We are living in an age where tasks and systems are fusing together with the power of IoT to have a more efficient system of working and to execute jobs quickly. In the recent decades, Urbanization has increased tremendously. At the same phase there is an increase in waste production. Waste management has been a crucial issue to be considered. This paper is a way to achieve this good cause, due to negligence of authorities and carelessness of the public may lead to long term problems. Breeding of insects and mosquitoes can create nuisance around promoting an unclean environment. This may even cause dreadful diseases. The main objective of the project Trash Bot will keep the environment clean and also eco friendly. If the dustbin is not maintained then these can cause an unhealthy environment and can cause pollution that affects our health. Since the technologies are getting smarter day by day to clean the environment, we are designing a smart dustbin by using Arduino Opens and closes its lid if it sees any trash in front of it and the lid of the can will open automatically and will wait for you to feed it more than after a certain delay it will automatically close. This will help toward health and hygiene. In this project we have designed a smart dustbin using ARDUINO UNO, along with an ultrasonic sensor, servo motor, and battery jumper wire. After all hardware and software connection, now Smart Dustbin program will be run. Dustbin lid will open automatically when someone comes near. Then will wait for the user to put garbage and close it. The main advantage in terms of society is that it will help toward health and hygiene.
Sentiment analysis is one among the distinguished fields of knowledge and pattern mining that deals with the identification and analysis of sentiment within the text. The main challenges in sentiment analysis are word ambiguity and multi polarity. The problem of word ambiguity is to define polarity because the polarity for words is context dependent. The tweets are initially preprocessed. The preprocessing includes the removal of stop words, and lower case conversion. The tweets are then passed to the feature extraction techniques. Then the data is splitted as training and testing data. The trained data is passed to the different machine learning algorithm like Naive Bayes. Support Vector machine, Random forest, and Decision Tree and k-NN algorithm. The accuracy obtained using the Naive Bayes. Support Vector machine, random forest, and Decision Tree, k-NN and Logistic regression algorithm is 80%, 77%, 72%, 61% ,56% and 78%. The naïve bayes algorithm has achieved a better accuracy when compared to the other algorithm. KEYWORDS: SVM, Naive bayes, Decision tree, Random forest
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