1. Condominium Security Enhancement with Computer Vision

Project Overview:

Implemented an advanced security system for a condominium complex, leveraging computer vision for facial recognition of residents and automatic number plate detection for vehicles. This innovative system enhances security measures, streamlines entry processes, and provides a seamless, automated experience for both residents and visitors.

Key Technologies:

• Facial Recognition for Residents: Utilizes state-of-the-art facial recognition technology to instantly identify residents, enabling automatic gate opening without the need for physical keys or access cards.
• Non-Resident Registration and Entry: Captures the faces of non-residents at the entrance, allowing for quick registration and authorized entry, managed through a secure, web-based dashboard.
• Automatic Number Plate Recognition (ANPR): Employs computer vision to detect and recognize vehicle number plates, facilitating automatic entry for registered resident vehicles and enhancing parking security.

Key Features:

• Automated Resident Identification: Provides swift, automated access for residents through facial recognition, reducing wait times and enhancing convenience.
• Visitor Management System: Integrates a visitor management system that captures and stores visitor data for security purposes, streamlining the entry process for guests.
• Real-Time Security Monitoring: Offers real-time monitoring capabilities, including instant alerts and notifications for security breaches or unauthorized access attempts.
• Comprehensive Reporting: Generates detailed reports on entry and exit patterns, visitor logs, and security incidents, aiding in the efficient management of condominium security.

Impact:

• Enhanced Security and Surveillance: Significantly improves the security of the condominium complex with advanced surveillance and access control measures.
• Increased Operational Efficiency: Reduces the need for manual security checks and entry processes, freeing up resources for other critical security tasks.
• Improved Resident Satisfaction: Elevates the living experience for residents by providing a secure, efficient, and technologically advanced living environment.

2. Real-Time Social Media Analytics for PR Agencies

Project Overview:

This project entails the development of a cutting-edge social media analytics platform for PR agencies, leveraging Microsoft Fabric, OpenAI's generative AI technologies, and PySpark for efficient data engineering. The platform is designed to aggregate, analyze, and summarize social media posts across various platforms in real-time, providing PR agencies with instant insights into public sentiment and engagement related to their clients.

Key Technologies:

• Microsoft Fabric: Utilized for building a robust and scalable cloud infrastructure capable of handling large volumes of data.
• OpenAI and Generative AI: Employed for advanced text analysis, including sentiment analysis, summarization, and content generation.
• PySpark: Used for fast, distributed data processing, enabling the handling of vast datasets from multiple social media platforms efficiently.

Key Features:

• Real-Time Data Collection: Automates the collection of social media posts using PySpark, ensuring efficient processing of large data volumes in real time.
• Client-Specific Insights: Classifies and tags social media posts according to the relevant client, utilizing AI for accurate identification and categorization.
• Sentiment Analysis: Applies generative AI models to assess the sentiment of social media posts, providing PR agencies with a gauge of public perception.
• Content Summarization: Offers concise summaries of social media posts, enabling quick understanding of content trends and key discussion points.
• Interactive Dashboard: A user-friendly dashboard presents processed data, sentiment analysis, and summaries, allowing for real-time monitoring and strategic decision-making.

Impact:

• Empowers PR agencies with the capability to monitor social media landscapes comprehensively and in real time, ensuring that they stay ahead of public sentiment and trends.
• Enhances strategic communications planning by providing detailed insights into the effectiveness of PR campaigns and public engagement.
• Facilitates targeted and timely responses to emerging trends, sentiment shifts, and public discussions, significantly improving client representation and image management.

3. Computer Vision: Employee and Student Attendance System

Project Overview:

Developed an automated attendance management system using computer vision and facial recognition technology to accurately track employee and student attendance in real-time. This system was designed to replace traditional manual attendance methods, improving accuracy and efficiency.

Key Features:

• Facial Recognition Attendance: Utilizes advanced facial recognition algorithms to identify individuals and record attendance automatically.
• Real-Time Tracking: Offers real-time attendance tracking and reporting, accessible via a web dashboard.
• Alerts and Notifications: Sends automatic alerts for absentees and generates weekly/monthly attendance reports.

Impact:

• Significantly reduced time and resources spent on manual attendance tracking.
• Enhanced security and accuracy in attendance management.
• Provided valuable data insights for attendance patterns and punctuality.

4. Machine Learning: Student Course Selection Advisor

Project Overview:

Implemented a machine learning-based advisor system to assist students in selecting courses based on their interests, academic performance, and career goals. The system also predicts sales conversion rates, the potential academic performance in selected courses, and the probability of securing employment post-completion.

Key Features:

• Personalized Course Recommendations: Analyzes student profiles using machine learning to provide personalized course recommendations.
• Performance and Sales Forecasting: Predicts the likelihood of a student enrolling in recommended courses and their potential academic outcomes.
• Employment Probability Estimation: Estimates the probability of a student securing a job after course completion, based on course selection and performance predictions.

Impact:

• Improved student satisfaction through tailored course recommendations.
• Enabled data-driven decision-making for course offerings and marketing strategies.
• Enhanced the institution's ability to provide career-oriented education.

5. NLP and Machine Learning: Predictive Analytics for Student Success and Placement

Project Overview:

Developed a predictive analytics platform that leverages NLP and machine learning to analyze student essays, feedback, and academic records to predict academic success, suitability for specific courses, and job placement probabilities.

Key Features:

• Text Analysis for Course Suitability: Uses NLP to analyze student submissions and feedback to determine their interests and suitability for certain academic paths.
• Academic Performance Prediction: Applies machine learning models to predict a student's potential academic performance in various courses.
• Job Placement Probability: Integrates with job market data to predict the likelihood of job placement in relevant fields post-graduation.

Impact:

• Empowered academic advisors with tools to guide students more effectively.
• Enhanced student engagement and motivation by aligning courses with interests and job market trends.
• Improved placement rates through targeted career advice and course selection.