A Real-Time Hadoop Bigdata Maintenance Model using A Software-Defined and U-Net Deep Learning Mode

Authors

  • G. Nanda Kishor Kumar Professor, Department Computer Science and Engineering, Malla Reddy University, Maisammaguda, Dulapally, Hyderabad, Telangana 500043
  • M. Srinivasa Reddy Department of Electrical and Electronics Engineering, MLR institute of Technology, Hyderabad, India
  • D. Naga Malleswari Department of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation, Green Fileds, Vaddeswaram, A.P. – 522302.
  • K. Madhusudhana Rao Professor, Dept of ECE, KKR & KSR Institute of Technology & Sciences, Guntur
  • K. Saikumar School of engineering, department of CSE, Malla Reddy University, Maisammaguda, Dulapally, Hyderabad, Telangana 500043

Keywords:

big data, U-net, deep learning, Hadoop, sparks

Abstract

An advanced big data platform will include several functions, such as the ability to administer servers, the cloud, and Hadoop. However, the present big data infrastructure, which employs Hadoop models, has limitations that prevent the quick delivery of dynamic operations. Problems with storage and latency are threatening the robustness of applications. The failure to leverage the Internet and big data platforms to direct manufacturing activities is another problem with cloud storage maintenance. It is critical to handle these challenges by continuously growing and evolving big data cloud systems, which are driven by the effective processing of enormous data at cloud gateways. The sponsored software arrangement enables the DL-enabled Operations Facilities concept, which is introduced in this work. Through the use of intelligent closed-loop video monitoring, this technology expedites the processing and updating of procedures for managing large data files. The primary objective of this research is to develop more efficient methods for creating large data files on the cloud. The research is conducted using U-net, a network architecture that has been tested on Python 3.7 and is built on Hadoop and Spark. The achieved performance parameters outperform the industry standard with values of 97.23 percent recall, 98.92 percent sensitivity, and 99.23 percent throughput. The presented U-net-based big data analytics solution outperforms state-of-the-art technology.

Downloads

Download data is not yet available.

References

Shi, Y., Han, Q., Shen, W., & Zhang, H. (2019). Potential applications of 5G communication technologies in collaborative intelligent manufacturing. IET Collaborative Intelligent Manufacturing, 1(4), 109-116.

Yang, C., Lan, S., Shen, W., Huang, G. Q., Wang, X., & Lin, T. (2017). Towards product customization and personalization in IoT-enabled cloud manufacturing. Cluster Computing, 20(2), 1717-1730.

Yang, C., Shen, W., & Wang, X. (2018). The internet of things in manufacturing: Key issues and potential applications. IEEE Systems, Man, and Cybernetics Magazine, 4(1), 6-15.

Marjani, M., Nasaruddin, F., Gani, A., Karim, A., Hashem, I. A. T., Siddiqa, A., & Yaqoob, I. (2017). Big IoT data analytics: architecture, opportunities, and open research challenges. ieee access, 5, 5247-5261.

O'donovan, P., Gallagher, C., Bruton, K., & O'Sullivan, D. T. (2018). A fog computing industrial cyber-physical system for embedded low-latency machine learning Industry 4.0 applications. Manufacturing letters, 15, 139-142.

Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., & Zhang, J. (2019). Edge intelligence: Paving the last mile of artificial intelligence with edge computing. Proceedings of the IEEE, 107(8), 1738-1762.

Li, S., Da Xu, L., & Zhao, S. (2018). 5G Internet of Things: A survey. Journal of Industrial Information Integration, 10, 1-9.

Mei, H., & Guo, Y. (2018). Toward ubiquitous operating systems: A software-defined perspective. Computer, 51(1), 50-56.

Yang, C., Lan, S., Shen, W., Huang, G. Q., & Wang, L. (2019, August). Software-defined cloud manufacturing in the context of industry 4.0. In 2019 WRC Symposium on Advanced Robotics and Automation (WRC SARA) (pp. 184-190). IEEE.

Ordonez-Lucena, J., Ameigeiras, P., Lopez, D., Ramos-Munoz, J. J., Lorca, J., & Folgueira, J. (2017). Network slicing for 5G with SDN/NFV: Concepts, architectures, and challenges. IEEE Communications Magazine, 55(5), 80-87.

Kailasam, S., Achanta, S.D.M., Rama Koteswara Rao, P., Vatambeti, R., Kayam, S. (2022). An IoT-based agriculture maintenance using pervasive computing with machine learning technique. International Journal of Intelligent Computing and Cybernetics, 15(2), pp. 184–197.

Saikumar, K., Rajesh, V. A machine intelligence technique for predicting cardiovascular disease (CVD) using Radiology Dataset. Int J Syst Assur Eng Manag (2022). https://doi.org/10.1007/s13198-022-01681-7.

Nagendram, S., Nag, M. S. R. K., Ahammad, S. H., Satish, K., & Saikumar, K. (2022, January). Analysis For The System Recommended Books That Are Fetched From The Available Dataset. In 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 1801-1804). IEEE.

Shravani, C., Krishna, G. R., Bollam, H. L., Vatambeti, R., & Saikumar, K. (2022, January). A Novel Approach for Implementing Conventional LBIST by High Execution Microprocessors. In 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 804-809). IEEE.

Kiran, K. U., Srikanth, D., Nair, P. S., Ahammad, S. H., & Saikumar, K. (2022, March). Dimensionality Reduction Procedure for Bigdata in Machine Learning Techniques. In 2022 6th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 836-840). IEEE.

Murugan, S., Vijayakumar, K., Sivakumar, V., Manikandan, R., Kumar, A., & Saikumar, K. (2022). Impact of Internet of Health Things (IoHT) on COVID-19 Disease Detection and Its Treatment Using Single Hidden Layer Feed Forward Neural Networks (SIFN). In How COVID-19 is Accelerating the Digital Revolution: Challenges and Opportunities (pp. 31-50). Cham: Springer International Publishing.

Srinivas Rao, K., Divakara Rao, D. V., Patel, I., Saikumar, K., & Vijendra Babu, D. (2023). Automatic Prediction and Identification of Smart Women Safety Wearable Device Using Dc-RFO-IoT. Journal of Information Technology Management, 15(Special Issue), 34-51.

Sreelakshmi, D., Sarada, K., Sitharamulu, V., Vadlamudi, M. N., & Saikumar, K. (2023). An Advanced Lung Disease Diagnosis Using Transfer Learning Method for High-Resolution Computed Tomography (HRCT) Images: High-Resolution Computed Tomography. In Digital Twins and Healthcare: Trends, Techniques, and Challenges (pp. 119-130). IGI Global.

Saikumar, K., Rajesh, V., & Rahman, M. Z. U. (2022). Pretrained DcAlexnet Cardiac Diseases Classification on Cognitive Multi-Lead Ultrasound Dataset. International Journal of Integrated Engineering, 14(7), 146-161.

Maddileti, T., Sirisha, J., Srinivas, R., & Saikumar, K. (2022). Pseudo Trained YOLO R_CNN Model for Weapon Detection with a Real-Time Kaggle Dataset. International Journal of Integrated Engineering, 14(7), 131-145.

Reddy, D. H., & Sirisha, N. (2022). Multifactor Authentication Key Management System based Security Model Using Effective Handover Tunnel with IPV6. International Journal of Communication Networks and Information Security, 14(2), 273-284.

Reddy, D. A., Shambharkar, S., Jyothsna, K., Kumar, V. M., Bhoyar, C. N., & Somkunwar, R. K. (2022, September). Automatic Vehicle Damage Detection Classification framework using Fast and Mask Deep learning. In 2022 Second International Conference on Computer Science, Engineering and Applications (ICCSEA) (pp. 1-6). IEEE.

Kumar, A., Janakirani, M., Anand, M., Sharma, S., Vivekanand, C. V., & Chakravarti, A. (2022). Comparative Performance Study of Difference Differential Amplifier Using 7 nm and 14 nm FinFET Technologies and Carbon Nanotube FET. Journal of Nanomaterials, 2022.

Vijetha, T., Mallick, P. S., Karthik, R., & Rajan, K. (2022). Effect of Scattering Angle in Electron Transport of AlGaN and InGaN. Advances in Materials Science and Engineering, 2022.

Kumar, H., Prasad, R., Kumar, P., & Hailu, S. A. (2022). Friction and Wear Response of Friction Stir Processed Cu/ZrO 2 Surface Nano-Composite. Journal of Nanomaterials, 2022.

Bhanuprakash, Lokasani, Soney Varghese, and Sitesh Kumar Singh. "Glass Fibre Reinforced Epoxy Composites Modified with Graphene Nanofillers: Electrical Characterization." Journal of Nanomaterials (2022).

Jangam, N. R., Guthikinda, L., & Ramesh, G. P. (2022). Design and Analysis of New Ultra Low Power CMOS Based Flip-Flop Approaches. In Distributed Computing and Optimization Techniques: Select Proceedings of ICDCOT 2021 (pp. 295-302). Singapore: Springer Nature Singapore.

Chandrasekaran, S., Satyanarayana Gupta, M., Jangid, S., Loganathan, K., Deepa, B., & Chaudhary, D. K. (2022). Unsteady radiative Maxwell fluid flow over an expanding sheet with sodium alginate water-based copper-graphene oxide hybrid nanomaterial: an application to solar aircraft. Advances in Materials Science and Engineering, 2022.

Shareef, S. K., Sridevi, R., Raju, V. R., & Rao, K. S. (2022). An Intelligent Secure Monitoring Phase in Blockchain Framework for Large Transaction. IJEER, 10(3), 536-543.

Koppula, N., Rao, K. S., Nabi, S. A., & Balaram, A. (2023). A novel optimized recurrent network-based automatic system for speech emotion identification. Wireless Personal Communications, 128(3), 2217-2243.

Ali, F., Kumar, T. A., Loganathan, K., Reddy, C. S., Pasha, A. A., Rahman, M. M., & Al-Farhany, K. (2023). Irreversibility analysis of cross fluid past a stretchable vertical sheet with mixture of Carboxymethyl cellulose water based hybrid nanofluid. Alexandria Engineering Journal, 64, 107-118.

Pittala, C. S., Vijay, V., & Reddy, B. N. K. (2022). 1-Bit FinFET carry cells for low voltage high-speed digital signal processing applications. Silicon, 1-12.

Saran, O. S., Reddy, A. P., Chaturya, L., & Kumar, M. P. (2022). 3D printing of composite materials: A short review. Materials Today: Proceedings.

Dasari, K., Anjaneyulu, L., & Nadimikeri, J. (2022). Application of C-band sentinel-1A SAR data as proxies for detecting oil spills of Chennai, East Coast of India. Marine Pollution Bulletin, 174, 113182.

Rao, A. D., Chaitanya, A. K., Seshaiah, T., & Bridjesh, P. (2022). An Integrated Approach by Using Various Approaches for a Green Supplier Selection Problem. In Recent Advances in Manufacturing, Automation, Design and Energy Technologies: Proceedings from ICoFT 2020 (pp. 909-919). Springer Singapore.

Yakaiah, P., & Naveen, K. (2022). An Approach for Ultrasound Image Enhancement Using Deep Convolutional Neural Network. In Advanced Techniques for IoT Applications: Proceedings of EAIT 2020 (pp. 86-92). Springer Singapore.

Kumar, C. A., & Haribabu, K. (2022). A Great Adaptive SNR Assumed Low Power LDPC Decoder. In Advanced Techniques for IoT Applications: Proceedings of EAIT 2020 (pp. 443-451). Springer Singapore.

Thottempudi, P., Dasari, V. S. C. B., & Sista, V. S. P. (2022). Recognition of Moving Human Targets by Through the Wall Imaging RADAR Using RAMA and SIA Algorithms. In Advanced Techniques for IoT Applications: Proceedings of EAIT 2020 (pp. 544-563). Springer Singapore.

Arun, V., Reddy, D. L., & Rao, K. N. (2022). A Novel Analysis of Efficient Energy Architecture in Cryptography. In Advanced Techniques for IoT Applications: Proceedings of EAIT 2020 (pp. 339-345). Springer Singapore.

Amareswer, E., & Raju Naik, M. (2022). Smart Erobern of Vehicles on Crosswalks. In Advanced Techniques for IoT Applications: Proceedings of EAIT 2020 (pp. 489-497). Springer Singapore.

Downloads

Published

05.12.2023

How to Cite

Kumar, G. N. K. ., Reddy, M. S. ., Malleswari, D. N. ., Rao, K. M. ., & Saikumar, K. (2023). A Real-Time Hadoop Bigdata Maintenance Model using A Software-Defined and U-Net Deep Learning Mode. International Journal of Intelligent Systems and Applications in Engineering, 12(7s), 364–376. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4080

Issue

Vol. 12 No. 7s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.