Open Access Original Research Article

Estimating the Strength of the Impact of Rushing Attempt in NFL Game Outcomes

Xupin Zhang, Benjamin Rollins, Necla Gunduz, Ernest Fokoue

Journal of Advances in Mathematics and Computer Science, Page 1-12
DOI: 10.9734/BJMCS/2017/31565

In this paper, we use estimators of variable importance from the ensemble learning technique of random forest to consistently discover and extract the knowledge that Rush Attempt is strongly related with winning football games in the NFL. Almost all researchers before us have consistently made claims of the impact/influence other statistics in the outcomes of NFL games, with Third Down Conversion Percentage and Takeaways almost universally considered as having the greatest impacts in game outcomes. Rushing as a factor of NFL success has also been mentioned, but mostly in terms of number of rushing yards per game. The novelty in this present work lies in the fact that not only do we discover Rush Attempt differential to be the strongest and most dominant variable, but we also establish its dominance throughout the years, namely with 14 seasons worth of NFL games data providing firm evidence of the ubiquitous appearance of Rush Attempt at the root of every classification tree.

Open Access Original Research Article

Asymptotics of Solution of a Boundary Value Problem for Quasilinear Non-Classical Type Differential Equation of Arbitrary Odd Order

Mahir M. Sabzaliev, Ilhama M. Sabzalieva

Journal of Advances in Mathematics and Computer Science, Page 1-19
DOI: 10.9734/BJMCS/2017/33704

In a rectangle domain, a boundary value problem is considered for a singularly perturbed quasilinear non-classical type equation of arbitrary odd order, degenerating into a hyperbolic equation. Asymptotic expansion of the generalized solution of the problem under consideration is constructed to within any positive degree of a small parameter, and the residual term is estimated.

Open Access Original Research Article

Extensions of Locally Compact Abelian, Torsion-Free Groups by Compact Torsion Abelian Groups

Hossein Sahleh, Ali Akbar Alijani

Journal of Advances in Mathematics and Computer Science, Page 1-5
DOI: 10.9734/BJMCS/2017/32966

Let be a compact torsion abelian group. In this paper, we show that an extension of Fp by splits where Fpis the p-adic number group and a prime number. Also, we show that an extension of a torsion-free, non-divisible LCA group by X is not split.

Open Access Original Research Article

Magnetic Curves According to Bishop Frame and Type-2 Bishop Frame in Euclidean 3-Space

Ahmet Kazan, H. Bayram Karadağ

Journal of Advances in Mathematics and Computer Science, Page 1-18
DOI: 10.9734/BJMCS/2017/33330

In this paper, we de ne the notions of T-magnetic, N1-magnetic, N2-magnetic curves according to Bishop frame and YYY.PNG-magnetic, XXXX.PNG-magnetic, B-magnetic curves according to type-2 Bishop frame in Euclidean 3-space. Also, we obtain the magnetic vector field V when the curve is T-magnetic, N1-magnetic, N2-magnetic trajectory of V according to Bishop frame andYYY1.PNG-magnetic,XXXX1.PNG-magnetic, B-magnetic trajectory of according to type-2 Bishop frame. Finally, we give an example for magnetic curves according to Bishop frame and type-2 Bishop frame.

Open Access Original Research Article

Text Summarization versus CHI for Feature Selection

R. S. Jabri, E. Al-Thwaib

Journal of Advances in Mathematics and Computer Science, Page 1-8
DOI: 10.9734/BJMCS/2017/33615

Text Classification is an important technique for handling the huge and increasing amount of text documents on the web. An important problem of text classification is features selection. Many feature selection techniques were used in order to solve this problem, such as chi-square (CHI). Rather than using these techniques, this paper proposes a method for feature selection based on text summarization. We demonstrate this method on Arabic text documents and use text summarization for feature selection. Support Vector Machine (SVM) is then used to classify the summarized documents and the ones processed by CHI. The classification indicators (precision, recall, and accuracy) achieved by text summarization are higher than the ones achieved by CHI. However, text summarization has negligible higher execution time.