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We are developing an intelligent parking management system that combines Artificial Intelligence, IoT, and Web Technologies to provide a smart and automated parking solution. The system uses AI-based vehicle detection, number plate recognition, ESP32 controllers, online payment integration, and Google Navigation to improve parking efficiency and user experience.

We developed a real-time Computer Vision system that can automatically detect bottles, identify whether a bottle has a cap, and recognize its color using a live webcam feed. This project combines YOLOv8 object detection with OpenCV image processing to create a practical AI-based inspection system for industrial automation and quality control.

### Research & Description This project focuses on the development of a **Smart Vertical Parking System using Artificial Intelligence, Computer Vision, and IoT technology**. The main purpose of the system is to solve parking space problems in crowded areas by using vertical parking structures and intelligent vehicle detection. Traditional parking systems usually require more land, manual supervision, and extra time for drivers to find an available space. This project introduces a smarter and more automated approach. During the research phase, the main focus was on vehicle detection, parking slot monitoring, vehicle alignment, IoT-based control, and real-time dashboard communication. The system uses a camera with **OpenCV** to monitor the parking area. The camera detects incoming vehicles and checks whether a parking slot is available. It also helps determine whether the vehicle is correctly aligned below the selected vertical parking position. Once the vehicle reaches the correct position, the software sends a control signal to the **ESP32**. The ESP32 can then operate relays, motors, indicators, or other connected hardware required for the parking mechanism. The project also includes a real-time web dashboard that can show important information such as: * Available parking slots * Occupied parking slots * Vehicle detection status * Vehicle alignment status * Entry permission * Parking system alerts * Manual control options The dashboard can be developed using **Flask, Firebase, and Tailwind CSS**, while Wi-Fi communication allows the ESP32 and software system to exchange data. Another important part of the research was parking safety. The system should not move the parking platform unless the vehicle is correctly positioned. It should also stop new vehicles from entering when all parking spaces are occupied. For better vehicle detection, the system can be upgraded with **YOLO or another object detection model**. This can help improve vehicle recognition in different lighting conditions and busy parking environments. The project combines several technologies, including: * Artificial Intelligence * OpenCV * ESP32 * IoT * Python * Flask * Firebase * Computer Vision * Relay and Motor Control * Real-Time Monitoring The final goal of the project is to create a practical smart parking solution that can save space, reduce manual work, improve parking efficiency, and support future smart city applications. This system can be useful in shopping malls, offices, hospitals, residential buildings, airports, underground parking areas, and other locations where parking space is limited.

This project is designed to improve the performance, safety, and efficiency of electric vehicle battery banks using AIoT technology. The system monitors important battery parameters and predicts battery conditions in real time. It also controls the cooling system automatically to help prevent overheating and improve battery life. The project combines IoT, embedded systems, sensors, automation, and intelligent prediction to create a smart EV battery management solution.

For this project, I researched how businesses collect, analyze, and manage customer feedback. Many companies receive feedback through forms, emails, surveys, support tickets, and online reviews, but manually reading and organizing every message can take a lot of time. The main idea of this project was to use n8n automation with AI to make this process faster and more useful. The workflow is designed to automatically receive customer feedback, send it to an AI model, and analyze the message based on sentiment, category, priority, and main issue. The AI can identify whether the feedback is positive, negative, a complaint, a feature request, a technical problem, or a general suggestion. After the analysis, n8n automatically sends the feedback to the relevant team or stores it in a connected platform such as Google Sheets, a CRM, Notion, or a database. During the research phase, I focused on several important areas: Customer sentiment analysis AI-based text classification Feedback categorization Priority and urgency detection Automatic summarization Workflow routing Team notifications Data storage and reporting Agentic AI decision-making The project also explores how Agentic AI can go beyond simple text generation. Instead of only analyzing the feedback, the AI helps decide what action should happen next. For example, if a customer reports a serious payment issue, the system can classify it as high priority and send it directly to the support or technical team. If a customer shares a feature request, the workflow can send it to the product team. The final goal of the project is to turn unstructured customer feedback into clear, organized, and actionable information. This helps businesses respond faster, reduce manual work, and better understand what their customers need.

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This project is an Arduino-Based Smart Automated Plant Irrigation and Water Management System designed to automatically monitor water availability, soil conditions, and irrigate multiple plants

This project develops an Arduino-based distance alarm system using an HC-SR04 ultrasonic sensor. The system continuously measures the distance of nearby objects. Green, yellow, and red LEDs indicate safe, warning, and danger zones, while a buzzer sounds when an object is too close. The project demonstrates how sensors, LEDs, and buzzers can work together through Arduino programming.

The AI-Enabled IoT System for Smart Surveillance and Security is a smart four-layer door-locking project designed to improve safety in homes, offices, laboratories, and other restricted areas. The system combines RFID card verification, password entry, fingerprint scanning, and face recognition before giving access. An ESP32 microcontroller controls the hardware, while Firebase stores and updates data in real time. A Flutter mobile application allows users to check the lock status, manage users, update passwords, and control the system remotely. This project brings together IoT, artificial intelligence, biometrics, cloud technology, and mobile development in one practical security solution.

This project is an IoT and OpenCV-based Smart Parking System designed to make parking easier, faster, and more secure. The system uses IR sensors and Arduino to detect available and occupied parking spaces in real time. Users can create an account through a Flutter mobile application, check parking availability, reserve a slot, view parking time, and make digital payments. OpenCV is used for vehicle number plate detection, while OTP verification provides secure access to reserved parking spaces. Firebase manages user accounts, reservations, payments, and real-time parking data. The system also records vehicle entry and exit times and automatically calculates the parking bill. Overall, this project reduces manual work, saves time, improves security, and provides a convenient parking experience for both users and administrators.

This project is an IoT and OpenCV-based Smart Parking System designed to make parking easier, faster, and more secure. The system uses IR sensors and Arduino to detect available and occupied parking spaces in real time. Users can create an account through a Flutter mobile application, check parking availability, reserve a slot, view parking time, and make digital payments. OpenCV is used for vehicle number plate detection, while OTP verification provides secure access to reserved parking spaces. Firebase manages user accounts, reservations, payments, and real-time parking data. The system also records vehicle entry and exit times and automatically calculates the parking bill. Overall, this project reduces manual work, saves time, improves security, and provides a convenient parking experience for both users and administrators.

Food Bridge is a smart food redistribution platform developed to reduce food waste by connecting food suppliers with needy institutions such as orphanages and old age homes. The system provides a structured digital solution where suppliers can list surplus food, consumers can browse and request available items, volunteers can support delivery and handover, and the admin can manage approvals and monitor the complete workflow. The platform supports both free and paid food options, secure payment integration, OTP verification, notifications, and role-based access. Food Bridge helps improve food distribution efficiency, reduces unnecessary waste, and creates a practical social impact through technology.

This project presents an AI-based smart road accident and fire detection system that monitors live camera feeds and detects critical incidents in real time using the YOLOv8 model. The system is developed with a web-based dashboard using Flask, HTML, CSS, and JavaScript, while Firebase is used for real-time data storage and station management. When an accident or fire is detected, the system automatically identifies the relevant station and sends alerts through SMS, phone call, and email using ESP32 and SIM800L. The proposed solution helps reduce emergency response time, improves road safety, and provides an intelligent automated monitoring system for modern traffic environments.

The Fleet Management System is a smart and integrated solution designed to improve vehicle monitoring, transport safety, and operational efficiency. The system provides a single platform for managing buses, ambulances, and cargo vehicles in real time. It combines GPS-based live tracking, geofencing, driver fatigue detection, digital ticket checking, goods tracking, fuel level monitoring, and door status alerts. The project includes a driver mobile application, an admin web dashboard, a Python-based backend, and Firebase for real-time data storage and synchronization. Hardware components such as ESP32, ultrasonic sensor, and EMR relay are used to collect and transmit live vehicle data. This system helps organizations improve route monitoring, driver safety, cargo security, and overall fleet control.

GLUCOTWIN is an AI-driven healthcare application developed to support diabetes management in children aged 3 to 12 years. The system helps guardians by predicting insulin dosage based on important health factors such as blood pressure, body temperature, BMI, blood sugar condition, meal timing, and carbohydrate intake. The application is built using Flutter and Firebase, while a trained machine learning model processes health inputs and returns insulin predictions in real time. To improve safety and reliability, doctors can review the predicted results and provide medical recommendations. The system offers a structured, user-friendly, and intelligent platform for better child diabetes care

This project presents a Smart Attendance System Mobile Application designed to improve traditional attendance methods in educational institutions. The system uses Flutter for the mobile app and Firebase for real-time data management. It ensures secure and accurate attendance by combining geofencing and face recognition using Raspberry Pi. The system verifies both the student’s location and identity before marking attendance, which helps prevent proxy attendance and reduces manual errors. It provides separate dashboards for Admin, Teacher, and Student, making the system efficient, user-friendly, and suitable for real-time academic management.

This project presents a smart Sign Language Recognition and Translation System designed to reduce the communication gap between sign language users and non-sign language users. The system supports two-way communication by converting text and speech into sign language and translating sign language into text and speech. It uses machine learning and computer vision techniques to recognize hand signs and gestures in real time. YOLOv8 is used for sign alphabet detection, while MediaPipe is used for hand landmark tracking and gesture analysis. The system also stores gesture patterns and hand angle data in JSON format for sentence-level recognition. A Flask-based web interface is used to provide an interactive and user-friendly experience. This project offers a practical solution for real-time communication, accessibility, and learning support.

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