Intern @ FiVerity
Our project is a sophisticated fraud detection platform that has been meticulously designed and developed to enhance fraud detection accuracy within the financial industry.
- Risk Analysis of large scale User Credit data
- Feature Engineering for ID Theft identification
- Built secure AWS infrastructure for critical data
ML
Python
Kube
PostgreSQL
AWS
Graduate Research Assistant @ NEU
Our Supply Chain Transparency Analytics Platform is a cutting-edge solution designed to uncover suspicious relationships among companies within the global supply chain network.
- Full Stack Dashboard for large scale data
- ETL Pipelines with multiple sources
- KPI reports
Django
Python
Docker
PostgreSQL
ReactJS
Software Developer @ TCS
Designed and Developed multiple web applications used by millions of users
- Room Reservation Web Application
- Software Downloads
- Fresco Connect
ReactJS
Python
Spring(Java)
MySql
Docker
AWS
Kubernetes
iOS Developer @ TCS
Designed and implemented Enterprise native iOS applications used by millions of users
- Timesheet Application
- My Leaves
- Fresco Connect
- Fresco Raise
Swift
UIKit
SwiftUI
SqlLite
CreateML
AWS
CDN
Async-Await
Machine Learning Engineer @ TCS
Provided solutions to inventory problems using Data Science
- Developed a Classification model to identify significance of products in inventory
- Developed a Time Series Forecasting Model
- Created a Web Application incorporating ML models
Flask
Random Forest
Forecasting
Time Series
Project Tracker is an Application available for iOS, macOS and iPadOS which helps to track all your projects smartly.
- Stored user data in cloud for ubiquity
- Identifying Patterns from User Interaction
- On Device Recommendation System
- Adapting Face Unlock
SwiftUI
WidgetKit
Core ML
CloudKit
Content Based Filtering
Text miner is an Application available for iOS, iPadOS and macOS which helps in bringing Insights out of Text.
- Handling API Responses
- Implementing OAuth
- Creating Charts and Animations
- Increasing App Interaction by donating intents
SwiftUI
Natural Language
Core ML
SiriKit
Summarization
Sentiment Analysis
Multi Modal Sentiment Classification
Dependency between modes (text, audio and video) is taken into account for obtaining sentiment.
- Extract unimodal contextual representations using self attention and Crossed Attention representations using cross attention layers.
- The generated features are fed to GRU resulting in Deep multi modal contextual representations.
- These representations for each utterance are concatenated and fed to Fully Connected layers followed by softmax layer for final sentiment.
Natural Language
Pytorch
Sentiment Analysis
UnderWater Sonar Image Classification using AW-CNN
CNN is widely known for its application in image classification. However, there are some drawbacks even for this algorithm. So the application of CNN in every field has a variant of it.
- AW-CNN is a variant of CNN solving random initialisation problem of CNN
- AW-CNN uses feature matrix constructed from DBN to initialise the CNN
- The accuracy of AW-CNN was 4% higher than CNN without any overfitting
CNN
Pytorch
Tensorflow
Keras
Data Science
Data Preparation, EDA, Modelling, Model Evaluation and Model Deployment
iOS Development
Xcode, CoreData, UIKit, SwiftUI, CoreLocation, RestFul API management, CoreML, CreateML
Cloud Based Technologies
AWS, Docker, Kubernetes
Web Development
HTML, CSS, BootStrap, React JS, JavaScript, PHP, Node JS
Natural Language Processing
Language Modelling, Named Entity Recognition, Entity Linking, Question Answering