Fleet Management System

This project presents a smart and secure technology solution that combines Artificial Intelligence, IoT, and embedded systems to create an efficient automated system. The system uses advanced sensors, microcontrollers, and software integration to provide real-time monitoring, intelligent decision-making, and remote control features. It is designed to improve security, automation, and user convenience through a reliable and modern approach.

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.

### 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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