Volume 9 | Issue 11 |

 Abstract

The Internet of Things (IoT) inevitably changes the way organizations communicate and organize day-to-day business and industry processes. Its adoption has proven to be well suited to sectors that hold large amounts of assets and integrates complex and distributed processes. This study analyzes the enormous potential of using IoT technologies (i.e., data-driven applications or embedded automation and intelligent dynamic systems) to transform modern wars and offer similar benefits to those in the industry. Stock analysis using data mining will be useful for new investors to invest in the stock market depending on the various aspects considered by the software. The stock market includes day-to-day operations such as Sensex calculations, stock exchanges. The exchange offers an efficient and transparent market for equity trading, debt instruments and acquired assets. Our software will analyze Sensex based on the company's stock price. So in this project we will use a python based system on Raspberry Pi (IoT). The app will basically monitor the stock prices in the stock market. Also try to make a trader analysis, that it is easier for the stock market trader to choose a particular stock / buy stock.

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 Keywords

Stock Market Monitoring Using Raspberry Pi

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Nowadays, the prediction of share market prices and conditions has become a major researched topic amongst the data scientists, investment bankers, and stock brokers. As, the behavior of share market is very nonlinear and volatile in nature, it makes a very high-risk investment. Consequently, a lot of researchers have came up with their efforts to forecast the share market and average movement. Researchers have used various methods in computer science and economics in their illustrate to gain a piece of this volatile information and make great fortune out of the share market investment. The approaches like data mining and machine learning approaches can incorporate into Business Intelligence (BI) systems to help users for decision support in many real-life applications. This paper presents the brief survey of application of machine learning in share market prediction and investigates various techniques for the share market prediction using like Artificial Neural Network (ANN) and Support Vector Machine (SVM). ANN is non- linear and non- parametric classifier which is viable for forecasting of share prices. Support Vector Machine focuses on marginal values rather than average values for the classification predicting model. The aim of this paper is to provide a review of the application of machine learning in share market prediction to determine what can be done in the future.

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 Keywords

Share Market, Machine Learning

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The emotions, can be defined in simple words are what people feel. The face is probably the simplest approaches to separate the individual personality of one another Face recognition is a personal identification system that uses personal characteristics of a person to identify the person's identity and its Facial expressions. There are methods to identify expressions using machine learning and Artificial Intelligence techniques, this work attempts to use deep learning and image classification method to recognize facial expressions and classify these expressions according to the images. Various datasets are traversed for training expression recognition model are explained in this paper. We have used for expression recognition with FER2013 (Facial Expression Recognition).

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Face Emotion Recognition, Face Detection, Feature Extraction, Classification, Convolutional Neural Network

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 Abstract

Perceiving feeling from voice has become one the dynamic examination subjects in discourse preparing and in applications dependent on human-PC connection. This venture leads a trial concentrate on perceiving feelings from human discourse. The feelings considered for the analyses incorporate nonpartisan, outrage, happiness and pity. One of the principle highlight characteristic considered in the pre-arranged dataset was the top to-top distance acquired from the graphical portrayal of the discourse signals. The motivation behind this undertaking is to get ease acknowledgment of different feelings in voice utilizing python.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

MCQ , NLP , BERT , WordNet

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

It is hard for visionless individuals to peruse any kind of text like items or labels and more. In this manner, the advancement of framework that can give a sound yield to themis fundamental so they can undoubtedly move and take care of their job with no kind of hindrance. This application is for visionless individuals mainly for shopping so that it provides details written on tag through speech. Not many items can't keep going forever, particularly with regards to food and medicines it is important to know the the description, manufacture also, expiry date of items while purchasing. To give customers a sign of when the itemshould be utilized by, an expiry date is printed on the item. While remaining at home alone, if the outwardly disabled burns-through some terminated food or takes some terminated prescription, the outcome could even be life undermining. Keeping this in mind, this application depicts the advancement of Quick Response Code and items recognition through speech. Hence we have created an application by distinguishing QR code fromwhich the client gets data of items through discourse with the assistance of text-to-speech. From this application the clients can buy the items easily through earing the product information.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

QR Code, Visionless, text, speech

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

A Sentiment Analysis Classifier web-app, made using PyTorch and deployed in AWS with SageMaker. A required project for the Udacity Machine Learning Engineer Nanodegree. Sentiment Analysis web app is a notebook and collection of Python files to be completed. The result is a deployed RNN performing sentiment analysis on movie reviews complete with publicly accessible API and a simple web page which interacts with the deployed endpoint. This project assumes that you have some familiarity with SageMaker. Completing the XGBoost Sentiment Analysis notebook should suffice. The notebook and Python files provided, once completed, result in a simple web app which interacts with a deployed recurrent neural network performing sentiment analysis on movie reviews. This project assumes some familiarity with SageMaker, the mini-project, Sentiment Analysis using XGBoost, should provide enough background.

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Deployment A Sentiment Analysis Recurrent Neural Network Model Using Pytorch On Aws Segmaker.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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BLOCKCHAIN BASED ELECTRONIC HEALTHCARE RECORD MANAGEMENT SYSTEM

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Sentiment analysis refers to opinion mining and a machine learning task where one would like to work out what theoveral sentiment of a given document is. Using natural language processing and model training we will extract the subjective information of tweets froma particular dataset and checkitout to classify it consistent with its polarity like positive,neutral,or negative. The papers suggest a model created usingtheLSTM algorithmand some additional other layers of machinelearning for better sentiment analysis. Even though sentimentanalysis is really far away from being solved since the language is extremely complex because of objectivity/subjectivity, negation, vocabulary, grammar still this sequential model achieves the accuracy of approximately 81%. Thus , this work shows that there wide applications yet to be done.

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 Keywords

Twitter, Sentiment Analysis, Natural Language processing, Long Short Term Memory (LSTM)

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Deep Learning, Neural Networks, Machine Learning, Business intelligence, Sentimental analysis.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

With increase in the popularity on social media and millions and billions of people using it every day. This popularity of applications like Facebook, Twitter and Instagram grabs the attention of spammers. As through these they can trap genuine users with malicious activities. There is a significant amount of research done in this field. The primary focus of these researches is generally based on the accounts or users whose activity poses them as suspicious. These activities include posting of the same content, posting tweets that have no relevance to the trending topics and tagging them as one of the trending topics, sending bulk direct messages or users that have similar contents and are created on the same day. However, much of the research done focuses on spam accounts. There is little to none research done based on a model that marks tweets as spam and along with a sentiment analysis. In the proposed system, we propose a Machine learning system that would detect tweets as spam or ham. This spam detection would be done considering factors such as: shortened URLs, Emails that lead to malicious sites etc. The tweets would also be marked as spam based on the language. Using NLP, we would form a system that would mark tweets as spam if they have the potential to hurt sentiments of other users. The model would be trained and tested on a previously labelled dataset. This model would then be incorporated in a website that would take tweets as an input from the user. The result would be creation of a model that would give the tweet as spam or not based on the sentiment and spammer tactics.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Machine Learning, Spam Tweets, Ham Tweets, Sentiment Analysis, Natural Language Processing, Logistic Regression , Decision Tree, Random Forest, K - nearest Neighbour, Datasets, Feature Extraction

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Creative Commons Attribution 4.0 and The Open Definition