Deployment of Artificial Intelligence (AI) in daily life will show a new way to the future, the complex task will become easy. One of such evolution of AI technology is the creation of the Self-Driving cars. But according to the statistics, there are some cases where the Self-Driving cars killed people due to the failure in the prediction or hardware problem. So, to overcome those kinds of situations the AI models should be accurate and also humans should take part in making them perfect. For example, when there are some passengers in a self-driving car and the car has lost its control due to the fault in the hardware of the computer installed in the car then an authorized person in the car should be able to instruct it using voice commands to control the car at that moment. The main focus of this paper relies on the development of Self-Driving Cars using the Convolutional Neural Networks (CNN), drawbacks and solutions for the drawbacks. The Self-Driving Car CNN model was trained using the Asphalt-8 game data and the Voice command prediction CNN model was trained with 3 different persons (1-Kid, 1-Man, 1-Woman) voices. The accuracy of both the CNN models were 99% and was tested on the same game where they have produced their best results.

In the human body, the most important and the complex organs work with billions of cells in the brain. The abnormal growth or uncontrolled division of cells around the brain will cause a brain tumor. These group of cells which affect the functioning of the brain and also destroys the human cells. In the olden days, the detection of brain tumors is way much harder than nowadays. The usage of modern computer vision techniques has made the detection to be more accurate and easy. In this paper, firstly the detection of tumor in the brain was performed using a Sequential Neural Network (SNN) model which classifies the symptoms, as the brain tumor and then Magnetic Resonance Images (MRI) Scans are used for the further confirmation. The SNN model has an accuracy of 99.36% whereas the Convolutional Neural Network (CNN) Model used in this paper is 99.89% accurate.

Sentiment Analysis (SA) is nothing but mining the emotion from many sources. Some of them include texts, audio, video, etc. Every individual has their own opinion, hence own reviews and ratings. Based on these reviews if we classify the sentiment of the opinion of the public, the profit or loss calculations on the product or application is directly found. Our algorithm takes Naïve Bayes (NB) as a foundation of classification of textual data taken from the public and categorizes the tweets accordingly. To this, we are adding an organic emotion factor called Average Impact Factor (AMF). In the market, there were several algorithms which can be used in mining the sentiment from the given textual data. But this sentiment has flaws as it cannot detect the true emotion from the text or it overfits the opinion of the public. Based on this idea, we integrated the AMF on the public tweets and reviews to evaluate the true sentiment and to improve the time factor too of the opinion mining. We used data of tweets related to Demonetization that happened in India, 2016. When compared to NB Classifier and Support Vector Machine (SVM) algorithms, there is an improvement in time constraint and accuracy too in our Integrated Sentimental Analysis (ISA) classifier.

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