An Improved Deep Neuro Fuzzy Framework for Big Data Management in Ultra-Dense Cloud Servers

As how much information keeps on filling dramatically in the present computerized world, overseeing and dissecting this information has turned into a significant test for organizations. Conventional strategies for information the executives, for example, rule-based approaches, frequently should be extended to adjust to the intricacies and vulnerabilities of enormous information. To resolve this issue, we propose a better Deep Neuro-Fuzzy (DNF) structure for huge information the executives in super thick cloud servers. This system use the force of both profound learning and fluffy rationale to deal with the intricacies of large information in cloud servers. The DNF structure adjusts to the changing climate by utilizing profound learning methods to learn and refresh its boundaries. Simultaneously, the fluffy rationale system gives interpretability and adaptability in navigation. The DNF system comprises of three fundamental parts: information pre-handling, Deep Neuro-Fuzzy Network (DNFN), and information post-handling. In the pre-handling stage, the structure cleans and concentrates applicable highlights from the crude information. The DNFN, the center part of our structure, is a crossover brain network that consolidates the upsides of profound learning and fluffy rationale. It uses a versatile rule-based design to deal with enormous information and gives interpretability in the dynamic cycle. The information post-handling stage includes changing the result of the DNFN into significant bits of knowledge and representations. The proposed model arrived at 95.05% Adaptability, 95.38% asset the executives, 95.60% Security the board, 94.44% Energy Productivity, 95.21 % information reconciliation and 95.74% Data access. The system is versatile and can deal with huge volumes of information, making it appropriate for super thick cloud servers.