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International Journal of Scientific & Technology Research

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IJSTR >> Volume 9 - Issue 4, April 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Robotic Automation Of Employee Onboarding Using Neural Computing

[Full Text]

 

AUTHOR(S)

Sahil Sarthak Biswal, Ashwin Ganesh, Dr. P. Madhavan

 

KEYWORDS

Inverse Document Frequency, Natural Language Processing, Neural Computing, Onboarding of Employee, Robotic Process Automation, Similarity, Term Frequency, UiPath, Word2vec

 

ABSTRACT

With routine, repetitive, labour-intensive tasks in the IT industry, there is a lot of human resources involved in handling these systems for business support and operations. The very fact that remains is if these mundane tasks were machine-driven, employees would be able to concentrate on higher-value activities with improved speed, productivity, at considerably reduced costs to the organisation. Robotic Process Automation (RPA) can do this by applying automation software to perform tasks and operations in applications and process them in the same way as a human would. It delivers direct profitability while improving accuracy across entire business functions and can be leveraged irrespective of industry and application. It is already having an impact at organizations currently deploying virtual workforces and delivered game-changing results for many organizations. The main objective of this proposed work is to bring a change in the employee onboarding process wherein the paperwork can be automated at a regular interval of time with hours of work can be saved. All the documents during the recruitment process can be a part of the onboarding documentation using automation that can be quickly finished with the probability of mistakes that might happen if performed manually can be avoided.

 

REFERENCES

[1] Chaithra K, Vinod Kumar H P, 2019, ‘Robotic Process Automation: Strategic Technology Solutions for IT’ NCARES 2019 Volume 7, Issue 10.
[2] Anusha N D, Baishali Rawat, Renuka J, Sahana S, Vijayshree H P, 2019,’RPA for Human Resource Operation’ International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Vol. 8 Issue 04.
[3] Audrey Bourgouin, Abderrahmane Leshob, and Laurent Renard, 2018, ‘Towards a Process Analysis Approach to Adopt Robotic Process Automation’ IEEE 15th International Conference on e-Business Engineering (ICEBE)
[4] Solomiya Yatskiv, Iryna Voytyuk, Nataliia Yatskiv, Oksana Kushnir, Yuliia Trufanova and Valentyna Panasyuk, 2019, ‘Improved Method of Software Automation Testing Based on Robotic Process Automation Technology’, 9th International Conference on Advanced Computer Information Technologies (ACIT)
[5] Ruchi Issac, Riya Muni and Kenali Desai, 2018, ‘Delineated Analysis of Robotic Process Automation Tools’, Second International Conference on Advances in Electronics, Computers and Communications (ICAECC)
[6] KP Naveen Reddy and Undavalli Harichandana, 2019, ’A Study of Robotic Process Automation Among Artificial Intelligence’ International Journal of Scientific and Research Publications (IJSRP), volume 9, issue 2
[7] Anagnoste S., 2018, ‘Robotic Automation Process – The operating system for the digital enterprise’ Proceedings of the 12th International Conference on Business Excellence 2018, DOI:10.2478/picbe-2018-0007, pp. 54-69, ISSN 2558-9652
[8] Henrik Leopold, Han van der Aa, and Hajo A. Reijers (2018). Identifying Candidate Tasks for Robotic Process Automation in Textual Process Descriptions. 19th International Conference, BPMDS 2018, 23rd International Conference, EMMSAD 2018.
[9] Cai-zhi Liu,Yan-xiu Sheng ,Zhi-qiang Wei , Yong-Quan Yang,2018,’Research of Text Classification Based on Improved TF-IDF Algorithm’ , 2018 IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE)
[10] Ammar Ismael Kadhim,2019, Term Weighting for Feature Extraction on Twitter: A Comparison Between BM25 and TF-IDF, 2019 International Conference on Advanced Science and Engineering (ICOASE)
[11] Haoying Wu,Na Yuan,2018, An Improved TF-IDF algorithm based on word frequency distribution information and category distribution information, ICIIP '18: Proceedings of the 3rd International Conference on Intelligent Information Processing