Career discrepancy and subjective well-being among undergraduate students in close liaison to career decision self- efficacy
The current study aims to establish a possible relation between three variables: career discrepancy. career decision self-efficacy, and subjective well-being. Respondents were 86 subjects currently enrolled as undergraduate students within the territorial boundaries of India. Subjects were to answer items present in the three forms while filling in certain demographic details. The current study had four hypotheses; a negative correlation between career discrepancy and self-efficacy; a positive correlation between self-efficacy and subjective well-being; a negative correlation between career discrepancy and subjective well-being; and career discrepancy and self-efficacy significantly predicting relationship with subjective well-being. Results concluded a significant agreement with the hypothesis, but all correlations were weak. Multiple linear regression revealed a moderate level of prediction, with both career discrepancy and career decision self-efficacy significantly predicting the subjective well-being
Published by: Riya Rajesh Shah, Anushka Thacker, Mitali Maral, Bhavi Mehta, Krutik Shah, Krisha Vyas, Hetvi Sanghavi, Kavya Shah
Author: Riya Rajesh Shah
Paper ID: V7I5-1231
Paper Status: published
Published: September 20, 2021
Operational Intelligence for Retail Industry
Operational intelligence in a new domain which is being explored by every business industry to know their customers in a better way possible and satisfy their need. Whenever customers want to purchase any product from a retail website they put a lot of time in searching for the right product at right price from right place. Its becomes difficult to get all this things at one place customer has to check all the sites on which the product is available and then compare and then decide even after deciding he is not sure whether he is getting the product at right price and getting all this information at real time is tedious task. The main objective involves, getting the required information of product price from different websites at real time whenever the customer wants to make a comparison. Giving appropriate discounts on the product so that the business is not in loss and the customers are retained, sending proper messages to respective customer at proper time regarding the best offers. Tracking the customer activities and making its use to the fullest for the benefits of the business industry. The websites which are present till date to not help customer find the right product at right price no real-time comparison of product prices are shown at one place. The customer select product after doing his research and then purchase the product depending on the reviews of the product and the feedback from other customers. There is no mechanism to check whether the reviews provided by other customers are genuine or not. Getting all the information about price at a single place will save time and won't keep a customer in a blind spot while purchasing the product from right place at right price. They will get instant messages regarding the rise or fall of prices or the discounts which will be provided along with that they do not need to worry about the feedback of the product as the products which are being provided is from same brand or company which other websites provide. The website is just a platform for B2C e-commerce
Published by: Shubham Singh, Rishabh Singh, Dilip Sharma, Abhishek Sand
Author: Shubham Singh
Paper ID: V7I5-1159
Paper Status: published
Published: September 20, 2021
A model to predict COVID-19 epidemics with application to India, Canada, and France
In this work, our gathering develops a differential conditions model of Coronavirus pandemics. We will most likely expect forward in time the future number of cases from early definite case data in regions all through the world. Our model unites the going with huge parts of Coronavirus pandemics: (1) the amount of asymptomatic compelling individuals (with uncommonly delicate or no signs), (2) the amount of intriguing declared overpowering individuals (with outrageous incidental effects) also, (3) the amount of demonstrative unreported overwhelming individuals (with less genuine indications). We apply our model to Covid plagues in India, Canada, and France.
Published by: Dheepa R.
Author: Dheepa R.
Paper ID: V7I5-1209
Paper Status: published
Published: September 20, 2021
Application of Operations Research in Financial Decision Making
“O.R. is the application of scientific methods, techniques and tools to problems involving the operations of a system so as to provide those in control of the system with optimum solution to the problem. -Churchman, Ackoff and Aronoff.” This report includes the application of OR (Operations research) in financial industry. How OR's instruments would avail in solving quandaries such as portfolio management, asset allocation, investment strategies and imperil evaluation etc. Finance management is a very delicate process and needs a very profound understanding in order to realise its functioning. Most of the times avail of experts is required to understand it’s working. OR gives a very efficient method to solve things and get the optimal solution thereby minimizing extra costs and jeopardizations. The topics discussed in the report are 1) Monte Carlo Simulation 2) Index Tracking Fund (Includes Quadratic Programming) 3) Data Envelopment Analysis (DEA). It is difficult to utilize operations research when you wish to make decisions but the conditions are not matched, and when differing objectives conflict with each other. The problems which are being solved through this report are 1) Financial Risk Management 2) Passive Portfolio Optimization 3) Banking Operations and Decision Making.
Published by: Harsh Bhalla, Prachi Kedia, Pulkit Ganeriwala, Moksh Khurrana, Avish Parakh
Author: Harsh Bhalla
Paper ID: V7I5-1240
Paper Status: published
Published: September 20, 2021
Plant Disease Recognition using Artificial Intelligence
The project Plant Disease recognition using AI is made for the farmers to take care of their farms and fields and to help them by recommending and suggesting to them a proper guideline for control measures to be taken. In this Project `Plant Disease Recognition using AI` we have focused on Detecting some plant diseases using Artificial Intelligence by using Various Algorithms like CNN Algorithm. We have used Hardware components like Sensors, Raspberry pi, and ADC convertors and created a sort of handheld device for taking readings from plants and by using face recognition algorithms like CNN and KNN algorithm we detect the disease by comparing the images from the standard images. In This project, we have used Thinkspeak which represents the data from IoT in a pure database format so that the data can be easily analyzed by using various DBMS Techniques. After getting the data from Thinkspeak that data will be undergoing through the various algorithm that we have mentioned in the project which is performed at the backend. In the frontend for the results to be displayed for the user, we will be creating a dashboard that will inform the user about the various diseases that the plant/crop is undergoing. For that we will be using app developing methods like react.js, node.js, ML5.js, etc. . We have used Tensor Flow which is one of the Machine Learning libraries for performing Machine Learning. IoT using Thinkspeak is used as this way we have combined all the software and the hardware aspects of our project to result in a hardcore product which would be very beneficial for the farmers of our country to take proper care of the crops and to take preventive measures so that there won't be any loss to farmers. This project is aiming to make the farmer’s life easy and better. The Sensor Programming is done in python and uploaded to the firebase.
Published by: Taha Qureshi, Durgesh Upadhyay, Rahul Prabhu, Adarsh Jadhav
Author: Taha Qureshi
Paper ID: V7I5-1246
Paper Status: published
Published: September 20, 2021
Application of Information Technology in Stock Market
India was introduced to the Internet in the early '90s and the users started to increase rapidly. India stands 5th in the list of countries by the number of internet users. The Stock markets introduced Internet trading (online trading) in February 2002 which has brought a tremendous change in trading. The National Stock Exchange started the first form of online trading wherein users/investors can buy/sell stocks in a day without much paperwork. Earlier it used to take around 60 days to buy/sell stocks in stock markets and the investor also had to go through loads of paperwork. But thanks to Internet the wait has been curbed and in the last ten years, Indian capital markets have recorded 1488% of growth in exchange turnover. The online trading system of NSE is used by companies to make IPOs through book building. It is a fully automated screen-based bidding system that allows trading members to enter bids on behalf of their clients. All bids received by the system are numbered, time-stamped, and stored in the book till the last day of the book-building process, and the offer price is determined after the bid closing date. While ensuring efficient price discovery, this system reduces the time taken for the completion of the issue process.
Published by: Disha Bhagat
Author: Disha Bhagat
Paper ID: V7I5-1230
Paper Status: published
Published: September 20, 2021
