Journal of Algorithms and Computation
Volume:54 Issue: 2, Dec 2022

Let $G=(V, E)$ be a simple graph. A dominating set of $G$ is a subset $D\subseteq V$ such that every vertex not in $D$ is adjacent to at least one vertex in $D$. The cardinality of the smallest dominating set of $G$, denoted by $\gamma(G)$, is the domination number of $G$. For $k \geq 1$, a $k$-fair dominating set ($kFD$-set) in $G$, is a dominating set $S$ such that $|N(v) \cap D|=k$ for every vertex $ v \in V\setminus D$. A fair dominating set in $G$ is a $kFD$-set for some integer $k\geq 1$. Let ${\cal D}_f(G,i)$ be the family of the fair dominating sets of a graph $G$ with cardinality $i$ and let $d_f(G,i)=|{\cal D}_f(G,i)|$. The fair domination polynomial of $G$ is $D_f(G,x)=\sum_{ i=1}^{|V(G)|} d_f(G,i) x^{i}$. In this paper, after computation of the fair domination number of power of cycle, we count the number of the fair dominating sets of certain graphs such as cubic graphs of order~$10$, power of paths, and power of cycles. As a consequence, all cubic graphs of order $10$ and especially the Petersen graph are determined uniquely by their fair domination polynomial.

We revised our technique for generating graphical renderings of the limitsets of Kleinian groups. The algorithm, relying on numerical base conversion,has been improved to shorten computation times. This method easily applies toany family of Kleinian groups with an arbitrary number of generators.Following an overview of this problem, we present the implementationguidelines in the form of pseudo-code.

One important criterion in decision-making when we want to purchase a product or a service is users’ reviews. When something is valuable, it’s fake and will be created as well. It is the same for users’ reviews. The purpose of these fake reviews is to deceive users, leading them to make a wrong choice. One challenging problem is when we can trust a review. Although many researchers attempted to address this problem, none of them pictured the problem in a streaming domain. With the help of the review network’s properties, we propose a model to find reliable reviews when reviews are coming in a stream. Our model is fast and online, that is, it is capable of identifying reliable reviews as they are been submitted, and scalable because it is a complementary model to offline models in detecting fake reviews.

In this paper, we consider only finite, undirected, and simple graphs. The concept of cordial labeling was introduced by Cahit[4]. Different types of cordial-related labeling were studied in [1, 2, 3, 5, 16]. In a similar line, the notion of pair difference cordial labeling of a graph was introduced in [8]. The pair difference cordial labeling behavior of several graphs like path, cycle, star, wheel, triangular snake, alternate triangular snake, butterfly, ladder, Mobius ladder, slanting ladder, and union of some graphs have been investigated in [ 8, 9, 10, 11, 12, 13, 14, 15]. The $m-$ copies of a graph $G$ is denoted by $mG$ [7]. In this paper, we investigate the pair difference cordial labeling behavior of $m-$ copies of Path, Star, Cycle, and Ladder graphs.

The selection of features is a crucial step in the analysis of high-dimensional data in machine learning and data mining. Gannet Optimization Algorithm (GOA) is a recently proposed metaheuristic algorithm that has not yet been investigated in terms of its capacity to solve feature selection problems. A new wrapper feature selection approach based on GOA is proposed to extract the best features. The GOA is a robust meta-heuristic algorithm that can deal with higher dimensions. A fitness function is used to account for the entropy of the sensitivity and specificity, as well as the accuracy of the classifier and the fraction of features selected. Additionally, four new algorithms are compared with the proposed algorithm in this paper. Based on the experimental results, fewer features can be obtained with a higher classification accuracy using the proposed algorithm.

Artificial neural networks that have been so popular in recent years, are inspired from biological neural networks in the nature. The aim of this work is to study the properties of biological neural networks to find out what is actually happening in these networks. To do so, we study on Caenrohibditis elegans neural network, which is the simplest and the only biological neural network that is fully mapped. We implemented the sub-circuit of C.elegans neural network that is associated with the sensation of aversive stimuli which results in forward and backward locomotion, and we found out that some of its neurons are ineffective in developing considered outputs. However, removing these neurons together has considerable effect on these outputs.

In this article, we examine different algorithms in the field of cryptography. And we analyze them from different aspects. Also, we introduce a new solution and algorithm, based on increasing data security. Our proposed method can perform the task optimally and efficiently compared to similar algorithms. In addition, it also reduces the need for storage space. Therefore, our proposed method works better and optimally by increasing productivity in operational environments. Our proposed algorithm can be used by increasing the security level for data, in various platforms and in all types of communication. Because the data is linked, and if we do not have the address of a data packet, it is not possible to decode the data. This increases data security.

Entropy is a measure of disorder in a system and is widely used in other scientific and engineering disciplines such as statistical mechanics and information theory. In a chaotic supply chain, the goal is to reduce chaotic behaviors and predict the future of the supply chain. In this case, relationships in a three-tier supply chain are considered in a continuous-time environment. In this paper, the chaotic supply chain is investigated and controlled using the entropy minimization method. Due to the dynamic nature of the supply chain and its chaotic behavior, Poincaré mapping of the system has been prepared by the stroboscopic method. Then, by defining Shannon entropy on the map, the entropy of the system is significantly reduced by the gradient descent algorithm.

In this paper, a linear programming problem is investigated in which the feasible region is formed as the intersection of fuzzy relational equalities and the harmonic mean operator is considered as fuzzy composition. Theoretical properties of the feasible region are derived. It is proved that the feasible solution set is comprised of one maximum solution and a finite number of minimal solutions. Furthermore, some necessary and sufficient conditions are additionally presented to determine the feasibility of the problem. Moreover, an algorithm is presented to find the optimal solutions of the problem and finally, an example is described to illustrate the algorithm.

threaten system self-worth by preventing them from seeing themselves as a good system, and it can generally erode trust in society. Lying may be considered a game. This paper is concerned with the effect of lying in a system containing two agents using the game theory. From a repeated measurement model, two agents (which constitute a system) play a global game and it is seen that during a repeated game, the system will be destroyed by laying an agent. The probability of failing negotiation is derived and its behavior is studied under different scenarios. Simulation results are proposed to support theoretical results. Finally, concluding remarks are given.

The demand for extracting sophisticated features, capable of effectively predicting gene interaction networks, from DNA or RNA sequences has increased in computational biology. The epigenetic modifications along with their patterns have been intensely recognized as appealing features affecting gene interaction. However, studying sequenced-based features highly correlated to this key element has remained limited. In this paper, the classification of 23 genes in the PPAR signaling pathway associated with muscle fat tissue in humans was proposed based on statistical distributions of the specified RNA-5-mers abundance. Then, we suggested that these 5-mers highly correlated to epigenetic modifications can efficiently categorize the different gene interactions, particularly co-expression interaction and physical interaction. Our results were evaluated according to GeneMania web interface shows that the geometric distribution of 5 mers in the epigenetic modifications region indicates the proportion of most physical interactions and the Poisson distribution the proportion of most Co-expression between genes

One of the important topics in oncology for treatment and prevention is the identification of genes that initiate cancer in cells. These genes are known as cancer driver genes (CDG). Identifying driver genes is important both for a basic understanding of cancer and for helping to find new therapeutic goals or biomarkers. Several computational methods for finding cancer-driver genes have been developed from genome data. However, most of these methods find key mutations in genomic data to predict cancer driver genes.  methods are dependent on mutation and genomic data and often have a high rate of false positives in the results. In this study, we proposed a network-based method, GeneIC, which can detect cancer driver genes without the need for mutation data. In this method, the concept of influence maximization and the independent cascade model is used. First, a cancer gene regulatory network was created using regulatory interactions and gene expression data. Then we implemented an independent cascade propagation algorithm on the network to calculate the coverage of each gene. Finally, the genes with the highest coverage were introduced as driver genes. The results of our proposed method were compared with 19 previous computational and network methods based on the F-measure metric and the number of detected drivers. The results showed that the proposed method has a better outcome than other methods. In addition, more than 25.49\% of the driver genes reported by GeneIC are new driver genes that have not been reported by any other computational method.