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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Farzana Sadia
Daffodil International University
Electrical & Electronics Engineering

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Modelling consumer’s intention to use IoT devices: role of technophilia Nusrat Jahan; Md. Abu Hosen Shawon; Farzana Sadia; Dilara Khanom Nitu; Md. Enam Kobir Ribon; Imran Mahmud
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 1: July 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i1.pp612-620

Abstract

The present study has been conducted to examine whether skills and general technology-related value (GTV) required to operate the internet of things (IoT). This study also investigates is there any effect of technophilia to adopt IoT. The research method we use in this quantitative study was the sample survey. For investigating results, 352 surveys were conducted where 26 surveys were led through online and 292 surveys were distributed to different age groups. The proposed model was examined using partial least square structural equation model where the results revealed that IoT skills and General knowledge on technology directly contribute to technophilia which covers behavioural, emotional, and cognitive aspects. That is if people have a fascination for new technologies then they are willing to use IoT.
Social crisis detection using Twitter based text mining-a machine learning approach Shoaib Rahman; Nusrat Jahan; Farzana Sadia; Imran Mahmud
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.3957

Abstract

Social-media and blogs are increasingly used for social-communication, an idea and thought publishing platform. Public intentions, wisdom, problems, solutions, mental states are shared in social media. Text is being the best and the most common way to communicate over social networks. All kinds of data shared in social sites like Facebook, Twitter, and Microblogs. People from different pursuance uses these media to publish thoughts and convey messages through text. Consequently, occurrences in social life are rapidly discussed in social blogs in daily manner. This work aims at discovering ongoing social crisis from the Twitter data. Text mining technique and sentiment analysis were applied to detect the current social crisis from the social sites. Twitter data were collected to identify the recent social crisis. Furthermore, the identified crisis was compared to reputed newspapers. A hybrid method used to detect recent social issues resulted nicely. However, our proposed analysis shows identifying rate 89%, 95%, 83%, 53%, and 98% for the top 5 identified crisis accordingly in the date between 27 February and 11 March 2020. The strategy used in this study for the detection of recent social crisis will contribute to the social life and findings of crisis will be eliminated easily.
Co-Authors Dilara Khanom Nitu Imran Mahmud Md. Abu Hosen Shawon Md. Enam Kobir Ribon Nusrat Jahan Shoaib Rahman