Employee attrition is one of the most significant business issues in human resource (HR) analytics. This research aims to identify the most critical elements that contribute to employee attrition. Businesses operate heavily on employee training in order to maximize the returns they will offer to the company in the future. By utilizing the employee information value concept, it has been discovered that employee features such as overtime, the total number of projects and job level have a significant impact on attrition.
To find the probability of new employee attrition, various classification algorithms such as decision trees (DT) classifier, logistic regression (LR), random forests (RF), and K-means clustering are used. A comparative analysis of the models with different rating scales is carried out for the highest accuracy. For prediction, four diverse machine learning (ML) algorithms such as LR, RF, DT classifier, and k-nearest neighbors (k-NN) are used. DT classifier outperforms with 97% of accuracy than other techniques. The effects of predictive ML techniques on the employee dataset show that RF evaluation outperforms other ML techniques followed by model of LR for the specific dataset if precision is the preferred metric. Identification of HR is forecasted using ML algorithms on employee data.
EMPLOYEE ATTRITION PREDICTION IN INDUSTRY USING MACHINE LEARNING TECHNIQUESIAEME Publication
Companies are always looking for ways to keep their professional personnel on board in order to save money on hiring and training. Predicting whether or not a specific employee would depart will assist the organisation in making proactive decisions. Human resource problems, unlike physical systems, cannot be defined by a scientific-analytical formula. As a result, machine learning approaches are the most effective instruments for achieving this goal. In this study, a feature selection strategy based on a Machine Learning Classifier is proposed to improve classification accuracy, precision, and True Positive Rate while lowering error rates such as False Positive Rate and Miss Rate. Different feature selection techniques, such as Information Gain, Gain Ratio, Chi-Square, Correlation-based, and Fisher Exact test, are analysed with six Machine Learning classifiers, such as Artificial Neural Network, Support Vector Machine, Gradient Boosting Tree, Bagging, Random Forest, and Decision Tree, for the proposed approach. In this study, combining Chi-Square feature selection with a Gradient Boosting Tree classifier improves employee attrition classification accuracy while lowering error rates.
Mantle Of Ml In Human Resource Management - PhdassistancePhD Assistance
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.
Three Major HR Functional Component:
1. Recruitment and selection Management
2. Performance Management
3. Knowledge Management
Learn More: https://bit.ly/31X49es
Contact Us:
Website: https://www.phdassistance.com/
UK NO: +44–1143520021
India No: +91–4448137070
WhatsApp No: +91 91769 66446
Email: info@phdassistance.com
This paper illustrates the similarities between the problems of customer churn and employee turnover. An example of employee turnover prediction model leveraging classical machine learning techniques is developed. Model outputs are then discussed to design \& test employee retention policies. This type of retention discussion is, to our knowledge, innovative and constitutes the main value of this paper.
Role of human intelligence and information technology in retaining intellectualIAEME Publication
This document discusses methods for valuing and retaining intellectual capital, specifically human capital, within information technology organizations. It proposes that employees should be evaluated based on their competencies in domains like domain expertise, technology skills, project management, initiative, and leadership. By assigning values to each competency, an employee's total value can be calculated. If this data is tracked over time, organizations can better understand the financial impact of employee turnover, especially for high-value employees. The document suggests this approach could help organizations structure compensation and retention efforts more effectively to maximize human capital.
Building the Digital HR Organization-pagesCeline Burgle
The document discusses how digital technologies are transforming HR and enabling the rise of "digital HR". Key points include:
- Mobile tools, analytics, social media and cloud/SaaS platforms allow HR to engage employees more and deliver services more flexibly.
- Technologies are driving the democratization of talent management by empowering employees and managers.
- HR can apply marketing techniques like customization and targeting to talent processes using technology.
- An integrated HR platform in the cloud provides data and tools to support digital HR capabilities.
- Case studies show how companies like Timken, Barry Callebaut are transforming HR with technologies.
Running Head: IT AND DECISION MAKING 1
7
IT AND DECISION MAKING
Comment by Lawrence Gross: See instructions no running head required
Stage 1: Analysis and System Recommendation on MTC
Joshua D. Musick
IFSM 300
Professor: Lawrence Gross
March 31, 2019
University of Maryland University College
Introduction
The reports shows the progress which the MTC company has managed to achieve in the last year years of its operation. The company has relatively succeeded in the IT consulting field and this can be measured by the number of clients it has managed to serve. The company management believes that success in information technology can only be realized through the identification of best talents within the consulting field and having a preference for best operation practices (Adler, 2007). Undoubtedly, the company exist in a competitive field and the IT world is equally demanding and harsh to any laxity in operation structure. The MTC structure of hiring process is responsive to technology compared to the manual hiring process. This report puts together the evaluation, analysis, and recommendation on how MTC can use technology to bring the existing gaps.
Use of Information technology Comment by Lawrence Gross: Your paper does not adhere to the prescribed outline.
The organization strategy
The MTC Company has plans to provide good consultation services for its clients. To achieve its objective the company, needs to get the best workforce on its projects in order to give the client companies evidence-based recommendations (Orges Ormanidhi, 2008). The company services are highly affected by the ever dynamic technology and, therefore, the company management appreciate the need to improve and use trending technologies to develop methods and practices in its operation and business (Wei zheng, 2010). The hiring structure considers the emerging trends despite the fact that the company is still using manual hiring structure. The MTC needs to advance and improve the its hiring process by ensuring it is technology-driven. An IT based hiring system positively affect the company structure and ensure consulting company have best practices in the market.
Competitive advantage
MTC operate in business field which is highly competitive. The company faces competition from Hewlett Packard and Booz Allen Hamilton who have already robust IT domain. Developing a better hiring process will push MTC to higher competitive levels and increase its attractiveness in the industry. Information from the hiring system will make the company make informed decisions in placements of new workers (Michael E Porter, 1985). The hiring system will make MTC identify and attract talented workers hence foot off the competitors. Continuous update of the business concept will help the company reduce threat of substitution (Wei zheng, 2010). It also boost the position of the company in market.
Strategic objectives.
The document discusses attrition rates at HCL Technologies. It provides background on HCL, outlines some key causes of attrition like lack of career growth opportunities and ineffective leadership. Some key findings from an employee satisfaction survey at HCL are presented, showing that 27% of employees expressed dissatisfaction, which could lead to increased attrition rates. The top two contributors to dissatisfaction were issues with career development and lack of proper recognition and rewards.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
Employee attrition refers to the decrease in staff numbers within an organization due to various reasons.
As it has a negative impact on long-term growth objectives and workplace productivity, firms have
recognized it as a significant concern. To address this issue, organizations are increasingly turning to
machine-learning approaches to forecast employee attrition rates. This topic has gained significant
attention from researchers, especially in recent times. Several studies have applied various machinelearning methods to predict employee attrition, producing different resultsdepending on the employed
methods, factors, and datasets. However, there has been no comprehensive comparative review of multiple
studies applying machine-learning models to predict employee attrition to date. Therefore, this study aims
to fill this gap by providing an overview of research conducted on applying machine learning to predict
employee attrition from 2019 to February 2024. A literature review of relevant studies was conducted,
summarized, and classified. Most studies agree on conducting comparative experiments with multiple
predictive models to determine the most effective one.From this literature survey, the RF algorithm and
XGB ensemble method are repeatedly the best-performing, outperforming many other algorithms.
Additionally, the application of deep learning to employee attrition prediction issues also shows promise.
While there are discrepancies in the datasets used in previous studies, it is notable that the dataset
provided by IBM is the most widely utilized. This study serves as a concise review for new researchers,
facilitating their understanding of the primary techniques employed in predicting employee attrition and
highlighting recent research trends in this field. Furthermore, it provides organizations with insight into
the prominent factors affecting employee attrition, as identified by studies, enabling them to implement
solutions aimed at reducing attrition rates.
EMPLOYEE ATTRITION PREDICTION IN INDUSTRY USING MACHINE LEARNING TECHNIQUESIAEME Publication
Companies are always looking for ways to keep their professional personnel on board in order to save money on hiring and training. Predicting whether or not a specific employee would depart will assist the organisation in making proactive decisions. Human resource problems, unlike physical systems, cannot be defined by a scientific-analytical formula. As a result, machine learning approaches are the most effective instruments for achieving this goal. In this study, a feature selection strategy based on a Machine Learning Classifier is proposed to improve classification accuracy, precision, and True Positive Rate while lowering error rates such as False Positive Rate and Miss Rate. Different feature selection techniques, such as Information Gain, Gain Ratio, Chi-Square, Correlation-based, and Fisher Exact test, are analysed with six Machine Learning classifiers, such as Artificial Neural Network, Support Vector Machine, Gradient Boosting Tree, Bagging, Random Forest, and Decision Tree, for the proposed approach. In this study, combining Chi-Square feature selection with a Gradient Boosting Tree classifier improves employee attrition classification accuracy while lowering error rates.
Mantle Of Ml In Human Resource Management - PhdassistancePhD Assistance
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.
Three Major HR Functional Component:
1. Recruitment and selection Management
2. Performance Management
3. Knowledge Management
Learn More: https://bit.ly/31X49es
Contact Us:
Website: https://www.phdassistance.com/
UK NO: +44–1143520021
India No: +91–4448137070
WhatsApp No: +91 91769 66446
Email: info@phdassistance.com
This paper illustrates the similarities between the problems of customer churn and employee turnover. An example of employee turnover prediction model leveraging classical machine learning techniques is developed. Model outputs are then discussed to design \& test employee retention policies. This type of retention discussion is, to our knowledge, innovative and constitutes the main value of this paper.
Role of human intelligence and information technology in retaining intellectualIAEME Publication
This document discusses methods for valuing and retaining intellectual capital, specifically human capital, within information technology organizations. It proposes that employees should be evaluated based on their competencies in domains like domain expertise, technology skills, project management, initiative, and leadership. By assigning values to each competency, an employee's total value can be calculated. If this data is tracked over time, organizations can better understand the financial impact of employee turnover, especially for high-value employees. The document suggests this approach could help organizations structure compensation and retention efforts more effectively to maximize human capital.
Building the Digital HR Organization-pagesCeline Burgle
The document discusses how digital technologies are transforming HR and enabling the rise of "digital HR". Key points include:
- Mobile tools, analytics, social media and cloud/SaaS platforms allow HR to engage employees more and deliver services more flexibly.
- Technologies are driving the democratization of talent management by empowering employees and managers.
- HR can apply marketing techniques like customization and targeting to talent processes using technology.
- An integrated HR platform in the cloud provides data and tools to support digital HR capabilities.
- Case studies show how companies like Timken, Barry Callebaut are transforming HR with technologies.
Running Head: IT AND DECISION MAKING 1
7
IT AND DECISION MAKING
Comment by Lawrence Gross: See instructions no running head required
Stage 1: Analysis and System Recommendation on MTC
Joshua D. Musick
IFSM 300
Professor: Lawrence Gross
March 31, 2019
University of Maryland University College
Introduction
The reports shows the progress which the MTC company has managed to achieve in the last year years of its operation. The company has relatively succeeded in the IT consulting field and this can be measured by the number of clients it has managed to serve. The company management believes that success in information technology can only be realized through the identification of best talents within the consulting field and having a preference for best operation practices (Adler, 2007). Undoubtedly, the company exist in a competitive field and the IT world is equally demanding and harsh to any laxity in operation structure. The MTC structure of hiring process is responsive to technology compared to the manual hiring process. This report puts together the evaluation, analysis, and recommendation on how MTC can use technology to bring the existing gaps.
Use of Information technology Comment by Lawrence Gross: Your paper does not adhere to the prescribed outline.
The organization strategy
The MTC Company has plans to provide good consultation services for its clients. To achieve its objective the company, needs to get the best workforce on its projects in order to give the client companies evidence-based recommendations (Orges Ormanidhi, 2008). The company services are highly affected by the ever dynamic technology and, therefore, the company management appreciate the need to improve and use trending technologies to develop methods and practices in its operation and business (Wei zheng, 2010). The hiring structure considers the emerging trends despite the fact that the company is still using manual hiring structure. The MTC needs to advance and improve the its hiring process by ensuring it is technology-driven. An IT based hiring system positively affect the company structure and ensure consulting company have best practices in the market.
Competitive advantage
MTC operate in business field which is highly competitive. The company faces competition from Hewlett Packard and Booz Allen Hamilton who have already robust IT domain. Developing a better hiring process will push MTC to higher competitive levels and increase its attractiveness in the industry. Information from the hiring system will make the company make informed decisions in placements of new workers (Michael E Porter, 1985). The hiring system will make MTC identify and attract talented workers hence foot off the competitors. Continuous update of the business concept will help the company reduce threat of substitution (Wei zheng, 2010). It also boost the position of the company in market.
Strategic objectives.
The document discusses attrition rates at HCL Technologies. It provides background on HCL, outlines some key causes of attrition like lack of career growth opportunities and ineffective leadership. Some key findings from an employee satisfaction survey at HCL are presented, showing that 27% of employees expressed dissatisfaction, which could lead to increased attrition rates. The top two contributors to dissatisfaction were issues with career development and lack of proper recognition and rewards.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
Employee attrition refers to the decrease in staff numbers within an organization due to various reasons.
As it has a negative impact on long-term growth objectives and workplace productivity, firms have
recognized it as a significant concern. To address this issue, organizations are increasingly turning to
machine-learning approaches to forecast employee attrition rates. This topic has gained significant
attention from researchers, especially in recent times. Several studies have applied various machinelearning methods to predict employee attrition, producing different resultsdepending on the employed
methods, factors, and datasets. However, there has been no comprehensive comparative review of multiple
studies applying machine-learning models to predict employee attrition to date. Therefore, this study aims
to fill this gap by providing an overview of research conducted on applying machine learning to predict
employee attrition from 2019 to February 2024. A literature review of relevant studies was conducted,
summarized, and classified. Most studies agree on conducting comparative experiments with multiple
predictive models to determine the most effective one.From this literature survey, the RF algorithm and
XGB ensemble method are repeatedly the best-performing, outperforming many other algorithms.
Additionally, the application of deep learning to employee attrition prediction issues also shows promise.
While there are discrepancies in the datasets used in previous studies, it is notable that the dataset
provided by IBM is the most widely utilized. This study serves as a concise review for new researchers,
facilitating their understanding of the primary techniques employed in predicting employee attrition and
highlighting recent research trends in this field. Furthermore, it provides organizations with insight into
the prominent factors affecting employee attrition, as identified by studies, enabling them to implement
solutions aimed at reducing attrition rates.
The document discusses implementing an applicant tracking system (ATS) and provides a strategic 3-step approach: 1) Analyze the current hiring process by interviewing stakeholders to understand workflows and gaps. 2) Define the future state and compare it to identify gaps. 3) Create an action plan to address findings and prepare for implementation. It emphasizes analyzing both the current and desired future processes to ensure the ATS can meet future needs and avoid costly workarounds. Conducting this analysis upfront can prevent risks associated with retrofitting processes to new technology.
Mantle Of Ml In Human Resource Management - PhdassistancePhD Assistance
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.
Three Major HR Functional Component:
1. Recruitment and selection Management
2. Performance Management
3. Knowledge Management
Learn More: https://bit.ly/31X49es
Contact Us:
Website: https://www.phdassistance.com/
UK NO: +44–1143520021
India No: +91–4448137070
WhatsApp No: +91 91769 66446
Email: info@phdassistance.com
EMPLOYEE ATTRITION PREDICTION USING MACHINE LEARNING MODELS: A REVIEW PAPERijaia
Employee attrition refers to the decrease in staff numbers within an organization due to various reasons. As
it has a negative impact on long-term growth objectives and workplace productivity, firms have recognized
it as a significant concern. To address this issue, organizations are increasingly turning to machine-learning
approaches to forecast employee attrition rates. This topic has gained significant attention from researchers,
especially in recent times. Several studies have applied various machine-learning methods to predict
employee attrition, producing different results depending on the employed methods, factors, and datasets.
However, there has been no comprehensive comparative review of multiple studies applying machinelearning models to predict employee attrition to date. Therefore, this study aims to fill this gap by providing
an overview of research conducted on applying machine learning to predict employee attrition from 2019 to
February 2024. A literature review of relevant studies was conducted, summarized, and classified. Most
studies agree on conducting comparative experiments with multiple predictive models to determine the most
effective one. From this literature survey, the RF algorithm and XGB ensemble method are repeatedly the
best-performing, outperforming many other algorithms. Additionally, the application of deep learning to
employee attrition prediction issues also shows promise. While there are discrepancies in the datasets used
in previous studies, it is notable that the dataset provided by IBM is the most widely utilized. This study
serves as a concise review for new researchers, facilitating their understanding of the primary techniques
employed in predicting employee attrition and highlighting recent research trends in this field. Furthermore,
it provides organizations with insight into the prominent factors affecting employee attrition, as identified by
studies, enabling them to implement solutions aimed at reducing attrition rates.
EMPLOYEE ATTRITION PREDICTION USING MACHINE LEARNING MODELS: A REVIEW PAPERijaia
Employee attrition refers to the decrease in staff numbers within an organization due to various reasons. As
it has a negative impact on long-term growth objectives and workplace productivity, firms have recognized
it as a significant concern. To address this issue, organizations are increasingly turning to machine-learning
approaches to forecast employee attrition rates. This topic has gained significant attention from researchers,
especially in recent times. Several studies have applied various machine-learning methods to predict
employee attrition, producing different results depending on the employed methods, factors, and datasets.
However, there has been no comprehensive comparative review of multiple studies applying machinelearning models to predict employee attrition to date. Therefore, this study aims to fill this gap by providing
an overview of research conducted on applying machine learning to predict employee attrition from 2019 to
February 2024. A literature review of relevant studies was conducted, summarized, and classified. Most
studies agree on conducting comparative experiments with multiple predictive models to determine the most
effective one. From this literature survey, the RF algorithm and XGB ensemble method are repeatedly the
best-performing, outperforming many other algorithms. Additionally, the application of deep learning to
employee attrition prediction issues also shows promise. While there are discrepancies in the datasets used
in previous studies, it is notable that the dataset provided by IBM is the most widely utilized. This study
serves as a concise review for new researchers, facilitating their understanding of the primary techniques
employed in predicting employee attrition and highlighting recent research trends in this field. Furthermore,
it provides organizations with insight into the prominent factors affecting employee attrition, as identified by
studies, enabling them to implement solutions aimed at reducing attrition rates.
Here are the key points about reconciliation based on the information provided:
- Reconciliation refers to changing or repairing a relationship after some conflict or factor has damaged it. It can apply to relationships just beginning or those being rebuilt.
- When discussing reconciliation, the focus is often more on the steps needed to achieve it, rather than reconciliation itself. People may resist these steps and thus reconciliation.
- Reconciliation processes can happen at various levels, from individuals to communities to societies. At the local community level, reconciliation may involve neighbors from different backgrounds cooperating on common issues like safety.
- For reconciliation to occur at the community level, people must be willing to work together in spaces that were once divided. This challenges them
Using Contextual Graphs as a Decision-making Tool in the Process of Hiring Ca...gerogepatton
Poor selection of employees can be a first step towards a lack of motivation, poor performance, and high turnover, to name a few. It's no wonder that organizations are trying to find the best ways to avoid these slippages by finding the best possible person for the job. Therefore, it is very important to understand the context of hiring process to help to understand which recruiting mistakes are most damaging to the organization in order to reduce the recruiting challenges faced by Human resource managers by building their capacity to ensure optimal HR performance. This paper initiates a research about how Contextual Graphs Formalism can be used for improving the decision making in the process of hiring potential candidates. An example of a typical procedure for visualization of recruiting phases is presented to show how to add contextual elements and practices in order to communicate the recruitment policy in a concrete and memorable way to both hiring teams and candidates.
Intranet Automation of Human Resource Management SystemIOSR Journals
This document summarizes the development of an intranet-based human resource management system (HRMS) for an organization. Key features of the system include automated employee records, skills assessments, and an online interview and confirmation process. A SWOT analysis is used to evaluate employees for project assignments based on their strengths, weaknesses, opportunities, and threats. The system was developed using technologies like ASP.NET for the interface, SQL for the database, and allows secure access via the organization's intranet. It aims to improve communication between employees and HR while streamlining HR processes through automation.
This document discusses how organizations can use workforce analytics to prepare their workforce for future needs. It recommends that HR leaders establish a single source of truth for employee data and use analytics tools to gain insights from this data. This will allow HR to monitor workforce trends, understand talent needs, and make strategic decisions. The document provides examples of how workforce analytics can help with talent retention, recruitment, and succession planning. It argues that analytics will help HR transform from a transactional to a strategic function and better support business goals.
Talent is the lifeblood of every company. Whether it’s a startup, SMB, or enterprise, there would be no systems or processes in place without the people to drive them. Learn 3 ways on how you can Improve Your Talent Pipeline with Technology
https://www.aberdeen.com/hcm-essentials/jim-stefanchin-3-ways-improve-talent-pipeline-technology/
504 PART 5 Meeting Other HR GoalsL05 Discuss the roleof .docxevonnehoggarth79783
504 PART 5 Meeting Other HR Goals
L05 Discuss the role
of HRM technology
in high-performance
work systems.
Compensation :
Organizations can reinforce the impact of this kind of performance managernent by
tinf,ing compensation in part to performance measures. Chapter 12 depcribed a num'
ber oimethtd, fot doing this, including merit pay, gainsharing, and ,profit sharing'
Lincoin Electric has for decades paid its production workers a piecework rate. Not
only does this motivare individual employees to look for the most efficient ways lo
do ih.it jobs, but because the company is known for this compensation method, it
atrracts workers who value rvorking hard in order to earn more. In addition, Lincoln
has been paying all of irs employees a profit-sharil^g bonus "every year since 193+,"
in che words of Lincoin's CEO John M. Stropkl Jr.29 Compensation systems also can
help ro creare the conditions that contribute to high per{ormance, including team'
*ork, .-po*efment, and job satisfaction. For example, as discussed in Chapter 12,
compensation can be linked to achievement of team objectives'
Organizations can increase empowerment and job satisfactiorrl by including
"*plo"y"",
in decisions about compensation and by communicating the basis for deci-
sio.rs abo.,t pay. When the organization designs a pay structure, it can set up a task
force that inciudes employees with direct experience in various types of jobs' Some
organizations share financial information with their employees and invite them to
,"Jo*rr"rrd pay increases for themselves, based on th.it contributkins. Employees
also may pu.li.ipur. in serting individual or group goals for which thiey can receive
bo.r,rr.r. R"r.u..h has found th"t .mploy"e participation in decisions about pay poli'
cies is linked to greater satisfaction with the pay and rhe job.rO And as pve discussed in
Chaprer 11, whln organizations expiain their pay structures to emplgyees,.the.cotn-
munication can er1hance employees' satisfaction and belief that the system is fair'
HRl Technology
Human resource departments can improve their own and their organization's perfor'
*"'r.. by appropriately using r-r..' t"ch.tology (see the "HR Oops!" box). New tech-
.roiogy r-,r"utiy i.r,rol,r", autJ-atian and. collaboranon-that is, using equipment and
information processing to per{orm activities that had been performed, by people and
faciiitating.l".tror-,i. Jommunication between people. Over thelast feq decades, auto-
,rrurio., hu", irrrprou"d HRM efficielcy by reducing the number of peopie needed to
per-
form routine tasks. Using automation can free HRM experts to concentrate on ways
to
determine how human resoufce managemenl can help the-organizatiorl meet its
goals,
,o t.ch.,ology also can make this function more valuable'rl For example, information
technoiogy irovicles ways ro build and improve systems for knowledge;generation
and
,t *i.rg, ir iurr of a iearning organization. Among the appiications .
Discussion 1a)When a company decides to use an outside party t.docxcuddietheresa
Discussion 1a)
When a company decides to use an outside party to conduct some tasks or projects, the process is called sourcing. Sometimes IT sourcing is a good idea because some companies do not have the resources or team to secure the IT structure. Some of the resources include skilled professionals, expensive equipment to support the structure, and so on. Similarly, Lazarus (2018) mentions that the outsourcing team usually gets the company great deals on prices. Their team has a list of competitive vendors that makes them excellent negotiators, which benefits the company. McKeen & Smith (2015) list some of the things that the managers need to focus on before they process IT sourcing.
They are:
Flexibility: When considering IT outsourcing, an organization needs to consider what kind of values they want and how soon do they want. Outsourcing IT might help them find a better deal, but sometimes it comes with longer response time.
Control: Outsourcing is right for some companies because it is easier for them to monitor if their expectations are meeting or not.
Knowledge Management: Some companies prefer not to outsource IT because they want to train their employees to learn knowledge and retain it. However, it is an expensive choice as it is cheaper to outsource IT and receive its benefits.
Business Exigency: Sometimes, different factors play in business that requires them to follow a non-stand process and employ unique decisions. Most of this time, outsourcing IT is a better deal, as it caters the specific needs of the business. In-house IT usually does not have the capability or the resource to respond to unique, non-standard functions.
1b) The main choice criteria that are required for IT managers to examine for making decision related to IT sourcing are as follows.
Flexibility
Flexibility is key thing that should be viewed while making choice in IT sectors. Flexibility in IT normally refers to scenario where, how speedy the IT functionality can be delivered and what are the capacities of those delivered functionality. It is equally crucial to display these elements of the decision. The great example of sourcing is IT body of workers nowadays. Many organizations have a tendency to hire group of workers from outside the business enterprise i.e. outsourcing or attempt to find the worker inside the company who have the competencies to operate the required task. For this, the part should take delivery of the terms and conditions (McKeen, 2017).
Control
Control refers to the excellent of the delivered IT functionalities. For example, its like checking the performance of the IT workforce that was employed from sourcing. For this, the work done via him has to be correct and should comply with the company requirements. Also, these works have be covered and protection measure need to be applied.
Knowledge enhancement
Knowledge enhancement is very essential procedure for making decision. Knowledge and continued learning are imperative in enterpr ...
Learning Analytics in Enterprise Performance ManagementTatainteractive1
http://www.tatainteractive.com/ : TIS is clear in its roadmap that learning analytics is gaining momentum and is here to stay while maturing over the next decade. TIS’ ability to harness the right set of data, transform data and use open source platforms to analyze data brings to our customers a high impact solution at reasonable investments. Visit http://www.tatainteractive.com/ for more.
This document discusses how human resource management must adapt to dynamic internal and external environmental factors. It outlines various technological, economic, political, social and local factors that influence HRM. It also discusses how organizations can manage workforce diversity and deal with issues like downsizing through effective HRM strategies. Total quality management and concepts like benchmarking and reengineering are important for organizations to effectively manage human resources in a changing environment.
Business Analysis using Machine LearningIRJET Journal
The document discusses using machine learning techniques like linear regression, random forest, and decision trees to analyze transaction data from a confectionery business in order to forecast product demand and sales. It applies these machine learning algorithms to a dataset containing over 20,000 transactions to analyze factors like product sales over time. The results can help the business optimize product offerings based on demand and improve profitability.
A STUDY TO REDUCE EMPLOYEE ATTRITION IN IT INDUSTRIESIAEME Publication
The research project entitled ‘Reduction of Employee Attrition’ is an attempt to understand the opinion and attitudes of the various categories of employees of IT sector towards the reduction of employee attrition in the organisations. Employee attrition is a serious issue, especially in today’s knowledge-driven marketplace where employees are the most important human capital assets, attrition directly or indirectly impacts on organization’s competitive advantage.
The document discusses the role of human resources in mergers and acquisitions. It outlines that HR helps determine if the cultures of the merging companies are compatible. It also discusses how HR assesses benefits structures and identifies any potential problems. Additionally, the document notes that HR helps address employee concerns about changes and uncertainty from the merger. Finally, it states that HR communicates changes to reporting structures, roles, and job descriptions as the organizations are integrated.
Emerging Trends in Accounting 08 Digital Transformation of Accounting-Big Data Analytics in Accounting-Cloud Computing in accounting- - Green Accounting-Human Resource Accounting, Inflation Accounting, Database Accounting
International Journal of Production ResearchVol. 49, No. 20,.docxnormanibarber20063
International Journal of Production Research
Vol. 49, No. 20, 15 October 2011, 5987–5998
A methodology for improving enterprise performance by
analysing worker capabilities via simulation
Brian Kernan*, Andrew Lynch and Con Sheahan
Department of Manufacturing Operations & Engineering, University of Limerick,
Engineering Research Building, Castletroy, Limerick, Ireland
(Received 20 May 2010; final version received 19 September 2010)
In this paper we outline a methodology for improving the overall performance of
small to medium sized enterprises (SMEs) by analysing worker capabilities
through simulation and modelling. We firstly examine key performance indica-
tors (KPIs) of the SME in its as-is state. The primary KPIs we examine are the
resource constraint metrics (RCMs) and customer misery index (CMI). The
RCMs help to identify the skill that is the biggest contributor to the overall
system constrainedness. The CMI is a measure of customer demand satisfaction.
By increasing the supply of the most heavily constrained skill we should increase
the flow of work orders through the system, which will in turn result in a reduced
CMI, or at least provide a potential for more work orders to flow through the
system. We run a set of experiments on data from a real factory, which upgrades
the skill sets of workers with the most heavily constrained skill, and then we look
at the system improvement. The overall impact of this experimental methodology
is that it can make recommendations to an organisation about which worker to
upgrade with which skill, and how the training should be implemented, to yield
the optimal improvement to the enterprise.
Keywords: worker; skill; training; SMEs; resource constraint metric;
customer misery index
1. Introduction
Managing directors of SMEs (small to medium sized enterprises) consistently find
personnel capability a limiting factor in influencing key performance measures.
Consequently worker training is viewed as an important tactical decision scenario for
an organisation. Previous research has shown that the implementation of training
programmes in companies can yield substantial productivity gains (Bartel 1994), and is
important for a company’s growth and its ability to stay competitive (Mital et al. 1999).
As part of our research we carried out a survey on 40 SME owner-managers on whether
or not a computer aid such as simulation would be a useful tool for making a decision
on worker training. The results are shown in Figure 1. Over 80% of participants either
agreed or strongly agreed that a computer tool that could rank candidate workers based
on the improvements they could bring to the enterprise would be useful in making a
decision on worker training.
*Corresponding author. Email: [email protected]
ISSN 0020–7543 print/ISSN 1366–588X online
� 2011 Taylor & Francis
DOI: 10.1080/00207543.2010.527387
http://www.informaworld.com
Discrete event simulation (DES) lends itself well to the training decision s.
Amazon products reviews classification based on machine learning, deep learni...TELKOMNIKA JOURNAL
In recent times, the trend of online shopping through e-commerce stores and websites has grown to a huge extent. Whenever a product is purchased on an e-commerce platform, people leave their reviews about the product. These reviews are very helpful for the store owners and the product’s manufacturers for the betterment of their work process as well as product quality. An automated system is proposed in this work that operates on two datasets D1 and D2 obtained from Amazon. After certain preprocessing steps, N-gram and word embedding-based features are extracted using term frequency-inverse document frequency (TF-IDF), bag of words (BoW) and global vectors (GloVe), and Word2vec, respectively. Four machine learning (ML) models support vector machines (SVM), logistic regression (RF), logistic regression (LR), multinomial Naïve Bayes (MNB), two deep learning (DL) models convolutional neural network (CNN), long-short term memory (LSTM), and standalone bidirectional encoder representations (BERT) are used to classify reviews as either positive or negative. The results obtained by the standard ML, DL models and BERT are evaluated using certain performance evaluation measures. BERT turns out to be the best-performing model in the case of D1 with an accuracy of 90% on features derived by word embedding models while the CNN provides the best accuracy of 97% upon word embedding features in the case of D2. The proposed model shows better overall performance on D2 as compared to D1.
Design, simulation, and analysis of microstrip patch antenna for wireless app...TELKOMNIKA JOURNAL
In this study, a microstrip patch antenna that works at 3.6 GHz was built and tested to see how well it works. In this work, Rogers RT/Duroid 5880 has been used as the substrate material, with a dielectric permittivity of 2.2 and a thickness of 0.3451 mm; it serves as the base for the examined antenna. The computer simulation technology (CST) studio suite is utilized to show the recommended antenna design. The goal of this study was to get a more extensive transmission capacity, a lower voltage standing wave ratio (VSWR), and a lower return loss, but the main goal was to get a higher gain, directivity, and efficiency. After simulation, the return loss, gain, directivity, bandwidth, and efficiency of the supplied antenna are found to be -17.626 dB, 9.671 dBi, 9.924 dBi, 0.2 GHz, and 97.45%, respectively. Besides, the recreation uncovered that the transfer speed side-lobe level at phi was much better than those of the earlier works, at -28.8 dB, respectively. Thus, it makes a solid contender for remote innovation and more robust communication.
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it has a negative impact on long-term growth objectives and workplace productivity, firms have recognized
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504 PART 5 Meeting Other HR GoalsL05 Discuss the roleof .docxevonnehoggarth79783
504 PART 5 Meeting Other HR Goals
L05 Discuss the role
of HRM technology
in high-performance
work systems.
Compensation :
Organizations can reinforce the impact of this kind of performance managernent by
tinf,ing compensation in part to performance measures. Chapter 12 depcribed a num'
ber oimethtd, fot doing this, including merit pay, gainsharing, and ,profit sharing'
Lincoin Electric has for decades paid its production workers a piecework rate. Not
only does this motivare individual employees to look for the most efficient ways lo
do ih.it jobs, but because the company is known for this compensation method, it
atrracts workers who value rvorking hard in order to earn more. In addition, Lincoln
has been paying all of irs employees a profit-sharil^g bonus "every year since 193+,"
in che words of Lincoin's CEO John M. Stropkl Jr.29 Compensation systems also can
help ro creare the conditions that contribute to high per{ormance, including team'
*ork, .-po*efment, and job satisfaction. For example, as discussed in Chapter 12,
compensation can be linked to achievement of team objectives'
Organizations can increase empowerment and job satisfactiorrl by including
"*plo"y"",
in decisions about compensation and by communicating the basis for deci-
sio.rs abo.,t pay. When the organization designs a pay structure, it can set up a task
force that inciudes employees with direct experience in various types of jobs' Some
organizations share financial information with their employees and invite them to
,"Jo*rr"rrd pay increases for themselves, based on th.it contributkins. Employees
also may pu.li.ipur. in serting individual or group goals for which thiey can receive
bo.r,rr.r. R"r.u..h has found th"t .mploy"e participation in decisions about pay poli'
cies is linked to greater satisfaction with the pay and rhe job.rO And as pve discussed in
Chaprer 11, whln organizations expiain their pay structures to emplgyees,.the.cotn-
munication can er1hance employees' satisfaction and belief that the system is fair'
HRl Technology
Human resource departments can improve their own and their organization's perfor'
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,rrurio., hu", irrrprou"d HRM efficielcy by reducing the number of peopie needed to
per-
form routine tasks. Using automation can free HRM experts to concentrate on ways
to
determine how human resoufce managemenl can help the-organizatiorl meet its
goals,
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technoiogy irovicles ways ro build and improve systems for knowledge;generation
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Discussion 1a)When a company decides to use an outside party t.docxcuddietheresa
Discussion 1a)
When a company decides to use an outside party to conduct some tasks or projects, the process is called sourcing. Sometimes IT sourcing is a good idea because some companies do not have the resources or team to secure the IT structure. Some of the resources include skilled professionals, expensive equipment to support the structure, and so on. Similarly, Lazarus (2018) mentions that the outsourcing team usually gets the company great deals on prices. Their team has a list of competitive vendors that makes them excellent negotiators, which benefits the company. McKeen & Smith (2015) list some of the things that the managers need to focus on before they process IT sourcing.
They are:
Flexibility: When considering IT outsourcing, an organization needs to consider what kind of values they want and how soon do they want. Outsourcing IT might help them find a better deal, but sometimes it comes with longer response time.
Control: Outsourcing is right for some companies because it is easier for them to monitor if their expectations are meeting or not.
Knowledge Management: Some companies prefer not to outsource IT because they want to train their employees to learn knowledge and retain it. However, it is an expensive choice as it is cheaper to outsource IT and receive its benefits.
Business Exigency: Sometimes, different factors play in business that requires them to follow a non-stand process and employ unique decisions. Most of this time, outsourcing IT is a better deal, as it caters the specific needs of the business. In-house IT usually does not have the capability or the resource to respond to unique, non-standard functions.
1b) The main choice criteria that are required for IT managers to examine for making decision related to IT sourcing are as follows.
Flexibility
Flexibility is key thing that should be viewed while making choice in IT sectors. Flexibility in IT normally refers to scenario where, how speedy the IT functionality can be delivered and what are the capacities of those delivered functionality. It is equally crucial to display these elements of the decision. The great example of sourcing is IT body of workers nowadays. Many organizations have a tendency to hire group of workers from outside the business enterprise i.e. outsourcing or attempt to find the worker inside the company who have the competencies to operate the required task. For this, the part should take delivery of the terms and conditions (McKeen, 2017).
Control
Control refers to the excellent of the delivered IT functionalities. For example, its like checking the performance of the IT workforce that was employed from sourcing. For this, the work done via him has to be correct and should comply with the company requirements. Also, these works have be covered and protection measure need to be applied.
Knowledge enhancement
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International Journal of Production ResearchVol. 49, No. 20,.docxnormanibarber20063
International Journal of Production Research
Vol. 49, No. 20, 15 October 2011, 5987–5998
A methodology for improving enterprise performance by
analysing worker capabilities via simulation
Brian Kernan*, Andrew Lynch and Con Sheahan
Department of Manufacturing Operations & Engineering, University of Limerick,
Engineering Research Building, Castletroy, Limerick, Ireland
(Received 20 May 2010; final version received 19 September 2010)
In this paper we outline a methodology for improving the overall performance of
small to medium sized enterprises (SMEs) by analysing worker capabilities
through simulation and modelling. We firstly examine key performance indica-
tors (KPIs) of the SME in its as-is state. The primary KPIs we examine are the
resource constraint metrics (RCMs) and customer misery index (CMI). The
RCMs help to identify the skill that is the biggest contributor to the overall
system constrainedness. The CMI is a measure of customer demand satisfaction.
By increasing the supply of the most heavily constrained skill we should increase
the flow of work orders through the system, which will in turn result in a reduced
CMI, or at least provide a potential for more work orders to flow through the
system. We run a set of experiments on data from a real factory, which upgrades
the skill sets of workers with the most heavily constrained skill, and then we look
at the system improvement. The overall impact of this experimental methodology
is that it can make recommendations to an organisation about which worker to
upgrade with which skill, and how the training should be implemented, to yield
the optimal improvement to the enterprise.
Keywords: worker; skill; training; SMEs; resource constraint metric;
customer misery index
1. Introduction
Managing directors of SMEs (small to medium sized enterprises) consistently find
personnel capability a limiting factor in influencing key performance measures.
Consequently worker training is viewed as an important tactical decision scenario for
an organisation. Previous research has shown that the implementation of training
programmes in companies can yield substantial productivity gains (Bartel 1994), and is
important for a company’s growth and its ability to stay competitive (Mital et al. 1999).
As part of our research we carried out a survey on 40 SME owner-managers on whether
or not a computer aid such as simulation would be a useful tool for making a decision
on worker training. The results are shown in Figure 1. Over 80% of participants either
agreed or strongly agreed that a computer tool that could rank candidate workers based
on the improvements they could bring to the enterprise would be useful in making a
decision on worker training.
*Corresponding author. Email: [email protected]
ISSN 0020–7543 print/ISSN 1366–588X online
� 2011 Taylor & Francis
DOI: 10.1080/00207543.2010.527387
http://www.informaworld.com
Discrete event simulation (DES) lends itself well to the training decision s.
Similar to Identification of human resource analytics using machine learning algorithms (20)
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In this study, a microstrip patch antenna that works at 3.6 GHz was built and tested to see how well it works. In this work, Rogers RT/Duroid 5880 has been used as the substrate material, with a dielectric permittivity of 2.2 and a thickness of 0.3451 mm; it serves as the base for the examined antenna. The computer simulation technology (CST) studio suite is utilized to show the recommended antenna design. The goal of this study was to get a more extensive transmission capacity, a lower voltage standing wave ratio (VSWR), and a lower return loss, but the main goal was to get a higher gain, directivity, and efficiency. After simulation, the return loss, gain, directivity, bandwidth, and efficiency of the supplied antenna are found to be -17.626 dB, 9.671 dBi, 9.924 dBi, 0.2 GHz, and 97.45%, respectively. Besides, the recreation uncovered that the transfer speed side-lobe level at phi was much better than those of the earlier works, at -28.8 dB, respectively. Thus, it makes a solid contender for remote innovation and more robust communication.
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Deep learning approach to DDoS attack with imbalanced data at the application...TELKOMNIKA JOURNAL
A distributed denial of service (DDoS) attack is where one or more computers attack or target a server computer, by flooding internet traffic to the server. As a result, the server cannot be accessed by legitimate users. A result of this attack causes enormous losses for a company because it can reduce the level of user trust, and reduce the company’s reputation to lose customers due to downtime. One of the services at the application layer that can be accessed by users is a web-based lightweight directory access protocol (LDAP) service that can provide safe and easy services to access directory applications. We used a deep learning approach to detect DDoS attacks on the CICDDoS 2019 dataset on a complex computer network at the application layer to get fast and accurate results for dealing with unbalanced data. Based on the results obtained, it is observed that DDoS attack detection using a deep learning approach on imbalanced data performs better when implemented using synthetic minority oversampling technique (SMOTE) method for binary classes. On the other hand, the proposed deep learning approach performs better for detecting DDoS attacks in multiclass when implemented using the adaptive synthetic (ADASYN) method.
The appearance of uncertainties and disturbances often effects the characteristics of either linear or nonlinear systems. Plus, the stabilization process may be deteriorated thus incurring a catastrophic effect to the system performance. As such, this manuscript addresses the concept of matching condition for the systems that are suffering from miss-match uncertainties and exogeneous disturbances. The perturbation towards the system at hand is assumed to be known and unbounded. To reach this outcome, uncertainties and their classifications are reviewed thoroughly. The structural matching condition is proposed and tabulated in the proposition 1. Two types of mathematical expressions are presented to distinguish the system with matched uncertainty and the system with miss-matched uncertainty. Lastly, two-dimensional numerical expressions are provided to practice the proposed proposition. The outcome shows that matching condition has the ability to change the system to a design-friendly model for asymptotic stabilization.
Implementation of FinFET technology based low power 4×4 Wallace tree multipli...TELKOMNIKA JOURNAL
Many systems, including digital signal processors, finite impulse response (FIR) filters, application-specific integrated circuits, and microprocessors, use multipliers. The demand for low power multipliers is gradually rising day by day in the current technological trend. In this study, we describe a 4×4 Wallace multiplier based on a carry select adder (CSA) that uses less power and has a better power delay product than existing multipliers. HSPICE tool at 16 nm technology is used to simulate the results. In comparison to the traditional CSA-based multiplier, which has a power consumption of 1.7 µW and power delay product (PDP) of 57.3 fJ, the results demonstrate that the Wallace multiplier design employing CSA with first zero finding logic (FZF) logic has the lowest power consumption of 1.4 µW and PDP of 27.5 fJ.
Evaluation of the weighted-overlap add model with massive MIMO in a 5G systemTELKOMNIKA JOURNAL
The flaw in 5G orthogonal frequency division multiplexing (OFDM) becomes apparent in high-speed situations. Because the doppler effect causes frequency shifts, the orthogonality of OFDM subcarriers is broken, lowering both their bit error rate (BER) and throughput output. As part of this research, we use a novel design that combines massive multiple input multiple output (MIMO) and weighted overlap and add (WOLA) to improve the performance of 5G systems. To determine which design is superior, throughput and BER are calculated for both the proposed design and OFDM. The results of the improved system show a massive improvement in performance ver the conventional system and significant improvements with massive MIMO, including the best throughput and BER. When compared to conventional systems, the improved system has a throughput that is around 22% higher and the best performance in terms of BER, but it still has around 25% less error than OFDM.
Reflector antenna design in different frequencies using frequency selective s...TELKOMNIKA JOURNAL
In this study, it is aimed to obtain two different asymmetric radiation patterns obtained from antennas in the shape of the cross-section of a parabolic reflector (fan blade type antennas) and antennas with cosecant-square radiation characteristics at two different frequencies from a single antenna. For this purpose, firstly, a fan blade type antenna design will be made, and then the reflective surface of this antenna will be completed to the shape of the reflective surface of the antenna with the cosecant-square radiation characteristic with the frequency selective surface designed to provide the characteristics suitable for the purpose. The frequency selective surface designed and it provides the perfect transmission as possible at 4 GHz operating frequency, while it will act as a band-quenching filter for electromagnetic waves at 5 GHz operating frequency and will be a reflective surface. Thanks to this frequency selective surface to be used as a reflective surface in the antenna, a fan blade type radiation characteristic at 4 GHz operating frequency will be obtained, while a cosecant-square radiation characteristic at 5 GHz operating frequency will be obtained.
Reagentless iron detection in water based on unclad fiber optical sensorTELKOMNIKA JOURNAL
A simple and low-cost fiber based optical sensor for iron detection is demonstrated in this paper. The sensor head consist of an unclad optical fiber with the unclad length of 1 cm and it has a straight structure. Results obtained shows a linear relationship between the output light intensity and iron concentration, illustrating the functionality of this iron optical sensor. Based on the experimental results, the sensitivity and linearity are achieved at 0.0328/ppm and 0.9824 respectively at the wavelength of 690 nm. With the same wavelength, other performance parameters are also studied. Resolution and limit of detection (LOD) are found to be 0.3049 ppm and 0.0755 ppm correspondingly. This iron sensor is advantageous in that it does not require any reagent for detection, enabling it to be simpler and cost-effective in the implementation of the iron sensing.
Impact of CuS counter electrode calcination temperature on quantum dot sensit...TELKOMNIKA JOURNAL
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
A progressive learning for structural tolerance online sequential extreme lea...TELKOMNIKA JOURNAL
This article discusses the progressive learning for structural tolerance online sequential extreme learning machine (PSTOS-ELM). PSTOS-ELM can save robust accuracy while updating the new data and the new class data on the online training situation. The robustness accuracy arises from using the householder block exact QR decomposition recursive least squares (HBQRD-RLS) of the PSTOS-ELM. This method is suitable for applications that have data streaming and often have new class data. Our experiment compares the PSTOS-ELM accuracy and accuracy robustness while data is updating with the batch-extreme learning machine (ELM) and structural tolerance online sequential extreme learning machine (STOS-ELM) that both must retrain the data in a new class data case. The experimental results show that PSTOS-ELM has accuracy and robustness comparable to ELM and STOS-ELM while also can update new class data immediately.
Electroencephalography-based brain-computer interface using neural networksTELKOMNIKA JOURNAL
This study aimed to develop a brain-computer interface that can control an electric wheelchair using electroencephalography (EEG) signals. First, we used the Mind Wave Mobile 2 device to capture raw EEG signals from the surface of the scalp. The signals were transformed into the frequency domain using fast Fourier transform (FFT) and filtered to monitor changes in attention and relaxation. Next, we performed time and frequency domain analyses to identify features for five eye gestures: opened, closed, blink per second, double blink, and lookup. The base state was the opened-eyes gesture, and we compared the features of the remaining four action gestures to the base state to identify potential gestures. We then built a multilayer neural network to classify these features into five signals that control the wheelchair’s movement. Finally, we designed an experimental wheelchair system to test the effectiveness of the proposed approach. The results demonstrate that the EEG classification was highly accurate and computationally efficient. Moreover, the average performance of the brain-controlled wheelchair system was over 75% across different individuals, which suggests the feasibility of this approach.
Adaptive segmentation algorithm based on level set model in medical imagingTELKOMNIKA JOURNAL
For image segmentation, level set models are frequently employed. It offer best solution to overcome the main limitations of deformable parametric models. However, the challenge when applying those models in medical images stills deal with removing blurs in image edges which directly affects the edge indicator function, leads to not adaptively segmenting images and causes a wrong analysis of pathologies wich prevents to conclude a correct diagnosis. To overcome such issues, an effective process is suggested by simultaneously modelling and solving systems’ two-dimensional partial differential equations (PDE). The first PDE equation allows restoration using Euler’s equation similar to an anisotropic smoothing based on a regularized Perona and Malik filter that eliminates noise while preserving edge information in accordance with detected contours in the second equation that segments the image based on the first equation solutions. This approach allows developing a new algorithm which overcome the studied model drawbacks. Results of the proposed method give clear segments that can be applied to any application. Experiments on many medical images in particular blurry images with high information losses, demonstrate that the developed approach produces superior segmentation results in terms of quantity and quality compared to other models already presented in previeous works.
Automatic channel selection using shuffled frog leaping algorithm for EEG bas...TELKOMNIKA JOURNAL
Drug addiction is a complex neurobiological disorder that necessitates comprehensive treatment of both the body and mind. It is categorized as a brain disorder due to its impact on the brain. Various methods such as electroencephalography (EEG), functional magnetic resonance imaging (FMRI), and magnetoencephalography (MEG) can capture brain activities and structures. EEG signals provide valuable insights into neurological disorders, including drug addiction. Accurate classification of drug addiction from EEG signals relies on appropriate features and channel selection. Choosing the right EEG channels is essential to reduce computational costs and mitigate the risk of overfitting associated with using all available channels. To address the challenge of optimal channel selection in addiction detection from EEG signals, this work employs the shuffled frog leaping algorithm (SFLA). SFLA facilitates the selection of appropriate channels, leading to improved accuracy. Wavelet features extracted from the selected input channel signals are then analyzed using various machine learning classifiers to detect addiction. Experimental results indicate that after selecting features from the appropriate channels, classification accuracy significantly increased across all classifiers. Particularly, the multi-layer perceptron (MLP) classifier combined with SFLA demonstrated a remarkable accuracy improvement of 15.78% while reducing time complexity.
This study Examines the Effectiveness of Talent Procurement through the Imple...DharmaBanothu
In the world with high technology and fast
forward mindset recruiters are walking/showing interest
towards E-Recruitment. Present most of the HRs of
many companies are choosing E-Recruitment as the best
choice for recruitment. E-Recruitment is being done
through many online platforms like Linkedin, Naukri,
Instagram , Facebook etc. Now with high technology E-
Recruitment has gone through next level by using
Artificial Intelligence too.
Key Words : Talent Management, Talent Acquisition , E-
Recruitment , Artificial Intelligence Introduction
Effectiveness of Talent Acquisition through E-
Recruitment in this topic we will discuss about 4important
and interlinked topics which are
Open Channel Flow: fluid flow with a free surfaceIndrajeet sahu
Open Channel Flow: This topic focuses on fluid flow with a free surface, such as in rivers, canals, and drainage ditches. Key concepts include the classification of flow types (steady vs. unsteady, uniform vs. non-uniform), hydraulic radius, flow resistance, Manning's equation, critical flow conditions, and energy and momentum principles. It also covers flow measurement techniques, gradually varied flow analysis, and the design of open channels. Understanding these principles is vital for effective water resource management and engineering applications.
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
Road construction is not as easy as it seems to be, it includes various steps and it starts with its designing and
structure including the traffic volume consideration. Then base layer is done by bulldozers and levelers and after
base surface coating has to be done. For giving road a smooth surface with flexibility, Asphalt concrete is used.
Asphalt requires an aggregate sub base material layer, and then a base layer to be put into first place. Asphalt road
construction is formulated to support the heavy traffic load and climatic conditions. It is 100% recyclable and
saving non renewable natural resources.
With the advancement of technology, Asphalt technology gives assurance about the good drainage system and with
skid resistance it can be used where safety is necessary such as outsidethe schools.
The largest use of Asphalt is for making asphalt concrete for road surfaces. It is widely used in airports around the
world due to the sturdiness and ability to be repaired quickly, it is widely used for runways dedicated to aircraft
landing and taking off. Asphalt is normally stored and transported at 150’C or 300’F temperature
Accident detection system project report.pdfKamal Acharya
The Rapid growth of technology and infrastructure has made our lives easier. The
advent of technology has also increased the traffic hazards and the road accidents take place
frequently which causes huge loss of life and property because of the poor emergency facilities.
Many lives could have been saved if emergency service could get accident information and
reach in time. Our project will provide an optimum solution to this draw back. A piezo electric
sensor can be used as a crash or rollover detector of the vehicle during and after a crash. With
signals from a piezo electric sensor, a severe accident can be recognized. According to this
project when a vehicle meets with an accident immediately piezo electric sensor will detect the
signal or if a car rolls over. Then with the help of GSM module and GPS module, the location
will be sent to the emergency contact. Then after conforming the location necessary action will
be taken. If the person meets with a small accident or if there is no serious threat to anyone’s
life, then the alert message can be terminated by the driver by a switch provided in order to
avoid wasting the valuable time of the medical rescue team.
Identification of human resource analytics using machine learning algorithms
1. TELKOMNIKA Telecommunication Computing Electronics and Control
Vol. 20, No. 5, October 2022, pp. 1004~1015
ISSN: 1693-6930, DOI: 10.12928/TELKOMNIKA.v20i5.21818 1004
Journal homepage: http://telkomnika.uad.ac.id
Identification of human resource analytics using machine
learning algorithms
Elham Mohammed Thabit A. Alsaadi1
, Sameerah Faris Khlebus2
, Ashwak Alabaichi3
1
Department of Information Technology, College of Computer Science and Information Technology, University of Kerbala,
Karbala, Iraq
2
Department of Business Information Technology, College of Business Administration of Informatics, University of Information
Technology and Communications, Baghdad, Iraq
3
Department of Biomedical Engineering, College of Engineering, University of Kerbala, Karbala, Iraq
Article Info ABSTRACT
Article history:
Received Sep 23, 2021
Revised Jul 05, 2022
Accepted Jul 13, 2022
Employee attrition is one of the most significant business issues in human
resource (HR) analytics. This research aims to identify the most critical
elements that contribute to employee attrition. Businesses operate heavily on
employee training in order to maximize the returns they will offer to the
company in the future. By utilizing the employee information value concept,
it has been discovered that employee features such as overtime, the total
number of projects and job level have a significant impact on attrition.
To find the probability of new employee attrition, various classification
algorithms such as decision trees (DT) classifier, logistic regression (LR),
random forests (RF), and K-means clustering are used. A comparative
analysis of the models with different rating scales is carried out for the
highest accuracy. For prediction, four diverse machine learning (ML)
algorithms such as LR, RF, DT classifier, and k-nearest neighbors (k-NN)
are used. DT classifier outperforms with 97% of accuracy than other
techniques. The effects of predictive ML techniques on the employee dataset
show that RF evaluation outperforms other ML techniques followed by
model of LR for the specific dataset if precision is the preferred metric.
Identification of HR is forecasted using ML algorithms on employee data.
Keywords:
Decision tree classifier
Logistic regression
Machine learning
Random forest
This is an open access article under the CC BY-SA license.
Corresponding Author:
Elham Mohammed Thabit A. Alsaadi
Department of Information Technology, College of Computer Science and Information Technology
University of Kerbala, Karbala, Iraq
Email: elham.thabit@uokerbala.edu.iq
1. INTRODUCTION
Human resource (HR) is the organization of a company that is in charge of analysis, recruiting,
monitoring and training job candidates, and managing employee benefit programs [1]. It refers to both the
people who are working for a company or an organization and the division in charge of dealing with all
employee related issues [2], [3]. Employees are among the most important resources in any organization or
company. HR are critical in enabling businesses to engage with a business in a socially responsible business
atmosphere and a higher improvement in the quality workers in the twenty-first decade [4], [5].
Employee churn is the most serious issue that a company faces, and it has a wide-ranging impact on
how the company operates. In this age of intense competition, there are many variables such as salary,
working conditions, and so on that lead to employee dissatisfaction [6]. Long working hours, family pressure,
work experience, job role, distance traveled, office building, office conveniences, perks, and a variety of
other factors may all play a role in employee attrition. It is critical for HR department for recognizing all of
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Identification of human resource analytics using … (Elham Mohammed Thabit A. Alsaadi)
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these variables in order to improve employee fulfilment. As the quality has a direct impact on the workers’
employee’s productivity. Sometimes the employee has no problems in the company, but other companies
may offer an improved profile with a better pay package. As a result, the employee may be willing to resign.
Retaining a single employee necessitates extensive knowledge in a variety of areas. In this study, we attempt
to identify significant contributors to employee attrition [7]. The primary motivation is to design employee
turnover and why an employee is leaving the company, which will be beneficial to the company in the future.
Until the employee submits his resignation, HR department should devise a strategy [8], [9]. As the primary
course of HR management, employee turnover could provide administrators with decision support. In this
research, we inability to discover employee turnover using a variety of continuous variables. Employee and job
status classes are determined as two factors, with variable value varying based on the estate’s nation. We should
offer a dataset of employee information to show how these models are work in practice. Lastly, it is
demonstrated that the approach proposed in this study is extremely useful to managers when developing
succession methods, supporting guidelines, and retirement legislation.
This research comprises a comparative study of different algorithms utilizing model measurement
measures such as accuracy, reminder, FN rate, F-measuring, in contrast to the majority of current focusing on
a single company-specific algorithm. The reason for using several algorithms is that the special benefits and
pitfalls of each algorithm. For example, if the rate of events is low, the logistic model fails, while random
forests surpass. When the total attributes contain a large number of important attributes. Decision tree
executes linear models when the data is highly non-linear. Through comparative study all the advantages and
falls that allow companies to choose the best model for their data would be taken into account [10]. In this
paper, a strategy dependent on artificial intelligence (AI) model that gives us the knowledge of worker’s
turnover of an organization by recognizing its significant variables has been suggested. The referenced model is
an all-encompassing way to deal with pick the most noteworthy weighted highlights in two stages. Our primary
objective is to diminish the quantity of highlights and take out the most noticeable ones from the component
determination process [11].
In circumstances where a dataset requires enormous space for usefulness and might be liable to over
fitting. There are two methods to follow so as to gain a reasonable perception and to abstain from overfitting:
one is the system of dimensionality decrease and the other is the choice of highlights. We utilized the element
choice methodology, in any case, to diminish the quantity of highlights while choosing the most noticeable
highlights. Likewise, for most estimations, the precision of the lessened number of specific features and the
accuracy of complete features are almost the proportional, prescribing that not all features are on a very basic
level critical. Again, with the inconsequential picked features, only two or three figurings like support vector
machine (SVM) give insignificantly high precision. We utilized AI situations like scikit-learn [12], Pandas and
Matplotlib. The turnover degree is characterized as the association’s enlisting and end necessities. For various
reasons, a representative may leave the activity. Here the turnover and attrition are the terms of business that are
consistently in struggle. In an association, there are various types of turnover. The decrease in the quantity of
representatives is commonly known as wearing down. These phrasings can be utilized conversely to examine
the information on labor and different advances required for labor planning. This happens when an agent leaves
the association trimming down similarly as turnover. Agent trimming down can be depicted as laborer
hardship for any of the going with reasons: singular reasons, low work satisfaction, low wages and poor
business conditions. The turnover of workers can be isolated into two classes: deliberate and programmed
trimming down. The programmed end occurs for several reasons, for example, poor implementation of
delegates, business necessities, or when their supervisor terminates the workers [13]. Then again, in willful
wearing down, high-performing laborers choose to leave the association, regardless of an exertion by the
organization to keep them. Early retirement or work offers from various associations, for example, can
achieve stubborn debilitating. While associations that comprehend the noteworthiness of their delegates
generally put assets into their workforce by giving liberal getting ready and an unprecedented work
environment, they are moreover encountering purposeful wearing out and the loss of proficient authorities.
Another issue, the contracting of substitutions, powers critical costs on the client, including the costs of
enrolling, securing and getting ready [14]. Anticipating the steady loss of laborers at an association will assist
the executives with reacting all the more adequately by reinforcing their inner approaches and techniques.
Where skilled specialists with a possibility of leaving can be given different answers for limit their likelihood
of stopping, for example, a compensation increment or appropriate preparing. Utilizing models of AI can
assist organizations with foreseeing the turnover of workers. Investigators can create and prepare an AI
model utilizing chronicled information held in HR divisions that can anticipate workers leaving the
organization [15]. These models are set up to dismember the relationship among dynamic and terminated
laborer characteristics. This was developed using comprehensive retailer HRIS data by pushing the topic of
trimming as a a gathering limit and proving it with stewardship frameworks. This is by distinguishing the
XGBoost classifier’s predominant precision and different frameworks and identifying the reasoning behind
their unique offer [16], [17]. There are several useful algorithms, both qualitative and quantitative,
3. ISSN: 1693-6930
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for anticipating staff turnover in order to stabilise a company. However, these methodologies have a number
of drawbacks when it comes to predicting staff turnover, including the following:
1) Inadequate consideration of data that is uneven. The data on employee turnover included in these
studies only represents a small part of the total workforce.
2) Inefficient data processing due to high dimensionality. Employees have a variety of feature dimensions,
including both static and dynamic ones.
3) There is no rating. Because the purpose of the prediction is to reduce employee turnover, we must
identify and prioritise its aspects.
The objective of this work is to target the model to recognize the individuals who will leave, with the goal
that the organization can intercede, act and forecast the correct fitment for yearning worker, predict whittling
down particularly among superior workers and predict how pay esteems will work out [18], [19].
The HR department can utilise the results of our model to design strategy before the employee
introduces his resignation. differtn the majority of current research work, which they focus on a single
business problem-solving algorithm, this paper compares several algorithms using model evaluation
measures including as accuracy, precision and sensitivity. The reason for adopting verouis algorithms is that
each has its own set of benefits and drawbacks.
Data preprocessing, feature selection and measurement, the modeling utilising various techniques,
and finally evaluation of models using model evaluation metrics are all part of the analytics project as shown
in Figure 1. Following evaluation, the best model is used to generate predictions on the data. A raw data set is
used for data pre-processing, followed by feature selection and scaling, model building after that, evaluation
and model tuning are performed, and finally deloyment and monitoring step will be performed.
Figure 1. Analytic prosses
− Meaning of the job satisfaction
People bring their mental and physical skills to their work and time. Most people by working
attempt to make a diversity in their lives and in others’ lives. A pay check is not the only explanation why an
individual wants a job. Jobs can be worked to accomplish personal goals. When a job meets the expectations
of an individual, he or she also experiences positive emotions. Such optimistic thoughts are job satisfaction.
There are many different ways of defining job satisfaction or employee satisfaction. Many say it’s
just how satisfied a person is with his or her employment, that is, whether they like the job or particular
aspects or facets of jobs, such as the nature of work or supervision. Others think it isn’t that easy and require
multidimensional psychological reactions to one’s job instead. Studies have noted that tests of job
satisfaction vary in how much they measure job-related emotions (emotional job satisfaction) or job regard
cognitions (cognitive job satisfaction). Often job satisfaction is measured through how well outcomes match,
or exceed expectations. It reflects many behaviors linked to that.
2. LITERATURE SURVAY
This section presents some of different studies in the field of HR analytics. As a part of this research
work, different reviews are studied to understand the background of the HR system, different approaches.
In this section also study the most critical elements that contribute to employee attrition. The literature review
provides detailed information on the topic under consideration in this research work along with the gaps in
literature.
Zhao et al. [20] analyzed HR with supervised methods of machine learning, demonstrated and
analyzed within an enterprise for the estimation of employee turnover. Machine learning (ML) is able to
identify the factor of human resource. In this analysis, computational tests are conducted with a decision tree
system, a random forest technique, a gradient boosting trees technique, an extreme gradient boosting
technique, a logistic regression technique, for actual and virtual human resource datasets representing
organizations of small, medium, and large employee populations.
Chourey et al. [21] analyzed that human resource attrition is nowadays important in the industry.
It is the big problem that stands out in all the organizations. Attrition is the steady decline in the numbers of
employees by way of retirement, resignation or death. ML technique can predict dataset vary effectively.
4. TELKOMNIKA Telecommun Comput El Control
Identification of human resource analytics using … (Elham Mohammed Thabit A. Alsaadi)
1007
Talent retention and professional employee retention is a crucial dilemma for HR manager particularly in the
manufacturing industry. This research identified certain complex factors that are main responsible for the
turnover of employees in selected organizations. The most appealing environment to make employees stay
back in company is the ethical work culture, cordial employee relationship and the execution of
organizational policies.
Gao et al. [22] focused on human resource analyze the performance of employee turnover. HR analyze
the major issue for many companies and businesses. The issue is important because it affects not only the
viability of the job but also the quality of the preparation and culture of the enterprise. ML methods analyze
the employee performance. This research aims to improve the ability to predict employee turnover, and
present a new approach based on algorithm of an improved random forest to tackle this need. Studying offers
a new empirical approach which can allow human resource departments to more accurately predict employee
turnover and its experimental results afford valuable tools to minimize employee turnover.
Karande et al. [23] suggested that human resource employee turnover in different sectors is now
becoming a big problem. This study focused on identifying key characteristics of volunteer employee
turnover and how they can be resolved in advance. The question is to figure out whether an employee is
going to leave or stay. ML methods analyze the employee performance. Instead of focusing on algorithm of a
single classifier, the suggested work will utilize ensemble learning to resolve the problem, combining weak
learning algorithms for getting a stronger ensemble model. When the need for scalability and high
availability without sacrificing performance, the Apache Cassandra database is the way to go. Cassandra’s
support for replicating across various datacenters is is best in class, giving your users lower latency and the
piece of mind that comes with knowing you can endure regional disruptions. Ensemble model is also used in
this research.
Alghamlar and Alabduljabbar [24], a clear picture of the state of the art, describing each typical
phase of the data mining process, the variations and similarities across this research, and making additional
recommendations as a result, this survey presents a detailed path for using data mining to improve employee
attrition. This by using algorithms of ML for evolving a web-based application which assist in predicting the
suitability of IT students’ skills for the recruitment. The result displays that some hard skills such as (Linux
systems, image and video processing, and some programming languages are below average. On the other
hand, all soft skills are above average. In addition, the rate of 82% of respondents do not have any IT
certification.
Kowsher et al. [25] to create a linear function, two shifting vectors dubbed support direction vector
(SDV) and support origin vector (SOV) were employed. With both the target class data and the non target
class data, these vectors construct a linear function for measuring cosine-angle. When considering target data
points, the linear function positions itself in such a way that its angle with target class data is minimised and
its angle with non-target class data is maximised. The linear function’s positional error has been characterised
as a loss function that has been iteratively refined with the gradient descent algorithm. Three different
standard datasets have used to demonstrate the acceptability of this strategy. The model’s accuracy was
comparable to that of a normal supervised classification algorithm.
3. METHODOLOGY
This section provides the theoretical and technical walk-through of the research method used to
construct an analytical analysis and prediction of employee data set using python and ML forecast employee
turnover and how to identify the successful employee, thereby saving the company’s HRM budget on hiring
new employees as shown in Figure 2. This chapter explains the collection of methods used to perform the
investigation and estimate the accuracy and evaluates the data set. It also includes the techniques used to
collect data and interpret data.
Figure 2. Stages of analytics
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Figure 3 displays set of data, the work architecture, recognizes and selects features with variable
significance. So many features are performed independently with multiple algorithms previously discussed in
order to achieve the individual performance. Then weights are designated for each model to model the learning
model of the ensemble and provided to the model of the ensemble. The classifier is categorized, as well as the
accuracy is estimated.
Figure 3. Architecture diagram
3.1. Modelling
Once we’ve identified important features and data is ready to format, we can proceed the project’s
predictive analysis section. The most used algorithms are classification and regression trees (CART). Some
more unique, and advanced algorithms are also available, such as ridge and lasso, Naïve Bayes, linear
discrimination and supporting vector machines used to compare the results of this work. As few algorithms
are impacted because numerical factors are different, it is always recommended that the feature is scaled
before analysis. Algorithms such as lasso and ridge, while other CART algorithms, are highly influenced due
to scaling of the functions.
The modelling begins when the information collected is divided into a training phase and a test set.
In the training dataset, algorithms are implemented and tested. The training set is assigned randomly 80% of
the data and the test set is assigned 20% of the data. Since our response variable is binary, we will use
classification algorithms. The characters “yes” and “no” of the attrition factor are transformed to 1 and 0 for
convenience respectively.
3.1.1. Linear discriminant analysis
It is a method used to generalize the linear discriminant of Fisher. It is used in the statistics to find a
linear combination of factors to differentiate two or more categories of objects or events for pattern
recognition and ML. Basically, the result or the dependent variable can be continuous with a linear
discriminant analysis and may contain an effect on another variable and find a sequential combination of
characteristics that best characterize or separate the categories 2 or more. Linear descriminent analysis (LDA)
is thus used for fine-tune the linear combination of characteristics to the best describe or separate two groups,
which indicate if the employee is attrition or not. At begining, the total data set is partitioned or randomly
divided into 80% and 20% training and testing data sets. Figure 4 shows that the receiver operating
characteristic (ROC) curve for various classifiers utilizing false positives rate (FPR), and true positive rate
(TPR).
Figure 4. Receiver operating characteristic
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The higher region below the curve, the more specifically the classifiers. In this work for plotting a
ROC curve probability of employee being classified as leaver and actual class (target variable) will be used.
ROC Curve implies different grouping by different coordinates. The node (0,1) for example, implies FPR = 0,
TPR = 1. We can get false positive (FP) = 0 and false negative (FN) = 0 according to the TPR and FPR formula.
And this is the perfect case because it does all manner of classifications. For node (1,0), meaning FPR = 1, and
TPR = 0. Thus, we can obtain true positive (TP) = 0, TN = 0. This is the worst situation in fact, because it
prevents all correct classification. We can get FPR=TPR = 0, for the node (0,0), so FP=TP = 0. Therefore, it
declares everything to be negative class. Similarly, we can get FPR=TPR = 1 and FP = TP =1 for node (1,1).
3.1.2. Logistic regression
Logistic regression is a way by which ML is characterized. In this calculation, a logistic regression
is used to display the probabilities reflecting possible outcomes of a separate trial.
𝑃(𝑐ℎ𝑢𝑟𝑛|𝑤) =
1
1+𝑒
−[𝑤0+∑ 𝑤𝑖𝑥𝑖]
𝑁
𝑖=1
(1)
𝑦 = 𝑙𝑛 (
𝑃(𝑌=1|𝑥
1−𝑃(𝑌 = 1|𝑥)
) = 𝑤0 + ∑ 𝑤𝑖.𝑥𝑖
𝑃
𝑖=1 (2)
𝑥 is a vevtor of independent varibles of dimension 𝑝 and 𝑦 is the logit (log odds). 𝑤0, 𝐿, 𝑤𝑃 are model
parameters. Logistic regression is a kind of regression that matches the values to logistic function. It is
suitable in situations where the dependent variable is categorical. The common form of a model is:
𝑃 (𝑌 𝑋−
, 𝑊) =
1
1+𝑒
−(𝑤0+ ∑ 𝑤𝑖𝑥𝑖)
(3)
3.1.3. Random forest
The random forest is an ineffable decision tree building block. The decision trees are often regarded
as a weak learner as their prediction accuracy is incredibly low. Random forests have been employed to
classify the endogenous earthquake sources. A random forest nevertheless collects and incorporates a group
of decision makers to achieve particularly powerful program based. This concept, that somehow a collection
of weak students is used to create a strong learner, validates the basis for classification techniques, which are
often found in machine learning. Random forest samples randomized exercise data to replace each decision
tree called classifier. In order to make a single decision, each decision tree returns to a class and then
incorporates luggage. Random forests are an accordion of decision-making objects that should be more
effective and accurate.
1) Pick 𝑘 usefulness arbitrarily from the all-out 𝑚 usefulness.
2) Where 𝑘 < 𝑚 2 is concerned. Calculate the hub 𝑑 utilizing the best part point among the 𝑘 highlights.
3) Use the right split to partition the hub into little girl hubs.
4) Repeat 1 to 3 stages until coming to l number of hubs.
5) Create woods to assemble 𝑛 number of trees by rehashing stages 1 to 4 for 𝑛 number occasions.
3.1.4. Decision tree classifier
A decision tree is utilized as a structure similar to a tree for construction regression and
classification models. Decision trees have utilized to categorize hotspot events, it divides set of data into few
more subsets while concurrently an associated decision tree is established [15]. The last result is a tree with
the required decision nodes. The decision tree is an identification. The other independent variables are the
(decision nodes). First of all, the entire data set is splitted randomly into the 80% ratio of a train and the 20%
test data. Calculation for decision tree:
− Step 1: having an unfilled 𝑁 hub.
− Step 2: on the off chance that examples are the entirety of a similar class 𝑐, return 𝑁 as a class
𝑐-named leaf hub;
− Step 3: in the event that the characteristic rundown is vacant in the record, at that point return 𝑁 as a
leaf hub set apart with the most widely recognized example name;
− Step 4: pick the check quality, the property in the record between the trait set;
− Step 5: mark hub (𝑁) with characteristic for the checking;
− Step 6: for each realized check property estimation 𝑎𝑖;
− Step 7: grow a branch for the test attribute = 𝑎𝑖 condition from hub 𝑁;
− Step 8: leave 𝑆𝑖 alone the example set for which attribute = 𝑎𝑖 test;
− Step 9: in the event that 𝑆𝑖 is unfilled, include a leaf hub set apart with the most widely recognized
example class;
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− Step 10: at that point include the hub returned by the choice tree age (𝑆𝑖, test-characteristic, quality
rundown).
Decision tree model is then applied to the training data set using the “rpart” instruction on the “attrition”
based or output variable with all other impartial data set factors. The whole decision tree algorithm on this
data set is compiled with the “fancyRpartPlot” command.
Required predictions are made on the test data set utilising the model of train dataset decision tree to
predict whether or not a given new employee will attrition based based on probability values ranging from
0 to 1 and then using a specific threshold of 0.5 (in this case), these predictions are cut to 0 and 1. Confusion
matrix has been described in Table 1 to Table 3. A decision tree is a type of tree-like structure that is used to
develop classification or regression models. Alduayj and Rajpoot [9] classified hotspot occurrences using
decision trees. It splits the data set into smaller and smaller number of subsets during developing an
accompanying decision tree in stages
The end result is a tree with all of the necessary decision and leaf nodes. A classification or decision
is represented by a leaf node (i.e., the goal or dependent variable “attrition” for this data set). The decision
nodes are represented by the other independent variables. To begin, the complete dataset is randomly
separated or split into train and test datasets in a ratio of 80% train and 20% test. Then the decision tree model
is applied to the training data set using the “rpart” command on the dependent or output variable “attrition”
along with all other independent variables. The command “fancyRpartPlot” is used to visualise the full decision
tree model on this training dataset. Required predictions are made on the test dataset using this decision tree
model of the train dataset to predict whether or not a particular new employee will be on the decline based on
probability values ranging from 0 to 1 and beyond by taking a threshold Specific to 0.5 (in this case),
predictions are divided into two ratings 0 and 1 classifications (i.e. the given employee will not be in attrition).
Table 1. Confusion matrix and
evaluation to decision tree
Table 2. Confusion matrix and
evaluation to random forests
Table 3. Confusion matrix and
evaluation to logistic regression
Predicted Actual
0 0 1
1 467 13
667 220
(Model evaluation metrics)
Accuracy
rate
98
Precision
rate
98
Recall rate 93
F1-measure 95
Predicted Actual
0 0 1
1 230 19
450 650
(Model evaluation metrics)
Accuracy
rate
99
99
96
97
Precision
rate
Recall rate
F1-measure
Predicted Actual
0 0 1
1 290 14
450 367
(Model evaluation metrics)
Accuracy rate 80
83
92
87
Precision rate
Recall rate
F1-measure
The random forest approach which was first advanced by Breiman in 2001, can be classified as an
ensemble model [26]. What’s the point of an ensemble? the omnipresent decision tree is the foundation of a
random forest. Because of its low prediction accuracy, the decision tree is frequently referred to as a weak
learner. Random forests were utilised to classify endogenous seismic sources in a landslide. A random forest,
on the other hand, assembles a group (or ensemble) of decision trees and combines their prediction ability to
achieve comparatively good predictive performance-strong learner. This notion of bringing together a group of
weak learners to generate a strong learner is at the heart of ensemble methods, which are commonly used in
machine learning. Bagging is when RF takes a random sample of training data and replaces it with new data
before generating each decision tree. Each decision tree returns a class, which is subsequently combined by
bagging to provide a unique choice. Random forest is an ensemble of decision trees that is supposed to
perform better and, as a result, provide more accuracy.
4. RESULTS AND DISCUSSION
To predict employee turnover, we should predict who will leave. This is a binomial classification
problem, where the group of employees has to be splitted into two groups based on some characteristics, one
with higher risk of retention and one with lower risk. Commonly used ML algorithms for binomial
classification problem are: decision trees, logistic regression, random forest. The following algorithm is used
to predict the model accuracy and calculate the confusion matrix. Only a couple of information mining
procedures were utilized in the current frameworks for information expectation. Here we additionally utilized
the representative informational index highlight determination technique. The representative informational
index for the most part contains data about specialists, for example, number of activities, left, pay, level of
fulfillment, and so forth. We can pick those fitting highlights from the worker informational index for our
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survey by utilizing highlight determination. The steps used in arriving at the results are: 1) dataset is
collected. In research, Kaggle dataset is used, 2) predictive model for data visualization and analysis for the
attrition is improved, 3) usage of machine algorithms such as random forest, linear regression and decision
tree, and 4) finding the best results for the parameters of accuracy, sensitivity and precision.
4.1. Data overview
Kaggle dataset was used where, there are 15,000 specialists in the dataset. Among these, 271
laborers are set apart as turnover, which means as far as the whittling down qualities, they are set apart as
“yes”. The turnover proportion is 13.5% and is plainly a lopsided information issue. Here we are showing the
main parameters for the prediction and implementation in Table 4, these 10 are the following parameters.
It includes all the characteristics or parameters of the employee. After the data set is collected, the data to be
modelled must be determined and cleaned up and shaped. The process of data preparation may be followed
by choose the metric results and predictor variables, determine how much data can be modelled, cleaned and
prepared. For each variable various data types and measuring levels should be identified in the dataset. Data
for outliers, missing or incorrect values should then be evaluated. Skew and high cardinality in data should be
removed. We use Python with SciPy, NumPy, Pandas, scikit-learn, and Matplotlib in the test and create three
apparatuses: one instrument is for RF-based component positioning, one is a visual device for breaking down
element factors and target variable, and one is RF-put together demonstrating device based with respect to
the weighted F-measure. The dataset we chose contains 10 different attributes such as satisfaction level, last
evaluation, average monthly hours, number of projects, time spent in company, left, work accident,
promotion in last 5 years, sales, salary. These are represented as a range of values depending on the
corresponding feature which is represented in Table 4.
Table 4. Brief of dataset
No. Feature Value range
1. Satisfaction level 0.4 – 0.99
2. Last evaluation 0.5 − 1
3. Average monthly hours 96 – 350
4. Number of projects 2 – 8
5. Time spent in company 2 – 10
6. Left 0 – 1
7. Work accident 0 – 1
8. Promotion in last 5 years 0 – 1
9. Sales Sales, support, technical, IT, marketing, accounting, HR, product mng, RandD
10. Salary Low, medium, high
Here the predictive model for the attrition is improved with the help of anaconda-command-prompt
to first utilise Python Jupyter. But then to improve the prediction of data visualisation and analysis, here the
ML learning and python have been decided. However, it was too time intensive and complicated to download
libraries and set up a system, machine-learning, data visualisation and the logic of predictive and decision-making
analytics were unlikely. It was therefore a way of achieving the role of ML and data visualization.
The following steps are used to pre-process the data to a usable format before using data for the ML
algorithm training and evaluating the test data:
1) Remove values from null.
2) No unique value columns removed.
3) Unnecessary columns have been removed.
4) Modification of the outdated the unicode transformation format (UTF) for UTF support.
5) The weight of each column depended on the columns rearranged.
6) Save data that was cleaned in csv format.
Once the important features are identified, and the data is in a model-ready format, we can begin the
predictive analytics part of the project. Modeling begins by dividing the available data into a training set and
a test set. Algorithms are deployed in the training set and tested in the test set. 80% of the data is randomly
assigned to the training set and 20% of the data is assigned to the test set. We will use classification
algorithms because our response variable is binary. The ‘yes’ and ‘no’ characters in the attrition variable are
converted to 1 and 0 respectively to make it easier.
4.2. Performance matrices
In this paper, Table 5 provides the performance measures used for the methodology assessments,
and these entries are used for identifying and measuring classification accuracy. By using the confusion
matrix, performance metrics like, accuracy, precision, specificity and sensitivity of the calculated classifier
and assembly model and their results as displayed in Table 5. There will be (four cases) if the result is
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positive it can be expected to be TP. It is a false negative if it is expected to be a FN. If the result is negative
and can be expected to be negative, it is true negative (TN); if the result is positive and can be expected to be
positive, it is FP.
Table 5. Performance metrics & their definition
Metric Equation Definition
Accuracy (TP + TN) / (P+N) Ratio of the total number of predictions which are correct
Sensitivity TP / (TP + FN) Ratio of the positive cases which are correctly identified
Specificity TN / (FP / TN) Ratio of the negative cases which are correctly identified
Precision TP / (TP + FP) Ratio of the predicted positive cases which are correct
4.3. Matrix of confusion for different methodologies
This matrix describes the Predictive Analysis methodology on HR data, in order to analyze the use
of predictive analysis for HR. The following chosen matrix for prediction is employee turnover, because of
its high importance for organization. Thus, goal of the thesis is to predicting the employee turnover. Recall,
F1 performance, and accuracy are all infuriating matrix metrics. (Positives 1) and (negatives 0) will occur
when we divide a sample into two parts. There will be (four cases) if the result is positive it can be expected
to be positive (TP). It is a false negative if it is expected to be a negative (FN). If the result is negative and
can be expected to be negative, it is TN; If the result is positive and can be expected to be positive, it is FP.
Figure 5 to Figure 7 predicting the following algorithms used here such as decision tree, logistic
regression, random forest tree. The accuracy provided by the decision tree is 97%. The accuracy provided by
random forest algorithm is 98%. The accuracy provided by the logistic regression is 78%. Hence, there
calculate and get the best accuracy from random forest algorithm of the employee data set.
Figure 5. Decision tree matrix Figure 6. Random forest matrix
Figure.7. Logistic regression
To conclude, random forest classifier performed better than other tested models, on the Kaggle
sample datasets. Therefore, random forest model has been used to predict employee turnover in company.
Prediction results have been saved in flat file, including prediction probabilities for each employee.
In addition, for interpretation of the decision path, decision trees were represented as graphs. Furthermore,
feature importance has been evaluated to better understand variables that influenced the decision most.
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Such influencers are monthly income, satisfaction, last evaluation, and left, all in correlation with over time.
In this work, data preparation has been done on HR datasets as shown in Figure 8. Example dataset was used
for demonstrating methods used for cleaning and preparing data. First of all, employee data from multiple
sources has been gathered and unified. Then missing values has been imputed, outliers have been removed
and skew has been reduced. Afterwards parameters for selected ML algorithms have been tuned using
different methodologies. Later, on pre-processed employee data different ML algorithms have been applied
and algorithm with best prediction accuracy has been selected.
From Table 6, above outcomes describes, compared to the other model, the model has various TP
and TN. The accuracy obtained by using decision tree classifier with a 97% efficiency outperforms other
mining techniques. Considering the sensitivity parameter, decision tree classifier has the highest value.
The effects of predictive ML techniques on the employee dataset show that random forest evaluation
outperforms other ML techniques followed by model of logistic regression for the specific dataset if
precision is the preferred metric. Identification of human resources is forecasted using ML algorithms on
employee data. This suggests that the prediction of the employees interest in maintaining their job or not was
better estimated by this method compared to the other techniques. Several implementations are inferior to
other frameworks in consistency, reliability, and specialization. We conclude from this that the model’s
consistency is stronger than other models.
Figure.8. Analysis the parameters
Table 6. Performance metrics
Algorithms Precision Sensitivity Accuracy
Logistic regression 83 81 78%
Random forest 99 82 97%
Decision tree 98 84 98%
5. CONCLUSION
There are several useful algorithms, both qualitative and quantitative, for anticipating staff turnover
in order to stabilise a company. However, these methodologies have a number of drawbacks when it comes
to predicting staff turnover, such as the following: Inadequate consideration of inconsistent data. The data
provided in these studies regarding employee turnover only reflects a small portion of the entire workforce.
Inefficient data processing due to high dimensionality. There are a range of feature dimensions, both static
and dynamic, that characterize employees. In addition, there is no evaluation, given that the aim of the
forecast is to reduce employee turnover, we must define and prioritize its components.
In this paper, identification of human In this paper, identification of human resource is forecasted
using ML algorithms on employee data. Employee dataset has been collected from Kaggle. The emphasis is
on utilizing various ML algorithms and mixtures of several recognizes the importance for efficient and
effective employee attrition reduction using best ML algorithms, as employee attrition is one of the most
crucial business problems. On the kaggle employee data, following algorithms has been implemented such as
logistic regression, decision tree classification, linear discriminant analysis and random forest. We discovered
that the accuracy obtained by using a random forest analysis model with a 99% efficiency outperforms
mining techniques. As a result, the effects of predictive ML techniques on the employee dataset show that
random forest evaluation outperforms other ML techniques followed by model of logistic regression for this
specific dataset if precision is the preferred metric. The biggest drawback of random forest is that it can
become very slow and ineffective for real-time predictions if there are too many trees. Generaly, these
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algorithms are quick to learn but take a long time to make predictions after they have been taught.
The applications of this method is used to determine the efficiency of staff members, relationship of
retirement behaviors on employee turnover, like, job content, lateness and absenteeism, length of service, and
demographics. It has the application of the logit, and probit models for voluntary employee turnover
predictions and to explore different personnel and job factors influencing the voluntary turnover of
employees. It is used to investigate employee attrition using multiple decision tree algorithms and to correlate
data mining techniques to calculate employee inconvenience. In random forest, the future study can be on
improving the accuracy by utilising different attribute split measurements, different combine functions, or
both. Increasing the diversity of base classifiers is a continuous process of quality improvement that will
improve accuracy. As a result, discovering new approaches to achieve diversity will undoubtedly be a
research topic in the future. For enhancing the accuracy of random forest classifiers, out of bag (OOB)
estimates, proximity computation, and variable importance features can be used more extensively.
The random forest technique generates a large number of classification trees, each of which is independent of
the others. As a result, random forest is an excellent option for parallel processing. Furthermore, data mining
is typically done on very large datasets, and random forest can handle datasets with a lot of predictors.
As indicated in section 4, each parallel random forest implementation is tailored to a particular platform or
language. As a result, there is room for a generalised random forest parallel algorithm. Business data is
dispersed around the globe due to the geographical spread of business and the world’s connection to the
Internet. As a result, developing a distributed random forest algorithm is an important future research topic.
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BIOGRAPHIES OF AUTHORS
Elham Mohammed Thabit A. Alsaadi BSc. in Computer Scince from Al-
Mustansiriya University -Iraq, MSc. in Real Time Systems from UK. Ph.D. from College of
Information Technology at Bybilon University-Iraq. She is a lecturer in College of Computer
Science & Information Technology, Karbala University. Her research interests include:
Artificial Intelligence, Computer Vision, Image Processing, Database, System Analysis, and
E- business. She can be contacted at email: elham.thabit@uokerbala.edu.iq.
Sameerah Faris Khlebus MSc.in Data Security from department of data security.
She is a lecturer in University of Information Technology & Communications. Her research
interests include: data security, artificial intelligence, information systems and information
technology. She can be contacted at email: sameerah.alradh@uoitc.edu.iq and
sameera_alradhi@yahoo.com.
Ashwak Alabaichi Msc. in steganalysis. Ph.D. in cryptography from Computing
of School, Uiversiti Utara Malaysia, Kedah, Malaysia. She is a lecturer in Department of
Biomedical Engineering, College of Engineering, University of Kerbala, Iraq. Her research
interests include cryptography, Image Processing, Stegoanalysis, and Data security. She can be
contacted at email: ashwaq.mahmood@uokerbala.edu.iq.