Sight is viewed as the most significant sense and the visually impaired individuals are seen upon with feel sorry for by others. Innovation encourages the visually impaired individuals to speak with nature, the correspondence procedure and the dispersal of data has gotten quick and on a more extensive scale to incorporate all pieces of the world which incredibly influenced to the human life, subsequently expanding the methods for amusement and comfort and diminished affliction and hardship in numerous things. We have surveyed the existing solutions meant for autonomous mobility for the visually impaired people. In this paper, we have proposed a novel structure, Smart Shoes with sensors installed in them to control an outwardly debilitated individual smoothly and to alarm him/her of the impediments that lay in front of him in his way. The structure is meant to build up a simple to utilise processing power of Arduino in conjunction with the object detection capability of ultrasonic sensor to oblige the extraordinary needs, used to manage the individual coextending the highlights of the Smart Shoes.

Format has been changed for submitting it to IEEE LISS 2020 Conference.

This paper describes the author's vision in developing a flexible workflow infrastructure for enforcing the pragmatic interoperability in industries like manufacturing and healthcare. This vision is based on business continuity planning, web services interoperability, Node-Red and IFTTT workflow technologies.

Social media sites are gaining popularity day by day. They are best for communication, business, entertainment, and many other things. After more than a decade, social media have become very influential. On the flip side, fake news, hate speech, and online trolls are the biggest concerns because of social media. So, a solution to curb this issue is needed, especially in regional languages. Many social media platforms support regional languages. This paper will provide a machine learning-based solution to this problem. The focus of this paper is to classify comments written in regional languages. Firstly, a dataset has been created in Gujarati, Hindi, English, Marathi, and Punjabi languages. After that, different machine learning and deep learning models are applied to the multilingual dataset. At last, a comparison of all model performances was made.

This is the era of internet and there are a lot of Social Media platforms out there that facilitate the sharing a vast variety of user generated content. LinkedIn is a business and employment oriented social platform whereas Twitter is used to share recent news trends and updates. Pinterest is about discovering new content and ideas while Facebook is more about catching up with friends and family. These networks and virtual communities cast a tremendous amount of influence on their users. Here we have a similar content-oriented platform called Social. It is designed and built for the users to connect and share digital content (like text, images and/or gifs) related to community, social, healthcare and welfare services. MERN stands for MongoDB, ExpressJS, ReactJS and NodeJS (the four key technologies that make the stack) and is used to built this fully responsive web application in conjugation with other APIs and tools.

The chat application is something that allows users to get in touch directly. People can interact using this online application. People can interact using this online application effectively even far apart. To be utilized by many people, this web-based application is supposed to be real-time and multiplatform. Chatterbox is a real-time chat application developed using MERN stack development thus it is easily extendible and developed with recent technologies. Also, it has features like voice calls, video calls, screen sharing, chatting, and much more for users in one application. Particularly the application is more secure and comes with the option of screenshare which other applications mostly don’t have making it handy and easily reliable as well.  Also, with further system demands for running higher ends of the project, multiple features can be added to the application due to the extensible code and the application can be extended.

Due to the increased use and popularity of social media platforms in the most recent technological period, sentiment categorization has emerged as an important research area among those platforms. When it comes to Twitter, the main problem of previous research is, they all did a sentiment classification on the document level (Tweet level). It cannot classify the sentiment for any particular aspect. When it comes to the review of any multifunctional product and service, gathering an overall positive or negative mood may not be helpful to the firms as it is more crucial to ascertain precisely what their customers are happy or upset about, to bring the updates and changes on that particular product and service. In addition to this, what if someone wants to know the sentiments about recently generated data or tweets? What if someone wants to know the sentiment for data between a particular date range? What if users want to get sentiment of the tweets regarding current ongoing events and happenings? Along with this, very few of them performed aspect-based sentiment analysis on other platforms and they are using the same data set for training purposes as well as analytics purposes. So here we come up with the idea of Aspect based sentiment analysis on twitter, in which we train our model with a publicly available dataset, and then the user will give a particular hashtag and aspects. Our system will get tweets related to that specific hashtag from publicly available daily search twitter API and our model will take those tweets as input for analytics. Then machine learning operations will be performed on those tweets to find sentiment analysis for that hashtag’s tweet and its aspects with the best accuracy. In that way, we can get responses from people on any event, feedback or national issue, or matter of people’s support. The experimental findings also showed that our method beats current state-of-the-art approaches.

Everyone may now readily communicate and share their sentiments with individuals around the world thanks to the various social networking platforms. In the current scenario, some people are more extroverted on social media than others around them. In the end, whenever they get into trouble or any situation where they need other people’s help who gives them motivation or who cares, they would not be there because that time sufferer prefers to express their feeling on social media rather than any close once. Therefore, if they are warned in advance, there are some strategies that reduce the stress and mental  health issues that they are experiencing. Due to rising these issues globally, attract many researchers to  focus on the subject and provide some viable solutions, where there is still a need for more research that  provides some efficient results. So finally, we built a project in which it takes users’ tweets as input, and  it will give the result in the form of depression or not using the help of a machine learning algorithm. For  the segmentation of tweets, we are using LSTM (Long Short-Term Memory) machine learning algorithm  which is best suited for this task. Not only this but LSTM performs best out of available machine learning  algorithms. All the emotions are identified as neutral, positive, or negative which assists to provide a  solution toward sentiments. Apart from this, we are not focusing on only the English language but we  will try to counter this issue in different languages by translating other languages into English, which  other researchers have missed out on in their research. With the help of these results, the company will  take different steps to mitigate the stress from those users by providing motivational feeds in their feed  section or any other way.

Nothing is rock-steady in the stock market, which isa very volatile market. Nevertheless, there are a variety of ways and approaches one may utilise to learn about this dynamic movement and be prepared for it as technology develops. The focus of this essay is on different methods for quickly identifying market trends. The suggested strategy is comprehensive because it includes pre-processing the stock market dataset, a range of feature engineering techniques, and the integration of a customised deep learning-based system for forecasting stock market price patterns. The best and most suggested method for prediction is the model with the least amount of error. In order to conduct this study, we used three distinct models and ran sentiment analysis on news articles mentioning the firm or the stock. The results of this classification have given investors additional information to help them make decisions about where to stake their money as well as clear and incisive insight into the market's irregular ups and downs.

Improving Cataract Surgery Procedure using Machine Learning and Thick Data Analysis

This is a stage 1 of our ongoing research to build bedside machine learning predictors to identify septic shocks at early stages. It is an important research as the current COVID-19 causes such septic shocks and predicting them early save lives.

The aim is to optimise the usage of resources, so that more customers can be accommodated and each can pay less in return.