This topic is an attempt to develop an open source vehicle simulator for use by anyone needing realistic vehicle data delivered as it would be in a real vehicle. As you probably know, most vehicles nowadays have an On-Board Diagnostic (OBD) connector which is wired up to the car’s internal computer. It is used in many garages where the mechanic can probe the car through the OBD connector and read out parameters onto a display. In an Internet of Things (IoT ) world where the car can be easily connected to the Internet (perhaps via Bluetooth to a smart phone), it could automatically search an online knowledge base and not only report a fault code but also let you know the most likely cause either based on the car’s personal history or on the environment (an expert system could conclude “it is minus 15 degrees Celsius outside, and there is a water leak, and it is likely to be a cracked hose due to the cold temperature – and there was a manufacturer recall notice concerning this hose”). There will be whole sectors of applications such as, safer driver operation of the vehicle, fewer distractions, more automation, safer mechanical performance, better & more timely maintenance warnings, lower cost operation and lower maintenance costs.
Sentiment analysis is a rapidly growing field in natural language processing that aims to extract subjective information from text data. One of the most common applications of sentiment analysis is in the movie industry, where it is used to gauge public opinion on films. In this research paper, a sentimental analysis of movie reviews has been presented using a dataset of over 25,000 reviews collected from various sources. A machine learning model with different classifiers was built using Naïve Bayes, Logistic Regression and Support Vector Machines for classifying movie reviews as positive, negative or neutral. A comparison of three popular machine learning algorithms was made. After pre-processing the dataset by removing stop words, a stemming technique was applied to reduce the dimensionality of the dataset. The recognition algorithms were evaluated in terms of performance matrices such as accuracy, precision, recall and F1-score. Compared to others, it was observed that the SVM algorithm performed the best among all three algorithms, achieving an accuracy of 73%. The results of this analysis demonstrated the effectiveness of the model in accurately classifying movie reviews and provided valuable insights into the current state of public opinion on films. The comparison of the three algorithms provided insight into the best algorithm to be used for a specific dataset and scenario.
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