CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS

The book collects the short papers presented at the 13th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS). The meeting has been organized by the Department of Statistics, Computer Science and Applications of the University of Florence, under the auspices of the Italian Statistical Society and the International Federation of Classification Societies (IFCS). CLADAG is a member of the IFCS, a federation of national, regional, and linguistically-based classification societies. It is a non-profit, non-political scientific organization, whose aims are to further classification research

1996 Laboratory directed research and development annual report

This report summarizes progress from the Laboratory Directed Research and Development (LDRD) program during fiscal year 1996. In addition to a programmatic and financial overview, the report includes progress reports from 259 individual R&D projects in seventeen categories. The general areas of research include: engineered processes and materials; computational and information sciences; microelectronics and photonics; engineering sciences; pulsed power; advanced manufacturing technologies; biomedical engineering; energy and environmental science and technology; advanced information technologies; counterproliferation; advanced transportation; national security technology; electronics technologies; idea exploration and exploitation; production; and science at the interfaces - engineering with atoms

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed

Computer science: the hardware software and heart of IT

1st edition, 201