International Research Journal of Engineering and Technology (IRJET) Volume: 09 Issue: 09 | Sep 2022 www.irjet.net
e-ISSN: 2395-0056 p-ISSN: 2395-0072
A Machine learning based framework for Verification and Validation of Massive Scale Image Data Bhavani A M1 Dept. of MCA, Vidya Vikas Institute of Engineering and Technology, Karnataka, India ---------------------------------------------------------------------***--------------------------------------------------------------------Because it is an internet-based venture, the firm has decided Abstract – Machine learning algorithms are now involved to use advanced advertising as a means of expanding its business while simultaneously building a worldwide reputation and brand.
in more and more aspects of everyday life from what one can read and watch, to how one can shop, to who one can meet and how one can travel. For example, consider fraud detection.
2.EXISTING SYSTEM
Images supply large amounts of data, that need appropriate statistical and numerical techniques, in order to achieve their restoration and validation. In this work some procedures of data processing are presented; they combine suitably optimality from the statistical point of view and practicability from the numerical one.
To investigate the state of the art of ML in Autism research, and whether there is an effect of sample size on reported ML performance, a literature search was performed using search terms “Autism” AND “Machine learning”. Most of the surveyed studies had a small number of subjects . The studies used various types of data to classify autistic and non-autistic individuals as there wwere many drawbacks, with the majority from the brain imaging domain. Other studies used, microarray, clinical chemistry, cognitive, motion and eye tracking data.
Image data verification and validation makes a very important part of Machine Learning. In order to proceed successfully in our technical inventions its must to bring up strategies for verification process of massive image data. We describe the design of the proposed framework with CMA as the case study. The effectiveness of the framework is demonstrated through verifying and validating the data set, software systems and algorithms in CMA.
Disadvantage: As huge capital is invested in the large scale production, it is very difficult to bring about a change in the scale of production according to the circumstances.
Key Words: Validation, Verification, CMA,ML. 1.INTRODUCTION
The combined file sizes of all the images in the series ended up causing significant delays in page load performance.
In computer graphics and digital imaging we see an imaging, image scaling refers to the resizing of a digital image. In video technology, the magnification of digital material is known as upscaling or resolution enhancement.
Massive scale images are usually not flexible and are too large. CMA is used as a case study to demonstrate the creation of the conceptual methodology.
New data management models, designed to sustain billions billion data operations per second, are being driven by the demands of big data, while old relational models are evolving to keep up. As that the product environment changes, the authors present practical techniques to help data managers select candidate solutions and ways match their acceptance criteria.
3. PROPOSED SYSTEM The fields of application of the presented procedure are very broad. Indeed they range from preprocessing techniques, to achieve image quality assessment and to generate datasets suitable for analysis and measurements, to algorithms for geometric determinat ion and analysis of image data ( e.g. feature extraction, image matching), including the semantic aspects of image understanding.
Performing machine learning for image recognition at the edges makes it possible to overcome the limitations of the cloud in terms of privacy, real-time performance, efficacy, robustness, and more.
Data validation is forecasted to be one of the biggest challenges e-commerce websites are likely to experience in 2020. In this article, we will go over key statistics highlighting the main data validation issues that currently impact big data companies. The article’s final aim is to propose a quality improvement solution for tech teams.
Hence, the use of Edge AI for computer vision makes it possible to scale image recognition applications in realworld scenarios.
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