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All Journal Aptisi Transactions on Technopreneurship (ATT) Startupreneur Business Digital (SABDA Journal)
Arora, Kawal
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Social Sciences

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The Use of Data Science in Digital Marketing Techniques: Work Programs, Performance Sequences and Methods. Arora, Kawal; Alwiyah; Faisal, Muhammad
Startupreneur Business Digital (SABDA Journal) Vol. 1 No. 2 (2022): Startupreneur Business Digital (SABDA)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1130.062 KB) | DOI: 10.33050/sabda.v1i2.110

Abstract

Over the past decade, there have been tremendous advances in the use of data science to facilitate decision making and extract and thus act upon insights from large data sets in digital business environments. However, despite these advances, there is still a lack of relevant evidence on actions to improve data science management in digital businesses. To fill this gap in the literature. The purpose of this study is to review (i) analytical methods, (ii) usage, and performance metrics based on (iii). Data science used in digital business techniques and strategies. To this end, a comprehensive literature search was carried out on the important scientific contributions made so far in this area of ​​research. The results provide an overview of the most important applications of data science in digital business. Generate insights related to the creation of innovative data mining and knowledge discovery techniques. Important theoretical implications are discussed and a list of topics for further research in this area is provided. This report aims to develop recommendations for enhancing digital business strategies for enterprises, marketing, and non-technical researchers, and identify directions for further research on innovative data mining and discovery applications in science.
A Novel Approach for Facial Attendance:AttendXNet Arora, Kawal; Bist, Ankur Singh; Prakash, Roshan; Chaurasia, Saksham
Aptisi Transactions On Technopreneurship (ATT) Vol 2 No 2 (2020): September
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v2i2.86

Abstract

Recent advancements in the area of facial recognition and verification introduced the possibility of facial attendance for various use cases. In this paper we present a system named as AttendXNet. Our method uses the ResNet and Multi-layer feed forward network to achieve the state of art results. Extensive analysis of various deep learning and machine learning techniques is described. Face anti-spoofing is a major challenge in facial attendance. Extended-MobileNet is used to resolve the same issue. We also introduced the end to end pipeline to implement an attendance system for various use cases.
Custom OCR for Identity Documents:OCRXNet Arora, Kawal; Bist, Ankur Singh; Prakash, Roshan; Chaurasia, Saksham
Aptisi Transactions On Technopreneurship (ATT) Vol 2 No 2 (2020): September
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v2i2.87

Abstract

Recent advancements in the area of Optical Character Recognition (OCR) using deep learning techniques made it possible to use for real world applications with good accuracy. In this paper we present a system named as OCRXNet. OCRXNetv1, OCRXNetv2 and OCRXNetv3 are proposed and compared on different identity documents. Image processing methods and various text detectors have been used to identify best fitted process for custom ocr of identity documents. We also introduced the end to end pipeline to implement OCR for various use cases.
Artificial Intelligence Based Drug Discovery Techniques for COVID-19 Detection Arora, Kawal; Bist, Ankur Singh
Aptisi Transactions On Technopreneurship (ATT) Vol 2 No 2 (2020): September
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v2i2.88

Abstract

Recent advancements in the area of drug discovery using artificial intelligence made it possible to speed up the hunt for new pharmaceuticals. Drugs like arbidol, atazanavir, remdesivir & favipiravir are under testing phase to cure COVID-19. In this paper, we present systematic study of AI based drug discovery techniques suitable for COVID-19 detection.
Co-Authors Alwiyah Alwiyah Bist, Ankur Singh Chaurasia, Saksham Muhammad Faisal Prakash, Roshan