Nowadays, there is a tremendous increase in the use of online secure payment systems. Even roadside shops all over India and other countries have access to online payment which made each and every person to go cash-free. But there is one important issue we need to consider which is the security during online transactions. There are several new techniques that are being developed nowadays but each has its own pros and cons. In order to enhance the security during online transaction, we have introduced a two-way authentication system which increases the security during online transaction. This system includes facial and proxy detection before entering the UPI pin. The algorithm used in this paper, will further improve the security and efficiency of facial recognition since it embeds 128 feature points of the user’s face thereby increasing the accuracy of the system. While the data is being transmitted for verification, we have included tripleDES encryption to further enhance the security of the work. After the verification is done by the system, the bank balance of the user is verified and the transaction is done. The proposed system has better efficiency when compared to the existing works and also its simple UI makes it easy to use.

Machine translation is one of the most powerful natural language processing applications for preserving and upgrading low-resource language. Mizo language is considered as low-resource since there is limited availability of resources. Therefore, it is a challenging task for English-Mizo language pair translation. Moreover, Mizo is a tonal language, where a word can express different meanings depending on a variety of tones. There are four variations of tones, namely high, low, rising, and falling. A tone marker is used to represent each of the tones, which is added to the vowels to indicate tone variation. Addressing tonal words in machine translation for such a low-resource pair is another challenging issue. In this paper, the English-Mizo corpus is developed where parallel sentences having tonal words are incorporated. The different machine translation models are explored based on statistical machine translation and neural machine translation for the baseline systems. Furthermore, the proposed approach attempts to augment the train data by expanding parallel data having tonal words and achieves state-of-the-art results for both forward