The rapid advancement in artificial intelligence and data analytics has opened new avenues for enhancing vehicle safety and management systems.

The rapid advancement in artificial intelligence and data analytics has opened new avenues for enhancing vehicle safety and management systems. Our project, titled "AI-Based Vehicle Monitoring System," aims to leverage these technologies to monitor and analyze vehicle data in real-time, thereby improving riding quality and ensuring driver safety.
The primary objective of this project is to continuously monitor various vehicle parameters such as speed, temperature, acceleration, Location, Fuel Consumption and driver health. By integrating sophisticated AI algorithms, the system can detect anomalies and potential signs of accidents early. This proactive approach not only helps in preventing unusual riding such as sudden acceleration/ declaration, over speeding, drunk driving, overload or unusual fuel consumption but also contributes to overall vehicle maintenance and performance optimization.
The system collects data through a network of sensors installed in the vehicle. This data is then transmitted to a central server where it is analyzed using machine learning models to identify patterns and potential risks. The real-time analysis enables timely interventions, providing alerts to the driver and relevant authorities in case of emergencies.
Our findings indicate that the AI-based system significantly enhances the accuracy of early accident detection, thereby reducing response times and potentially saving lives. Additionally, the system offers valuable insights into driving behavior and vehicle conditions, facilitating better decision-making for vehicle maintenance and fleet management.

1. NATIONAL CONFERENCE ON EMERGING APPROACHES TOWARDS SUSTAINABILITY 2024 Smart Plant Guard: IoT Based Plant Water Monitoring System Authors Abhirup Dutta Sumanta Dey Abhishek Saha Mita Halder Abstract ID: 33

2. Introduction Objective Methodology Result analysis Conclusion References NCEATS 2024 2

3. NCEATS 2024 3 Introduction Conventional Watering Methods use a lot of water and needless to say, a lot of it is wasted on a daily basis. With the help of advanced methods of watering, which should ideally use just the correct amount of water needed for the plants, we could save a considerable amount of water. This system uses ESP32 wifi module and Moisture sensor to provide adequate amount of water needed by plant This system works automatically without any human intervention

4. NCEATS 2024 4 Objective The objectives of the Plant Water Monitoring System project are as follows: 1. Real Time Soil Moisture Monitoring 2. Automated Watering System 3. Environmental Monitoring 4. User Interface Development 5. Manual Control Capability 6. Energy Efficiency Optimization 7. Data Logging and Analysis By achieving these objectives, the project aims to create an efficient and user friendly plant water monitoring system that promotes healthier plant growth and simplifies plant care for users.

5. NCEATS 2024 5 Methodology 1. Requirements Analysis: Define the specific requirements and functionalities of the system. 2. Component Selection: Choose appropriate hardware components such as the ESP32 microcontroller, soil moisture sensor, temperature and humidity sensors, water pump, tubing, power supply, and enclosure based on project requirements and compatibility. 3. Circuit Design and Prototyping: Design the circuitry to interface the selected components. Prototype the circuit on a breadboard. 4. Software Development: Program the ESP32 microcontroller using the Arduino IDE to read data from the sensors. Develop an IoT application using platforms like Blynk 5. Integration and Testing: Integrate the hardware components with the software system, ensuring proper communication and functionality. Conduct thorough testing of the system. 6. User Interface Refinement: Refine the user interface of the IoT application based on user feedback, ensuring ease of use and intuitive navigation. 7. Monitoring and Maintenance: Monitor the system's performance over time, including soil moisture levels, environmental conditions, and overall functionality. and Perform regular maintenance tasks

6. NCEATS 2024 6 Flowchart

7. NCEATS 2024 7 Result Analysis: Fig 1 . Testing of a proposed system in real-time Fig 2.  Normal conditions of Plant monitoring on Blynk app.

8. NCEATS 2024 8 Result Analysis: The system successfully monitors soil moisture levels, temperature, and humidity in real-time using the ESP32 microcontroller and sensors(Fig 1). Automated watering functionalities are implemented, ensuring plants receive water when soil moisture levels fall below predefined thresholds. A user-friendly IoT application is developed, enabling users to remotely monitor soil moisture, temperature, and humidity levels and control the system as needed(Fig 2).Manual control options are integrated into the user interface, allowing users to initiate watering manually or adjust system settings. Overall, the results and analysis demonstrate that the Plant Water Monitoring System effectively automates plant care tasks, improves plant health, and provides valuable insights for optimized plant growth and maintenance.

9. NCEATS 2024 9 Conclusion: The IoT-based Smart Plant Monitoring System is a powerful and versatile system that allows users to monitor and control various aspects of their plants remotely. It is a highly customizable and cost-effective solution for monitoring and controlling plants remotely. The system can be easily integrated with other devices and platforms and can be used for a wide range of applications, such as greenhouse monitoring, crop monitoring, and irrigation control. With the help of this system, the user can keep track of the temperature, humidity, soil moisture, and other important parameters of their plants and take appropriate actions to ensure the optimal growth and health of their plants. The system can be extended further by adding more sensors or actuators to monitor and control other aspects of the plants.

10. NCEATS 2024 10 References: Hati, A.J.; Singh, R.R. Smart Indoor Farms: Leveraging Technological Advancements to Power a Sustainable Agricultural Revolution.  AgriEngineering   2021 ,  3 , 728–767. [ Google Scholar ] [ CrossRef ] Chen, C.-H.; Jeng , S.-Y.; Lin, C.-J. Fuzzy Logic Controller for Automating Electrical Conductivity and pH in Hydroponic Cultivation.  Appl. Sci.   2022 ,  12 , 405. [ Google Scholar ] [ CrossRef ] Shirsath , D.O.; Kamble , P.; Mane, R.; Kolap , A.; More, R.S. IOT Based Smart Greenhouse Automation Using Arduino.  Int. J. Innov . Res. Comput . Sci. Technol.   2017 ,  5 , 234–238. [ Google Scholar ] [ CrossRef ] Zhang, X.; Zhang, J.; Li, L.; Zhang, Y.; Yang, G. Monitoring Citrus Soil Moisture and Nutrients Using an IoT Based System.  Sensors   2017 ,  17 , 447. [ Google Scholar ] [ CrossRef ] [ PubMed ] Muangprathuba , J.; Boonnama , B.; Kajornkasirat , S.; Lekbangpong , N.; Wanichsombat , A.; Nillaor , P. IoT and agriculture data analysis for the smart farm.  Comput . Electron. Agric.   2019 ,  156 , 467–474. [ Google Scholar ] [ CrossRef ] Nobrega , L.; Golcalves , P.; Pedreiras , P.; Pereira, J. An IoT-Based Solution for Intelligent Farming.  Sensors   2019 ,  19 , 603. [ Google Scholar ] [ CrossRef ] [ PubMed ] Touseau , L.; Le Sommer, N. Contribution of the Web of Things and the Opportunistic Computing to the Smart Agriculture: A Practical Experiment.  Sensors   2019 ,  11 , 33. [ Google Scholar ] [ CrossRef ]