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In recent years, natural language processing (NLP) has seen a lot of advancements thanks to deep learning models such as BERT (Bidirectional Encoder Representations from Transformers). These models can be fine-tuned on specific tasks to achieve state-of-the-art results. One such task is question answering, where the goal is to provide a concise answer to a question posed in natural language. In this project, we build a simple Employee Experience bot using the pre-trained BERT model. The bot is able to answer HR-related questions asked by the user in natural language.

Tech Stack: Python, Natural Language Processing, Deep Learning

 The URLAnalyzer project is a pioneering initiative in the realm of cybersecurity, focused on developing an advanced tool that leverages the power of machine learning and web scraping to detect and classify potentially malicious websites. The primary objective of this project is to accurately identify phishing and malware-laden URLs, thereby significantly reducing the risk of cyber threats for individuals and organizations. In an era where digital security is paramount, URLAnalyzer stands as a crucial asset, providing essential defenses against the ever-evolving landscape of online threats.

The Optimizing Information Retrieval project aims to improve the navigation and information access experience on university websites. By incorporating natural language processing (NLP) techniques, the search engine is designed to process complex academic queries, delivering relevant and accurate results to users. Moving beyond traditional keyword-based searches, the system employs semantic analysis and contextual understanding to better align user intent with suitable resources on the university website. The implementation of this NLP-powered search engine serves to enhance user experience and facilitate information retrieval within the academic community.

Analyze incoming customer queries to categorize them and automatically route them to the appropriate department or provide instant solutions if the topic has been previously addressed. 

The Data Breach Avoidance System leverages the Honeypot Strategy to provide a proactive cybersecurity framework specifically designed for the MyBankCardsManager app. By deploying a sacrificial database alongside the original one, the system distracts would-be attackers, effectively monitoring and mitigating cyber threats. The architecture is built on MS Azure SQL Server and employs machine learning algorithms, Intrusion Detection Systems (IDS), and User and Entity Behavior Analytics (UEBA) to offer a robust security solution. 

 This novel cloud load balancer employs Reinforcement Learning (RL) algorithms to dynamically distribute incoming traffic across multiple servers. Unlike traditional load balancing techniques, which rely on static rules or pre-defined conditions, the RL-based approach adapts to varying network conditions and user demands in real time. By continuously learning from its environment, the load balancer optimizes server selection to minimize latency, enhance resource utilization, and improve overall system performance. Experimental results demonstrate significant advantages over existing methods in terms of scalability, adaptability, and efficiency.

Dataset: Google Cluster Data 2019 Sample

The Azure-Based File Sharing Web Application is a simplified yet effective solution for sharing files among friends and colleagues. Utilizing the robust and scalable Azure cloud platform, this web application provides a secure and user-friendly environment for file management and sharing.

Core Features:

1. File Storage Using Azure Blob: The application leverages Azure Blob storage for storing uploaded files. Azure Blob is known for its high availability, security, and scalability, making it an ideal choice for handling diverse file types and sizes.
2. SMTP for Link Sharing: The application incorporates Simple Mail Transfer Protocol (SMTP) to facilitate the sharing of file links. Users can easily send secure links to their files to friends or colleagues via email, enhancing the sharing experience while maintaining security.
3. User Account Creation: Users can create their own accounts, allowing for personalized experiences. Each user can manage their own files, track shared items, and customize their settings for optimal use.
4. Intuitive User Interface: The web application is designed with a focus on simplicity and ease of use. Users can navigate effortlessly through the application, upload files, and share them without requiring technical expertise.
5. Security and Privacy: Emphasizing security, the application ensures that all files are stored and transmitted securely. User privacy is also a priority, with robust measures in place to protect personal information and shared content.
6. Cross-Platform Accessibility: Being a web-based application, it is accessible from various devices and platforms, enabling users to share files on-the-go.

Use Case:

The application is ideal for individuals and small teams who need a straightforward, secure, and reliable way to share files. Whether it’s for collaborative projects, sharing personal media, or simply keeping files accessible across devices, this application offers a versatile solution.

Overall, this Azure-Based File Sharing Web Application represents a blend of modern cloud storage technology with user-friendly features, catering to the needs of users who require a simple yet efficient file sharing tool.

The project aims at bolstering the security framework within autonomous robotic systems by devising and implementing AI-driven encrypted communication protocols. In the face of evolving security threats, traditional cryptographic methods may falter, making a case for intelligent, adaptive solutions. The project explores the fusion of machine learning models with encryption mechanisms to foster a dynamically secure communication environment among robotic entities. Through a series of simulated and real-world tests, the proposed protocols demonstrated a significant enhancement in thwarting security threats and ensuring seamless communication. The successful implementation underscores the potential of AI in advancing the security landscape of autonomous robotic networks, laying a solid foundation for more secure, reliable, and intelligent robotic ecosystems in the future. 

This project proposes an advanced method for analyzing e-commerce product reviews, employing the BERT (Bidirectional Encoder Representations from Transformers) model to perform both sentiment analysis and aspect-based categorization. The focus is on processing the extensive Amazon Product Review dataset from Kaggle, aiming to not only classify reviews by sentiment - positive, negative, or neutral - but also to categorize them according to specific product aspects like quality, price, and usability. This dual approach marks a significant improvement over conventional review analysis methods, which often fail to capture the intricacies and specificities of consumer feedback. By leveraging the sophisticated language processing capabilities of BERT, the project aims to extract more nuanced and actionable insights from customer reviews. The anticipated result is a robust analytical tool for e-commerce platforms, enabling a deeper, more structured understanding of customer opinions and experiences. Such a tool has the potential to inform targeted product development, refine customer service strategies, and enhance overall customer satisfaction. This project represents a step forward in transforming the vast, unstructured dataset of customer reviews into a strategic resource for data-driven decision-making in the e-commerce sector.  

The VTU Question Classification System Based on BERT and Bloom's Taxonomy project is designed to address the pressing need for an advanced natural language processing (NLP) system that can intelligently classify VTU (Verbs, Time, and Units) questions into their respective cognitive complexity levels based on Bloom's Taxonomy. By leveraging state-of-the-art deep learning techniques, particularly BERT (Bidirectional Encoder Representations from Transformers), this project seeks to offer a powerful solution to enhance educational assessment and align it with specific learning outcomes.

 Overview: This project aims to harness the power of data analytics and AI to revolutionize app monetization strategies and feature development in the Google Play Store. It consists of two interrelated components: the development of a Dynamic Pricing Model for Paid Apps, and an App Feature Success Analyzer.

Objective:

1. Dynamic Pricing Model: To devise an AI-driven model that dynamically suggests optimal pricing for paid apps based on market trends, user demand, and app characteristics.
2. App Feature Success Analyzer: To analyze user reviews and ratings to uncover key app features that drive success and user satisfaction.

Methodology:Utilizing the datasets from the Google Play Store, the project involves:

• Data Preprocessing: Cleaning and normalization of the datasets, including NLP preprocessing for user reviews.
• Dynamic Pricing Model: Employing machine learning algorithms like Random Forest or Gradient Boosting to predict optimal app pricing, with an exploration into Reinforcement Learning for adaptive pricing strategies.
• Feature Success Analysis: Implementing NLP techniques to extract app features from user reviews and conducting sentiment analysis to understand user preferences. This is complemented by statistical analysis to correlate these features with app success metrics like ratings and downloads.

Innovation:The project stands out in its comprehensive approach, blending predictive analytics with market and sentiment analysis. The dynamic pricing model will be informed by real user data and market conditions, while the feature success analyzer will provide a deeper understanding of user preferences and their impact on app success.

Expected Outcomes:

• A robust dynamic pricing strategy that adapts to market changes and maximizes revenue.
• Detailed insights into which app features resonate with users, guiding developers in feature prioritization and enhancement.
• A comprehensive report and presentation detailing methodologies, insights, and practical implications for app developers and marketers.

Impact:This project is poised to offer valuable contributions to the field of app development and digital marketing. By intelligently analyzing market data and user sentiment, it aims to aid developers in making data-driven decisions for pricing and feature development, ultimately leading to increased user satisfaction and revenue generation in the competitive app market.

  The TA Management Suite is an innovative solution designed to address the multi-faceted challenges of managing Teaching Assistant (TA) allocations in higher education institutions. The suite centralizes and simplifies the application, selection, and performance review processes through a user-friendly web-based interface.

The system caters to four primary user groups: TA applicants, department staff, TA committee members, and instructors, each with tailored functionalities. TA applicants can register, submit applications, and track their status; department staff can manage course listings and perform preliminary TA-course matches; committee members review and finalize TA assignments; and instructors provide performance assessments.

Developed using the Flask framework in Python, the suite leverages a SQL database for robust data management and integrates with Azure for cloud-based functionalities, ensuring scalability and security. The design process incorporated iterative feedback, and the testing phase emphasized thorough unit, integration, and user acceptance tests to ensure reliability.

The Executive Summary encapsulates the core value proposition of the TA Management Suite: a streamlined TA management process that enhances the educational infrastructure's efficiency and effectiveness.

In addressing the challenges of counterfeiting and scalping in traditional event ticketing systems, this project proposes a Python-based ticketing system that employs Non-Fungible Tokens (NFTs). The use of NFTs ensures ticket authenticity and uniqueness, addressing key issues such as ticket fraud. This system enhances security and transparency, and introduces functionalities like peer-to-peer ticket transfer, real-time compliance checks, and detailed analytics, while maintaining a user-friendly interface. This approach differs from conventional methods by integrating the advantages of blockchain technology with the flexibility of Python, offering a practical and improved solution in the realm of event ticketing that focuses on security, user privacy, and operational efficiency.