2021-06-20T18:11:40Z
https://jac.ut.ac.ir/?_action=export&rf=summon&issue=10135
Journal of Algorithms and Computation
J. Algorithm Comput.
2476-2776
2476-2776
2020
52
2
$4$-total mean cordial labeling in subdivision graphs
R
Ponraj
S
SUBBULAKSHMI
S
Somasundaram
Let $G$ be a graph. Let $f:V\left(G\right)\rightarrow \left\{0,1,2,\ldots,k-1\right\}$ be a function where $k\in \mathbb{N}$ and $k>1$. For each edge $uv$, assign the label $f\left(uv\right)=\left\lceil \frac{f\left(u\right)+f\left(v\right)}{2}\right\rceil$. $f$ is called $k$-total mean cordial labeling of $G$ if $\left|t_{mf}\left(i\right)-t_{mf}\left(j\right) \right| \leq 1$, for all $i,j\in\left\{0,1,2,\ldots,k-1\right\}$, where $t_{mf}\left(x\right)$ denotes the total number of vertices and edges labelled with $x$, $x\in\left\{0,1,2,\ldots,k-1\right\}$. A graph with admit a $k$-total mean cordial labeling is called $k$-total mean cordial graph.
corona
subdivision of star
subdivision of bistar
subdivision of comb
subdivision of crown
subdivision of double comb
subdivision of ladder
2020
12
01
1
11
https://jac.ut.ac.ir/article_78640_417b0db101ba534580bcc1065d70cdd3.pdf
Journal of Algorithms and Computation
J. Algorithm Comput.
2476-2776
2476-2776
2020
52
2
Linear optimization constrained by fuzzy inequalities defined by Max-Min averaging operator
A.
Ghodousian
Sara
Falahatkar
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 Max-Min\textquotedblright \ 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.
Fuzzy relation
fuzzy relational inequality
Linear programming
fuzzy compositions and fuzzy averaging operator graph
2020
12
01
13
28
https://jac.ut.ac.ir/article_79080_dbd6cabd14838434f023bf778552f3e4.pdf
Journal of Algorithms and Computation
J. Algorithm Comput.
2476-2776
2476-2776
2020
52
2
Crypto- Currency Price Prediction with Decision Tree Based Regressions Approach
ali
naghib moayed
Reza
Habibi
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).
Crypto currency price prediction
Decision Tree
ARIMA
2020
12
01
29
40
https://jac.ut.ac.ir/article_79110_6156628d39397c2b78824976d69d9b12.pdf
Journal of Algorithms and Computation
J. Algorithm Comput.
2476-2776
2476-2776
2020
52
2
Implementation of Combinational Logic Circuits Using Nearest-Neighbor One-Dimensional Four-State Cellular Automata
Abolfazl
Javan
Maryam
Jafarpour
Ali
Moieni
Mohammad
Shekaramiz
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 nearest-neighbor one-dimensional four-state 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 logic-circuits in the conventional CA fashion. This can open a new window to the computation using cellular automata.
cellular automata
Cellular Machine
Combinational Logic Circuits
universality
2020
12
01
41
56
https://jac.ut.ac.ir/article_79225_bb5fb4256d7b683d0530681b354a0cb1.pdf
Journal of Algorithms and Computation
J. Algorithm Comput.
2476-2776
2476-2776
2020
52
2
On the domination number of generalized Petersen graphs
Abolfazl
Poureidi
Let $n$ and $k$ be integers such that $3\leq 2k+ 1 \leq n$.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 $D\subseteq V$ is a dominating set of $GP(n, k)$ if for each $v\in V\setminus D$ there is a vertex $u\in D$ 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 $k=\mathcal{O}(1)$.
Dominating set
Algorithm
Dynamic Programming
Generalized Petersen graph
2020
12
01
57
65
https://jac.ut.ac.ir/article_79236_83169ff58aaf301dce65ecd29d8d6030.pdf
Journal of Algorithms and Computation
J. Algorithm Comput.
2476-2776
2476-2776
2020
52
2
On Hardy's Apology Numbers
Dr.
Koppelaar
Peyman
Nasehpour
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.
Hardy's apology numbers
Armstrong numbers
Dudeney numbers
Wells numbers
2020
12
01
67
83
https://jac.ut.ac.ir/article_79248_56d3c030088a170a3906b627fcde5388.pdf
Journal of Algorithms and Computation
J. Algorithm Comput.
2476-2776
2476-2776
2020
52
2
LP Problems on the max - “Fuzzy Or” inequalities systems
A.
Ghodousian
Parmida
Mirhashemi
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.
Fuzzy relation
fuzzy relational inequality
linear optimization
fuzzy compositions and fuzzy averaging operator
2020
12
01
85
98
https://jac.ut.ac.ir/article_79249_c198efc6bb40dcd88841addc34b6ec2c.pdf
Journal of Algorithms and Computation
J. Algorithm Comput.
2476-2776
2476-2776
2020
52
2
Generalization of DP Curves and Surfaces
Davood
Bakhshesh
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.
B'{e}zier curve
DP curve
CAGD
2020
12
01
99
108
https://jac.ut.ac.ir/article_79264_7b24762116d5b5830b5d4ad475da353f.pdf
Journal of Algorithms and Computation
J. Algorithm Comput.
2476-2776
2476-2776
2020
52
2
On the optimization of Hadoop MapReduce default job scheduling through dynamic job prioritization
Narges
Peyravi
Ali
Moeini
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.
Hadoop MapReduce
Job scheduling
Prioritization
dynamic priority score
2020
12
01
109
126
https://jac.ut.ac.ir/article_79266_1ccbac12d443ad1cb51ac9305190b1b3.pdf
Journal of Algorithms and Computation
J. Algorithm Comput.
2476-2776
2476-2776
2020
52
2
Fuzzy Cumulative Distribution Function and its Properties
Mehdi
Shams
Gholamreza
Hesamian
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.
Cumulative Distribution Function
Fuzzy random variable
fuzzy parameter
ranking method
convergence
divergence to infinity
2020
12
01
127
136
https://jac.ut.ac.ir/article_79267_82f43625d638278d11153074cd36964c.pdf
Journal of Algorithms and Computation
J. Algorithm Comput.
2476-2776
2476-2776
2020
52
2
Two different inverse eigenvalue problems for nonsymmetric tridiagonal matrices
Ferya
Fathi
Mohammad Ali
Fariborzi Araghi
Seyed Abolfazl
Shahzadeh Fazeli
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.
Inverse eigenvalue problem
Tridiagonal matrix
Principal submatrix
2020
12
01
137
148
https://jac.ut.ac.ir/article_79269_ecc0d219b5c8c3f9e1df951b33d76cdb.pdf
Journal of Algorithms and Computation
J. Algorithm Comput.
2476-2776
2476-2776
2020
52
2
A note on the approximability of the tenacity of graphs
Vahid
Heidari
Dara
Moazzami
In this paper we show that, if $NP\neq ZPP$, for any $\epsilon > 0$, the tenacity of graphwith $n$ vertices is not approximable in polynomial time within a factor of$\frac{1}{2} \left( \frac{n-1}{2} \right) ^{1-\epsilon}$.
$NP$-complete problem
Tenacity
Tenacious
$NP$-hard
2020
12
01
149
157
https://jac.ut.ac.ir/article_79270_0a4d1e9aa72c099beb4fcfe521d1bc23.pdf