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All Journal IAES International Journal of Robotics and Automation (IJRA) Indonesian Journal of Electrical Engineering and Computer Science
Dwivedi, Ritesh Kumar
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Automotive Engineering Electrical & Electronics Engineering

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An introduction to using QR codes in web portals for synchronizing calendar events over phones Sethi, Inder Pal Singh; Gupta, Om Pradyumana; Bhaisare, Sulbha; Dwivedi, Ritesh Kumar; Kapoor, Misha
IAES International Journal of Robotics and Automation (IJRA) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v13i4.pp469-475

Abstract

An optical label with machine-readable information about the object it is attached to is called a quick response (QR) code. QR codes frequently hold information for a tracker, locator, or identifier that directs users to a website or application. To efficiently store data, a QR code has four standardized encoding modes: kanji, byte or binary, alphanumeric, and numeric. As a means of identifying a wide range of commercial goods, including transactions, ads, and other public notices, the QR code gained popularity. In our web portal, the proposed QR code model synchronizes all the event details synchronously in the mobile calendar. QR code is used for web-to-mobile data transfer, saving events or meeting details in the mobile calendar. Anyone with a smartphone can view the data encoded in a QR code by scanning it. Although it makes it easier for end users to decode QR codes, verifying access to the encoded data is a cause for worry. Our proposed model validates access to data through the QR code, allowing only authorized personnel to access data. To ensure accessibility control, the proposed model has the functionality of a one-time password (OTP) that enhances application security. The model achieved an average decoding speed of 157 milliseconds with an error rate of 0.38%.
Attention based English to Indo-Aryan and Dravidian language translation using sparsely factored NMT Dwivedi, Ritesh Kumar; Nand, Parma; Pal, Om
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp250-256

Abstract

Neural machine translation (NMT) is a sophisticated technique that employs a large, singular neural network to learn and execute automatic translation tasks. Unlike statistical machine translation systems, NMT handles the entire translation process in an end-to-end manner, removing the need for additional components. This approach has shown significant promise in translation quality and has become the prevalent method. In this study, we apply sparsely factored NMT to English and several Indo-Aryan (Hindi, Bengali) and Dravidian (Tamil, Malayalam) language pairs. Specifically, we develop the machine translation system using an attention-based mechanism. A significant problem with traditional transformers is the huge memory requirement. Therefore, a sparsely factored NMT (SFNMT) is used to reduce the memory requirement but also improves the training time, thereby, reducing the computing time. In this paper, take inspiration from Vaswani transformer and modify it to get the best results. The system’s performance was evaluated using the BLEU metric. The proposed model indtrl achieves a BLUE score of 32.13 (en→hi), 29.31 (en→be), 31.21 (en→ta), 21.12 (en→ml) and 32.67 (en→hi), 29.38 (en→be), 31.75 (en→ta), 21.17 (en→ml) without backtranslation and with backtranslation. To evaluate the performance of the system, we compared the results with those of existing systems. The developed system demonstrated a marginally higher BLEU score than both AnglaMT and Google translate.
Co-Authors Bhaisare, Sulbha Gupta, Om Pradyumana Kapoor, Misha Nand, Parma Pal, Om Sethi, Inder Pal Singh