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Published in: New Generation Computing 2/2023

16-03-2023

A Systematic Literature Review and Future Perspectives for Handling Big Data Analytics in COVID-19 Diagnosis

Authors: Nagamani Tenali, Gatram Rama Mohan Babu

Published in: New Generation Computing | Issue 2/2023

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Abstract

In today’s digital world, information is growing along with the expansion of Internet usage worldwide. As a consequence, bulk of data is generated constantly which is known to be “Big Data”. One of the most evolving technologies in twenty-first century is Big Data analytics, it is promising field for extracting knowledge from very large datasets and enhancing benefits while lowering costs. Due to the enormous success of big data analytics, the healthcare sector is increasingly shifting toward adopting these approaches to diagnose diseases. Due to the recent boom in medical big data and the development of computational methods, researchers and practitioners have gained the ability to mine and visualize medical big data on a larger scale. Thus, with the aid of integration of big data analytics in healthcare sectors, precise medical data analysis is now feasible with early sickness detection, health status monitoring, patient treatment, and community services is now achievable. With all these improvements, a deadly disease COVID is considered in this comprehensive review with the intention of offering remedies utilizing big data analytics. The use of big data applications is vital to managing pandemic conditions, such as predicting outbreaks of COVID-19 and identifying cases and patterns of spread of COVID-19. Research is still being done on leveraging big data analytics to forecast COVID-19. But precise and early identification of COVID disease is still lacking due to the volume of medical records like dissimilar medical imaging modalities. Meanwhile, Digital imaging has now become essential to COVID diagnosis, but the main challenge is the storage of massive volumes of data. Taking these limitations into account, a comprehensive analysis is presented in the systematic literature review (SLR) to provide a deeper understanding of big data in the field of COVID-19.

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Literature
1.
go back to reference Ranjan, J., Foropon, C.: Big data analytics in building the competitive intelligence of organizations. Int. J. Inf. Manage. 56, 102231 (2021) CrossRef Ranjan, J., Foropon, C.: Big data analytics in building the competitive intelligence of organizations. Int. J. Inf. Manage. 56, 102231 (2021) CrossRef
2.
go back to reference Mohamed, A., Najafabadi, M.K., Wah, Y.B., Zaman, E.A.K., Maskat, R.: The state of the art and taxonomy of big data analytics: view from new big data framework. Artif. Intell. Rev. 53(2), 989–1037 (2020) CrossRef Mohamed, A., Najafabadi, M.K., Wah, Y.B., Zaman, E.A.K., Maskat, R.: The state of the art and taxonomy of big data analytics: view from new big data framework. Artif. Intell. Rev. 53(2), 989–1037 (2020) CrossRef
3.
go back to reference Mariani, M.M., Wamba, S.F.: Exploring how consumer goods companies innovate in the digital age: the role of big data analytics companies. J. Bus. Res. 121, 338–352 (2020) CrossRef Mariani, M.M., Wamba, S.F.: Exploring how consumer goods companies innovate in the digital age: the role of big data analytics companies. J. Bus. Res. 121, 338–352 (2020) CrossRef
4.
go back to reference Mikalef, P., Krogstie, J.: Examining the interplay between big data analytics and contextual factors in driving process innovation capabilities. Eur. J. Inf. Syst. 29(3), 260–287 (2020) CrossRef Mikalef, P., Krogstie, J.: Examining the interplay between big data analytics and contextual factors in driving process innovation capabilities. Eur. J. Inf. Syst. 29(3), 260–287 (2020) CrossRef
5.
go back to reference Holmlund, M., Van Vaerenbergh, Y., Ciuchita, R., Ravald, A., Sarantopoulos, P., Ordenes, F.V., Zaki, M.: Customer experience management in the age of big data analytics: a strategic framework. J. Bus. Res. 116, 356–365 (2020) CrossRef Holmlund, M., Van Vaerenbergh, Y., Ciuchita, R., Ravald, A., Sarantopoulos, P., Ordenes, F.V., Zaki, M.: Customer experience management in the age of big data analytics: a strategic framework. J. Bus. Res. 116, 356–365 (2020) CrossRef
6.
go back to reference Wong, Z.S., Zhou, J., Zhang, Q.: Artificial intelligence for infectious disease big data analytics. Infect. Dis. Health 24(1), 44–48 (2019) CrossRef Wong, Z.S., Zhou, J., Zhang, Q.: Artificial intelligence for infectious disease big data analytics. Infect. Dis. Health 24(1), 44–48 (2019) CrossRef
7.
go back to reference Manogaran, G., Shakeel, P.M., Baskar, S., Hsu, C.H., Kadry, S.N., Sundarasekar, R., Kumar, P.M., Muthu, B.A.: FDM: fuzzy-optimized data management technique for improving big data analytics. IEEE Trans. Fuzzy Syst. 29(1), 177–185 (2020) CrossRef Manogaran, G., Shakeel, P.M., Baskar, S., Hsu, C.H., Kadry, S.N., Sundarasekar, R., Kumar, P.M., Muthu, B.A.: FDM: fuzzy-optimized data management technique for improving big data analytics. IEEE Trans. Fuzzy Syst. 29(1), 177–185 (2020) CrossRef
8.
go back to reference Li, W., Chai, Y., Khan, F., Jan, S.R.U., Verma, S., Menon, V.G., Li, X.: A comprehensive survey on machine learning-based big data analytics for IoT-enabled smart healthcare system. Mobile Netw. Appl. 26(1), 234–252 (2021) CrossRef Li, W., Chai, Y., Khan, F., Jan, S.R.U., Verma, S., Menon, V.G., Li, X.: A comprehensive survey on machine learning-based big data analytics for IoT-enabled smart healthcare system. Mobile Netw. Appl. 26(1), 234–252 (2021) CrossRef
9.
go back to reference Yasmin, M., Tatoglu, E., Kilic, H.S., Zaim, S., Delen, D.: Big data analytics capabilities and firm performance: an integrated MCDM approach. J. Bus. Res. 114, 1–15 (2020) CrossRef Yasmin, M., Tatoglu, E., Kilic, H.S., Zaim, S., Delen, D.: Big data analytics capabilities and firm performance: an integrated MCDM approach. J. Bus. Res. 114, 1–15 (2020) CrossRef
10.
go back to reference Ghasemaghaei, M.: The role of positive and negative valence factors on the impact of bigness of data on big data analytics usage. Int. J. Inf. Manage. 50, 395–404 (2020) CrossRef Ghasemaghaei, M.: The role of positive and negative valence factors on the impact of bigness of data on big data analytics usage. Int. J. Inf. Manage. 50, 395–404 (2020) CrossRef
11.
go back to reference Sousa, M.J., Pesqueira, A.M., Lemos, C., Sousa, M., Rocha, Á.: Decision-making based on big data analytics for people management in healthcare organizations. J. Med. Syst. 43(9), 1–10 (2019) CrossRef Sousa, M.J., Pesqueira, A.M., Lemos, C., Sousa, M., Rocha, Á.: Decision-making based on big data analytics for people management in healthcare organizations. J. Med. Syst. 43(9), 1–10 (2019) CrossRef
12.
go back to reference Aljumah, A.I., Nuseir, M.T., Alam, M.M.: Traditional marketing analytics, big data analytics and big data system quality and the success of new product development. Business Process Manag. J. 27, 1108 (2021) CrossRef Aljumah, A.I., Nuseir, M.T., Alam, M.M.: Traditional marketing analytics, big data analytics and big data system quality and the success of new product development. Business Process Manag. J. 27, 1108 (2021) CrossRef
13.
go back to reference Peters, E., Kliestik, T., Musa, H., Durana, P.: Product decision-making information systems, real-time big data analytics, and deep learning-enabled smart process planning in sustainable industry 4.0. J. Self-Governance Manag. Econ. 8(3), 16–22 (2020) CrossRef Peters, E., Kliestik, T., Musa, H., Durana, P.: Product decision-making information systems, real-time big data analytics, and deep learning-enabled smart process planning in sustainable industry 4.0. J. Self-Governance Manag. Econ. 8(3), 16–22 (2020) CrossRef
14.
go back to reference Mishra, S., Mishra, B.K., Tripathy, H.K. and Dutta, A.: Analysis of the role and scope of big data analytics with IoT in health care domain. In: Handbook of data science approaches for biomedical engineering, pp. 1–23. Academic Press. (2020) Mishra, S., Mishra, B.K., Tripathy, H.K. and Dutta, A.: Analysis of the role and scope of big data analytics with IoT in health care domain. In: Handbook of data science approaches for biomedical engineering, pp. 1–23. Academic Press. (2020)
15.
go back to reference Rehman, A., Naz, S. and Razzak, I.: Leveraging big data analytics in healthcare enhancement: trends, challenges and opportunities. Multimedia Syst 1–33. (2021) Rehman, A., Naz, S. and Razzak, I.: Leveraging big data analytics in healthcare enhancement: trends, challenges and opportunities. Multimedia Syst 1–33. (2021)
16.
go back to reference Jia, Q., Guo, Y., Wang, G., Barnes, S.J.: Big data analytics in the fight against major public health incidents (Including COVID-19): a conceptual framework. Int. J. Environ. Res. Public Health 17(17), 6161 (2020) CrossRef Jia, Q., Guo, Y., Wang, G., Barnes, S.J.: Big data analytics in the fight against major public health incidents (Including COVID-19): a conceptual framework. Int. J. Environ. Res. Public Health 17(17), 6161 (2020) CrossRef
17.
go back to reference Ahn, P.D., Wickramasinghe, D.: Pushing the limits of accountability: big data analytics containing and controlling COVID-19 in South Korea. Account. Audit. Account. J (2021) Ahn, P.D., Wickramasinghe, D.: Pushing the limits of accountability: big data analytics containing and controlling COVID-19 in South Korea. Account. Audit. Account. J (2021)
18.
go back to reference Awotunde, J.B., Ogundokun, R.O., Misra, S.: Cloud and IoMT-based big data analytics system during COVID-19 pandemic. In Efficient data handling for massive internet of medical things (pp. 181–201). Springer, Cham. (2021) Awotunde, J.B., Ogundokun, R.O., Misra, S.: Cloud and IoMT-based big data analytics system during COVID-19 pandemic. In Efficient data handling for massive internet of medical things (pp. 181–201). Springer, Cham. (2021)
19.
go back to reference Verma, S. and Gazara, R.K.: Big data analytics for understanding and fighting COVID-19. In Computational intelligence methods in COVID-19: Surveillance, prevention, prediction and diagnosis (pp. 333–348). Springer, Singapore. (2021) Verma, S. and Gazara, R.K.: Big data analytics for understanding and fighting COVID-19. In Computational intelligence methods in COVID-19: Surveillance, prevention, prediction and diagnosis (pp. 333–348). Springer, Singapore. (2021)
20.
go back to reference Shinde, P.P., Desai, V.P., Katkar, S.V., Oza, K.S., Kamat, R.K., Thakar, C.M.: Big data analytics for mask prominence in COVID pandemic. Mater. Today 51, 2471–2475 (2022) Shinde, P.P., Desai, V.P., Katkar, S.V., Oza, K.S., Kamat, R.K., Thakar, C.M.: Big data analytics for mask prominence in COVID pandemic. Mater. Today 51, 2471–2475 (2022)
21.
go back to reference Zhou, H., Sun, G., Fu, S., Liu, J., Zhou, X., Zhou, J.: A big data mining approach of PSO-based BP neural network for financial risk management with IoT. IEEE Access 7, 154035–154043 (2019) CrossRef Zhou, H., Sun, G., Fu, S., Liu, J., Zhou, X., Zhou, J.: A big data mining approach of PSO-based BP neural network for financial risk management with IoT. IEEE Access 7, 154035–154043 (2019) CrossRef
22.
go back to reference Zhang, P., Shi, X., Khan, S.U.: QuantCloud: enabling big data complex event processing for quantitative finance through a data-driven execution. IEEE Transact. Big Data 5(4), 564–575 (2018) CrossRef Zhang, P., Shi, X., Khan, S.U.: QuantCloud: enabling big data complex event processing for quantitative finance through a data-driven execution. IEEE Transact. Big Data 5(4), 564–575 (2018) CrossRef
23.
go back to reference Wensheng, D.: Rural financial information service platform under smart financial environment. IEEE Access 8, 199944–199952 (2020) CrossRef Wensheng, D.: Rural financial information service platform under smart financial environment. IEEE Access 8, 199944–199952 (2020) CrossRef
24.
go back to reference Zhou, H., Sun, G., Fu, S., Wang, L., Hu, J., Gao, Y.: Internet financial fraud detection based on a distributed big data approach with node2vec. IEEE Access 9, 43378–43386 (2021) CrossRef Zhou, H., Sun, G., Fu, S., Wang, L., Hu, J., Gao, Y.: Internet financial fraud detection based on a distributed big data approach with node2vec. IEEE Access 9, 43378–43386 (2021) CrossRef
25.
go back to reference Dos Anjos, J.C., Matteussi, K.J., De Souza, P.R., Grabher, G.J., Borges, G.A., Barbosa, J.L., Gonzalez, G.V., Leithardt, V.R., Geyer, C.F.: Data processing model to perform big data analytics in hybrid infrastructures. IEEE Access 8, 170281–170294 (2020) CrossRef Dos Anjos, J.C., Matteussi, K.J., De Souza, P.R., Grabher, G.J., Borges, G.A., Barbosa, J.L., Gonzalez, G.V., Leithardt, V.R., Geyer, C.F.: Data processing model to perform big data analytics in hybrid infrastructures. IEEE Access 8, 170281–170294 (2020) CrossRef
26.
go back to reference Yang, L., Yang, Y., Mgaya, G.B., Zhang, B., Chen, L., Liu, H.: Novel fast networking approaches mining underlying structures from investment big data. IEEE Transact Syst Man Cybern. 51(10), 6319–6329 (2020) CrossRef Yang, L., Yang, Y., Mgaya, G.B., Zhang, B., Chen, L., Liu, H.: Novel fast networking approaches mining underlying structures from investment big data. IEEE Transact Syst Man Cybern. 51(10), 6319–6329 (2020) CrossRef
27.
go back to reference Ruan, J., Jiang, H., Yuan, J., Shi, Y., Zhu, Y., Chan, F.T., Rao, W.: Fuzzy correlation measurement algorithms for big data and application to exchange rates and stock prices. IEEE Trans. Industr. Inf. 16(2), 1296–1309 (2019) CrossRef Ruan, J., Jiang, H., Yuan, J., Shi, Y., Zhu, Y., Chan, F.T., Rao, W.: Fuzzy correlation measurement algorithms for big data and application to exchange rates and stock prices. IEEE Trans. Industr. Inf. 16(2), 1296–1309 (2019) CrossRef
28.
go back to reference Sohangir, S., Wang, D., Pomeranets, A., Khoshgoftaar, T.M.: Big data: deep learning for financial sentiment analysis. J. Big Data 5(1), 1–25 (2018) CrossRef Sohangir, S., Wang, D., Pomeranets, A., Khoshgoftaar, T.M.: Big data: deep learning for financial sentiment analysis. J. Big Data 5(1), 1–25 (2018) CrossRef
29.
go back to reference Hassib, E.M., El-Desouky, A.I., El-Kenawy, E.S.M., El-Ghamrawy, S.M.: An imbalanced big data mining framework for improving optimization algorithms performance. IEEE Access 7, 170774–170795 (2019) CrossRef Hassib, E.M., El-Desouky, A.I., El-Kenawy, E.S.M., El-Ghamrawy, S.M.: An imbalanced big data mining framework for improving optimization algorithms performance. IEEE Access 7, 170774–170795 (2019) CrossRef
30.
go back to reference Liu, B.: Text sentiment analysis based on CBOW model and deep learning in big data environment. J. Ambient. Intell. Humaniz. Comput. 11(2), 451–458 (2020) CrossRef Liu, B.: Text sentiment analysis based on CBOW model and deep learning in big data environment. J. Ambient. Intell. Humaniz. Comput. 11(2), 451–458 (2020) CrossRef
31.
go back to reference Zhai, G., Yang, Y., Wang, H., Du, S.: Multi-attention fusion modeling for sentiment analysis of educational big data. Big Data Mining Anal. 3(4), 311–319 (2020) CrossRef Zhai, G., Yang, Y., Wang, H., Du, S.: Multi-attention fusion modeling for sentiment analysis of educational big data. Big Data Mining Anal. 3(4), 311–319 (2020) CrossRef
32.
go back to reference Rodrigues, A.P. and Chiplunkar, N.N.: A new big data approach for topic classification and sentiment analysis of Twitter data. Evolut. Intell. 1–11 (2019) Rodrigues, A.P. and Chiplunkar, N.N.: A new big data approach for topic classification and sentiment analysis of Twitter data. Evolut. Intell. 1–11 (2019)
33.
go back to reference Lau, R.Y.K., Zhang, W., Xu, W.: Parallel aspect-oriented sentiment analysis for sales forecasting with big data. Prod. Oper. Manag. 27(10), 1775–1794 (2018) CrossRef Lau, R.Y.K., Zhang, W., Xu, W.: Parallel aspect-oriented sentiment analysis for sales forecasting with big data. Prod. Oper. Manag. 27(10), 1775–1794 (2018) CrossRef
34.
go back to reference Johnson, J.M., Khoshgoftaar, T.M.: The effects of data sampling with deep learning and highly imbalanced big data. Inf. Syst. Front. 22(5), 1113–1131 (2020) CrossRef Johnson, J.M., Khoshgoftaar, T.M.: The effects of data sampling with deep learning and highly imbalanced big data. Inf. Syst. Front. 22(5), 1113–1131 (2020) CrossRef
35.
go back to reference Juez-Gil, M., Arnaiz-González, Á., Rodríguez, J.J., García-Osorio, C.: Experimental evaluation of ensemble classifiers for imbalance in Big Data. Appl. Soft Comput. 108, 107447 (2021) CrossRef Juez-Gil, M., Arnaiz-González, Á., Rodríguez, J.J., García-Osorio, C.: Experimental evaluation of ensemble classifiers for imbalance in Big Data. Appl. Soft Comput. 108, 107447 (2021) CrossRef
36.
go back to reference Al, S., Dener, M.: STL-HDL: A new hybrid network intrusion detection system for imbalanced dataset on big data environment. Comput. Secur. 110, 102435 (2021) CrossRef Al, S., Dener, M.: STL-HDL: A new hybrid network intrusion detection system for imbalanced dataset on big data environment. Comput. Secur. 110, 102435 (2021) CrossRef
37.
go back to reference Juez-Gil, M., Arnaiz-González, Á., Rodríguez, J.J., López-Nozal, C., García-Osorio, C.: Approx-SMOTE: fast SMOTE for big data on apache spark. Neurocomputing 464, 432–437 (2021) CrossRef Juez-Gil, M., Arnaiz-González, Á., Rodríguez, J.J., López-Nozal, C., García-Osorio, C.: Approx-SMOTE: fast SMOTE for big data on apache spark. Neurocomputing 464, 432–437 (2021) CrossRef
38.
go back to reference Gupta, A., Lohani, M.C., Manchanda, M.: Financial fraud detection using naive bayes algorithm in highly imbalance data set. J. Discrete Math. Sci. Cryptogr. 24(5), 1559–1572 (2021) MathSciNetCrossRef Gupta, A., Lohani, M.C., Manchanda, M.: Financial fraud detection using naive bayes algorithm in highly imbalance data set. J. Discrete Math. Sci. Cryptogr. 24(5), 1559–1572 (2021) MathSciNetCrossRef
39.
go back to reference Kwon, J.M., Jung, M.S., Kim, K.H., Jo, Y.Y., Shin, J.H., Cho, Y.H., Lee, Y.J., Ban, J.H., Jeon, K.H., Lee, S.Y., Park, J.: Artificial intelligence for detecting electrolyte imbalance using electrocardiography. Ann. Noninvasive Electrocardiol. 26(3), e12839 (2021) CrossRef Kwon, J.M., Jung, M.S., Kim, K.H., Jo, Y.Y., Shin, J.H., Cho, Y.H., Lee, Y.J., Ban, J.H., Jeon, K.H., Lee, S.Y., Park, J.: Artificial intelligence for detecting electrolyte imbalance using electrocardiography. Ann. Noninvasive Electrocardiol. 26(3), e12839 (2021) CrossRef
40.
go back to reference Sobanadevi, V. and Ravi, G.: Handling data imbalance using a heterogeneous bagging-based stacked ensemble (HBSE) for credit card fraud detection. In: Intelligence in Big Data Technologies—Beyond the Hype, pp. 517–525. Springer, Singapore. (2021) Sobanadevi, V. and Ravi, G.: Handling data imbalance using a heterogeneous bagging-based stacked ensemble (HBSE) for credit card fraud detection. In: Intelligence in Big Data Technologies—Beyond the Hype, pp. 517–525. Springer, Singapore. (2021)
41.
go back to reference Johnson, J.M. and Khoshgoftaar, T.M.: Thresholding strategies for deep learning with highly imbalanced big data. In: Deep Learning Applications, vol 2. Springer, Singapore, pp. 199–227 (2021) Johnson, J.M. and Khoshgoftaar, T.M.: Thresholding strategies for deep learning with highly imbalanced big data. In: Deep Learning Applications, vol 2. Springer, Singapore, pp. 199–227 (2021)
42.
go back to reference Javaid, N., Jan, N., Javed, M.U.: An adaptive synthesis to handle imbalanced big data with deep siamese network for electricity theft detection in smart grids. J. Parallel Distributed Comput. 153, 44–52 (2021) CrossRef Javaid, N., Jan, N., Javed, M.U.: An adaptive synthesis to handle imbalanced big data with deep siamese network for electricity theft detection in smart grids. J. Parallel Distributed Comput. 153, 44–52 (2021) CrossRef
43.
go back to reference Arif, A., Javaid, N., Aldegheishem, A., Alrajeh, N.: Big data analytics for identifying electricity theft using machine learning approaches in microgrids for smart communities. Concurr. Comput. 33(17), e6316 (2021) CrossRef Arif, A., Javaid, N., Aldegheishem, A., Alrajeh, N.: Big data analytics for identifying electricity theft using machine learning approaches in microgrids for smart communities. Concurr. Comput. 33(17), e6316 (2021) CrossRef
44.
go back to reference Arif, A., Alghamdi, T.A., Khan, Z.A., Javaid, N.: Towards efficient energy utilization using big data analytics in smart cities for electricity theft detection. Big Data Res. 27, 100285 (2022) CrossRef Arif, A., Alghamdi, T.A., Khan, Z.A., Javaid, N.: Towards efficient energy utilization using big data analytics in smart cities for electricity theft detection. Big Data Res. 27, 100285 (2022) CrossRef
45.
go back to reference Hou, C., Wu, J., Cao, B., Fan, J.: A deep-learning prediction model for imbalanced time series data forecasting. Big Data Mining and Analytics 4(4), 266–278 (2021) CrossRef Hou, C., Wu, J., Cao, B., Fan, J.: A deep-learning prediction model for imbalanced time series data forecasting. Big Data Mining and Analytics 4(4), 266–278 (2021) CrossRef
46.
go back to reference Xia, D., Zhang, M., Yan, X., Bai, Y., Zheng, Y., Li, Y., Li, H.: A distributed WND-LSTM model on MapReduce for short-term traffic flow prediction. Neural Comput. Appl. 33(7), 2393–2410 (2021) CrossRef Xia, D., Zhang, M., Yan, X., Bai, Y., Zheng, Y., Li, Y., Li, H.: A distributed WND-LSTM model on MapReduce for short-term traffic flow prediction. Neural Comput. Appl. 33(7), 2393–2410 (2021) CrossRef
47.
go back to reference Bawankule, K.L., Dewang, R.K. and Singh, A.K.: Historical data based approach to mitigate stragglers from the Reduce phase of MapReduce in a heterogeneous Hadoop cluster. Cluster Comput. 1–19 (2022) Bawankule, K.L., Dewang, R.K. and Singh, A.K.: Historical data based approach to mitigate stragglers from the Reduce phase of MapReduce in a heterogeneous Hadoop cluster. Cluster Comput. 1–19 (2022)
48.
go back to reference Asif, M., Abbas, S., Khan, M.A., Fatima, A., Khan, M.A., Lee, S.W.: MapReduce based intelligent model for intrusion detection using machine learning technique. J. King Saud Univ.-Comput. Inform. Sci. 34, 9723 (2021) Asif, M., Abbas, S., Khan, M.A., Fatima, A., Khan, M.A., Lee, S.W.: MapReduce based intelligent model for intrusion detection using machine learning technique. J. King Saud Univ.-Comput. Inform. Sci. 34, 9723 (2021)
49.
go back to reference Wang, X., Wang, C., Bai, M., Ma, Q., Li, G.: HTD: heterogeneous throughput-driven task scheduling algorithm in MapReduce. Distributed Parallel Databases 40(1), 135–163 (2022) CrossRef Wang, X., Wang, C., Bai, M., Ma, Q., Li, G.: HTD: heterogeneous throughput-driven task scheduling algorithm in MapReduce. Distributed Parallel Databases 40(1), 135–163 (2022) CrossRef
50.
go back to reference Pandey, V., Saini, P.: A heuristic method towards deadline-aware energy-efficient mapreduce scheduling problem in Hadoop YARN. Clust. Comput. 24(2), 683–699 (2021) CrossRef Pandey, V., Saini, P.: A heuristic method towards deadline-aware energy-efficient mapreduce scheduling problem in Hadoop YARN. Clust. Comput. 24(2), 683–699 (2021) CrossRef
51.
go back to reference Baruah, A.J., Baruah, S.: Data augmentation and Deep Neuro-Fuzzy network for student performance prediction with MapReduce framework. Int. J. Autom. Comput. 18(6), 981–992 (2021) MathSciNetMATHCrossRef Baruah, A.J., Baruah, S.: Data augmentation and Deep Neuro-Fuzzy network for student performance prediction with MapReduce framework. Int. J. Autom. Comput. 18(6), 981–992 (2021) MathSciNetMATHCrossRef
52.
go back to reference Patan, R., Kallam, S., Gandomi, A.H., Hanne, T., Ramachandran, M., Gaussian relevance vector MapReduce-based annealed Glowworm optimization for big medical data scheduling. J. Operat. Res. Soc. 1–12. (2021) Patan, R., Kallam, S., Gandomi, A.H., Hanne, T., Ramachandran, M., Gaussian relevance vector MapReduce-based annealed Glowworm optimization for big medical data scheduling. J. Operat. Res. Soc. 1–12. (2021)
53.
go back to reference Chawla, T., Singh, G., Pilli, E.S.: MuSe: a multi-level storage scheme for big RDF data using MapReduce. J. Big Data 8(1), 1–26 (2021) CrossRef Chawla, T., Singh, G., Pilli, E.S.: MuSe: a multi-level storage scheme for big RDF data using MapReduce. J. Big Data 8(1), 1–26 (2021) CrossRef
54.
go back to reference Narayana, S., Chandanapalli, S.B., Rao, M.S., Srinivas, K.: Ant cat swarm optimization-enabled deep recurrent neural network for big data classification based on map reduce framework. Comput. J. 65, 3167 (2021) CrossRef Narayana, S., Chandanapalli, S.B., Rao, M.S., Srinivas, K.: Ant cat swarm optimization-enabled deep recurrent neural network for big data classification based on map reduce framework. Comput. J. 65, 3167 (2021) CrossRef
55.
go back to reference Ramsingh, J., Bhuvaneswari, V.: An efficient map reduce-based hybrid NBC-TFIDF algorithm to mine the public sentiment on diabetes mellitus–a big data approach. J. King Saud University-Comput. Inform. Sci. 33(8), 1018–1029 (2021) Ramsingh, J., Bhuvaneswari, V.: An efficient map reduce-based hybrid NBC-TFIDF algorithm to mine the public sentiment on diabetes mellitus–a big data approach. J. King Saud University-Comput. Inform. Sci. 33(8), 1018–1029 (2021)
56.
go back to reference Roy, S., Bhattacharya, S., Omkar, S.N.: Automated Large-Scale Mapping of the Jahazpur Mineralised Belt by a MapReduce Model with an Integrated ELM method. PFG J. Photogr. Remote Sens. Geoinform. Sci. 90(2), 191–209 (2022) Roy, S., Bhattacharya, S., Omkar, S.N.: Automated Large-Scale Mapping of the Jahazpur Mineralised Belt by a MapReduce Model with an Integrated ELM method. PFG J. Photogr. Remote Sens. Geoinform. Sci. 90(2), 191–209 (2022)
57.
go back to reference Pham, T.A., Dang, X.K., Vo, N.S.: Optimising Maritime Big Data by K-means Clustering with Mapreduce Model. In International Conference on Industrial Networks and Intelligent Systems (pp. 136–151). Springer, Cham, (2022) Pham, T.A., Dang, X.K., Vo, N.S.: Optimising Maritime Big Data by K-means Clustering with Mapreduce Model. In International Conference on Industrial Networks and Intelligent Systems (pp. 136–151). Springer, Cham, (2022)
58.
go back to reference Arunadevi, N., Thulasiraaman, V.: Cuckoo search augmented mapreduce for predictive scheduling with big stream data. I. J. Sociotechnol. Knowledge Develop. 14(1), 1–18 (2022) CrossRef Arunadevi, N., Thulasiraaman, V.: Cuckoo search augmented mapreduce for predictive scheduling with big stream data. I. J. Sociotechnol. Knowledge Develop. 14(1), 1–18 (2022) CrossRef
59.
go back to reference Kumar, D., Jha, V.K.: An improved query optimization process in big data using ACO-GA algorithm and HDFS map reduce technique. Distributed Parallel Databases 39(1), 79–96 (2021) CrossRef Kumar, D., Jha, V.K.: An improved query optimization process in big data using ACO-GA algorithm and HDFS map reduce technique. Distributed Parallel Databases 39(1), 79–96 (2021) CrossRef
60.
go back to reference Agarwal, G. and Om, H.: Parallel training models of deep belief network using MapReduce for the classifications of emotions. Int. J. Syst. Assurance Eng. Manag. 1–16. (2021) Agarwal, G. and Om, H.: Parallel training models of deep belief network using MapReduce for the classifications of emotions. Int. J. Syst. Assurance Eng. Manag. 1–16. (2021)
61.
go back to reference Pang, Z., Wu, S., Huang, H., Hong, Z., Xie, Y.: AQUA+: Query Optimization for Hybrid Database-MapReduce System. Knowl. Inf. Syst. 63(4), 905–938 (2021) Pang, Z., Wu, S., Huang, H., Hong, Z., Xie, Y.: AQUA+: Query Optimization for Hybrid Database-MapReduce System. Knowl. Inf. Syst. 63(4), 905–938 (2021)
62.
go back to reference Maheswari, K., Ramakrishnan, M.: Kernelized Spectral Clustering based Conditional MapReduce function with big data. Int. J. Comput. Appl. 43(7), 601–611 (2021) Maheswari, K., Ramakrishnan, M.: Kernelized Spectral Clustering based Conditional MapReduce function with big data. Int. J. Comput. Appl. 43(7), 601–611 (2021)
63.
go back to reference Thanga Selvi, R., Muthulakshmi, I.: An optimal artificial neural network based big data application for heart disease diagnosis and classification model. J. Ambient. Intell. Humaniz. Comput. 12(6), 6129–6139 (2021) CrossRef Thanga Selvi, R., Muthulakshmi, I.: An optimal artificial neural network based big data application for heart disease diagnosis and classification model. J. Ambient. Intell. Humaniz. Comput. 12(6), 6129–6139 (2021) CrossRef
64.
go back to reference Ed-Daoudy, A. and Maalmi, K.: Real-time machine learning for early detection of heart disease using big data approach. In 2019 international conference on wireless technologies, embedded and intelligent systems (WITS) (pp. 1–5). IEEE (2019) Ed-Daoudy, A. and Maalmi, K.: Real-time machine learning for early detection of heart disease using big data approach. In 2019 international conference on wireless technologies, embedded and intelligent systems (WITS) (pp. 1–5). IEEE (2019)
65.
go back to reference Vaishali, G. and Kalaivani, V.: Big data analysis for heart disease detection system using map reduce technique. In 2016 International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE'16) (pp. 1–6). IEEE (2016) Vaishali, G. and Kalaivani, V.: Big data analysis for heart disease detection system using map reduce technique. In 2016 International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE'16) (pp. 1–6). IEEE (2016)
66.
go back to reference Rastogi, R., Chaturvedi, D.K., Satya, S. and Arora, N.: Intelligent heart disease prediction on physical and mental parameters: a ML based IoT and big data application and analysis. In: Machine Learning with Health Care Perspective, pp. 199–236. Springer, Cham (2020) Rastogi, R., Chaturvedi, D.K., Satya, S. and Arora, N.: Intelligent heart disease prediction on physical and mental parameters: a ML based IoT and big data application and analysis. In: Machine Learning with Health Care Perspective, pp. 199–236. Springer, Cham (2020)
67.
go back to reference Nayak, S., Gourisaria, M.K., Pandey, M. and Rautaray, S.S.: Comparative analysis of heart disease classification algorithms using big data analytical tool. In: International Conference on Computer Networks and Inventive Communication Technologies, pp. 582–588. Springer, Cham, (2019) Nayak, S., Gourisaria, M.K., Pandey, M. and Rautaray, S.S.: Comparative analysis of heart disease classification algorithms using big data analytical tool. In: International Conference on Computer Networks and Inventive Communication Technologies, pp. 582–588. Springer, Cham, (2019)
68.
go back to reference Nair, L.R., Shetty, S.D., Shetty, S.D.: Applying spark based machine learning model on streaming big data for health status prediction. Comput. Electr. Eng. 65, 393–399 (2018) CrossRef Nair, L.R., Shetty, S.D., Shetty, S.D.: Applying spark based machine learning model on streaming big data for health status prediction. Comput. Electr. Eng. 65, 393–399 (2018) CrossRef
69.
go back to reference Saluja, M.K., Agarwal, I., Rani, U. and Saxena, A.: Analysis of diabetes and heart disease in big data using MapReduce framework. In: International Conference on Innovative Computing and Communications, pp. 37–51. Springer, Singapore (2021) Saluja, M.K., Agarwal, I., Rani, U. and Saxena, A.: Analysis of diabetes and heart disease in big data using MapReduce framework. In: International Conference on Innovative Computing and Communications, pp. 37–51. Springer, Singapore (2021)
70.
go back to reference Kılınç, D.: A spark-based big data analysis framework for real-time sentiment prediction on streaming data. Software 49(9), 1352–1364 (2019) Kılınç, D.: A spark-based big data analysis framework for real-time sentiment prediction on streaming data. Software 49(9), 1352–1364 (2019)
72.
go back to reference Park, K., Baek, C. and Peng, L.: A development of streaming big data analysis system using in-memory cluster computing framework: Spark. In: Advanced Multimedia and Ubiquitous Engineering, pp. 157–163. Springer, Singapore, (2016) Park, K., Baek, C. and Peng, L.: A development of streaming big data analysis system using in-memory cluster computing framework: Spark. In: Advanced Multimedia and Ubiquitous Engineering, pp. 157–163. Springer, Singapore, (2016)
73.
go back to reference Carcillo, F., Dal Pozzolo, A., Le Borgne, Y.A., Caelen, O., Mazzer, Y., Bontempi, G.: Scarff: a scalable framework for streaming credit card fraud detection with spark. Inform. Fusion 41, 182–194 (2018) CrossRef Carcillo, F., Dal Pozzolo, A., Le Borgne, Y.A., Caelen, O., Mazzer, Y., Bontempi, G.: Scarff: a scalable framework for streaming credit card fraud detection with spark. Inform. Fusion 41, 182–194 (2018) CrossRef
74.
go back to reference Rathore, M.M., Son, H., Ahmad, A., Paul, A., Jeon, G.: Real-time big data stream processing using GPU with spark over hadoop ecosystem. Int. J. Parallel Prog. 46(3), 630–646 (2018) CrossRef Rathore, M.M., Son, H., Ahmad, A., Paul, A., Jeon, G.: Real-time big data stream processing using GPU with spark over hadoop ecosystem. Int. J. Parallel Prog. 46(3), 630–646 (2018) CrossRef
75.
go back to reference Zhou, B., Li, J., Wang, X., Gu, Y., Xu, L., Hu, Y., Zhu, L.: Online internet traffic monitoring system using spark streaming. Big Data Mining Anal. 1(1), 47–56 (2018) CrossRef Zhou, B., Li, J., Wang, X., Gu, Y., Xu, L., Hu, Y., Zhu, L.: Online internet traffic monitoring system using spark streaming. Big Data Mining Anal. 1(1), 47–56 (2018) CrossRef
76.
go back to reference Xiao, W., Hu, J.: SWEclat: a frequent itemset mining algorithm over streaming data using Spark Streaming. J. Supercomput. 76(10), 7619–7634 (2020) CrossRef Xiao, W., Hu, J.: SWEclat: a frequent itemset mining algorithm over streaming data using Spark Streaming. J. Supercomput. 76(10), 7619–7634 (2020) CrossRef
77.
go back to reference Subramaniyan, S., Regan, R., Perumal, T. and Venkatachalam, K.: Semi-supervised machine learning algorithm for predicting diabetes using big data analytics. In Business Intelligence for Enterprise Internet of Things, pp. 139–149. Springer, Cham, (2020) Subramaniyan, S., Regan, R., Perumal, T. and Venkatachalam, K.: Semi-supervised machine learning algorithm for predicting diabetes using big data analytics. In Business Intelligence for Enterprise Internet of Things, pp. 139–149. Springer, Cham, (2020)
78.
go back to reference AlZubi, A.A.: Big data analytic diabetics using map reduce and classification techniques. J. Supercomput. 76(6), 4328–4337 (2020) CrossRef AlZubi, A.A.: Big data analytic diabetics using map reduce and classification techniques. J. Supercomput. 76(6), 4328–4337 (2020) CrossRef
79.
go back to reference Hatua, A., Subudhi, B.N., Veerakumar, T., Ghosh, A.: Early detection of diabetic retinopathy from big data in hadoop framework. Displays 70, 102061 (2021) CrossRef Hatua, A., Subudhi, B.N., Veerakumar, T., Ghosh, A.: Early detection of diabetic retinopathy from big data in hadoop framework. Displays 70, 102061 (2021) CrossRef
80.
go back to reference Sivakumar, N.R. and Karim, F.K.D.: An IoT based big data framework using equidistant heuristic and duplex deep neural network for diabetic disease prediction. J. Ambient Intell. Humanized Comput. 1–11. (2021) Sivakumar, N.R. and Karim, F.K.D.: An IoT based big data framework using equidistant heuristic and duplex deep neural network for diabetic disease prediction. J. Ambient Intell. Humanized Comput. 1–11. (2021)
81.
go back to reference Mamatha Bai, B.G., Nalini, B.M., Majumdar, J.: Analysis and detection of diabetes using data mining techniques—a big data application in health care. In: Emerging research in computing, information, communication and applications, pp. 443–455. Springer, Singapore. (2019) Mamatha Bai, B.G., Nalini, B.M., Majumdar, J.: Analysis and detection of diabetes using data mining techniques—a big data application in health care. In: Emerging research in computing, information, communication and applications, pp. 443–455. Springer, Singapore. (2019)
82.
go back to reference Sisodia, A., Jindal, R.: An effective model for healthcare to process chronic kidney disease using big data processing. J. Ambient Intell. Humanized Comput. 1–17 (2022) Sisodia, A., Jindal, R.: An effective model for healthcare to process chronic kidney disease using big data processing. J. Ambient Intell. Humanized Comput. 1–17 (2022)
83.
go back to reference Diez-Sanmartin, C., Sarasa-Cabezuelo, A., Belmonte, A.A.: The impact of artificial intelligence and big data on end-stage kidney disease treatments. Expert Syst. Appl. 180, 115076 (2021) CrossRef Diez-Sanmartin, C., Sarasa-Cabezuelo, A., Belmonte, A.A.: The impact of artificial intelligence and big data on end-stage kidney disease treatments. Expert Syst. Appl. 180, 115076 (2021) CrossRef
84.
go back to reference Abdelaziz, A., Salama, A.S., Riad, A.M. and Mahmoud, A.N.: A machine learning model for predicting of chronic kidney disease based internet of things and cloud computing in smart cities. In: Security in smart cities: models, applications, and challenges, pp. 93–114. Springer, Cham, (2019) Abdelaziz, A., Salama, A.S., Riad, A.M. and Mahmoud, A.N.: A machine learning model for predicting of chronic kidney disease based internet of things and cloud computing in smart cities. In: Security in smart cities: models, applications, and challenges, pp. 93–114. Springer, Cham, (2019)
85.
go back to reference Mansour, R.F., Escorcia-Gutierrez, J., Gamarra, M., Díaz, V.G., Gupta, D., Kumar, S.: Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images. Neural Comput. Appl. 1–13. (2021) Mansour, R.F., Escorcia-Gutierrez, J., Gamarra, M., Díaz, V.G., Gupta, D., Kumar, S.: Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images. Neural Comput. Appl. 1–13. (2021)
86.
go back to reference HS, S.K. and Karibasappa, K.: An approach for brain tumour detection based on dual-tree complex Gabor wavelet transform and neural network using Hadoop big data analysis. Multimedia Tools Appl. 1–24 (2022) HS, S.K. and Karibasappa, K.: An approach for brain tumour detection based on dual-tree complex Gabor wavelet transform and neural network using Hadoop big data analysis. Multimedia Tools Appl. 1–24 (2022)
87.
go back to reference Chew, A.W.Z., Pan, Y., Wang, Y., Zhang, L.: Hybrid deep learning of social media big data for predicting the evolution of COVID-19 transmission. Knowl.-Based Syst. 233, 107417 (2021) CrossRef Chew, A.W.Z., Pan, Y., Wang, Y., Zhang, L.: Hybrid deep learning of social media big data for predicting the evolution of COVID-19 transmission. Knowl.-Based Syst. 233, 107417 (2021) CrossRef
88.
go back to reference Elghamrawy, S.: An h 2 o’s deep learning-inspired model based on big data analytics for coronavirus disease (covid-19) diagnosis. In Big data analytics and artificial intelligence against COVID-19: Innovation Vision and Approach, pp. 263–279. Springer, Cham (2020) Elghamrawy, S.: An h 2 o’s deep learning-inspired model based on big data analytics for coronavirus disease (covid-19) diagnosis. In Big data analytics and artificial intelligence against COVID-19: Innovation Vision and Approach, pp. 263–279. Springer, Cham (2020)
89.
go back to reference Jamshidi, M., Lalbakhsh, A., Talla, J., Peroutka, Z., Hadjilooei, F., Lalbakhsh, P., Jamshidi, M., La Spada, L., Mirmozafari, M., Dehghani, M., Sabet, A.: Artificial intelligence and COVID-19: deep learning approaches for diagnosis and treatment. Ieee Access 8, 109581–109595 (2020) CrossRef Jamshidi, M., Lalbakhsh, A., Talla, J., Peroutka, Z., Hadjilooei, F., Lalbakhsh, P., Jamshidi, M., La Spada, L., Mirmozafari, M., Dehghani, M., Sabet, A.: Artificial intelligence and COVID-19: deep learning approaches for diagnosis and treatment. Ieee Access 8, 109581–109595 (2020) CrossRef
90.
go back to reference Oh, Y., Park, S., Ye, J.C.: Deep learning COVID-19 features on CXR using limited training data sets. IEEE Trans. Med. Imaging 39(8), 2688–2700 (2020) CrossRef Oh, Y., Park, S., Ye, J.C.: Deep learning COVID-19 features on CXR using limited training data sets. IEEE Trans. Med. Imaging 39(8), 2688–2700 (2020) CrossRef
91.
go back to reference Alakus, T.B., Turkoglu, I.: Comparison of deep learning approaches to predict COVID-19 infection. Chaos Solitons Fractals 140, 110120 (2020) MathSciNetCrossRef Alakus, T.B., Turkoglu, I.: Comparison of deep learning approaches to predict COVID-19 infection. Chaos Solitons Fractals 140, 110120 (2020) MathSciNetCrossRef
92.
go back to reference Luo, Y., Xu, X.: Comparative study of deep learning models for analyzing online restaurant reviews in the era of the COVID-19 pandemic. Int. J. Hosp. Manag. 94, 102849 (2021) CrossRef Luo, Y., Xu, X.: Comparative study of deep learning models for analyzing online restaurant reviews in the era of the COVID-19 pandemic. Int. J. Hosp. Manag. 94, 102849 (2021) CrossRef
93.
go back to reference Prasanth, S., Singh, U., Kumar, A., Tikkiwal, V.A., Chong, P.H.: Forecasting spread of COVID-19 using google trends: a hybrid GWO-deep learning approach. Chaos Solitons Fractals 142, 110336 (2021) MathSciNetCrossRef Prasanth, S., Singh, U., Kumar, A., Tikkiwal, V.A., Chong, P.H.: Forecasting spread of COVID-19 using google trends: a hybrid GWO-deep learning approach. Chaos Solitons Fractals 142, 110336 (2021) MathSciNetCrossRef
94.
go back to reference Ramanathan, S., Ramasundaram, M.: Accurate computation: COVID-19 rRT-PCR positive test dataset using stages classification through textual big data mining with machine learning. J. Supercomput. 77(7), 7074–7088 (2021) CrossRef Ramanathan, S., Ramasundaram, M.: Accurate computation: COVID-19 rRT-PCR positive test dataset using stages classification through textual big data mining with machine learning. J. Supercomput. 77(7), 7074–7088 (2021) CrossRef
95.
go back to reference Ghosh, T., Al Banna, M.H., Al Nahian, M.J., Taher, K.A., Kaiser, M.S. and Mahmud, M.: A hybrid deep learning model to predict the impact of COVID-19 on mental health form social media big data (2021) Ghosh, T., Al Banna, M.H., Al Nahian, M.J., Taher, K.A., Kaiser, M.S. and Mahmud, M.: A hybrid deep learning model to predict the impact of COVID-19 on mental health form social media big data (2021)
96.
go back to reference Panwar, H., Gupta, P.K., Siddiqui, M.K., Morales-Menendez, R., Singh, V.: Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet. Chaos Solitons Fractals 138, 109944 (2020) MathSciNetCrossRef Panwar, H., Gupta, P.K., Siddiqui, M.K., Morales-Menendez, R., Singh, V.: Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet. Chaos Solitons Fractals 138, 109944 (2020) MathSciNetCrossRef
97.
go back to reference Kaur, H., Ahsaan, S.U., Alankar, B., Chang, V.: A proposed sentiment analysis deep learning algorithm for analyzing COVID-19 tweets. Inf. Syst. Front. 23(6), 1417–1429 (2021) CrossRef Kaur, H., Ahsaan, S.U., Alankar, B., Chang, V.: A proposed sentiment analysis deep learning algorithm for analyzing COVID-19 tweets. Inf. Syst. Front. 23(6), 1417–1429 (2021) CrossRef
98.
go back to reference Wang, Y., Zeng, D.: Development of sports industry under the influence of COVID-19 epidemic situation based on big data. J. Intell. Fuzzy Syst. 39(6), 8867–8875 (2020) CrossRef Wang, Y., Zeng, D.: Development of sports industry under the influence of COVID-19 epidemic situation based on big data. J. Intell. Fuzzy Syst. 39(6), 8867–8875 (2020) CrossRef
99.
go back to reference Awan, M.J., Bilal, M.H., Yasin, A., Nobanee, H., Khan, N.S., Zain, A.M.: Detection of COVID-19 in chest X-ray images: A big data enabled deep learning approach. Int. J. Environ. Res. Public Health 18(19), 10147 (2021) CrossRef Awan, M.J., Bilal, M.H., Yasin, A., Nobanee, H., Khan, N.S., Zain, A.M.: Detection of COVID-19 in chest X-ray images: A big data enabled deep learning approach. Int. J. Environ. Res. Public Health 18(19), 10147 (2021) CrossRef
100.
go back to reference Wang, L., Lin, Z.Q., Wong, A.: Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Sci. Rep. 10(1), 1–12 (2020) Wang, L., Lin, Z.Q., Wong, A.: Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Sci. Rep. 10(1), 1–12 (2020)
101.
go back to reference Yang, D., Martinez, C., Visuña, L., Khandhar, H., Bhatt, C., Carretero, J.: Detection and analysis of COVID-19 in medical images using deep learning techniques. Sci. Rep. 11(1), 1–13 (2021) Yang, D., Martinez, C., Visuña, L., Khandhar, H., Bhatt, C., Carretero, J.: Detection and analysis of COVID-19 in medical images using deep learning techniques. Sci. Rep. 11(1), 1–13 (2021)
102.
go back to reference Ohata, E.F., Bezerra, G.M., das Chagas, J.V.S., Neto, A.V.L., Albuquerque, A.B., de Albuquerque, V.H.C. and Reboucas Filho, P.P.: Automatic detection of COVID-19 infection using chest X-ray images through transfer learning. IEEE/CAA Journal of Automatica Sinica, 8(1), 239-248 (2020) Ohata, E.F., Bezerra, G.M., das Chagas, J.V.S., Neto, A.V.L., Albuquerque, A.B., de Albuquerque, V.H.C. and Reboucas Filho, P.P.: Automatic detection of COVID-19 infection using chest X-ray images through transfer learning. IEEE/CAA Journal of Automatica Sinica, 8(1), 239-248 (2020)
103.
go back to reference Chowdhury, N.K., Rahman, M., Kabir, M.A.: PDCOVIDNet: a parallel-dilated convolutional neural network architecture for detecting COVID-19 from chest X-ray images. Health Inform. Sci. Syst. 8(1), 1–14 (2020) Chowdhury, N.K., Rahman, M., Kabir, M.A.: PDCOVIDNet: a parallel-dilated convolutional neural network architecture for detecting COVID-19 from chest X-ray images. Health Inform. Sci. Syst. 8(1), 1–14 (2020)
104.
go back to reference Canayaz, M.: MH-COVIDNet: Diagnosis of COVID-19 using deep neural networks and meta-heuristic-based feature selection on X-ray images. Biomed. Signal Process. Control 64, 102257 (2021) CrossRef Canayaz, M.: MH-COVIDNet: Diagnosis of COVID-19 using deep neural networks and meta-heuristic-based feature selection on X-ray images. Biomed. Signal Process. Control 64, 102257 (2021) CrossRef
105.
go back to reference Aboutalebi, H., Abbasi, S., Shafiee, M.J. and Wong, A.: COVID-Net CT-S: 3D convolutional neural network architectures for COVID-19 severity assessment using Chest CT Images. arXiv preprint arXiv:​2105.​01284. (2021) Aboutalebi, H., Abbasi, S., Shafiee, M.J. and Wong, A.: COVID-Net CT-S: 3D convolutional neural network architectures for COVID-19 severity assessment using Chest CT Images. arXiv preprint arXiv:​2105.​01284. (2021)
106.
go back to reference Pavlova, M., Terhljan, N., Chung, A.G., Zhao, A., Surana, S., Aboutalebi, H., Gunraj, H., Sabri, A., Alaref, A. and Wong, A.: Covid-net cxr-2: an enhanced deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Front. Med. 9 (2022) Pavlova, M., Terhljan, N., Chung, A.G., Zhao, A., Surana, S., Aboutalebi, H., Gunraj, H., Sabri, A., Alaref, A. and Wong, A.: Covid-net cxr-2: an enhanced deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Front. Med. 9 (2022)
107.
go back to reference Farooq, M. and Hafeez, A.: Covid-resnet: A deep learning framework for screening of covid19 from radiographs. arXiv preprint arXiv:​2003.​14395. (2020) Farooq, M. and Hafeez, A.: Covid-resnet: A deep learning framework for screening of covid19 from radiographs. arXiv preprint arXiv:​2003.​14395. (2020)
108.
go back to reference Awasthi, N., Dayal, A., Cenkeramaddi, L.R., Yalavarthy, P.K.: Mini-COVIDNet: efficient lightweight deep neural network for ultrasound based point-of-care detection of COVID-19. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 68(6), 2023–2037 (2021) CrossRef Awasthi, N., Dayal, A., Cenkeramaddi, L.R., Yalavarthy, P.K.: Mini-COVIDNet: efficient lightweight deep neural network for ultrasound based point-of-care detection of COVID-19. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 68(6), 2023–2037 (2021) CrossRef
109.
go back to reference Hasan, N., Bao, Y., Shawon, A., Huang, Y.: DenseNet convolutional neural networks application for predicting COVID-19 using CT image. SN Comput. Sci. 2(5), 1–11 (2021) CrossRef Hasan, N., Bao, Y., Shawon, A., Huang, Y.: DenseNet convolutional neural networks application for predicting COVID-19 using CT image. SN Comput. Sci. 2(5), 1–11 (2021) CrossRef
110.
go back to reference Liu, Q., Leung, C.K., Hu, P.: A two-dimensional sparse matrix profile DenseNet for COVID-19 diagnosis using chest CT images. IEEE Access 8, 213718–213728 (2020) CrossRef Liu, Q., Leung, C.K., Hu, P.: A two-dimensional sparse matrix profile DenseNet for COVID-19 diagnosis using chest CT images. IEEE Access 8, 213718–213728 (2020) CrossRef
111.
go back to reference Xiao, B., Yang, Z., Qiu, X., Xiao, J., Wang, G., Zeng, W., Li, W., Nian, Y., Chen, W.: PAM-DenseNet: a deep convolutional neural network for computer-aided COVID-19 diagnosis. IEEE Transact. Cybern. 52, 12163 (2021) CrossRef Xiao, B., Yang, Z., Qiu, X., Xiao, J., Wang, G., Zeng, W., Li, W., Nian, Y., Chen, W.: PAM-DenseNet: a deep convolutional neural network for computer-aided COVID-19 diagnosis. IEEE Transact. Cybern. 52, 12163 (2021) CrossRef
112.
go back to reference Tang, S., Wang, C., Nie, J., Kumar, N., Zhang, Y., Xiong, Z., Barnawi, A.: EDL-COVID: Ensemble deep learning for COVID-19 case detection from chest X-ray images. IEEE Trans. Industr. Inf. 17(9), 6539–6549 (2021) CrossRef Tang, S., Wang, C., Nie, J., Kumar, N., Zhang, Y., Xiong, Z., Barnawi, A.: EDL-COVID: Ensemble deep learning for COVID-19 case detection from chest X-ray images. IEEE Trans. Industr. Inf. 17(9), 6539–6549 (2021) CrossRef
113.
go back to reference Abdani, S.R., Zulkifley, M.A. and Zulkifley, N.H.: A lightweight deep learning model for covid-19 detection. In: 2020 IEEE Symposium on Industrial Electronics & Applications (ISIEA) (pp. 1–5). IEEE (2020) Abdani, S.R., Zulkifley, M.A. and Zulkifley, N.H.: A lightweight deep learning model for covid-19 detection. In: 2020 IEEE Symposium on Industrial Electronics & Applications (ISIEA) (pp. 1–5). IEEE (2020)
114.
go back to reference Aminu, M., Ahmad, N.A., Noor, M.H.M.: Covid-19 detection via deep neural network and occlusion sensitivity maps. Alex. Eng. J. 60(5), 4829–4855 (2021) CrossRef Aminu, M., Ahmad, N.A., Noor, M.H.M.: Covid-19 detection via deep neural network and occlusion sensitivity maps. Alex. Eng. J. 60(5), 4829–4855 (2021) CrossRef
115.
go back to reference Kumar, M.D., Ramana, K.: Cardiac Segmentation from MRI images using Recurrent & Residual Convolutional Neural Network based on SegNet and Level Set methods. Annals of the Romanian Society for Cell Biology, pp.1536–1545, (2021) Kumar, M.D., Ramana, K.: Cardiac Segmentation from MRI images using Recurrent & Residual Convolutional Neural Network based on SegNet and Level Set methods. Annals of the Romanian Society for Cell Biology, pp.1536–1545, (2021)
116.
go back to reference Kumar, M.D., Ramana, K.V.: Cardiovascular disease prognosis and severity analysis using hybrid heuristic methods. Multimedia Tools Appl. 80(5), 7939–7965 (2021) CrossRef Kumar, M.D., Ramana, K.V.: Cardiovascular disease prognosis and severity analysis using hybrid heuristic methods. Multimedia Tools Appl. 80(5), 7939–7965 (2021) CrossRef
Metadata
Title
A Systematic Literature Review and Future Perspectives for Handling Big Data Analytics in COVID-19 Diagnosis
Authors
Nagamani Tenali
Gatram Rama Mohan Babu
Publication date
16-03-2023
Publisher
Springer Japan
Published in
New Generation Computing / Issue 2/2023
Print ISSN: 0288-3635
Electronic ISSN: 1882-7055
DOI
https://doi.org/10.1007/s00354-023-00211-8

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