Biodiesel is the new form of automotive fuel that the world is now concerning and several researches are going on for the production of an efficient form of Bio-Diesel because of the fact that Diesel and Petrol are going to be exhaustible in nearly 60 years. In order to produce an efficient fuel, it is inevitable to calculate the emission characteristics concerning the fuel. This project deals with the efficient and intelligent way of analyzing and calculating the engine emission characteristics of Bio-diesel operated IC engines. A Machine Learning based model using TensorFlow library has been developed using python programming for the calculation of emission characteristics such as Carbon-monoxide (CO) and Carbon-dioxide (CO2) of an IC engine upon injection of Bio-diesel as fuel in different proportions. These investigations and data-sets are considered for a four stroke internal combustion engine. In this Machine Learning model TensorFlow library has been used for the better visualization of the results and error rectification. The results of the developed TensorFlow model are then compared with an existing Fuzzy model for the same application. The results predicted by this model clearly are in good correlation with the actual values which depicts that this method is effective and the total error of the developed model was found to be ±0.02 which is comparatively lower than that of the existing Fuzzy model. Concludingly, the Machine learning model using TensorFlow was found to be the best model for the calculation of engine emission characteristics of Bio-diesel operated IC engines as it offers more visualization tools and better predictive analysis.

The challenges in classical way of inventory log are human mathematical ability, paper maintenance, untidy process of editing and chances of missing the data of invoice and outward. The proposed idea is to develop an inventory management application using Tkinter and SQLite platform that creates a way towards progressively from traditional applications to a fully connected and customized inventory management system. This application uses password encryption to ensure the data privacy between the administrators and other workers of the industry. The system utilizes the integration of Tkinter and SQLite for the effective Graphical User Interface interfaced with Relational Database Management System. This customized application enables the administrator to add a new employee or remove the existing employee, he can update the stock quantity, and can back up the invoice history. Due to the login feature, the details of the product outward such as who withdrew and when it was withdrawn are automatically stored in the database. This digitization of inventory management system increases flexibility, reliability, smart storage, resource utilization, easy access to product location and warehouse management. It would be one time investment no further investment needed if at all any problem or errors occur in the app by human mishandling. The application saves us a lot of time because it uses SQL in it which is structured language, the data can be retrieved soon without any time delay and the human errors are avoided due to its structured nature.