An empirical analysis of the growth of artificial intelligence in socio economic environment

Research Article
Open access

An empirical analysis of the growth of artificial intelligence in socio economic environment

Sangeetha S.K.B 1 , Rajeswari Rajesh Immanuel 2 , Sukumar R. 3 , Sandeep Kumar M. 4*
  • 1 Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, India.    
  • 2 Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, India.    
  • 3 School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.    
  • 4 School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.    
  • *corresponding author sandeepkumarm322@gmail.com
ACE Vol.5
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-57-7
ISBN (Online): 978-1-915371-58-4

Abstract

Artificial Intelligence (AI) is very evident in our daily lives and our economy, and it has had an impact on our environment in various ways. The competition to reap its benefits is growing worldwide, and world leaders - the US and Asia - are already taking action. Many people see AI as a way to improve productivity and economic development. It can improve process efficiency and greatly improve decision-making processes by examining large amounts of data. It can also lead to the creation of new products, services, markets, and sectors, leading to increased customer demand and new sources of revenue. Some are concerned that it could lead to the development of large corporations - institutions of wealth and knowledge - that would hurt the entire economy. It has the potential to widen the gap between developed and developing countries, as well as to increase the need for highly skilled workers while firing others; the latest trends can have a profound effect on the labor market. Experts also worry that inequality could worsen, reduce wages, and lower the tax base. The aim of this study was to learn how artificial intelligence is used in today's economy and what society thinks about its use.

Keywords:

Artificial Intelligence, Digital Transformation, Industry 4.0, Industrial Revolution, Social Economic Environment

S.K.B,S.;Immanuel,R.R.;R.,S.;M.,S.K. (2023). An empirical analysis of the growth of artificial intelligence in socio economic environment. Applied and Computational Engineering,5,625-630.
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References

[1]. Sathishkumar, V. E., Venkatesan, S., Park, J., Shin, C., Kim, Y., & Cho, Y. (2020, April). Nutrient water supply prediction for fruit production in greenhouse environment using artificial neural networks. In Basic & Clinical Pharmacology & Toxicology (Vol. 126, pp. 257-258). 111 RIVER ST, HOBOKEN 07030-5774, NJ USA: WILEY.

[2]. Zhongshan, C., Xinning, F., Manickam, A., & Sathishkumar, V. E. (2021). Facial landmark detection using artificial intelligence techniques. Annals of Operations Research, 1-19.

[3]. Shanthi, N., VE, S., Upendra Babu, K., Karthikeyan, P., Rajendran, S., & Allayear, S. M. (2022). Analysis on the Bus Arrival Time Prediction Model for Human-Centric Services Using Data Mining Techniques. Computational Intelligence & Neuroscience.

[4]. Kogilavani, S. V., Sathishkumar, V. E., & Subramanian, M. (2022, May). AI Powered COVID-19 Detection System using Non-Contact Sensing Technology and Deep Learning Techniques. In 2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS) (pp. 400-403). IEEE.

[5]. Pavithra, E., Janakiramaiah, B., Narasimha Prasad, L. V., Deepa, D., Jayapandian, N., & Sathishkumar, V. E. (2022). Visiting Indian Hospitals Before, During and After COVID. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems.

[6]. Sathishkumar, V. E., Park, J., & Cho, Y. (2020). Using data mining techniques for bike sharing demand prediction in metropolitan city. Computer Communications, 153, 353-366.

[7]. Krishnamoorthy, N., Prasad, L. N., Kumar, C. P., Subedi, B., Abraha, H. B., & Sathishkumar, V. E. (2021). Rice leaf diseases prediction using deep neural networks with transfer learning. Environmental Research, 198, 111275.

[8]. Easwaramoorthy, S., Sophia, F., & Prathik, A. (2016, February). Biometric Authentication using fingernails. In 2016 international conference on emerging trends in engineering, technology and science (ICETETS) (pp. 1-6). IEEE.

[9]. VE, S., & Cho, Y. (2020). Season wise bike sharing demand analysis using random forest algorithm. Computational Intelligence.

[10]. Zhang, M., Wang, X., Sathishkumar, V. E., & Sivakumar, V. (2021). Machine learning techniques based on security management in smart cities using robots. Work, 68(3), 891-902.

[11]. Subedi, B., Sathishkumar, V. E., Maheshwari, V., Kumar, M. S., Jayagopal, P., & Allayear, S. M. (2022). Feature learning-based generative adversarial network data augmentation for class-based few-shot learning. Mathematical Problems in Engineering, 2022.

[12]. Subramanian, M., Shanmuga Vadivel, K., Hatamleh, W. A., Alnuaim, A. A., Abdelhady, M., & VE, S. (2022). The role of contemporary digital tools and technologies in Covid‐19 crisis: An exploratory analysis. Expert systems, 39(6), e12834.

[13]. Subramanian, M., Lv, N. P., & VE, S. (2022). Hyperparameter optimization for transfer learning of VGG16 for disease identification in corn leaves using Bayesian optimization. Big Data, 10(3), 215-229.


Cite this article

S.K.B,S.;Immanuel,R.R.;R.,S.;M.,S.K. (2023). An empirical analysis of the growth of artificial intelligence in socio economic environment. Applied and Computational Engineering,5,625-630.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN:978-1-915371-57-7(Print) / 978-1-915371-58-4(Online)
Editor:Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.5
ISSN:2755-2721(Print) / 2755-273X(Online)

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References

[1]. Sathishkumar, V. E., Venkatesan, S., Park, J., Shin, C., Kim, Y., & Cho, Y. (2020, April). Nutrient water supply prediction for fruit production in greenhouse environment using artificial neural networks. In Basic & Clinical Pharmacology & Toxicology (Vol. 126, pp. 257-258). 111 RIVER ST, HOBOKEN 07030-5774, NJ USA: WILEY.

[2]. Zhongshan, C., Xinning, F., Manickam, A., & Sathishkumar, V. E. (2021). Facial landmark detection using artificial intelligence techniques. Annals of Operations Research, 1-19.

[3]. Shanthi, N., VE, S., Upendra Babu, K., Karthikeyan, P., Rajendran, S., & Allayear, S. M. (2022). Analysis on the Bus Arrival Time Prediction Model for Human-Centric Services Using Data Mining Techniques. Computational Intelligence & Neuroscience.

[4]. Kogilavani, S. V., Sathishkumar, V. E., & Subramanian, M. (2022, May). AI Powered COVID-19 Detection System using Non-Contact Sensing Technology and Deep Learning Techniques. In 2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS) (pp. 400-403). IEEE.

[5]. Pavithra, E., Janakiramaiah, B., Narasimha Prasad, L. V., Deepa, D., Jayapandian, N., & Sathishkumar, V. E. (2022). Visiting Indian Hospitals Before, During and After COVID. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems.

[6]. Sathishkumar, V. E., Park, J., & Cho, Y. (2020). Using data mining techniques for bike sharing demand prediction in metropolitan city. Computer Communications, 153, 353-366.

[7]. Krishnamoorthy, N., Prasad, L. N., Kumar, C. P., Subedi, B., Abraha, H. B., & Sathishkumar, V. E. (2021). Rice leaf diseases prediction using deep neural networks with transfer learning. Environmental Research, 198, 111275.

[8]. Easwaramoorthy, S., Sophia, F., & Prathik, A. (2016, February). Biometric Authentication using fingernails. In 2016 international conference on emerging trends in engineering, technology and science (ICETETS) (pp. 1-6). IEEE.

[9]. VE, S., & Cho, Y. (2020). Season wise bike sharing demand analysis using random forest algorithm. Computational Intelligence.

[10]. Zhang, M., Wang, X., Sathishkumar, V. E., & Sivakumar, V. (2021). Machine learning techniques based on security management in smart cities using robots. Work, 68(3), 891-902.

[11]. Subedi, B., Sathishkumar, V. E., Maheshwari, V., Kumar, M. S., Jayagopal, P., & Allayear, S. M. (2022). Feature learning-based generative adversarial network data augmentation for class-based few-shot learning. Mathematical Problems in Engineering, 2022.

[12]. Subramanian, M., Shanmuga Vadivel, K., Hatamleh, W. A., Alnuaim, A. A., Abdelhady, M., & VE, S. (2022). The role of contemporary digital tools and technologies in Covid‐19 crisis: An exploratory analysis. Expert systems, 39(6), e12834.

[13]. Subramanian, M., Lv, N. P., & VE, S. (2022). Hyperparameter optimization for transfer learning of VGG16 for disease identification in corn leaves using Bayesian optimization. Big Data, 10(3), 215-229.