PROJECT TITLE :

The Visual Causality Analyst: An Interactive Interface for Causal Reasoning

ABSTRACT:

Uncovering the causal relations that exist among variables in multivariate datasets is one amongst the ultimate goals in knowledge analytics. Causation is connected to correlation but correlation does not imply causation. While a range of casual discovery algorithms are devised that eliminate spurious correlations from a network, there aren't any guarantees that all of the inferred causations are indeed true. Hence, bringing a website skilled into the casual reasoning loop can be of great profit in identifying erroneous casual relationships advised by the discovery algorithm. To handle this need we tend to gift the Visual Causal Analyst-a unique visual causal reasoning framework that permits users to apply their experience, verify and edit causal links, and collaborate with the causal discovery algorithm to spot a valid causal network. Its interface consists of both an interactive 2D graph view and a numerical presentation of salient statistical parameters, such as regression coefficients, p-values, and others. Each help users in gaining a smart understanding of the landscape of causal structures significantly when the amount of variables is massive. Our framework is also novel in that it can handle both numerical and categorical variables within one unified model and come plausible results. We demonstrate its use via a collection of case studies using multiple practical datasets.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE : Deep Spatial and Temporal Network for Robust Visual Object Tracking ABSTRACT: For visual tracking, there are two crucial components: (a) the appearance of the object and (b) the motion of the object. Since deep
PROJECT TITLE : Visual Correspondences for Unsupervised Domain Adaptation on Electron Microscopy Images ABSTRACT: For Electron Microscopy volumes, we provide an Unsupervised Domain Adaptation approach. Pretrained models are able
PROJECT TITLE : A Blind Stereoscopic Image Quality Evaluator With Segmented Stacked Autoencoders Considering the Whole Visual Perception Route ABSTRACT: Blind stereoscopic image quality assessment (SIQA) methods currently in use
PROJECT TITLE : Deep Visual Saliency on Stereoscopic Images ABSTRACT: Quality of stereoscopic 3D images has been demonstrated to have a significant impact on visual saliency in S3D images. As a result, this dependency is critical
PROJECT TITLE : Fundamental Visual Concept Learning From Correlated Images and Textí_ ABSTRACT: The visual notions in heterogeneous web media, such as objects, situations, and activities, cannot be dissected semantically. Learning

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry