Open Access Short Research Article

Experimental Study on Class Imbalance Problem Using an Oil Spill Training Data Set

Xi Qin Ouyang, Yuan Ping Chen, Bing Hui Wei

Journal of Advances in Mathematics and Computer Science, Page 1-9
DOI: 10.9734/BJMCS/2017/32860

There is a paucity of research on one of the key issues in oil spill detection: the imbalanced training set learning problem. This paper performs experiments to show the influence of the imbalanced learning problem (ILP) on oil spill detection and devises a novel framework to tackle this problem. Experimental results show that an imbalanced training set degenerate the performance of oil spill detection, and our proposed framework achieves a better performance based on F-measure.

Open Access Original Research Article

Global Stability of Equilibrium Points of Typhoid Fever Model with Protection

J. K. Nthiiri

Journal of Advances in Mathematics and Computer Science, Page 1-6
DOI: 10.9734/BJMCS/2017/32690

A non-linear mathematical model of typhoid fever diseases incorporating protection is hereby considered to study the global stability of equilibrium points. To study the global stability of the disease free equilibrium point and endemic equilibrium point, the method by Castillo-Chavez and a suitable Lyapunov function are used respectively. The disease free equilibrium point was found not to be globally asymptotically stable while the endemic equilibrium point is globally asymptotically stable. This implies that the disease transmission can be kept quiet low or manageable with minimal deaths in the presence of protection.

Open Access Original Research Article

The Topp-Leone Burr-XII Distribution: Properties and Applications

Hesham M. Reyad, Soha A. Othman

Journal of Advances in Mathematics and Computer Science, Page 1-15
DOI: 10.9734/BJMCS/2017/33053

In this paper we introduce a new generalization of the Burr-XII distribution using the genesis of the Topp-Leone distribution and is named as Topp-Leone Burr-XII (TLBXII) distribution. The statistical properties of this distribution including the mean, variance, coefficient of variation, quantile function, median, ordinary and incomplete moments, skewness, kurtosis, moment and probability generating functions, reliability analysis, Lorenz, Bonferroni and Zenga curves, Rényi of entropy and order statistics are studied. We consider the method of maximum likelihood for estimating the model parameters and the observed information matrix is derived. Three real data sets are presented to demonstrate the effectiveness of the new model.q

Open Access Original Research Article

Boolean Product of Zero-one Matrices Application to Truth Values of Logical Connectives of Several Propositions

Etop E. Ndiyo

Journal of Advances in Mathematics and Computer Science, Page 1-5
DOI: 10.9734/BJMCS/2017/22054

In this paper, the Boolean product of zero-one matrices are applied to obtain the truth values of several propositions with logical connectives. The propositional matrices are given in relation to the matrix algebric properties. We state and prove a theorem for which a given conditional connetives is a tautology.

Open Access Review Article

A Literature Study on Traditional Clustering Algorithms for Uncertain Data

S. Sathappan, S. Sridhar, D. C. Tomar

Journal of Advances in Mathematics and Computer Science, Page 1-21
DOI: 10.9734/BJMCS/2017/32697

Numerous traditional Clustering algorithms for uncertain data have been proposed in the literature such as k-medoid, global kernel k-means, k-mode, u-rule, uk-means algorithm, Uncertainty-Lineage database, Fuzzy c-means algorithm. In 2003, the traditional partitioning clustering algorithm was also modified by Chau, M et al. to perform the uncertain data clustering. They presented the UK-means algorithm as a case study and illustrate how the proposed algorithm was applied. With the increasing complexity of real-world data brought by advanced sensor devices, they believed that uncertain data mining was an important and significant research area. The purpose of this paper is to present a literature study as foundation work for doing further research on traditional clustering algorithms for uncertain data, as part of PhD work of first author.