Mantle Of Ml In Human Resource Management - Phdassistance

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MANTLE OF ML IN HUMAN RESOURCE MANAGEMENT
Dr. Nancy Agnes, Head,
Technical Operations, Phdassistance
info@phdassistance.com
In Brief :
Transformation of HRM from normal
execution to ML and AI algorithms'
implementations will clear the
Organization's growth's bright path.
HR functional components which have
proved to be more effective and successful
by integrating ML
ML algorithms will improve the HR
department's performance, which will
boost the entire Organization for efficient
performance.
Academic Keywords:
Human Resource Management, Machine
Learning algorithms, Knowledge
Management, Recruitment Management,
Performance management,
Classification, and Regression
techniques, Decision Tree algorithms,
Text mining algorithms, Pre-hire
analysis, Post-hire analysis, Artificial
Intelligence, Big Data Analytics,
Production, and Profits, Data-based
decision making, Performance
improvements
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Topics
I. INTRODUCTION
Today's digital world requires, innovative
HR implementation to improve employee
performance and engagement. Recently
HRM has been ascertained to join the
league to more advanced headway like

Artificial Intelligence (AI) and ML (
Machine Learning ). The presence of
HRM in the Organization waves the
employees' performance to improve or
maintain their stability. The transformation
of HRM from Normal Execution to ML
and AI algorithms' implementations will
clear the Organization's growth's bright
path. 1
II. NEW AGE HRM- ANALYTICS TO
AI AND ML
Machine Learning is a Solicitation of
Artificial Intelligence programmed to
access data to make predictions and give
the required output without human
interference.
To stimulate the regular presence of the
Employee in an organization hits
productivity and profitability. The
objective-based gain measure system will
understand and provide reliable concern
for the Employee.2
The three major HR functional
components which have proved to be more
effective and successful by integrating ML
are
1. Recruitment and selection
Management
2. Performance Management
3. Knowledge Management
The research article concludes that the
integration of machine learning in HRM is
highly efficient with specific functions like
Decision trees, text-mining algorithms
3
These algorithms will improve the HR
department's performance, which will
boost the entire Organization for efficient
performance.
1. Recruitment and selection
Management
The current trend demands a more
comprehensive, more strategic perspective
to recruit, utilize and conserve valuable
human resources. Using algorithms like
Classification and Regression will evaluate
the candidate profile is legitimate and fit
for the required job profile.
Recruitment is a process that determines
the need for staffing for a particular
position in the Organization and works to
fulfil that need by identifying the right
candidate for the job.

Machine Learning kickstarts the role of
HRM by supporting and filtering the
candidates based on some criteria. Based
on the job posts, The candidates who will
apply for the required job position can be
screened using some denominations. Like,
Age, Gender, Experience, Location, Skill
sets, with cost-effectiveness (Figure 1).
This approach is narrated through the
article. 4
Figure 1. Relationship between age and
absenteesim at an organization into
different sub-groups of employees. The
LHS indicates the simple partition of age
and RHS expresses the series of pattern
derived by CART model. 4
ML assists organizations in sourcing the
right candidate who meets their
organizational goals by storing and
managing a large mass of online
applications.
The ML application designed to store
applicants' databases is also linked with
various social media websites that provide
additional information about the candidate.
This approach to recruitment and selection
helps to recognize the right person for the
right job and helps recruit at the right time
by abbreviating the time required for the
entire proceeding.
The work proposed by Irad Ben Gal and
et.al 5
implies that a particular model for
Pre-hire prediction of employees. This
model enhances the high accuracy and
interoperability of HR.
2. Performance Management
After successfully recruiting the precise
candidate for the job, the human resources
department becomes liable to ensure that
the Employee is utilized to his maximum
capabilities and retained to serve the
Organization long term. The results will
deviate the contrast between well-
performed candidates and Bad performed
candidates. Based on the results, the HRM
will act accordingly.
An organization's success largely depends
on its ability to identify, measure, and
develop performance and, in turn, link
each individual's performance and
objectives to the Organization's overall
mission and goals. Using Extreme
Gradient Boosting (XG Boost)
methodology will evaluate the Employee's

overall performance and efficiency. This
methodology and concept is innovated by
Rohit Punnose et. al 6.
Machine Learning Technology Software is
to strengthen the Organization's internal
communications in the following ways.
1. A centralized database helps keep track
of individual contributions to
organizational goals.
2. ML encourages employee engagement
in the Organization through feedback
forms and chatbots
3. This application is available round the
Clock for the Employee's perusal.
4. Automated rewards and appraisals
designed to boost employee morale.
The article 7
prescribes a Gaussian Naïve
Bayes classifier to annotates the
performance of the employees of the
Organization. It is used to filtrate the fillers
or remarks if they occurred for the
Employee.
3. Knowledge Management
Many of the employees will quit the
Organization due to a lack of knowledge
and enhance their career knowledge. But
sometimes, the reason may remain
unknown, which leads to a hidden issue in
the Organization. The article drives the
Classification techniques like Support
Vector Machines, Naïve Bayes, Random
forests techniques to volume-up this issue.
These classifiers filter and figure out the
causation of termination or any other
problems in the Organization.8
This technique augments individual
learning for the employees and proves
methodical to the Organization in a broad
sense.
Acquire more knowledge in Artificial
Intelligence, and Machine Learning
Algorithms supports a lot in Knowledge
management, enhancing the Skillset in all
the management teams, which are
mandatory for Human Resources and
Management. The literature 9
kickstarts the
KMC(Knowledge Management
Capability), which positively maintains the
frosty relationship between HRM and the
entire Organization.
Table 1. Methodologies and Concepts of
HRM using ML

III. FUTURE SCOPE
These are the Research Areas where the
Machine Learning with respect to HRM is
booming to core,
1. Post-hire analysis process will enhance
the exact requirement for the Organization
based on the pre-hire analysis.
2. ML with HRM is in the early stage. To
establish more efficiency in the future, HR
experts and ML specialists work together
is mandatory.
3. Using Machine learning algorithms with
tools and languages will enhance the
analytics process, improving organization
production.
4. Based on the performance, appraisal,
incentives will be automatically generated
to the HR and also the employees. If any
escalation has appeared for the candidate,
the reaction will be according to it.
REFERENCE
[1] Zehir C., Karaboğa T., Başar D. (2020) The
Transformation of Human Resource Management and
Its Impact on Overall Business Performance: Big Data
Analytics and AI Technologies in Strategic HRM. In:
Hacioglu U. (eds) Digital Business Strategies in
Blockchain Ecosystems. Contributions to
Management Science. Springer, Cham.
https://doi.org/10.1007/978-3-030-29739-8_12
[2] Singer, G.; Cohen, I. An Objective-Based Entropy
Approach for Interpretable Decision Tree Models in
Support of Human Resource Management: The Case of
Absenteeism at Work. Entropy, 2020, 22, 821.
https://doi.org/10.3390/e22080821
[3] Garg, Swati, Shuchi Sinha, Arpan Kumar Kar, and
Mauricio Mani. "A review of machine learning
applications in human resource
management." International Journal of Productivity and
Performance Management (2021).
[4] D. Jagan Mohan Reddy, S. Regella and S. R. Seelam,
"Recruitment Prediction using Machine Learning," 2020
5th International Conference on Computing,
Communication, and Security (ICCCS), Patna, India,
2020, pp. 1-4, doi: 10.1109/ICCCS49678.2020.9276955
[5] Dana Pesach, Gonen Singer, Dan Avrahami, Hila
Chaulutz Ben Gal, Erez Shemuli, Irad Ben Gal.
"Employees Recruitment: The prescriptive analysis
approach via Machine learning and mathematical
programming" (2020)
[6] Pankaj Ajit, Rohit Punnoose, "Prediction of
Employee Turnover in Organizations using Machine
Learning Algorithms" (2016)
[7] Fallucchi, Francesca, Marco Coladangelo, Romeo
Giuliano, and Ernesto William De Luca. "Predicting
Employee Attrition Using Machine Learning
Techniques." Computers 9, no. 4 (2020): 86.
[8] Anil Jadhav, "Churn Prediction of Employees Using
Machine Learning Techniques" (2021)
[9] Hui Li, Mathida Khamkhoutlavong, Phong Ba Le,
"Journal Of Knowledge management.", (2021)

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