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Abstract

nGoogle play store consists of millions of applications and several thousand apps are added on the play store every day. The competition is so fierce that it is really difficult for the developers to find out whether the app that is the product of his hard work is going to be successful or not. The main goal of the study is to create a tool that helps developers and organizations to understand the current trends in the Play Store and the user bias for applications and utilize the data for making optimal decisions regarding their upcoming app. The success of an app can be determined by factors like ratings, number of installs and reviews. In this study we demonstrate using applied exploratory data analysis to discover correlaations among features of an app and direct contribution of features towards success of the app to predict which apps will succeed. Data from Google Play Store was used to train various supervised learning models for prediction of the rating of an app, the models being-Random Forest, Support Vector Machine and Linear Regression. The user reviews are used to learn input bias of users towards applications using natural language processing techniques.n

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References

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1. Tuckerman CJ. Predicting mobile application success. 2014 Dec.
2. Singh K, Wajgi R. Data analysis and visualization of sales data. 2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave). 2016; 1–6.
3. Muneez Abdul Ahmed, Khushba Islam, Tuba Iqbal, Waqqas. Exploratory Data Analysis and Success Prediction of Google Play Store Apps. BRAC University; 2018.
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5. Bird Steven, Loper Edward, Klein Evan. Natural Language Processing with Python. OReilly Media Inc.; 2009.
6. Aralikatte R, Sridhara G, Gantayat N, Mani S. Fault in your stars: an analysis of android app reviews. In Proceedings of the ACM India Joint International Conference on Data Science and Management of Data. 2018; 57–66.
7. Maskey Sameer. MapReduce for Statistical NLP/Machine Learning. Columbia University; 2012 Oct 17.
8. Harman M, Jia Y, Zhang Y. App store mining and analysis: MSR for app stores. In 2012 9th IEEE Working Conference on Mining Software Repositories (MSR). 2012; 108–111.
9. Ruiz IJM, Nagappan M, Adams B, Berger T, Dienst S, Hassan E. Examining the rating system used in mobile-app stores. IEEE Software. 2016; 33(6): 86–92.

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Journal of Open Source Developments

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[if 344 not_equal=””]ISSN: 2395-6704[/if 344]

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Abstract

nThese days, PC vision has arrived at its pinnacle, where a PC can recognize its proprietor utilizing a straightforward program of picture handling. In this advanced stage, people are incorporating this vision into various aspects of daily life, such as face recognition, color identification, automatic vehicles, and so on. In this project, computer vision is used to create an optical mouse and console that uses hand signals. The camera of the PC will peruse the picture of various signals performed by an individual’s hand and as indicated by the development of the motions the mouse or the cursor of the PC will move, even perform both ways click, utilizing various signals. Essentially, the console capacities might be utilized for a variety of signals, signals, such as utilizing one finger motion for letter set select and four-figure motion to swipe left and right. It will go about as a virtual mouse and console with no wire or outside gadgets. The main equipment part of the undertaking is a webcam, and the coding is done on python, utilizing Anaconda stage. Here, the convex frame deserts are first created, and then a calculation is created using the deformity estimations, and the mouse and console capacities are planned with the imperfections.n

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1. Desale Rakesh D, Ahire Vandana S. A Study on Wearable Gestural Interface–A Sixth Sense Technology. IOSR Journal of Computer Engineering (IOSR-JCE). 2013; 10(5): 10–16.
2. Sadhana Rao S. Sixth Sense Technology. Proceedings of the International Conference on Communication and Computational Intelligence. 2010; 336–339.
3. Christy A, Vaithyasubramanian S, Mary VA, Naveen Renold J. Artificial intelligence based automatic decelerating vehicle control system to avoid misfortunes. International Journal of Advanced Trends in Computer Science and Engineering (IJATCSE). 2019; 8(6): 3129–3134.
4. Gandhi GM, Salvi. Artificial Intelligence Integrated Blockchain for Training Autonomou Cars. 2019 5th International Conference on Science Technology Engineering and Mathematics (ICONSTEM), Chennai, India. 2019; 157–161.
5. Jesudoss A, Subramaniam NP. EAM: Architecting Efficient Authentication Model for Internet Security using Image-Based One Time Password Technique. Indian J Sci Technol. 2016 Feb; 9(7): 1–6.
6. Praveena MDA, Eriki MK, Enjam DT. Implementation of smart attendance monitoring using open-CV and python. J Comput Theor Nanosci. 2019 Aug; 16(8): 3290–3295.
7. Roobini MS, Lakshmi M. Classification of Diabetes Mellitus using Soft Computing and Machine Learning Techniques. International Journal of Innovative Technology and Exploring Engineering (IJITEE). 2019; 8(6S4): 1541–1545.
8. Game PM, Mahajan AR. A gestural user interface to Interact with computer system. Int J Sci Technol (IJSAT). 2011 Jan–Mar; II(I): 018–027.
9. Arashdeep Kaur. Virtual Switching Panel by Detecting Position and Color of Object. Int J Latest Trends Eng Technol. 7(4): 55–62.
10. Siddiqui Mohammad F, Darade Milind M. Failures in Construction: Types and Causes and Its Assessment. Imp J Interdiscip Res (IJIR). 2017; 3(4): 878–881.

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