097 Citation: Ashraf S (2020) Towards Shrewd Object Visualization Mechanism. Trends Comput Sci Inf Technol 5(1): 097-102. DOI: https://dx.doi.org/10.17352/tcsit.000030 https://dx.doi.org/10.17352/tcsit DOI: 2641-3086 ISSN: ENGINEERING GROUP Introduction Visualization is the key bridge between the data’s quantitative content and human experience, and it can be argued that there is nothing (including mathematical constructs) that we cannot imagine in any way that we can truly understand or intuitively understand. Human beings possess an excellent pattern-recovery mechanism in our brains, which is critically dependent on our ability to conduct efficient and scalable visual discovery in data-based research. This may be one of the most critical methodological problems in 21st century for data-rich research [1]. With the passage of time, the 3D visualisation technology is demystifying rapidly and it becomes crucial utilizing the Augmented Reality (AR), Virtual Reality (VR) and high-quality displays for generation [2], of abstract and highly interactive tools for data analysis. There exists some extensive and established phenomenon emphasizing how to avail the Virtual Reality (VR) and Immersive Virtual Reality (IVR) [3]. Due to high cost and lack of portability, limited processing capacity and bespoke supporting software frameworks have hindered broader application. Due to a continue chain upgradation in sensor technology mainly used for Electronic Warfare (EW), Intelligence and Cyber Warfare has led to massive stores of data, which requires some assisted process to generate actionable knowledge [4]. Virtual reality has been shown in areas where the key dimensions are spatial to contribute to better exploration. It has shown that data visualization facilitates possessed highly abstract multidimensional analyses [5]. Many researchers are looking to display large datasets and fusion of interactive virtual and abstract visualization and the interplay between new technologies and human perception are one of the aims we strive to explore. In order to enhance scientific results, VR and abstract data visualization have both been independently demonstrated, but to the best of our knowledge, there is no assessment of how interactive virtual reality can use abstract representation of high-dimensional data to promote scientific research [6]. Since immersive visualization will improve the productivity of desktop visualization, we believe that immersive visualization is one of the foundations for exploring the greater dimensionality and abstraction of “big data”. Some crucial cases are the studies being cited, they appear to be very case- specific (or one-of) and we know of no VR method that has tried to leverage the visual and interactive problem-solving of abstract, multi-dimensional data for the general purpose [7]. In other words, the use of interactive VR for data exploration as a general-purpose tool. Traditional methods require the use of very complex, costly and non-portable facilities. Our objective is to develop efficient, scalable, portable data visualization software available with low prices, commercially developed hardware on a standard affordable desktop or laptop. Abstract In order to measure the accurate outcome of different visualization mechanism it is imperative to adopt a shrewd strategy. Indeed, the outcome of experiment is focusing to assess the value of complex visualization approaches when comparing with alternative methods for data analysis. The interaction between participant prior knowledge and experience, a diverse range of experimental or real -world data sets and a dynamic interaction with the display system presents challenges when seeking timely, affordable and statistically relevant results. A hybrid approach proposed is being proposed to deal with complex interactive data analysis tools. This approach involves a structured survey completed after free engagement with the software platform by expert participants. The survey captures objective and subjective data points relating to the experience with the goal of making an assessment of the software performance supported by statistically significant experimental results. This work is particularly applicable to field of network analysis for cyber security and also military cyber operations and intelligence data analysis. Research Article Towards Shrewd Object Visualization Mechanism Shahzad Ashraf* College of Internet of Things Engineering, Hohai University Changzhou, Jiangsu, China Received: 31 August, 2020 Accepted: 28 November, 2020 Published: 30 November, 2020 *Corresponding author: Shahzad Ashraf, College of Internet of Things Engineering, Hohai University Chang- zhou, Jiangsu, China, E-mail: Keywords: Network forensics; Human computer interaction; Cyber warfare https://www.peertechz.com