Browsing by Author "Rahman, Shoaib"

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IOT Based Smart Health Monitoring System for Diabetes Patients Using Neural Network
(Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Springer, 2020-07-30) Efat, Md. Iftekharul Alam; Rahman, Shoaib; Rahman, Tasnim
In improvement of the quality of health care services, Internet of Things (IoT) has evolved rapidly for monitoring patient from distance. However, notifying health status based on continuous change of health condition for immediate healing to patient, existing systems has some limitations. In this paper, we demonstrate a smart health monitoring technology for diabetic patients which follows up their health condition depending on sugar level, heart pulse, food intake, sleep time and exercise. To illustrate, this technology takes the variables (data) as input through sensors continuously and process with neural network to evaluate the data, resulting four modes of health risk status: low, medium, high and extreme. The range of the risk status can differ based on patient’s type and previous histories of their health. In addition, an automatic phone call and/or SMS notification is being sent to patient’s relative along with patient’s location if his/her health condition is at high or extreme risk. Besides, it also calls patients nearest hospital in case of extreme risk. However, the system provides allied instruction as voice command to patient’s mobile in both cases. This technology has been experimented on 25 diabetic patients successfully and achieved 84.29% accuracy to identify the proper risk level, which is a highly acceptable level of identifying health risk status.
Social Crisis Detection Using Twitter Based Text Mining-A Machine Learning Approach
(Springer Nature, 2023-04-15) Rahman, Shoaib; Jahan, Nusrat; Sadia, Farzana; Mahmud, Imran
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.