i-manager Publications

Comparison of Programming Languages for Human Gender Classification

G.D.K.Kishore*, Babu Reddy Mukkamalla**

* Research Scholar, Department of Computer Science, Krishna University, Andhra Pradesh, India.

** Assistant Professor and Head, Department of Computer Science, Krishna University, Andhra Pradesh, India.

Periodicity:June - August'2017
DOI : https://doi.org/10.26634/jcom.5.2.13908

Abstract

Nowadays, the investigation of Human gender classification (Yong et al., 2012; Mahmood et al., 2012) is capable and powerful, of predicting human gender by utilizing statistical tools and methods. There are a few numerical computational tools that fill in as instructive tools and are likewise accessible for business use to contrast different tools. In this paper, statistical tools like Python, R and MATLAB along with their advantages, working environment, features and challenges are discussed. Here, a review on the most important software tools for image classification applications is presented. Additionally, which software tool is best for beginning learners to learn image learn classification with accurate results and for analysing output results perfectly are also discussed.

Keywords

Classification, R, Python, MATLAB, Data Analysis Tools

How to Cite this Article?

Kishore, G. D. K., and Reddy, M.B. (2017). Comparison of Programming Languages for Human Gender Classification. i-manager’s Journal on Computer Science, 5(2), 34-39. https://doi.org/10.26634/jcom.5.2.13908

References

[1]. Muenchen. R. (2017). Data Science Job Report 2017: R Passes SAS, But Python Leaves Them Both Behind [Blog post]. Retrieved from http://r4stats.com/2017/02/28/rpasses- sas/

[2]. The R Foundation. (2017). The R Project for Statistical Computing. Retrieved from https://www.r-project.org/

[3]. Python vs. Matlab. (pizo.org). Pyzo. Retrieved from http://www.pyzo.org/python_vs_matlab.html

[4]. Revolution Analytics. (2014, February 20). What is R? Retreived from http://www.inside-r.org/what-is-r, February 20, 2014.

[5]. Yong, C. Y., Chew, K. M., Mahmood, N. H., Ariffin, I. (2012). A Survey of Image Processing Tools Package in Medical Imaging. International Conference on Teaching and Learning & In Higher Education in conjunction with Regional Conference on Engineering Education & Research in Higher Education (RCEERHed 2012).

[6]. Mahmood, N. H., Yong, C. Y., Chew, K. M., & Ariffin, I. (2012). Image Processing Software Package in Medical Imaging: A review. International Journal of Computational Engineering Research (IJCER), 2(1), 199-203.

[7]. Ozgur, C., Hall, U., Colliau, T., Rogers, G., & Hughes, Z. (2017). MatLab vs. Python vs. R. Journal of Data Science, 15(3), 355-372.

[8]. Eglen SJ (2009). A Quick Guide to Teaching R Programming to Computational Biology Students. PLoS Comput, Biol., 5(8): e1000482. https://doi.org/10.1371/ journal.pcbi.1000482.

[9]. Feldman, P. M. (2015). Eight Advantages of Python Over Matlab. Retrieved from http://phillipmfeldman.org/ Python/Advantages_of_Python_Over_Matlab.html

[10]. Guo, P. (2014). Python is Now the Most Popular Introductory Teaching Language at Top U.S. Universities. Retrieved from http://cacm.acm.org/blogs/blogc a cm/ 1 7 6 4 5 0 - p y t h o n - i s - n o w-t h e -mo s t- p o p u l a ri n t r o d u c t o r y t e a c h i n g - l a n g u a g e - a t - t o p - u s - universities/fulltext

[11]. Guo, P. (2007). Why Python is a great language for teaching beginners in introductory programming classes. Retrieved from http://pgbovine.net/pythonteaching. htm

[12]. Hughes, Z. (2015). Personal Experience Section in MatLab vs. Python vs. R

[13]. Markham, K. (2016, February 2). Should you teach Python or R for data science? Retrieved from http://www.dataschool.io/python-or-r-for-data-science/

[14]. Kishore, G. D. K. & Reddy, M. B. A Literature Survey on Object Classification Techniques, IJATES (International Journal of Advanced Technology in Engineering and Science), An International Refereed Electronic Journal, 5 (3), 779-786.

[15]. Kishore, G. D. K. & Reddy, M. B. (2017). Comparative Analysis between Classification Algorithms and Datasets (1:N & N:1) Through WEKA, Open Access International Journal of Science and Engineering (OAIJSE), 2(5), 23-28.

If you have access to this article please login to view the article or kindly login to purchase the article

Single Article

	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Online	15	15

ADD TO CART

Options for accessing this content:

If you would like institutional access to this content, please recommend the title to your librarian.
If you already have i-manager's user account: Login above and proceed to purchase the article.
New Users: Please register, then proceed to purchase the article.