JAAV id

Github Link:https://github.com/Vmaster2000/JAAVid

Inspiration:  All of our parents are immigrants, and the countries that we come from have seen a plethora of refugees who have faced several challenges adapting to the new environment. Thus, we felt this problem directly connected with our hearts. Hence, we created a project that help make a fluid and seamless transition for the new immigrants.

What it does: Our project is a platform which allows immigrants to quickly adapt into the new environment. It does this through a variety of different features are incorporated in our app. Some things which it does it allows immigrants to quickly find jobs based on their skill set, verify documents using the sentimental analysis and OCR scanning. 

How we built it: We built this through the following languages:

Python( Front end)

HTML and CSS(Front end) 

Mongo DB and Neurelo( Back end) 

Challenges we ran into: We faced numerous challenges throughout this app’s construction. We planned to develop a self-learning/integrated AI model that used a wide range of document datasets to make decisions for processing qualitative data in technical documents such as Land Records, Language Applications for proof of citizenship, or refugee protection documents that had long wait times (weeks to months to years) even with active government personnel verification, and fulfilled important services for refugees such as housing availability, and access to health-care.  However, we severely underestimated the amount of public document records/data sets we’d have to feed our AI model to actually begin grading applications from a fixed criteria. Thus, we used sentimental analysis to look for specific words/criteria in 5 refugee-related documents with straightforward applications. We also hoped to improve immigrant lives by collecting valuable info about them, and suggesting different housing options, and likeable communities, and job opportunities that would fit their experience and desired demographic, for a sustainable start in Canadian life. However, we’d need to store a humungous database of all available houses through google maps, which would connect to the filters provided by immigrants, for which we didn’t have much space in our computer. Thus, we used an AI tracker to track nearby jobs through job scanner databases, giving immigrants adequate opportunities based on their education and experiences, and detecting housing options in a 5-10 km radius of the job company. 

Accomplishments that we are proud of: We learnt how to use UI and sentimental analysis for the purposes of analysing text as opposed to qualitative analysis

Benefit to Job Availability and Efficiency in the Governement Document Processing Market:

It provides a range of jobs a diverse range of skillsets for different workers in the government. For instance, if the automated extraction and validation processes minimize the risk of errors, ensuring the accuracy of critical property information. Then, you don’t require high-skillled workers filling such positions when AI is completing their task. Furthermore, with faster processing times, government agencies can provide more timely responses to inquiries, applications, and legal matters related to land records. This way, more jobs can be shifted to actual public engagement rather than document verification (contributing to the availability of the HR Sector - a huge applicant pool of jobs for this sector are in Canada). 

This way, you can calm the job disparity in the market for any operation that automation touches. 

Resource Optimization: IDP allows agencies to allocate human resources more strategically, focusing on higher-value tasks while routine document processing is handled by AI.

This is mainly for higher skilled workers to focus their attention on improving government systems, and innovation, instead of step-by-step straightforward and time consuming actions that can be readily done through Automation.