Digitalized Ration Card

© 2020 by IJCTT Journal
Volume-68 Issue-4
Year of Publication : 2020
Authors : Ms. A. Abirami, Muthulakshmi K, Pritheevi VA, Tamilselvan G
DOI :  10.14445/22312803/IJCTT-V68I4P113

How to Cite?

Ms. A. Abirami, Muthulakshmi K, Pritheevi VA, Tamilselvan G, "Digitalized Ration Card," International Journal of Computer Trends and Technology, vol. 68, no. 4, pp. 73-76, 2020. Crossref,

The processes to avail items from the Ration shops are being complex nowadays due to excess crowd. The entire PDS works on ration cards. In order to overcome this difficulty, the mobile application named Online Ration System can be used. The commodities can be ordered online and payment can be made through online payment system in this application. Naive Bayes algorithm classifies the amount of commodities to be selected according to the members of the family. Once it is chosen and the payment is completed successfully, a set of time slots will be provided. Time slot and date to collect the items will be displayed. The commodities should be collected on the respective date and time slot. It also includes “Free Home Delivery” scheme for the senior citizens. Random Forest algorithm classifies the distance of delivery location. The distribution of commodities is completely supervised by the govt. It also has various disadvantages. The dealer tends to sell the commodities at a higher rate in the market. Due to this, we encounter a lack of transparency between the government and people. A good system has not been developed yet, by which the government is updates on the grain consumption by the people. This ration card management system works on android technology that replaces traditional ration cards.

Android, Public Distribution System (PDS), Naive Bayes, Random Forest

[1] Anindya Ghose and Panagiotis G. Ipeirotis, “Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics”, IEEE transactions on knowledge and data engineering, vol. 23, no. 10, October 2011.
[2] Vikas Sindhwani and Prem Melville, ” Document-Word Co-Regularization for Semi-supervised Sentiment Analysis”, 2008 Eighth IEEE International Conference on Data Mining.
[3] Tak-Lam Wong and Wai Lam,” Hot Item Mining and Summarization from Multiple Web Sites”, 2015 IEEE International Conference.
[4] Yang Liu, Xiangji Huang, Aijun An and Xiaohui Yu,” Modeling and Predicting the Helpfulness of Online Reviews”, 2015 IEEE International Conference.
[5] Tak-Lam Wong and Wai Lam,” A Probabilistic Approach for Adapting Information Extraction Wrappers and Discovering New Attributes”, 2017 IEEE International Conference.
[6] Yan Liu, Bin Guo, Chao Chen, He Du, Zhiwen Yu, Daqing Zhang and Huadong Ma,” Toward an Optimized Food Delivery Network based on Spatial Crowdsourcing”, TMC.2018.2861864, IEEE Transactions on Mobile Computing.
[7] Wei Tu, Tianhong Zhao, Baoding Zhou, Jincheng Jiang, Jizhe Xia, and Qingquan LI, “Online Crowdsourced Delivery for On-Demand Food” , JIOT.2019.2930984, IEEE Internet of Things.
[8] Mostafa Majidpour, Charlie Qiu, Peter Chu, Rajit Gadh and Hemanshu R.Pota, “Fast Prediction for Sparse Time Series”, IEEE transactions on industrial informatics, vol. 11, no. 1, February 2015.
[9] Santa Cruz de Tenerife “Digitalization in Engineering Education Research and Practice”,2018.
[10] Zhuojing Ma, Wangyang Yu, Xiaojun Zhai , and Menghan Jia,” A complex event processing-based online shopping user risk identification system”,2019