Volume-11 , Issue-2 , Feb - 2023 Go Back

Unemployment continues to be a major factor for both developed and developing countries, as a result of which they losing their overall financial and economic influence. Over the last few years, unemployment rate prediction has gained a lot of interest from researchers. The unemployment crisis has been going on for a long time. At the same time, the COVID-19 pandemic lockdown has had a devastating impact on India`s unemployment rate, with most private firms firing their staff. Predicting the growth and trend of the COVID-19 pandemic using machine learning. COVID19 is affecting lives in various ways. Unemployment is one of them. Unemployment can cause mental illness, stress, an increase in suicides, premature deaths, etc. That is why it is important to predict the pattern of unemployment in the post COVID19 situation. Machine Learning (ML) can be deployed effectively to predict the change in the unemployment rate. An ML-based model has been proposed to predict the post COVID19 unemployment rate in India. How the lockdown is affecting employment is shown and further future more effective analysis can be done by looking at various other aspects of the employment sector. The goal of our study is to look at the impact of the coronavirus on India`s unemployment rate. These models are proposed to provide the most accurate predictions for the future.

COVID-19; Machine Learning; Unemployment; Prediction

[1] M. Shuaib Khan, G.R Pasha, Ahmed Hesham Pasha, “Theoretical Analysis of Inverse Weibull Distribution”WSEAS TRANSACTIONS on MATHEMATICS, Issue 2 Vol. 7, pp. 30-38, 2008.
[2] Blanchard, O. J., & Leigh, D. “Growth forecast errors and fiscal multipliers” American Economic Review, 103(3), pp.117–120, 2013.
[3] Jha S, Goyal MK, Gupta B, Gupta AK, “A novel analysis of COVID 19 risk in India incorporating climatic and socioeconomic Factors”, Technol Forecast Soc Change 167:120679 ELSEVIER, 2021.
[4] Dr Mohd Akhter Ali and M.Kamraju, “A STUDY ON IMPACT OF COVID-19 PANDEMIC ON UNEMPLOYMENT IN INDIA”, The Bhopal School of Social Sciences, ISBN: 978-81-945302-8-2, pp. 50-61, 2020.
[5] Arun Khurana ,”Government Schemes to Tap Employment Potential in Various Sectors”, Employment News, Volume-48, 2018.
[6] Parul, “Unemployment during COVID-19 – the most severe impact in this pandemic faced by individuals and economies”, Inventiva , 2020.
[7] Dobriyal, K. (1970, January 01). “Unemployment in India Issues policy and perspective”. Retrieved Oct 05, 2022.
[8] Giri, V. (1973, January 01). “Labour Problems in Indian Industry”, third edition. Retrieved May 10, 2020.
[9] Vikas Barbate, Rajesh N. Gade and Shirish S. Raibagkar “COVID-19 and Its Impact on the Indian Economy” in.sagepub.com/journals-permissions-india DOI10.1177/09722 62921989126, Vol 25 issue 1, pp. 23-35, 2021.
[10] Hale, T., Webster, Sam, and Anna Petherick, Toby Phillips, B. K. “Oxford COVID- 19 Government Response Tracker”. 2020.
[11] Shania Bhalotia, Swati Dhingra, Fjolla Kondirolli, “City of dreams no more: The impact of Covid-19 on urban workers in India”, Centre for Economic Performance London School of Economics and Political Science Houghton Street, London WC2A 2AE, UK, pp. 1-29, 2020.
[12] Arup Mitra and Jitender Singh, “COVID-19 Pandemic and Livelihood Loss: Variations in Unemployment Outcomes and Lessons for Future”, INSTITUTE OF ECONOMIC GROWTH, 2020.
[13] Vijay Victor, Joshy Joseph Karakunnel , Swetha Loganathan and Daniel Francois Meyer, “From a Recession to the COVID-19 Pandemic: Inflation–Unemployment Comparison between the UK and India”, Economies, 9, 73. https://doi.org/10.3390/economies9020073, pp. 1-19, 2021.
[14] The Indian Express. “India’s GDP Contracts by 23.9% in April-June Quarter (2020)”. The Indian Express. Retrieved Sep 12, 2022.
[15] Ruan S. “Likelihood of survival of coronavirus disease” The Lancet Infectious Diseases.; 20(6):630–1. https://doi.org/10.1016/S1473-3099(20)30257-7 PMID: 32240633, 2020.
[16] Johns Hopkins University & Medicine (2020) “COVID-19 Dashboard” Available at: https://coronavirus.jhu.edu/map.html (Accessed: 22 Nov, 2022).
[17] Nhung Nghiem, Nick Wilson “Potential impact of COVID-19 related unemployment on increased cardiovascular disease in a high-income country: Modeling health loss, cost and equity” PLOS ONE | https://doi.org/10.1371/journal.pone.0246053 May 27, 2021.
[18] Gene Falk, Paul D. Romero “Unemployment Rates During the COVID-19 Pandemic”, Congressional Research Service, 2020.
[19] Dagmar Walter, “Implications of Covid?19 for Labour and Employment in India”, The Indian Journal of Labour Economics ,63 (Suppl 1):S47–S51, 2020.
[20] Avinash Raj David "Analysis of the Unemployment in India: Causes and Solutions", International Journal of Emerging Technologies and Innovative Research pp.125-131, 2019.
[21] Chatterjee, P., Dev, A. Labour Market Dynamics and Worker Flows in India: Impact of Covid-19. Ind. J. Labour Econ. https://doi.org/10.1007/s41027-022-00420-7 Springer- 2023.
[22] Purna Chandra Parida, Yogesh Suri, “Covid-19 Lockdown, Employment And Migration In India: Challenges And Prospects”, Manpower Journal, VOl. LIV No. 1 & 2, 2020.
[23] S. Tuli, S. Tuli, R. Tuli, and S. S. Gill, “Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing,” Journal Elsevier - Internet of Things 100222. Vol 11, pp. 1-16, 2020.
[24] Ramesh Chand, Jaspal Singh, “Workforce changes and employment: some findings from PLFS data series”, NITI Aayog Government of India pp.1-15, March 2022.
[25] Paez, Antonio. “Using Google Community Mobility Reports to Investigate the Incidence of COVID-19 in the United States.” Transport Findings, May. https://doi.org/10.32866/001c.12976, pp.1-7, 2020.
[26] Katris, Christos. “Prediction of Unemployment Rates with Time Series and Machine Learning Techniques”. Computational Economics. 10.1007/s10614-019-09908-9 pp.673-706, 2020.
[27] Viljanen, Markus & Pahikkala, Tapio, “Predicting Unemployment with Machine Learning Based on Registry Data”, International Conference on Research Challenges in Information Science, 10.1007/978-3-030-50316-1_21, Springer pp.352-368, 2020.
[28] Curbelo Montañez, Casimiro & Hurst, William, “A Machine Learning Approach for Detecting Unemployment Using the Smart Metering Infrastructure” IEEE, 10.1109/ACCESS.2020.2969468 pp.22525-22536, 2020.
[29] Xitao Liu and Lihui Li, “Prediction of Labor Unemployment Based on Time Series Model and Neural Network Model”, Hindawi Computational Intelligence and Neuroscience https://doi.org/10.1155/2022/7019078 pp.1-8 , 2022.
[30] Periklis Gogas, Theophilos Papadimitriou, Emmanouil Sofianos, “Forecasting unemployment in the euro area with machine learning”, Journal of Forecasting (WILEY), 2021.
[31] Mehmet Güney Celbi?, “Unemployment in Rural Europe: A Machine Learning Perspective”, Applied Spatial Analysis and Policy https://doi.org/10.1007/s12061-022-09464-0 Springer, 2022.

Nachiket Sainis, Reena Saini, "Post COVID-19 Unemployment Rate Prediction in India: A Machine Learning Perspective," International Journal of Computer Sciences and Engineering, Vol.11, Issue.2, pp.1-7, 2023.

Collaborative filtering recommender system is utilized as a significant method to suggest products to the users depends on their preferences. It is quite complicate when the user preference and rating data is sparse. Missing value occurs when there are no stored values for the specified dataset. Typical missing data are in three categories such as (i) Missing completely at random, (ii) Missing at random and (iii) Missing not at random. The missing values in dataset affect accuracy and causes deprived prediction outcome. In order to alleviate this issue, data imputation method is exploited. Imputation is the process of reinstating the missing value with substitute to preserve the data in dataset. It involves multiple approaches to evaluate the missing value. In this paper, we reviewed the progression of various imputation techniques and its limitations. Further, we endeavored k-recursive reliability-based imputation (k-RRI) to resolve the boundaries faced in existing approaches. Experimental results evince the studied methodology appreciably improves the prediction accuracy of recommendation system.

Sparse Data, Missing Value, Recommendation system, Missing Value Imputation, Recursive Imputation, Prediction

[1]. B. Alhijawi, G. Al-Naymat, N. Obeid and A. Awajan, “Novel predictive model to improve the accuracy of collaborative filtering recommender systems”, Inf. Syst., vol. 96, p. 101670. 2021.
[2]. S. Chinchanachokchai, P. Thontirawong and P.Chinchanachokchai, “A tale of two recommender systems: The moderating role of consumer expertise on artificial intelligence based product recommendations”, J. Retail. Consum. Serv. vol. 61, January. p. 102528. 2021.
[3]. RM. Sallam, M. Hussein, and HM. Mousa, “An Enhanced Collaborative Filtering-based Approach for Recommender Systems”, Int. J. Comput. Appl. vol. 176. no. 41. p. 9–15. 2020.
[4]. FT. Abdul Hussien AM. Rahma and HB. Abdul Wahab, “Recommendation Systems for E-commerce Systems An Overview”, J. Phys. Conf. Ser. vol. 1897, no.1. 2021.
[5]. L. Li, Y. Zhang and L. Chen, “EXTRA: Explanation Ranking Datasets for Explainable Recommendation”, SIGIR 2021 - Proc. 44th Int. ACM SIGIR Conf. Res. Dev. Inf. Retr. pp. 2463–2469. 2021.
[6]. BM. Marlin and RS. Zemel, “Collaborative prediction and ranking with non-random missing data”, RecSys’09 - Proc. 3rd ACM Conf. Recomm. Syst. pp. 5–12. 2009.
[7]. T. Emmanuel, T. Maupong, D. Mpoeleng, T. Semong, B. Mphago and O. Tabona, “A survey on missing data in machine learning”, vol. 8. no. 1. Springer International Publishing. 2021.
[8]. PV. Kumar and MV. Gopalachari, “A Review on Prediction of Missing Data in Multivariable Time Series”, Int. J. Comput. Appl. vol. 7. no. 6. pp. 84–91. 2017.
[9]. C. Ribeiro and AA. Freitas, “A data-driven missing value imputation approach for longitudinal datasets”, vol. 54. no. 8. Springer Netherlands. 2021.
[10]. G. Özbal, H. Karaman and FN. Alpaslan, “A content-boosted collaborative filtering approach for movie recommendation based on local and global similarity and missing data prediction”, Comput. J. vol. 54, no. 9. pp. 1535–1546. 2011.
[11]. Joenssen DW. and Bankhofer U. Hot deck methods for imputing missing data: The effects of limiting donor usage. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics). vol. 7376 LNAI. no. September. pp. 63–75. 2012.
[12]. Isinkaye FO. Folajimi YO. and Ojokoh BA. Recommendation systems: Principles, methods and evaluation. Egypt. Informatics J. vol. 16. no. 3. pp. 261–273. 2015.
[13]. Ihm SY. Lee SE. Park YH. Nasridinov A. Kim M and Park SH. A technique of recursive reliability-based missing data imputation for collaborative filtering. Appl. Sci. vol. 11. no. 8. 2021.
[14]. Kondo K. Recommendation system for CINEMA. no. January. 2008.
[15]. Berisha F. Quality of the predictions?: mean absolute error. accuracy and coverage. no. September 2018.
[16]. M. Murugeswari, S. Vimala, "The Recommender System for Smart E-Learning System Using Big Data: A Survey", International Journal of Computer Sciences and Engineering, Vol.8, Issue.10, pp.94-99, 2020.
[17]. Prachi Dahiya, Neelam Duhan, "Comparative Analysis of Various Collaborative Filtering Algorithms", International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.347-351, 2019.

Thenmozhi Ganesan, Palanisamy Vellaiyan, "An Efficient Missing Data Prediction Technique using Recursive Reliability-Based Imputation for Book Recommendation System," International Journal of Computer Sciences and Engineering, Vol.11, Issue.2, pp.8-11, 2023.

Cloud computing is on demand and hosted solution mechanism on the internet which provides the facilities to the individual and industry to use needed infrastructure and application via Internet. It reduces infrastructure setup cost of resources and offers cost efficiency, scalability, availability, reliability and flexibility. Cloud computing offers a competitive edge over your competitors, in this way cloud services helps the users to access the latest applications any time without spending time and money on installing and mainlining hardware and software resources [1]. Load balancing is a technique in which load balancers are responsible to manage and maintain the workload on the back end servers, It also offer the mechanism to distribute the workload across the servers evenly in such a way that no any server is found under loaded or overloaded. Load balancing improves application responsiveness and overall system performance. In this paper, an attempt has been made to study a few of the existing performance measurement matrices of load balancers that distribute the workload across the back end servers and also analyse the various performance metrics that affect the load balancing process. The load balancer applies several load balancing algorithms to determine the appropriate resources. However, it faces several problems while distributing the load across the available resources. The main objective and motivation of this paper is to do a comparative study of different types of existing performance measurement matrices that are used by load balancer to manage the workload distribution task in cloud computing environment.

Cloud computing, performance measurement matrices, load balancing techniques, load balancer, public cloud, and cloud services.

[1]. Dimple Juneja, Atul Garg, “Collective Intelligence based framework for load balancing of web servers”, IJICT, Vol 3 No.1, 2012.
[2]. Jayant Adhikari, Sulbha Patil, “Load Balancing the Essential Factor in Cloud Computing”, International Journal of Engineering Research and Technology. ISSN: 2278- 0181 Vol.1 No.10, December 2012.
[3]. Mayank, Atul, “Comparative Survey of Load Balancing Algorithms in Cloud Computing Environment”, IJDCC, Vol.1, No.2, 2013.
[4]. Tushar, Jignesh, “A Survey on Various Load Balancing Techniques and Challenges in Cloud Computing”, IJSTR, Vol.2, No.11, 2013.
[5]. Tiwari M., Gautam K., and Katare K., “Analysis of Public Cloud Load Balancing using Partitioning Method and Game Theory”, International Journal of Advanced Research in Computer Science and Software Engineering, 4(2): pp. 807-812, (2014).
[6]. A.K. Singh et al, “Scheduling with load balancing in Cloud computing”, International Journal of scientific engineering and research, vol.2, no. 1, pp. 38-43, Jan-2014.
[7]. Tiwari M. et al., "Perspective study of Public Cloud: A highly scalable Cloud deployment model", International Journal of Science & Engineering Development Research, ISSN: 2455-2631, Vol.7, Issue 9, page no.848 - 853, September-2022.
[8]. Singh,A., Sahu, S., Gautam, K.,&Tiwari, M., “Private Cloud Scheduling with SJF, Bound Waiting, Priority and Load Balancing”, International Journal of Advanced Research in Computer Science and Software Engineering,4(1), 367-372, 2014
[9]. Armbrust, M. et al., “Above the Clouds: A Berkeley View of Cloud computing”, Technical Report No. UCB/ EECS-2009-28, University of California, Berkley, 2009.
[10]. Ananya Jain, 2021, “Study on Load Balancing Algorithms and Performance Metrics in Cloud Computing”, International Research Journal of Engineering and Technology (IRJET), Volume 08, Issue 07, July 2021.
[11]. Marc, Anthony, “Strategies for Dynamic Load Balancing on Highly Parallel Computers”, IEEE Trans. on Parallel and Distributed Systems, Vol.4, No.9, 1993.
[12]. Amandeep Kaur Sidhu and Supriya Kinger, “Analysis of Load Balancing Techniques in Cloud Computing”, International Journal of Computers & Technology, Vol.4 No. 2, March-April, 2013.
[13]. Meenakshi, “Comparative Study of Load Balancing Algorithms in Cloud Computing”, Environment, International Journal of Engineering Research and Technology (IJERT), Volume 02, Issue 10, October 2013.
[14]. Amit Kumar Dhingra, Dr Dinesh Rai, “Study on the Performance Analysis Tools and Techniques of Cloud Computing”, Journal of Critical Reviews, Volume 7, Issue 15, 2020.
[15]. Sushil Kumar, Deepak Singh Rana, “Various Dynamic Load Balancing Algorithms in Cloud Environment: A Survey”, International Journal of Computer Applications, Volume 129 – No.6, pp-0975 – 8887, November 2015.
[16]. Versha Thakur, Sanjay Kumar, “Load Balancing Approaches: Recent Computing Trends”, International Journal of Computer Applications, Volume 131, No.14, pp-0975–8887 December2015.
[17]. Pawan Kumar and Rakesh Kumar, “Issues and Challenges of Load Balancing Techniques in Cloud Computing: A Survey”. ACM Computing. Survey. 51, 6, Article 120, February 2019.
[18]. Rajesh and Jeykrishan, “A Survey on Load Balancing in Cloud Computing Environment”, IJARCEE, Vol.2, No.12, 2013.
[19]. Gokhroo, M.K.; Govil, M.C.; Pilli, E.S., “Detecting and mitigating faults in cloud computing environment”, In Proceedings of the International Conference on Computational Intelligence & Communication Technology (CICT), Ghaziabad, India, 9–10 February 2017.
[20]. Bushan and Urmila, “Pros and Cons of Load Balancing Algorithms for Cloud Computing”, International Conference on Information Systems and Networks, 2014.
[21]. Ritika and Harjot, “Load Balancing Techniques using Mobile Agents”, IPASJ-IIJCS, Vol.2, No.12, 2014.
[22]. Salimath, Nagesh and Kavitha, Dr. C. and Sheetlani, Dr. Jitendra, “Analysis of Resource Management and Security Management in Cloud Computing Environment”, Proceedings of the Second International Conference on Emerging Trends in Science & Technologies for Engineering Systems, ICETSE, 2019.
[23]. Nagesh Salimath & C. Kavitha, “Secure data compression and error detecting codes for networks and cloud storage”, International Journal of Research and Analytical Reviews (IJRAR), Vol.6, Issue 2, page no. 452 - 458, May-2019.
[24]. Nagesh Salimath & C. Kavitha, "Review of error detecting codes for networks and cloud storage", International Journal of Emerging Technologies and Innovative Research, ISSN: 2349-5162, Vol.6, Issue 5, page no. pp 370-375, May-2019.
[25]. Ray, S. and A. De Sarkar, “Execution analysis of load balancing algorithms in cloud computing environment”. International Journal on Cloud Computing: Services and Architecture, IJCCSA, 2(5), 2012.
[26]. Randles, M., D. Lamb, and A. Taleb-Bendiab. “A comparative study into distributed load balancing algorithms for cloud computing”. Advanced Information Networking and Applications Workshops (WAINA), IEEE 24th International Conference on. 2010. IEEE.
[27]. Mohapatra, S., K. Smruti Rekha, and S. Mohanty, “A Comparison of Four Popular Heuristics for Load Balancing of Virtual Machines in Cloud Computing”. International Journal of Computer Applications, 68(6): pp. 33-38, 2013.

Mangal Nath Tiwari, Nagesh Salimath, Vijay Anand Sullare, "Analysis of Various Performance Measurement Parameters (PMP) of Load Balancing Algorithms Used in Public Cloud Environment," International Journal of Computer Sciences and Engineering, Vol.11, Issue.2, pp.12-17, 2023.