It is absolutely not safe to drive in a punctured tire as there are chances of losing the control over a vehicle. Keep running with puncture will definitely destroy the tire that cannot be fixed any sooner. Until you keep your vehicle on a leveled surface you are safe. Driving vehicle on an unlevelled surface can immediately destroy your tire. Straight driving is safe with a punctured tire as turning and twisting your tire needs more pressure and struggle. If the air leaks slowly due to puncture (front or rear) the scooter will start to wobble and you can come to safe halt but if the air leaks instantly because of tube or tyre (in case of tubeless tyre) bursting you are most likely to fall and if its front tyre then it would be dangerous because the steering may turn to any side before you know anything. Keeping in mind the above difficulty faced by the rider during the vehicle’s tyre been punctured and inability to handle the situation safely, a portable emergency drive for two wheelers will be fabricated as accessory, that can help the person to slowly run the vehicle safely to a destination, were it can get repaired without any damage to the tyre/s.

This paper presents the implementation of separating the linear and non-linear data using support vector machine (SVM) algorithm. First let us understand what linear and non-linear datasets are. Linear datasets are the data that can be easily separable using a straight line. Such data are usually easy to implement in Artificial Neural Networks as they require a smaller number of hidden layers for its computation. Less layers implies a smaller number of weights assigned to the nodes present between the layers and less amount of time needed is needed to compute and update the weights for the current neural network. Hence linear datasets are easy to train and model. Whereas non-linear datasets are those datasets which cannot be separated by a straight line. For such datasets more hidden layers and weights are required and also more time and computational power is needed for a system to update the weights and train the model to give a better and sophisticated output data. As a result, training and modeling such neural network is tedious due to its complexity. To solve this problem SVM comes into picture. SVM stands for Support Vector Machine which is a machine or an algorithm that helps in classification and diversifying the data given to it. The data provided to the Support Vector Machine (SVM) should be a labeled one. Then these datasets are given to a training model where the training process of the neural network is being undergone. Once the training is completed, the next step is to predict the output. For this process we have to provide a new data that may or may not belong to the dataset, so that the neural network can predict the output of it. If the prediction is wrong, again the training is done until we get the actual output matching with the desired output given by the designer for verification purposes. This is the basic working process under the SVM algorithm. The linear data that is used for this separation is an Iris dataset that contains various information about the different plant-life growing from 2002-2004.

To find solution for XOR problem we are considering two widely used software that is being relied on by many software developers for their work. The first software is Google Colab tool which can be used to implement ANN programs by coding the neural network using python. This tool is reliable since it supports python language for its implementation. The other major reason is that we can use GPU and TPU processors for the computation process of the neural network. The major advantage of this is that for complex Neural Networks instead of using CPU present in the user’s system we can use those two processors through online mode without purchasing such processors for our computation. The next software that can be used for implementing ANN is Matlab Simulink. This software is used for highly calculative and computational tasks such as Control System, Deep Learning, Machine Learning, Digital Signal Processing and many more. Matlab is highly efficient and easy to code and use when compared to any other software. This is because Matlab stores data in the form of matrices and computes them in this fashion. Matlab in collaboration with Simulink can be used to manually model the Neural Network without the need of any code or knowledge of any coding language. Since Simulink is integrated with Matlab we can also code the Neural Network in Matlab and obtain its mathematically equivalent model in Simulink. Also, Matlab has a dedicated tool in its library to implement neural network called NN tool. Using this tool, we can directly add the data for input, desired output, or target. After inserting the required data, we can train the network with the given dataset and receive appropriate graph’s which will be helpful in analyzing the data. Once the Neural Network is trained, we can test the network through this tool and verify the obtained results.