Algorithmic Trading with Deep Learning
In this paper, Artificial Neural Network (ANN) is used to predict trading signals like Strong Buy, Buy, Neutral, Strong Sell and Sell a total of 5 signals. The ANN outputs one of the trading signals based on the market data of cryptocurrencies. The ANN makes use of the candle-stick data along with additional data like Volume, Number of Trades, etc and technical indicators to predict the signals. The ANN is trained on multiple intervals of the market, i.e. 1 minute, 3 minutes, 5 minutes and so on up to 3 days in the intervals. Along with the market data, the paper also makes use of the news articles to better predict the trading signals, this is only used when the ANN predicts the Buy or Sell, and by using the news articles those Buy and Sell signals are converted to Strong Buy and Strong Sell respectively. The ANN was trained on 3 years of data, and Google’s BERT [1] model was trained on almost 1000 news article’s titles on topics related to cryptocurrencies and the model outputs whether it’s a positive or negative title. The accuracy of the ANN models for all the markets is in the range of 85 - 90% and the same accuracy is observed with the BERT model which is trained on the news article’s title.
Published by: Mayank Anuragi, Rakshit Ramnath Naik, Manoj Galanki, Kavitha S. Patil
Author: Mayank Anuragi
Paper ID: V7I4-1286
Paper Status: published
Published: July 12, 2021
City cleanliness using geo-tagged images with experimental results
During the process of smart city construction, city planners and managers always spend a lot of energy and money for cleaning street garbage due to the random appearances of street garbage. Consequently, visual street cleanliness assessment is particularly important. However, the existing assessment approaches have some clear disadvantages, such as the collection of street garbage information is not automated and street cleanliness information is not real-time. To address these disadvantages, this paper proposes a novel urban street cleanliness assessment approach using mobile edge computing and deep learning. First, the high resolution cameras installed on vehicles collect the street images. Mobile edge servers are used to store and extract street image information temporarily. Second, these processed street data is transmitted to the cloud data centre for analysis through city networks. At the same time, Faster Region-Convolutional Neural Network (Faster R-CNN) is used to identify the street garbage categories and count the number of garbage. Finally, the results are incorporated into the street cleanliness calculation framework to ultimately visualize the street cleanliness levels, which provides convenience for city managers to arrange clean-up personnel effectively. Index Terms— Smart cities, street cleaning, garbage detection, deep learning, mobile edge computing.
Published by: Krishna Sonawane, Prachi Pingle, Siddhant Lunawat, Trupti Zagade, Dr. Meenakshi Thalor
Author: Krishna Sonawane
Paper ID: V7I4-1285
Paper Status: published
Published: July 12, 2021
AI-based chatbots for providing health-related information
Chatbots in the health care systems may have the potential to provide patients with access to immediate medical information. Health care chatbots can help patients better manage their own health; improve access and timeliness to care. Al-based Chatbots for supplying health-related information. The System uses artificial intelligence which includes advanced Natural language processing to answer the query Tf-IDF weighting. The User can query any health-related activities through the system. The user does not have to personally go to the health for an inquiry. The software analyses the questions and then answers the user. The software answers the queries as if it is questioned by a person. The software answers using a Graphical user interface which implies that as if a pupil is talking to the user. The user has to register himself to the software and has to log in. After login user can access the bot. A chatbot is a program that communicates with people.
Published by: Dungi Pravalika Reddy, Dadala Shantha Shekinah, Gunisetty Pavani Suvarna Satya, Kolla Hasitha Naidu, Dr. K. Soumya
Author: Dungi Pravalika Reddy
Paper ID: V7I4-1326
Paper Status: published
Published: July 12, 2021
Size-specific variation in the rate of oxygen consumption, ammonia excretion, and O: N ratio of freshwater bivalve lamellicorns marginalis during the season of monsoon
Malacology means the study of molluscan animals and also conchology means the study of the molluscan shell. Body mass is one of the best known and most studied characteristics of aquatic animals on scaling of metabolic rates. We studied here how size-specific variation in the rate of oxygen consumption, ammonia excretion, and O: N ratio in Freshwater Bivalve Mollusc Lamellidens marginalis species in an attempt to know how size-specific variation affects their metabolism. The freshwater bivalve molluscs were chosen for experimental work from Bhima River at Siddhatek in August and September for the period of monsoon season with body size i.e. small (75-79 mm in shell-length) and large (90-93 mm in shell-length). In current work reported that the rate of oxygen consumption and O: N ratio was high in the small body-sized bivalve mollusc but the rate of ammonia excretion was low in small body-sized bivalves compared to large ones. The results are discussed in the flush of metabolic processes in fresh-water bivalve molluscs.
Published by: Pritesh Ramanlal Gugale
Author: Pritesh Ramanlal Gugale
Paper ID: V7I4-1320
Paper Status: published
Published: July 12, 2021
Helping business owners to find potential competitors
Opening a business in any place is not an easy task as it needs lots of things to be considered. One should consider its competitors before opening a business. This project is one attempt to help those business people to find their competitors. In this project, a taco palace is planned to open in the city of Monterrey, Mexico. Here the attempt is to find a neighborhood with not too many competitors with enough customers. To find the neighborhood, considering the latitude and longitude values of the city of Monterrey, Mexico, and can be able to locate the best place to open the restaurant. In this project Foursquare API is used to explore the neighborhoods and get the most common restaurants near that place and using this function clusters can be grouped. For clustering, a K-means clustering algorithm is used. Clusters are used to know the similar business in that areas. Clusters group the similar business and list out all its names. To visualize the neighborhoods Folium library is used.
Published by: Suraksha S. S., Darunya B. C., Priya D., Anisha B. S.
Author: Suraksha S. S.
Paper ID: V7I4-1279
Paper Status: published
Published: July 12, 2021
Social Cause Marketing (Pink Capitalism) and its impact on consumers’ brand preferences
As its name implies, cause-based marketing is the process of marketing a specific idea, cause, or goal, rather than a specific business, product, or service. These initiatives are often partnerships between a nonprofit organization – typically the driving force behind the” “messaging of the campaign itself – and either an ad agency or corporate partner, which typically handles the execution of the campaign. Although cause-based marketing campaigns can focus predominantly on PPC or social advertising, these campaigns can and often do incorporate” elements of guerilla marketing in their execution. Trying to grab people’s attention is no easy feat these days, and as such many organizations adopt more creative ways of getting their message out, as we’ll see later on. Many cause-based marketing campaigns are organic offshoots of grassroots marketing efforts, which also tend to focus on causes. This paper will discuss thoroughly cause-related marketing through the lenses of Pink Capitalism. It will also discuss how pink capitalism is not entirely an ethically incorrect concept and focus on the silver lining of the same, which would benefit both the NGO as well as the corporations.
Published by: Saniya Savant
Author: Saniya Savant
Paper ID: V7I4-1276
Paper Status: published
Published: July 12, 2021
