzy MaxMintextquotedblright averaging operator is considered as fuzzy composition. It is shown that a lower bound is always attainable for the optimal objective value. Also, it is proved that the optimal solution of the problem is always resulted from the unique maximum solution and a minimal solution of the feasible region. An algorithm is presented to solve the problem and an example is described to illustrate the algorithm.
0

13
28


A.
Ghodousian
University of Tehran, College of Engineering, Faculty of Engineering Science
Iran
a.ghodousian@ut.ac.ir


Sara
Falahatkar
University of Tehran, College of Engineering, Faculty of Engineering Science
Iran
sara.falahat@ut.ac.ir
Fuzzy relation
fuzzy relational inequality
Linear programming
fuzzy compositions and fuzzy averaging operator graph
1

Crypto Currency Price Prediction with Decision Tree Based Regressions Approach
https://jac.ut.ac.ir/article_79110.html
1
Generally, no one can reject the fact that crypto currency market is expanded rapidly during last few years as, nowadays, crypto currency market is attractive for both traders and business who are not willing to pay for FATF services for transferring money. With this in mind, crypto currency price prediction is crucial for many people and business entities. While there have been quite a few conventional statistical models to forecast crypto currency prices, we decided to make price prediction using decision Tree Based Regression. In this research we devised a decision tree models to predict Bitcoin which is the most renowned and frequently used crypto currency. we used Volume from, Volume to, New addresses, Active addresses, large transaction count, Block height, Hash rate, Difficulty, Current supply as predictor variables in addition to historical crypto currency price data during the with a total of 1000 Observations. We find that forecasting accuracy of decision tree models are higher than benchmark models such as linear regression and autoregressive integrated moving average(ARIMA).
0

29
40


ali
naghib moayed
Department of Statistics, Allameh Tabatabyee University
Iran
ali.ampmk@gmail.com


Reza
Habibi
Iran Banking Institute, Central Bank of Iran
Iran
r_habibi@ibi.ac.ir
Crypto currency price prediction
Decision Tree
ARIMA
1

Implementation of Combinational Logic Circuits Using NearestNeighbor OneDimensional FourState Cellular Automata
https://jac.ut.ac.ir/article_79225.html
1
Cellular automata are simple mathematical idealizations of natural systems. They consist of a lattice of discrete identical sites, each site taking on a finite set of, say, integer values. Over the years, scientists have been trying to investigate the computational capabilities of cellular automata by limiting the dimension, neighborhood radius, and the number of states.In this article, we represent a novel implementation of combinational logic circuits using nearestneighbor onedimensional fourstate cellular automata (CA). The novelty behind the proposed model is the reduction of the required number of states and yet being able to implement combinational logiccircuits in the conventional CA fashion. This can open a new window to the computation using cellular automata.
0

41
56


Abolfazl
Javan
University of Tehran, College of Engineering, Faculty of Engineerng Science, Department of Algorithms and Computation
Iran
ajavan@ut.ac.ir


Maryam
Jafarpour
Department of Algorithms and Computation, College of Engineering, University of Tehran Tehran, 1417613131, Iran
Iran
m.jafarpour@ut.ac.ir


Ali
Moieni
University of Tehran, College of Engineering, Faculty of Engineerng Science, Department of Algorithms and Computation
Iran
moeini@ut.ac.ir


Mohammad
Shekaramiz
Department of Electrical and Computer Engineering,
Utah State University
Logan, UT 843224120, USA
Iran
mohammad.shekaramiz@aggiemail.usu.edu
cellular automata
Cellular Machine
Combinational Logic Circuits
universality
1

On the domination number of generalized Petersen graphs
https://jac.ut.ac.ir/article_79236.html
1
Let $n$ and $k$ be integers such that $3leq 2k+ 1 leq n$.The generalized Petersen graph $GP(n, k)=(V,E) $ is the graph with $V={u_1, u_2,ldots, u_n}cup{v_1, v_2,ldots, v_n}$ and $E={u_iu_{i+1}, u_iv_i, v_iv_{i+k}: 1 leq i leq n}$, whereaddition is in modulo $n$. A subset $Dsubseteq V$ is a dominating set of $GP(n, k)$ if for each $vin Vsetminus D$ there is a vertex $uin D$ adjacent to $v$. The minimum cardinality of a dominating set of $GP(n, k)$ is called the domination number of $GP(n, k)$.
In this paper we give a dynamic programming algorithm for computing the domination number of a given $GP(n,k )$ in $mathcal{O}(n)$ time and space for every $k=mathcal{O}(1)$.
0

57
65


Abolfazl
Poureidi
Department of Mathematics, Shahrood University of Technology Shahrood, Iran
Iran
a.poureidi@shahroodut.ac.ir
Dominating set
Algorithm
Dynamic Programming
Generalized Petersen graph
1

On Hardy's Apology Numbers
https://jac.ut.ac.ir/article_79248.html
1
Twelve well known `Recreational' numbers are generalized and classified in three generalized types Hardy, Dudeney, and Wells. A novel proof method to limit the search for the numbers is exemplified for each of the types. Combinatorial operators are defined to ease programming the search.
0

67
83


Dr.
Koppelaar
Faculty of Electrical Engineering, Mathematics and Computer Science,
Delft University of Technology,
Delft,
The NetherlandsVredenburchstede 20
Iran
koppelaar.henk@gmail.com


Peyman
Nasehpour
Department of Engineering Science \
Golpayegan University of Technology
Iran
nasehpour@gut.ac.ir
Hardy's apology numbers
Armstrong numbers
Dudeney numbers
Wells numbers
1

LP Problems on the max  “Fuzzy Or” inequalities systems
https://jac.ut.ac.ir/article_79249.html
1
In this paper, optimization of a linear objective function with fuzzy relational inequality constraints is investigated whereby the feasible region is formed as the intersection of two inequality fuzzy systems and “Fuzzy Or” operator is considered as fuzzy composition. It is shown that a lower bound is always attainable for the optimal objective value. Also, it is proved that the optimal solution of the problem is always resulted from the unique maximum solution and a minimal solution of the feasible region. An algorithm is presented to solve the problem and an example is described to illustrate the algorithm.
0

85
98


A.
Ghodousian
University of Tehran, College of Engineering, Faculty of Engineering Science
Iran
a.ghodousian@ut.ac.ir


Parmida
Mirhashemi
University of Tehran, College of Engineering, Faculty of Engineering Science,
Iran
par.mirhashemi@ut.ac.ir
Fuzzy relation
fuzzy relational inequality
linear optimization
fuzzy compositions and fuzzy averaging operator
1

Generalization of DP Curves and Surfaces
https://jac.ut.ac.ir/article_79264.html
1
In CAGD, the DP curves are known as a normalized totally positive curves that have the linear computational complexity. Because of their geometric properties, these curves will have the shape preserving properties, that is, the form of the curve will maintain the shape of the polygon and optimal stability. In this paper, we first define a new basis functions that are called generalized DP basis functions. Based on these functions, the generalized DP curves and surfaced are defined which have most properties of the classical DP curves and surfaces. These curves and surfaces have geometric properties as the rational DP curves and surfaces. Furthermore, we show that the shape parameters can control the shape of the proposed curve without changing the control points.
0

99
108


Davood
Bakhshesh
Department of Computer Science, University of Bojnord, Bojnord, Iran.
Iran
d.bakhshesh@ub.ac.ir
B'{e}zier curve
DP curve
CAGD
1

On the optimization of Hadoop MapReduce default job scheduling through dynamic job prioritization
https://jac.ut.ac.ir/article_79266.html
1
One of the most popular frameworks for big data processing is Apache Hadoop MapReduce. The default Hadoop scheduler uses queue system. However, it does not consider any specific priority for the jobs required for MapReduce programming model. In this paper, a new dynamic score is developed to improve the performance of the default Hadoop MapReduce scheduler. This dynamic priority score is computed based on effective factors such as job runtime estimation, input data size, waiting time, and length or bustle of the waiting queue. The implementation of the proposed scheduling method, based on this dynamic score, not only improves CPU and memory performance, but also reduced waiting time and average turnaround time by approximately $45%$ and $40%$ respectively, compared to the default Hadoop scheduler.
0

109
126


Narges
Peyravi
Department of Computer Engineering and Information Technology, Faculty of Engineering, University of Qom, Qom, Iran
Iran
narges.peyravi@gmail.com


Ali
Moeini
Department of Algorithms and Computation, School of Engineering Science, College of Engineering, University of Tehran
Iran
moeini@ut.ac.ir
Hadoop MapReduce
Job scheduling
Prioritization
dynamic priority score
1

Fuzzy Cumulative Distribution Function and its Properties
https://jac.ut.ac.ir/article_79267.html
1
The statistical methods based on cumulative distribution function is a start point for many parametric or nonparametric statistical inferences. However, there are many practical problems that require dealing with observations/parameters that represent inherently imprecise. However, Hesamian and Taheri (2013) was extended a concept of fuzzy cumulative distribution function. Applying a common notion of fuzzy random variables, they extended a vague concept of fuzzy cumulative distribution function. However, the main properties of the proposed method has not yet been considered in fuzzy environment. This paper aims to extend the classical properties of the fuzzy cumulative distribution function in fuzzy environment.
0

127
136


Mehdi
Shams
Department of Mathematical Sciences, University of Kashan, Isfahan, Iran.
Iran
mehdishams@kashanu.ac.ir


Gholamreza
Hesamian
Department of Statistics, Payame Noor University, Tehran 193953697, Iran
Iran
gh.hesamian@pnu.ac.ir
Cumulative Distribution Function
Fuzzy random variable
fuzzy parameter
ranking method
convergence
divergence to infinity
1

Two different inverse eigenvalue problems for nonsymmetric tridiagonal matrices
https://jac.ut.ac.ir/article_79269.html
1
Inverse eigenvalue problems (IEPs) of tridiagonal matrices are among the most popular IEPs, this is due to the widespread application of this matrix. In this paper, two different IEPs with different eigen information including eigenvalues and eigenvectors are presented on the nonsymmetric tridiagonal matrix. A recursive relation of characteristic polynomials of the leading principal submatrices of the required matrix is presented to solve the problems. The application of the problems in graph and perturbation theory is studied. The necessary and sufficient conditions for solvability of the problems are obtained.The algorithms and numerical examples are given to show the applicability of the proposed scheme.
0

137
148


Ferya
Fathi
Department of Mathematics, Central Tehran Branch, Islamic Azad University, Tehran,
Iran
Iran
ferya.fathi@gmail.com


Mohammad Ali
Fariborzi Araghi
Department of Mathematics, Faculty of Sciences, Central Tehran branch, Islamic Azad university, Tehran, Iran.
Iran
fariborzi.araghi@gmail.com


Seyed Abolfazl
Shahzadeh Fazeli
Department of Computer Science, Yazd University, Yazd, Iran.
Iran
fazeli@yazd.ac.ir
Inverse eigenvalue problem
Tridiagonal matrix
Principal submatrix
1

A note on the approximability of the tenacity of graphs
https://jac.ut.ac.ir/article_79270.html
1
In this paper we show that, if $NPneq ZPP$, for any $epsilon > 0$, the tenacity of graphwith $n$ vertices is not approximable in polynomial time within a factor of$frac{1}{2} left( frac{n1}{2} right) ^{1epsilon}$.
0

149
157


Vahid
Heidari
University of Tehran, Department of Algorithms and Computation.
Iran
vahid.heidari@ut.ac.ir


Dara
Moazzami
University of Tehran, College of Engineering, Faculty of Engineering Science
Iran
dmoazzami@ut.ac.ir
$NP$complete problem
Tenacity
Tenacious
$NP$hard