performance analysis of ann models for the prediction of i...

4th International Conference on System Modeling & Advancement in Research Trends (SMART)College of Computing Sciences and Information Technology (CCSIT) ,Teerthanker Mahaveer University , Moradabad 2015

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Performance Analysis of ANN models for the prediction of I.T. Professional’s Occupational

stress Using MATLAB 1Naveen Kumar Pandey, 2Prof. Abhay Saxena, 3Dr. Ashutosh Bhatt

1Research Scholar, Dept. of Computer Science, Dev SanskritiVishwavidyalaya, Shantikunj, Haridwar, Uttarakhand 2Dept. of Computer Science, Dev SanskritiVishwavidyalaya, Shantikunj, Haridwar, Uttarakhand

3Dept. of Computer Science, Birla Institute of Technology, Bhimtal [email protected]

[email protected] [email protected]

Abstract:-The current research paper present occupational stress analysis and prediction model, which are vital for the IT Professional healthcare. In this procedure we will used Artificial Neural Network techniques implemented by using MATLAB Tools. The development of the Occupational Stress Prediction model involves 2 basic steps, training/learning and testing/validation. With this model we will able to measure and predict the Occupational Stress level of the IT Professional, so that they cure on time before encounter with some major chronic diseases. As a result this model would be beneficial for the HR managers, Psychologist to keep their human resources well. In this research paper we have develop five neural network model with the help of MATLAB N.N. Toolbox, out of them “network5” performance was good, on the regression point 0.97768, MSE = 0.043 and best validation point 0.007500.TRAINCGF training function were used in the network5. Keywords-Occupational Stress, Artificial Neural Network, IT Professionals

I. INTRODUCTION: Occupational stress is a serious problem in an Indian

IT Professional, According to survey (Regus, 2012) by the workspace provider Regus conducted among over 16,000 professionals worldwide, over half 51% of Indian respondents said their stress levels have risen over the past year. The survey said most stress triggers due to work pressure and personal finances. Besides, a number of factors such as work, money, and commute to work as well as continuing instability in the world economy have fuelled this growing pressure. With the introduction of new technologies there are number of working conditions in different sectors which leads to making work stressful. IT profession is one of the major professions which are more likely to experience both psychologically and physiologically stress. With the hustle and bustle of life and challenges in the workplace people are more inclined with the deadline rather than health and still work until the major health problem arise.In this study we will measure and predict the Occupational Stress Level by using Artificial Neural

Network techniques implemented by using MATLAB Tools. so that they cure on time before encounter with some major chronic diseases. As a result this model would be beneficial for the HR managers, Psychologist to keep their human resources well.

II. STRRESS AND OCCUPATIONAL STRESS

Stress:In physics, stress is a pressure exerted on a body. Sources of physical stress are found in tons of rock crushing the earth, in cars smashing one another, and in stretching rubber bands. Psychological stresses also “press,” “push,” and “pull.” People can feel “crushed” by the need to make a life changing decision. They can feel “smashed” by a disaster, or “stretched” to the brink of “splitting” (Steber, 1998). Stress is your mind and body’s response or reaction to a real or imagined threat, event or change. The threat, event or changes are commonly called stressors. Stressors can be internal (thoughts, beliefs, attitudes) or external (loess, tragedy, change) (Hanna, 2012)

Occupational stress:Occupational stress can be defined as the harmful physical and emotional responses that occur when the requirements of the job do not match the capabilities, resources, or needs of the worker (Cincinnati, 1999).

Stress related with job or occupation is called occupational stress. Occupational stress refers to a situation where occupation related factors interact with employee to change, disrupts or enhance his psychological and physiological conditions such that the person is forced to deviate from normal functioning. The person cannot work efficiently due to stress. An employee’s job role is composed of quality work life and is responsible for bringing in maximum amount of job satisfaction or minimum amount of job stress &


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anxiety. Occupational stress usually results from conflicting incompatible or unclear expectation that is derived from work environment (P., 2012)

Sources of occupational stress: Attempts to identify the sources of occupational stress have discovered many culprits. (Cooper, 1983), has developed a concise yet complete list of six sources of work stress, Job Conditions - Quantitative & qualitative work overload, people decisions, physical danger, and techno stress. Role Stress - Role ambiguity, sex bias and sex-role stereotypes. Interpersonal Factors - Poor work and social support systems, lack of management concern for the worker, political rivalry, jealousy, or anger. Career Development - Under promotion, over promotion, job security, frustrated ambitions. Organizational Structure - Rigid and impersonal structure, political battles, inadequate supervision or training, non-participative decision making. Home-work Interface - Spillover, lack of support from spouse, marital conflict, dual career stress Artificial neural network:

The term neural network was traditionally used which refers to a network or circuit of biological neurons. The modern usage of the term often refers to artificial neural networks, which are composed of artificial neurons or nodes. Thus the term may refer to either biological neural networks, made up of real biological neurons, or artificial neural networks, for solving artificial intelligence problems (Monterola, 2008). These artificial networks may be used for predictive modeling, adaptive control and applications where they can be trained via a dataset (Saxena A. B., 2012). There are numerous fields where ANN techniques are being applied i.e. medical field, education sector, entertainment, games, security etc. Psychology is one of the emerging fieldswhere ANN techniques are being used to assess human behavior, personality traits prediction and prediction of stress level, Human capacity assessment through time series prediction (Saxena, Pandya, & Bhat, 2012). Assessment of Human Capacity based on Conjugated Gradient Techniques using Artificial Neural Network (Saxena & Pandya, 2013)theseresearches are the best examples of that.

III. RESEARCH METHODOLOGY:

Input Parameter (Independent Variable): after review of literature we have found 12 source of occupational stress that is known as independent variable. And in terms of ANN also known as input parameter or input variable. Following are the name of 12 Input parameter

1. Role Overload 2. Role ambiguity 3. Role Conflict 4. Unreasonable Group and Political Pressure 5. Under participation 6. Powerlessness 7. Poor peer relations intrinsic 8. Impoverishment 9. Low status 10. Strenuous working conditions 11. Unprofitability 12. Personal Problem/ Family Problem

Output Parameter (Dependent Variable): Level of Occupational Stress of the employee

Sample size- 50 (BHEL Employee) Sampling Techniques: Simple Random Sampling Tools Used: MATLAB: Used to analyze data, develop algorithms, and create models and applications. OSI: Occupational Stress Index, Prepared By A.K. Srivastava & Dr. A.P. Singh, BHU, The scale had 46 items each to be rated on the five-point scale. Out of 46 items, 28 are “true keyed” and the balance 18 is “false keyed”.

ANN Model for Prediction of Occupational Stress:Artificial Neural Network Model Training Parameter

Network Type- Feed Forward Backpropagation Training Function – TRAINLM, TRAINBFG,

TRAINBR, TRAINCGB, TRAINCGF Adaption Learning Function – LEARNGDM Performance function – MSE Number of Layer – 2 Number of Neurons -10 RESULT ANALYSIS AND DISCUSSION:

Performance (MSE= Mean Squire Error) chart of ANN Models


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ANN Model

Training Function

Training of the

network MSE

Best Validation

Performance

At Epoch

Network 1 TRAINLM

1 Iteration 0.750 0.2171 5

2 Iteration 0.067 0.1137 1

3 Iteration 0.067 0.0656 0

Network 2 TRAINBFG

1 Iteration 1.09 0.125 4

2 Iteration 0.142 0.874 1

3 Iteration 0.301 0.125 2

Network 3 TRAINBR

1 Iteration 1.09 1.075 803

2 Iteration 1.08 1.097 1000

3 Iteration 1.10 1.097 1000

Network 4 TRAINCGB

1 Iteration 0.141 0.085 2

2 Iteration 0.054 0.047 1

3 Iteration 0.040 0.073 0

Network 5 TRAINCGF

1 Iteration 1.60 0.000395 21

2 Iteration 0.043 0.007500 0

3 Iteration 0.0523 0.037422 2

Table 1.0: Shows the performance of all the neural networks models

Regression analysis chart of ANN Models

ANN Model

Training Function Training Validation Test

All Regress

ion

Network 1

TRAINLM

Output

Target

Output

Target

Output

Target

0.98 +0.048 0.67 +0.4

1 1.1 +0.45 0.9573

0.97 +0.088 0.87 +0.0

77 1 +0.098 0.95908

0.98 +0.088 0.91 +0.0

94 0.99 0.059 0.95908

Network 2

TRAINBFG

0.93 +0.33 1 +0.0

91 0.97 +0.68 0.87152

0.99 +0.13 0.72 +1.2 0.75 +0.8

7 0.87148

0.99 +0.1 0.94 +0.2 1 +0.0 0.91708

5 8 43

Network 3

TRAINBR

0.96 +0.079 - - - - 0.9865

0.96 +0.084 - - - - 0.98629

0.96 +0.84 - - - - 0.98629

Network 4

TRAINCGB

0.91 +0.18 0.9 +0.2

3 0.93 +0.12 0.96818

0.89 +0.2 0.89 +0.23 0.84 +0.1

5 0.97287

0.9 +0.17 0.88 +0.2

4 0.92 +0.05 0.97227

Network 5

TRAINCGF

0.95 +0.093 0.98 +0.0

23 0.99 +0.2 0.97227

0.97 +0.1 0.92 +0.16 0.94 +0.0

6 0.97227

0.93 +0.14 0.97 +0.0

9 0.98 +0.02 0.97768

Table 2.0: Shows the Regression Value of Training, Validation and Testing of the neural network models

Performance Graph and Regression Plot of the ANN Models:

Network 1:Training Function – TRAINLM and all parameters of the network will be same.


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Graph 1.0 a= Best Validation Perfomrance, b= Neural Network Training Regression [First Training of the Network]

Graph 1.1 a= Best Validation Perfomrance, b= Neural Network Training Regression [Second Training of the Network]


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Graph 1.2 a= Best Validation Perfomrance, b= Neural Network Training Regression [Third Training of the Network]

Network 2:Training Function – TRAINBFG and all parameters of the network will be same.



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Graph 2.2 a= Best Validation Perfomrance, b= Neural Network Training Regression [Third Training of the Network] Network 3:Training Function – TRAINBR and all parameters of the network will be same



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Network 4: Training Function – TRAINCGB and all parameters of the network will be same



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Network 5:Training Function – TRAINCGF and all parameters of the network will be same


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Graph 5.2 a= Best Validation Perfomrance, b= Neural Network Training Regression [Third Training of the Network] Interpretation and Discussion:In this study we have created five neural network model with the help of MATLAB N.N. Toolbox i.e. network 1, network 2, network 3, network 4 and network 5, based on the above mention parameter. But every single neural network has different Training Function that gives the basis for comparative analysis of the network. We have train every single network three times and get the difference on MSE value and Regression Plot. As we have mention on the Graph 1.0 (a) (b) Graph 1.1 (a) (b) and in the same way Graph 2.0 (a) (b) and so on. Where (a) represents Validation Performance Graph on MSE (Mean Squire Error) and (b) represents regression plot. On the basis of acquired score we have created a performance table Table 1.0 and regression table Table 2.0.As Table 1.0 shows MSE value of network 5, iteration 2 is 0.043 that is very less than other networks of the table and Table 2.0 shows the overall regression value of the network 5, iteration 2 is 0.97227 that indicates model are more closer to desired output. So that network5 with TRAINCGF is best model and out of five

models for the prediction of the occupational stress of the IT Professionals.1

IV. CONCLUSION At this stage we can conclude thatProper analysis through artificial neural network techniques makes one attentive about their condition of occupational stress level. This model helps to recognize one’s stress level so that timely it can be cure by proper treatment and counseling. After result analysis we have found that network5 (Feed Forward Backpropagation network) with Training Function TRAINCGF has given best performance. Sample size was 50. So that this model can be used in the large set of data and get the desired output and satisfactory result.

REFERENCE: [1] Cincinnati, O. (1999). Stress. National Institute for Occupational Safety

and Health. [2] Cooper, C. (1983). Identifying Stressors at Work: Recent Research

Developments. Journal of Psychosomatic Research, 369-376.1 [3] Hanna, D. H. (2012). What is Stress? Retrieved 8 14, 2015, from The

American Institute of Stress: http://www.stress.org/what-is-stress/ [4] Monterola, C. R. (2008). Neural Networks . Retrieved 7 18, 2015, from

http://wiki.gis.com/: http://wiki.gis.com/wiki/index.php/Neural_network [5] P., S. M. (2012). Occupational Stress Amongst Teachers of Professional

Colleges in Punjab. Journal of Educational Research and Development. [6] Regus. (2012, 9 6). Stress level rising among employees: Survey.

Retrieved 8 14, 2015, from The Economic Times: http://articles.economictimes.indiatimes.com/2012-09-06/news/33650170_1_stress-indian-respondents-madhusudan-thakur

[7] Saxena, A. B. (2012). Assessment of Human Capcity with respect to social values and professional attitude: An Artificial Neural Network Based methodology. International Conference on Emerging Trends in Engineering and Technology. Moradabad : TMU.

[8] Saxena, A., & Pandya, P. (2013). Assessment of Human Capacity based on Conjugated Gradient Techniques using Artificial Neural Network. Artifical Conciousness and Computer Science.

[9] Saxena, A., Pandya, P., & Bhatt, D. (2012). Human capacity assessment through time series prediction. S & T Review an international journal on science and Technology , 2231-5160.

[10] Steber, W. (1998). Occupational Stress Among Frontline Corrections. Menomonie: University of Wisconsin-Stout.

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