ABSTRACT
Faced with an increasing amount of unstructured multimodal data appearing on various social platforms (e.g., Twitter/Instagram), we seek to effectively understand the complex social events portrayed on these platforms. However, conventional information extraction systems cannot understand these data because they cannot handle real-world analysis or require extensive tuning and many manually annotated examples to successfully comprehend these events. To solve this problem, this paper develops a knowledge-oriented artificial intelligence system that can identify and analyze these data and complex events and bring them to the user's attention. Our research aims to understand complex events described in multimedia inputs by developing a semi-automated system that identifies, links, and temporally sequences their subsidiary elements, the participants involved, as well as the complex event type. This project proposes a systematic analysis of world events, such as the Boston Marathon bombing, Capital Riots, Covid-19, etc. We have successfully evaluated our system on various datasets and have shown significant improvement compared to other previous methods. © 2022 IEEE.
ABSTRACT
Automated information retrieval and servicing systems are a priority demand system in today's businesses to ensure instantaneous customer satisfaction. The chatbot system is an incredible technological application that enables communication channels to automatically respond to end-users in real-time and 24 hours a day. By providing effective services for retrieving information and electronic documents continuously and automating the information service system, the coronavirus disease (COVID-19) is challenging to promote graduate school programs, update news, and retrieve student information in this era. This article discusses automated information retrieval and services based on the architecture, components, technology, and experiment of chatbots. The chatbot system's primary functions are to deliver the course and contact information, answer frequency questions, and provide a link menu to apply for our online course platform. We manage the entire functional process of gathering course information and submitting an application for a course online. The final results compare end users' perceptions of chatbot system usage to onsite services to ensure that the chatbot system can be integrated into the university's information system, supporting university-related questions and answers. We may expand our chatbot system's connection to the university's server to provide information services to students in various informative areas for future research. © 2022 Institute of Advanced Engineering and Science. All rights reserved.
ABSTRACT
Statistics show that many infections of employees in medical institutions are associated with direct contact with patients. Furthermore, the deterioration of the patient's condition in the intervals between doctor's rounds was observed. Therefore, control and reduction of morbidity through introducing new monitoring methods without the direct involvement of human resources remains an urgent issue. This paper considers an alternative method for monitoring the state of infectious patients undergoing treatment. In this article, we have described the use of standard elements for scanning the medical state of people for the timely detection of pathologies with the implementation of instant notification of the attending physician. Based on the functionality of the Texas Instruments Robotics System Kit (TI-RLSK) developer board and additional sensors, an autonomous robot scanner was created. The considered embodiment of the model systemizes the provided recording recognition of a specific patient with the parameters of the health state in a database. In addition, the navigating system with an updated real-time map of the area enables the robot to deliver medicine to patients.
ABSTRACT
In the pandemic situation, people are quickly affected in our day-to-day life. Wearing the mask is being normal nowadays for controlling the spread of COVID-19. The government and public sectors will ask the public/customers to wear masks to control the spread of COVID-19. Mask detection has become an essential task to help society's well-being to protect our life. This paper provides a simplified approach to detect face masks using basic ML packages in PYTHON like tensor Flow, Keras, OpenCV. This paper helps to analyze an image to detect the face correctly and then identifies whether there is a mask on the face or not. It is a surveillance task to perform the security to create awareness among the people. This method attains the accuracy of scanning face up to 96.88% and 92.39% respectively. This detection is based on two datasets, one is about without wearing a mask and with wearing a mask. This mechanism helps to detect the mask on people's faces in real-time scenarios. © 2022 IEEE.
ABSTRACT
The number of COVID-19 cases is growing rapidly, while there is not enough healthcare workers which can help the patients. Even worse, the highly contagious nature of this disease, requires the medical staff to be more restrictive and wear the Personal Protective Equipment (PPE) all the time when handling the patients directly. In this situation, a remote system which can monitor patient progress from a distant is inevitable. The emerge of Internet of Thing (IoT) technology has been implemented in many domain. The availability of smart technology, where almost all devices around us has connectivity to the internet, allow people to automate process from distance. The implementation of IoT has also been shown very helpful in medical domain, especially during the pandemics. The IoT technology can be a suitable solution for monitoring patients with a highly contagious disease. The technology can also be very helpful for people who live far from healthcare facility. This can allow people to report immediately and even connect to the hospital system in real-time. In this paper, we propose the use of three different sensors, namely: heart-rate and pulse oximeter sensor (MAX30102), temperature sensor(DS18b20) and accelerometer sensor, which is integrated in a web-based early warning monitoring system for COVID-19 patients. © 2021 IEEE.