Navigating the Future: Exploring the Best Employee Management Systems of Today

Year : 2024 | Volume :01 | Issue : 02 | Page : 25-29
By

Satish Singh

Saurabh Sharma

Raj Singh

Arun Sharma

Suchi Pandey

  1. Student Bansal Institute of Engineering and Technology, Lucknow India
  2. Student Bansal Institute of Engineering and Technology, Lucknow India
  3. Student Bansal Institute of Engineering and Technology, Lucknow India
  4. Student Bansal Institute of Engineering and Technology, Lucknow India
  5. Student Bansal Institute of Engineering and Technology, Lucknow India

Abstract

In this paper, we discussed an effective employee management system with the help of Python GUI Technology. A company’s ability to successfully manage its workforce is essential to its success. The Employee Management System enables employers to easily manage all records. Python GUI Technology is used in the development of the application-based staff management system. In previous studies, there were two applications developed to manage employee details and another to mark their regular attendance. Even though office management software is widely available in many nations, certain offices still require this kind of system. Both organizations, whether it is private, or government use such type of application-based employee management systems to manage and store the details of staff.
Pen and paper are still widely used in India’s small-scale sectors for record-keeping, though. Though various cutting-edge technological methods are available to accomplish this task, they are all too expensive for these low-level industries. Employees will profit from tasks such as filing for leave, attending training, obtaining NOC for passports, and receiving notifications via email and SMS. This system will manage the details of each employee and salary management at the end of the month. It also manages their leave management so there is no chance of having a fake entry. This program helps avoid conflicts between the HR team and employees because it saves a lot of HR time and ensures no errors in the pay calculation.

Keywords: Employee management software, administration, employees, salaries, attendance, GUI

[This article belongs to International Journal of Electronics Automation(ijea)]

How to cite this article: Satish Singh, Saurabh Sharma, Raj Singh, Arun Sharma, Suchi Pandey. Navigating the Future: Exploring the Best Employee Management Systems of Today. International Journal of Electronics Automation. 2024; 01(02):25-29.
How to cite this URL: Satish Singh, Saurabh Sharma, Raj Singh, Arun Sharma, Suchi Pandey. Navigating the Future: Exploring the Best Employee Management Systems of Today. International Journal of Electronics Automation. 2024; 01(02):25-29. Available from: https://journals.stmjournals.com/ijea/article=2024/view=146376




Browse Figures

References

  1. Balci B, Saadati D, Shiferaw D Handwritten text recognition using deep learning. 1–8p.
  2. Kim SW, Gil J-M Research paper classification systems based on TF-IDF and LDA schemes. Human-centric Computing and Information Sciences 2019; 9(30): 1–21p.
  3. Berger, A & Lafferty, J. (1999). Information Retrieval as Statistical Translation. In Proceedings of the 22nd ACM Conference on Research and Development in Information Retrieval (SIGIRí99), 222–229.
  4. Friedman C, Hripcsak G Natural language processing and its future in medicine. Academic medicine: Journal of the Association of the American Medical Colleges 1999; 74(8): 890–5p.
  5. Ramos J Using TF-IDF to determine word relevance in document queries. 1999.
  6. Yi X, Allan J, Croft, WB Matching resumes, and jobs based on relevance models. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2007.
  7. Rodney, K. Valaskova and P. Durana, “The artificial intelligence recruitment process: how technological advancements have reshaped job application and selection practices,” Psychosociological Issues in Human Resource Management, vol. 7, no. 1, pp. 2–47, 2019.
  8. Mwaro PN, Ogada DK, Cheruiyot W, SCIT J. Applicability of naïve Bayes model for automatic resume classification. International Journal of Computer Applications Technology and Research. 2020;9(9):257–
  9. Roy PK, Singh SK, Das TK, Tripathy AK. Automated Resume Classification Using Machine Learning. Advances in Distributed Computing and Machine Learning: Proceedings of ICADCML 2022 2022 Jul 28 (pp. 307-316). Singapore: Springer Nature Singapore.
  10. Joachims. Making large-scale support vector machine learning practical. In Advances in Kernel Methods: Support Vector Learning. B. Schölkopf, C.J.C. Burges, and A.J. Smola (Eds.), MIT Press, 1998.
  11. Rosenblatt. The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review, 65:386-408, 1959.

Regular Issue Subscription Review Article
Volume 01
Issue 02
Received March 25, 2024
Accepted April 19, 2024
Published April 30, 2024