Sarmishta Sarangarajan, C. Kavi Chitra, S. Shivakumar
{"title":"Automation of Competency & Training Management using Machine Learning Models","authors":"Sarmishta Sarangarajan, C. Kavi Chitra, S. Shivakumar","doi":"10.1109/GHCI50508.2021.9513995","DOIUrl":"https://doi.org/10.1109/GHCI50508.2021.9513995","url":null,"abstract":"With the emerging intelligent technology and larger talent gaps training and development are the need of the hour. Organizations use competencies to remain adaptable and competitive in their respective fields. In this paper, we propose a technique to manage competency across a business unit to keep up with the want of the customers and ever-changing technology. The solution is powered by Artificial Intelligence and E-learning. We propose a technique to customize a learning roadmap for an individual by using a machine learning-based hybrid recommendation system. By using the technique managers can identify and optimize the skills required to deliver on an organization’s business strategy. The last section of the paper discusses a technique to auto-rank the algorithms used within a hybrid recommendation system based accuracy of predictions that help reduce the training time of the machine learning model itself. The solution indirectly boosts innovation, job satisfaction, and morale among employees resulting in higher levels of engagement, thus enabling a better productive workforce.","PeriodicalId":378325,"journal":{"name":"2021 Grace Hopper Celebration India (GHCI)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124936243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Application of ensemble classifiers for early diabetes detection","authors":"S. Srivatsan, T. Santhanam","doi":"10.1109/GHCI50508.2021.9514027","DOIUrl":"https://doi.org/10.1109/GHCI50508.2021.9514027","url":null,"abstract":"This research study proposes a decision making system after performing data analysis on the dataset to assist Endocrinologists to detect whether a person could become diabetic. The dataset is first preprocessed and then the cleaned dataset is used to increase the correctness of the model. This way, the system is more reliable. Diabetes is a metabolic disease with no cure but on early detection, it is easier to go to remission. In our study, feature selection is performed using L2 regularization method and with the reduced features, the data set is analysed by two ensemble classifiers namely a stacking classifier and Random Forest. Among the two classifiers Random Forest yielded better performance.","PeriodicalId":378325,"journal":{"name":"2021 Grace Hopper Celebration India (GHCI)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133741154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Debanjana Kar, M. Bhardwaj, Suranjana Samanta, A. Azad
{"title":"No Rumours Please! A Multi-Indic-Lingual Approach for COVID Fake-Tweet Detection","authors":"Debanjana Kar, M. Bhardwaj, Suranjana Samanta, A. Azad","doi":"10.1109/GHCI50508.2021.9514012","DOIUrl":"https://doi.org/10.1109/GHCI50508.2021.9514012","url":null,"abstract":"The sudden widespread menace created by the present global pandemic COVID-19 has had an unprecedented effect on our lives. Man-kind is going through humongous fear and dependence on social media like never before. Fear inevitably leads to panic, speculations, and spread of misinformation. Many governments have taken measures to curb the spread of such misinformation for public well being. Besides global measures, to have effective outreach, systems for demographically local languages have an important role to play in this effort. Towards this, we propose an approach to detect fake news about COVID-19 early on from social media, such as tweets, for multiple Indic-Languages besides English. In addition, we also create an annotated dataset of Hindi and Bengali tweet for fake news detection. We propose a BERT based model augmented with additional relevant features extracted from Twitter to identify fake tweets. To expand our approach to multiple Indic languages, we resort to mBERT based model which is fine tuned over created dataset in Hindi and Bengali. We also propose a zero shot learning approach to alleviate the data scarcity issue for such low resource languages. Through rigorous experiments, we show that our approach reaches around 89% F-Score in fake tweet detection which supercedes the state-of-the-art (SOTA) results. Moreover, we establish the first benchmark for two Indic-Languages, Hindi and Bengali. Using our annotated data, our model achieves about 79% F-Score in Hindi and 81% F-Score for Bengali Tweets. Our zero shot model achieves about 81% F-Score in Hindi and 78% F-Score for Bengali Tweets without any annotated data, which clearly indicates the efficacy of our approach.","PeriodicalId":378325,"journal":{"name":"2021 Grace Hopper Celebration India (GHCI)","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132235047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}