Nonlinear Dynamics in Distributed Ledger Blockchain and analysis using Statistical Perspective
Blockchain Technology; Data Transmission Optimization; Deep Regression Analysis; Healthcare Data Storage; Nonlinear Dynamics
More and more in healthcare is blockchain technology applied for safe and open data storage. Still, it is understudied how deeply regression analysis combined with nonlinear dynamics into distributed ledger systems performs. This kind of approach may help to increase data transfer efficiency and help storage management in blockchain systems. Data speed and storage efficiency restrictions make current blockchain systems difficult to handle for large amounts of healthcare data. Conventional methods find poor data retrieval and transfer due to the great complexity and nonlinear characteristics of healthcare data. Combining nonlinear dynamics with deep regression analysis, this paper proposes a fresh approach for maximizing data transfer and storage in blockchain systems. Inspired by nonlinear dynamics ideas, a deep regression model aimed at maximizing block storage and forecast data transmission requirements was assessed on a simulated healthcare dataset using a distributed ledger system with 1,000 blocks and a 500 GB total dataset size. Performance criteria covered transmission efficiency and storage consumption. The proposed technique improved data transmission efficiency by thirty percent over current techniques. Another clear improvement was using storage; block size needs fell 25%. The best model, according to numerical research, lowered an average transmission time from 120 to 84 minutes and storage overhead from 200 to 150 GB. 2024, International Publications. All rights reserved.
Acharjee P.B.; Purushothaman G.; Vijayammal B.K.P.; Dwivedi V.K.; Gupta S.; Akalyadevi K.
Communications on Applied Nonlinear Analysis, Vol-31, No. 6S, pp. 233-251.
https://doi.org/10.52783/cana.v31.1218 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212703272&doi=10.52783%2fcana.v31.1218&partnerID=40&md5=1b3d612b4fe5b979efdb83bc5b3255f9
Acharjee P.B., Department of Computer Science, CHRIST University, Karnataka, Bangaluru, India; Purushothaman G., Department of Mathematics, St. Joseph's College of Engineering, Tamilnadu, Chennai, India; Vijayammal B.K.P., Department of Science and Humanities (General Engineering Division), R.M.K. College of Engineering and Technology, Puduvoyal, India; Dwivedi V.K., Department of Mathematics, Vishwavidyalaya Engineering College, Surguja (C.G.), Ambikapur, India; Gupta S., Department of Accounting, College of Business Administration, University of Hail, Saudi Arabia; Akalyadevi K., Department of Mathematics, Bannari Amman Institute of Technology, Sathyamangalam, Erode, India