Use of API’s for Comparison of different product information under one roof: analysis using SVM
Автор: Priyanka Desai, G. R. Kulkarni
Журнал: International Journal of Information Technology and Computer Science @ijitcs
Статья в выпуске: 6 Vol. 10, 2018 года.
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The internet has grown in leaps and bounds and hence all the data is now available online; be it shopping, banking, private and public institutes or universities, private public sectors are all making their presence felt online. The online data is just a click away thanks to ubiquitous systems today. The browser does not require any specific program set up hence easier for the end user. Earlier the data online was static used HTML now it’s dynamic uses ASP, ASP.NET, Servlet, JSP and other operational tools therefore the internet operation is broken down into many categories. The problem arises with the customer while trying to buy something online. There are lots of online stores sometimes it’s difficult to browse through all products to get a better deal. The pricing of products are different on different sites, this is the first gap at the customer end. The second problem arises at the provider end. The second gap here is to understand the customer need. How can the variation in prices be checked? ; The existing prices available on sites cannot be changed but the customer can be provided with options to select the best deal of the same product. For the first problem the paper deals with an API implementation wherein the information of at least some products is compared under one roof. How can the provider know the genuine customer? ; The second problem is resolved by the use of SVM. Last problem is in detecting if a customer visiting a site will actually buy the product being compared. The paper focuses on the selection of ASP.NET to deal with the implementation problems stated and find solution to the forecasting problem using SVM. SVM and C4.5 are used for comparison.
HTML, ASP.NET, Application Program Interface, Support Vector Machines, C4.5
Короткий адрес: https://sciup.org/15016268
IDR: 15016268 | DOI: 10.5815/ijitcs.2018.06.02
Список литературы Use of API’s for Comparison of different product information under one roof: analysis using SVM
- Khampheth Bounnady , Khampaseuth Phanthavong ;, Somsanouk Pathoumvanh , Keokanlaya Sihalath (2016), “Comparison the processing speed between PHP and ASP.NET”, Electrical Engineering/Electronics, Computer, ,Telecommunications and Information Technology (ECTI-CON), 2016 13th International Conference on, DOI: 10.1109/ECTICon.2016.7561484
- P. Desai and G. R. Kulkarni (2014), "Necessity of customer inputs for online group shopping using Support Vector Machines," Proceedings of 3rd International Conference on Reliability, Infocom Technologies and Optimization, Noida, 2014, pp. 1-6.
- S.Subramanian, Laura Inozemtseva, Reid Holmes,” Live API Documentation”,(2014) International Conference on Software Engineering(ICSE), Hyderabad ,India 2014, Pages 643-652.
- F. Thung(2016), "API recommendation system for software development," 2016 31st IEEE/ACM International Conference on Automated Software Engineering (ASE), Singapore, pp. 896-899.
- Yi Yang ; Wenguang Chen (2016),”Taiga: performance optimization of the C4.5 decision tree construction algorithm”, Tsinghua Science and Technology , Volume: 21, Issue: 4 , DOI: 10.1109/TST.2016.7536719
- Tahira Mahboob , Sadaf Irfan , Aysha Karamat (2016),”A machine learning approach for student assessment in E-learning using Quinlan's C4.5, Naive Bayes and Random Forest algorithms”, Multi-Topic Conference (INMIC),19th International Conference, DOI: 10.1109/INMIC.2016.7840094
- Sardjoeni Moedjiono; Yosianus Robertus Isak ; Aries Kusdaryono (2016).”Customer loyalty prediction in multimedia Service Provider Company with K-Means segmentation and C4.5 algorithm”, Informatics and Computing (ICIC), DOI: 10.1109/IAC.2016.7905717
- Batra M., Agrawal R. (2018),” Comparative Analysis of Decision Tree Algorithms. In: Panigrahi B., Hoda M., Sharma V., Goel S. (eds) Nature Inspired Computing”, Advances in Intelligent Systems and Computing, vol 652. Springer, Singapore
- Seyyid Ahmed Medjahed, Mohammed Ouali, Tamazouzt Ait Saadi, Abdelkader Benyettou(2015),"An Optimization-Based Framework for Feature Selection and Parameters Determination of SVMs", IJITCS, vol.7, no.5, pp.1-9, 2015.DOI: 10.5815/ijitcs.2015.05.01
- A. Hajiha M. Shahriari, N. Vakilian (2014),“The role of perceived value on customer E-shopping intention using technology acceptance model, (TAM)”, Industrial Engineering and Engineering Management (IEEM), IEEE International Conference , DOI: 10.1109/IEEM.2014.7058816
- Munir Ahmad, Shabib Aftab(2017), "Analyzing the Performance of SVM for Polarity Detection with Different Datasets", International Journal of Modern Education and Computer Science(IJMECS), Vol.9, No.10, pp. 29-36, 2017.DOI: 10.5815/ijmecs.2017.10.04
- K. Maheswari ; P. Packia Amutha Priya(2017) “Predicting customer behavior in online shopping using SVM classifier”, Intelligent Techniques in Control, Optimization and Signal Processing (INCOS),IEEE International Conference, DOI:10.1109/ITCOSP.2017.8303085
- Ngoc, P.V., Ngoc, C.V.T., Ngoc, T.V.T. et al. (2017). Evolving Systems ,https://doi.org/10.1007/s12530-017-9180-1
- S. J. Lee, W. C. Su, C. E. Huang and J. L. You(2016), "Categorizing and Recommending API Usage Patterns Based osn Degree Centralities and Pattern Distances," 2016 International Computer Symposium (ICS), Chiayi, 2016, pp. 583-588.
- Grill T., Polacek O., Tscheligi M. (2012),” Methods towards API Usability: A Structural Analysis of Usability Problem Categories. In: Winckler M., Forbrig P., Bernhaupt R. (eds) Human-Centered Software Engineering. HCSE 2012. Lecture Notes in Computer Science, vol 7623. Springer, Berlin, Heidelberg