Showing papers by "Prin. L. N. Welingkar Institute of Management Development and Research published in 2020"

Covid-19 Pandemic Data Analysis and Forecasting using Machine Learning Algorithms

Abstract: India reported its first Covid-19 case on 30th Jan 2020 and the number of cases reported heavily escalated from March, 2020 This research paper analyses COVID -19 data initially at a global level and then drills down to the scenario obtained in India Data is gathered from multiple data sources-several authentic government websites The need of the hour is to accurately forecast when the numbers will reach at its peak and then diminish It will be of huge help to public welfare professionals to plan the preventive measures to be taken keeping the economic balance of the country as well Variables such as gender, geographical location, age etc have been represented using Python and Data Visualization techniques Time Series Forecasting techniques including Machine Learning models like Linear Regression, Support Vector Regression, Polynomial Regression and Deep Learning Forecasting Model like LSTM(Long short-term memory) are deployed to study the probable hike in cases and in the near future A comparative analysis is also done to understand which model fits the best for our data Data is considered till 30th July, 2020 The results show that a statistical model named sigmoid model is outperforming other models Also the Sigmoid model is giving an estimate of the day on which we can expect the number of active cases to reach its peak and also when the curve will start to flatten Strength of Sigmoid model lies in providing a count of date that no other model offers and thus it is the best model to predict Covid cases counts –this is unique feature of analysis in this paper Certain feature engineering techniques have been used to transfer data into logarithmic scale as is affords better comparison removing any data extremities or outliers Based on the predictions of the short-term interval, our model can be tuned to forecast long time intervals

An Exploration of Impact of COVID 19 on mental health -Analysis of tweets using Natural Language Processing techniques

Abstract: Twitter is one of the world’s biggest social media platforms for hosting abundant number of user-generated posts. It is considered as a gold mine of data. Majority of the tweets are public and thereby pullable unlike other social media platforms. In this paper we are analyzing the topics related to mental health that are recently (June, 2020) been discussed on Twitter. Also amidst the on-going pandemic, we are going to find out if covid-19 emerges as one of the factors impacting mental health. Further we are going to do an overall sentiment analysis to better understand the emotions of users. Executive Summery Novel Corona virus’s spread and its impact on various aspects of national and individual’s well-being has been at the center of lot of discussions across micro-blogging sites and various social media platforms ever since it commenced in December 2019. Users are voicing their opinions on several topics related to covid-19. Social distancing as prescribed by Government and Local Administration We all are aware that the Novel Corona virus has significantly affected our physical health; however the current social distancing norms are taking a toll on the psychological well-being of individuals. The research paper presents a two-phased analysis of most recent 2000 tweets related to mental health pulled out twice over a span of one month on 28 June 2020 and 28 July2020 respectively, thereby analyzing 4000 tweets in total. The second phase analysis was conducted exactly after a gap of one month to validate the results generated by the first analysis. The intention is to analyze to what extent people have discussed about mental health in the past few months based on the information disseminated on Twitter. Data was extracted using Twitter’s search application programming interface (API) and Python’s tweepy library. A predefined keyword like ‘mental health’ was given to find out if Covid-19 emerges as a reason for the same. Several natural language processing (NLP) techniques like tokenization, removing URL and stop words, stemming and lemmatization were used to pre-process the text data and make it ready for analysis. These collected tweets were analyzed using word frequencies of single and double words (unigram, bigram). A very unique feature of this analysis includes a network diagram that shows interconnections between the set of most common words used in to its and the connections (if any) are represented through links. Topic modeling technique in NLP visualizes the top concerns of tweeters through a word cloud. At present we have many methods to do topic modeling. In this paper we are using the Latent Dirichlet Allocation (LDA) method which is a probabilistic approach of modeling given by Prof David H.B in 2003. This model deals with distribution of topics to tweets and allocation of those topics to documents and words to topics. Finally a sentiment analysis is done using text mining techniques to analyze the sentiment of the tweets in the form of positive, negative and neutral.

GANTOON: Creative Cartoons Using Generative Adversarial Network

Abstract: We propose a methodology for generating creative cartoon art. The system generates cartoon by looking at various existing images of cartoon characters and learning about their posture/animation style. The proposed system is creative in nature as it generates unique cartoon art by deviating from the existing styles learned by the algorithm. We build over Generative Adversarial Networks (GAN) with unsupervised learning, which have shown the ability to learn to generate novel cartoons by simulating a given distribution. The proposed model exhibits an ability to generate cartoons which are creative and novel in design. We have conducted experiments by considering around 12K Tom’s cartoon images for training purposes. The results show that the increase in number of epochs resulted in better classification accuracy. The proposed system generates the character Tom’s cartoons which are novel and we have validated the same by applying Colton’s creativity benchmark.

Early Diagnosis of Parkinson’s Disease Using LSTM: A Deep Learning Approach

Abstract: Parkinson’s disease (PD) is one of the most rapidly growing neurodegenerative diseases in the world. Due to motor symptoms, it affects the normal life of a person. There is a severe need to identify PD in its early stage to avoid it getting worse and to control its symptoms easily. The advancements in Artificial Intelligence (AI) and the Internet of Things (IoT) open up new avenues for the analysis of various data points such as the gait of a person for early-stage detection. In this paper, we propose a methodology based on the use of Long Short-Term Memory (LSTM) architecture for PD diagnosis. We have used time series analysis to find the gait patterns and deep learning techniques to extract the features and to build a classifier model. The proposed model is predicting the PD disease with 85% testing accuracy and with an F1 score of 0.90. The validation is performed using Cohen’s Kappa statistical method and obtained a score of 0.631.

York University and Cupe 3903— Future of Collective Bargaining Rights:

Abstract: The Canadian Union of Public Employees (CUPE) 3903 view the introduction of the new Ontario government’s ‘back to work’ legislation (Bill 2) as detrimental and a threat to their fundamental bargain...

Speech Coach: A framework to evaluate and improve speech delivery

Abstract: Good speeches can have relevance for several decades or centuries and have the potential to impact people's minds and hearts forever. A good speech is centered around its substance, but how it is delivered is what makes a great speech. The aim of this project is to introduce a web-based platform to analyze, understand and improve elocution skills to help people deliver effective speeches, presentations or to improve business communications. We make use of various values and graphs of vocal elements related to speech delivery for a more visual and quantitative method of learning speech. Our framework uses free to use and open source products from the speech technology domain. It is tested and tailored for the English-speaking population of India. We aim to cater to the requirement of a convenient and user-friendly product that can be used to practice speech delivery, improve oratory skills, boost confidence, and deliver articulate speeches.

A Case Study on Impact of Value Stream Mapping as a Lean Diagnostic Tool on the operational performance of a Medium Enterprise

Abstract: India's quest is to stand out as a Global Manufacturing Hub, demands uncompromised contribution from the Small and Medium Enterprises. The Government of India has formulated various Policies under the Make in India Initiative that help industries achieve this international repute. Policies like Financial Support to MSMEs in ZED Certification Scheme, National Manufacturing Competitiveness Programme (NMCP), Lean Manufacturing Competitiveness for MSMEs, Technology and Quality Upgradation Support to MSMEs are a few to name, aligned to the goal of making India a Global Manufacturing Hub. The Government in partnership with the Overseas Human Resources Development Association (HIDA), Japan, and Confederation of Indian Industry is operating training programmes to enhance production management capability in the Indian manufacturing industry in order to achieve the idea of “Make in India” through Japanese-style Management. This is where the Principles of Lean Manufacturing come into action. Lean has a Customer oriented approach that focuses on elimination of non-value added activities in the process, making the system more robust in terms of Quality, Delivery, Productivity and Cost. Value Stream Mapping is a diagnostic tool that helps identify the non-value added activities in the present state of the Enterprise. Eliminating these nosn-value added activities leads to an improved efficiency. The research paper intends to demonstrate a case of a manufacturing industry, who used Value Stream Mapping as a diagnostic tool. After the diagnosis based on the current state of the value stream various lean tools were implemented to achieve a significant improvement in the operational performance of the organization. This case study presents how value stream mapping can help identify operational areas of improvement and channelize organizations to achieve significant improvements in their performance.

Use Innovativeness for New Product Development: A Study on Users of Titan Watches

Abstract: Developing a new product requires understanding of consumers, conducting ethnography research, empathizing with the customers, finding their needs, looking at pain points . But more importantly, it is the adoption of the product by the consumer at the end of the day which matters. Estimating the product usage and his need satisfaction level is what helps the product to penetrate in market. Use innovativeness involves the use of previously adopted products in novel ways. As conceptualized by Price and Ridgway use innovativeness encompasses five factors: creativity/curiosity, risk preferences, voluntary simplicity, creative re-use and multiple use potential. Use innovativeness is measured by above scales and a conceptual model of post adoption process is developed. Understanding, the technology adaption life cycle by grouping consumers into Innovators, Early Adaptors, Early Majority, Late majority and Laggards, helps us strike a fine relation between product developed according to its use innovativeness and the stage of technology adoption cycle your product will mostly fall under. The conceptual model is then clubbed with framework for technology adaption cycle which will be useful for companies mainly product based, to understand its target consumers and the target market in hitting with highest probability of success. Companies looking at pivoting a product when the product doesn’t work in the market, is a painful task. Killing a product and it’s usage involves lot of time, effort and money. Developing a product understanding one’s target consumers and looking at their probable adoption is a difficult relation to strike. The Study and Analysis developed here after research of consumers and their use innovativeness in target segment helps us to put the product in right market, at right time and with right adoption purpose based on user innovativeness and will have a future scope of arriving at a framework that can help companies to launch a new product.