This paper describes an integrated platform for machine learning and big data analysis. The integrated platform is configured in a way that builds a large distributed data processing environment in the computing environment that makes up the NVIDIA AI platform. In addition, this paper describes the background of this idea selection and the use of the software to build the unified platform. The technical details are shown in terms of how to create the proposed platform. In the anlaysis section, the methodology is provided and also the steps are explained as to how to use this integration platform. Finally, the expected effects are elaborated in the conclusion section.Keywords:Integrated Platform, Hadoop Eco System, Ambari, Virtual OS, Jetson TX-1, Dev Box, SSH1. Regeneration of the central nervous system presents a formidable challenge within regenerative medicine, as neurons in the brain and spinal cord have very limited potential for healing and reorganization. The Ile-Lys-Val-Ala-Val (IKVAV) peptide sequence, derived from laminin, has been incorporated into PAs for applications in neural regeneration in order to enhance neural attachment, migration, and neurite outgrowth. Variations in peptide sequence, while maintaining the alternating ionic hydrophilic and hydrophobic residues, have utilized mixed charged residues, such as repeat units of Arg-Ala-Asp-Ala (RADA) or repeat units of RARADADA. Although docking and scoring relies on many approximations, the application of our clustering techniques during lead optimization, with other computational methods, extended more traditional approaches to a Unified Platform for AI and Big Data Analytics Logical computations using algorithmic self-assembly RGD-FHRRIKA-RARADADA-IKVAV responsive peptide-modified mimetic triple-crossover hydrothermochemic molecules for tissue regeneration.
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