ABSTRACT
Artificial-Intelligence (AI) is becoming more widespread in several areas, from economics and government to consumer-services and even healthcare. In fact, in the latter, there was a big use increase in the past three years, also due to the COVID-19 pandemic. Several solutions have been implemented to tackle the several challenges imposed by this new disease, being one of such solutions chatbots. In this article, we present the results of a Systematic Literature Review (SLR) that identifies the Chatbots applications in COVID-19 disease. In this SLR, we identified 9987 papers from which we selected 30 studies, on which we performed a full-text analysis. From our research, we could conclude that several solutions were implemented, with good acceptance by citizens, despite several limitations, such as limited time to develop the solutions (which narrowed some features, such as AI voice conversation), lack of global implementation and infrastructure limitations. © 2022 Associacao Portuguesa de Sistemas de Informacao. All rights reserved.
ABSTRACT
Companion bots such as chatbots in cyberspace or robots in real space gained popularity during the COVID-19 pandemic as a means of comforting humans and reducing their loneliness. These bots can also help enhance the lives of elderly people. In this paper, we present how to design and implement a quick prototype of companion bots for elderly people. A companion bot named "Hello Steve"that is able to send emails, open YouTube to provide entertainment, and remember the times an elderly person must take medicine and remind them is designed and implemented as a quick prototype. In addition, the bot combines the features of a mobile robot and a chatbot. The experimental results show the effectiveness of the design through its very high accuracy when navigating mobile-robot-like tasks and responding to chatbot-like tasks via voice commands. © 2023 MYU K.K.
ABSTRACT
The Internet of Things (IoT) has had a significant impact on human existence. This branch of study will lead to the creation of technology and concepts that will enable humans to communicate with machines. that is specifically developed it for a specific job. Students and faculty members need to have access to educational service. When learning became fully electronic, during the Corona pandemic, every effort was made to strengthen services such as technical support and access to education materials. Chat bot is the most popular feature on Telegram which allows a third party or user to design bot functionalities based on user requirements. As a result, autoresponder messages can help solve a variety of issues, including searching for educational sources, accessing technical support channels, and FAQs, and easing the heavy burden on technical support that occurred during the COVID-19 pandemic. By developing this service, you may get reply by essential information to lecturers, students, and the academic community while saving time and handling many requests concurrently. Where the service has been developed to be available 24 hours to provide all data and access links directly without having to search for them. © 2023 IEEE.
ABSTRACT
The outbreak of Covid-19 has exposed the lack of medical resources, especially the lack of medical personnel. This results in time and space restrictions for medical services, and patients cannot obtain health information all the time and everywhere. Based on the medical knowledge graph, healthcare bots alleviate this burden effectively by providing patients with diagnosis guidance, pre-diagnosis, and post-diagnosis consultation services in the way of human-machine dialogue. However, the medical utterance is more complicated in language structure, and there are complex intention phenomena in semantics. It is a challenge to detect the single intent, multi-intent, and implicit intent of a patient's utterance. To this end, we create a high-quality annotated Chinese Medical query (utterance) dataset, CMedQ (about 16.8k queries in medical domain which includes single, multiple, and implicit intents). It is hard to detect intent on such a complex dataset through traditional text classification models. Thus, we propose a novel detect model Conco-ERNIE, using concept co-occurrence patterns to enhance the representation of pre-trained model ERNIE. These patterns are mined using Apriori algorithm and will be embedded via Node2Vec. Their features will be aggregated with semantic features into Conco-ERNIE by using an attention module, which can catch user explicit intents and also predict user implicit intents. Experiments on CMedQ demonstrates that Conco-ERNIE achieves outstanding performance over baseline. Based on Conco-ERNIE, we develop an intelligent healthcare bot, MedicalBot. To provide knowledge support for MedicalBot, we also build a Chinese medical graph, CMedKG (about 45k entities and 283k relationships). © 2023 Association for Computing Machinery.
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The coronavirus outbreak has far-reaching ramifications for civilizations all around the world. People are worried and have a lot of requests. A research department from Covid19 Awareness was our recommendation. We supplemented it with AI-based chatbot models to aid hospitals, patients, medical facilities, and congested areas such as airports. We propose to develop this chatbot to support current scenarios and enable hospitals or governments to achieve more to solve the objective, given the two primary factors that inexpensive and fast production is now necessary. It is an immediate necessity in this epidemic circumstance. We built this bot from the ground up to be open source, so that anybody or any institution can use it to fight Corona, and commercialization is strictly prohibited. This bot isn't for sale;instead, we'd like to devote it to the country to help with current pandemic situations. The design of advanced artificial intelligence is presented in this paper (AI). If patients are exposed to COVID-19, the chatbot assesses the severity of the illness and consults with registered clinicians if the symptoms are severe, evaluating the diagnosis and recommending prompt action. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Social influence characterizes the change of an individual's stances in a complex social environment towards a topic. Two factors often govern the influence of stances in an online social network: endogenous influences driven by an individual's innate beliefs through the agent's past stances and exogenous influences formed by social network influence between users. Both endogenous and exogenous influences offer important cues to user susceptibility, thereby enhancing the predictive performance on stance changes or flipping. In this work, we propose a stance flipping prediction problem to identify Twitter agents that are susceptible to stance flipping towards the coronavirus vaccine (i.e., from pro-vaccine to anti-vaccine). Specifically, we design a social influence model where each agent has some fixed innate stance and a conviction of the stance that reflects the resistance to change;agents influence each other through the social network structure. From data collected between April 2020 to May 2021, our model achieves 86% accuracy in predicting agents that flip stances. Further analysis identifies that agents that flip stances have significantly more neighbors engaging in collective expression of the opposite stance, and 53.7% of the agents that flip stances are bots and bot agents require lesser social influence to flip stances. © 2013 IEEE.
ABSTRACT
When public health emergencies occur, a large amount of low-credibility information is widely disseminated by social bots, and public sentiment is easily manipulated by social bots, which may pose a potential threat to the public opinion ecology of social media. Therefore, exploring how social bots affect the mechanism of information diffusion in social networks is a key strategy for network governance. This study combines machine learning methods and causal regression methods to explore how social bots influence information diffusion in social networks with theoretical support. Specifically, combining stakeholder perspective and emotional contagion theory, we proposed several questions and hypotheses to investigate the influence of social bots. Then, the study obtained 144,314 pieces of public opinion data related to COVID-19 in J city from March 1, 2022, to April 18, 2022, on Weibo, and selected 185,782 pieces of data related to the outbreak of COVID-19 in X city from December 9, 2021, to January 10, 2022, as supplement and verification. A comparative analysis of different data sets revealed the following findings. Firstly, through the STM topic model, it is found that some topics posted by social bots are significantly different from those posted by humans, and social bots play an important role in certain topics. Secondly, based on regression analysis, the study found that social bots tend to transmit information with negative sentiments more than positive sentiments. Thirdly, the study verifies the specific distribution of social bots in sentimental transmission through network analysis and finds that social bots are weaker than human users in the ability to spread negative sentiments. Finally, the Granger causality test is used to confirm that the sentiments of humans and bots can predict each other in time series. The results provide practical suggestions for emergency management under sudden public opinion and provide a useful reference for the identification and analysis of social bots, which is conducive to the maintenance of network security and the stability of social order. © 2022
ABSTRACT
Purpose: The study aimed to examine how different communities concerned with dementia engage and interact on Twitter. Design/methodology/approach: A dataset was sampled from 8,400 user profile descriptions, which was labelled into five categories and subjected to multiple machine learning (ML) classification experiments based on text features to classify user categories. Social network analysis (SNA) was used to identify influential communities via graph-based metrics on user categories. The relationship between bot score and network metrics in these groups was also explored. Findings: Classification accuracy values were achieved at 82% using support vector machine (SVM). The SNA revealed influential behaviour on both the category and node levels. About 2.19% suspected social bots contributed to the coronavirus disease 2019 (COVID-19) dementia discussions in different communities. Originality/value: The study is a unique attempt to apply SNA to examine the most influential groups of Twitter users in the dementia community. The findings also highlight the capability of ML methods for efficient multi-category classification in a crisis, considering the fast-paced generation of data. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2021-0208. © 2022, Emerald Publishing Limited.
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Now that the COVID-19 pandemic is serious, in order to maintain the quality of life and safety, it is very important to remind people of the outbreak. Because when the SARS pandemic occurred in the past, it was found that the pandemic situation had a certain relationship with the weather. Therefore, we tried to analyze the relationship between the pandemic situation and the weather by analyzing big data information, and we also tried to conclude the possible pandemic situation and climate-related prediction rules through big data. We hope to use the most popular instant messaging software - LINE in Taiwan to assist in the auxiliary reminders of the pandemic. When people use the weather pandemic robot, they can also find the correlation between the weather and the pandemic, and it also helps to remind the public to pay more attention to their own health. © 2022 IEEE.
ABSTRACT
Social influence characterizes the change of an individual's stances in a complex social environment towards a topic. Two factors often govern the influence of stances in an online social network: endogenous influences driven by an individual's innate beliefs through the agent's past stances and exogenous influences formed by social network influence between users. Both endogenous and exogenous influences offer important cues to user susceptibility, thereby enhancing the predictive performance on stance changes or flipping. In this work, we propose a stance flipping prediction problem to identify Twitter agents that are susceptible to stance flipping towards the coronavirus vaccine (i.e., from pro-vaccine to anti-vaccine). Specifically, we design a social influence model where each agent has some fixed innate stance and a conviction of the stance that reflects the resistance to change;agents influence each other through the social network structure. From data collected between April 2020 to May 2021, our model achieves 86% accuracy in predicting agents that flip stances. Further analysis identifies that agents that flip stances have significantly more neighbors engaging in collective expression of the opposite stance, and 53.7% of the agents that flip stances are bots and bot agents require lesser social influence to flip stances.
ABSTRACT
Self-isolation is step or effort to stop the spread of the Covid-19 virus that can carried out by individuals infected with the corona virus. However, they do not show enough symptoms seriously. This is one method to push amount Covid-19 cases. People who do self-isolation must stay at home around 7 days until they are free from Covid-19. To help monitoring by effective condition patient in self-isolation at home and reduce risk the symptoms of Covid-19 experienced, it requires a support system. In this research, it makes a system that can help inhabitant of village to monitor condition patients in the room during self-isolation through camera-based detection object and some sensors to monitor their health such as temperature, heart rate, and oxygen saturation. Camera can classify condition patient based on real-time video recording. If patient is detected lie down or fall on the floor, it will be assumed need help and message emergency sent to the telegram bot. However, if the patient is in a position like stand up, it will be assumed that patient in health condition. By using Mobilenet V2 320x320 SSD object model the average of accuracy is obtained by 86.8%. The results in this system could be monitored through web page. © 2022 IEEE.
ABSTRACT
Social Media is used by many as a source of information for current world events, followed by publicly sharing their sentiment about these events. However, when the shared information is not trustworthy and receives a large number of interactions, it alters the public's perception of authentic and false information, particularly when the origin of these stories comes from malicious sources. Over the past decade, there has been an influx of users on the Twitter social network, many of them automated bot accounts with the objective of participating in misinformation campaigns that heavily influence user susceptibility to fake information. This can affect public opinion on real-life matters, as previously seen in the 2020 presidential elections and the current COVID-19 epidemic, both plagued with misinformation. In this paper, we propose an agent-based social simulation environment that utilizes the social network Twitter, with the objective of evaluating how the beliefs of agents representing regular Twitter users can be influenced by malicious users scattered throughout Twitter with the sole purpose of spreading misinformation. We applied two scenarios to compare how these regular agents behave in the Twitter network, with and without malicious agents, to study how much influence malicious agents have on the general susceptibility of the regular users. To achieve this, we implemented a belief value system to measure how impressionable an agent is when encountering misinformation and how its behavior gets affected. The results indicated similar outcomes in the two scenarios as the affected belief value changed for these regular agents, exhibiting belief in the misinformation. Although the change in belief value occurred slowly, it had a profound effect when the malicious agents were present, as many more regular agents started believing in misinformation.
ABSTRACT
Purpose>The study aimed to examine how different communities concerned with dementia engage and interact on Twitter.Design/methodology/approach>A dataset was sampled from 8,400 user profile descriptions, which was labelled into five categories and subjected to multiple machine learning (ML) classification experiments based on text features to classify user categories. Social network analysis (SNA) was used to identify influential communities via graph-based metrics on user categories. The relationship between bot score and network metrics in these groups was also explored.Findings>Classification accuracy values were achieved at 82% using support vector machine (SVM). The SNA revealed influential behaviour on both the category and node levels. About 2.19% suspected social bots contributed to the coronavirus disease 2019 (COVID-19) dementia discussions in different communities.Originality/value>The study is a unique attempt to apply SNA to examine the most influential groups of Twitter users in the dementia community. The findings also highlight the capability of ML methods for efficient multi-category classification in a crisis, considering the fast-paced generation of data.Peer review>The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2021-0208.
ABSTRACT
Social media is not only an essential platform for the dissemination of public health-related information, but also an important channel for people to communicate during the COVID-19 pandemic. However, social bots can interfere with the social media topics that humans follow. We analyzed and visualized Twitter data during the prevalence of the Wuhan lab leak theory and discovered that 29% of the accounts participating in the discussion were social bots. We found evidence that social bots play an essential mediating role in communication networks. Although human accounts have a more direct influence on the information diffusion network, social bots have a more indirect influence. Unverified social bot accounts retweet more, and through multiple levels of diffusion, humans are vulnerable to messages manipulated by bots, driving the spread of unverified messages across social media. These findings show that limiting the use of social bots might be an effective method to minimize the spread of conspiracy theories and hate speech online.
Subject(s)
COVID-19 , Social Media , Humans , COVID-19/epidemiology , Public Opinion , Pandemics , Social Network AnalysisABSTRACT
Web of Things (IoT) with profound learning (DL) is definitely developing and assumes a critical part in numerous applications, including clinical and medical care frameworks. It can assist clients in this field with getting a benefit as far as upgraded touchless verification, particularly in spreading irresistible illnesses like Covid sickness 2019 (Coronavirus). Despite the fact that there is various accessible security frameworks, they experience the ill effects of at least one of issues, like character extortion, loss of keys and passwords, or spreading sicknesses through touch confirmation instruments. To beat these issues, IoT-based keen control clinical validation frameworks utilizing DL models are proposed to improve the security element of clinical and medical services puts actually. This work applies IoT with DL models to perceive human appearances for verification in savvy control clinical frameworks. We use Raspberry Pi (RPi) on the grounds that it has minimal expense and goes about as the principal regulator in this framework. The establishment of a brilliant control framework utilizing broadly useful info/yield (GPIO) pins of RPi likewise upgraded the antitheft for savvy locks, and the RPi is associated with shrewd entryways. For client validation, a camera module is utilized to catch the face picture and contrast them and information base pictures for gaining admittance. The proposed approach performs face location utilizing the Haar overflow procedures, while for face acknowledgment, the framework involves the accompanying advances. The initial step is the facial component extraction step, which is finished utilizing the pretrained CNN models (ResNet-50 and VGG-16) alongside direct twofold example histogram (LBPH) calculation. The subsequent step is the characterization step which should be possible utilizing a help vector machine (SVM) classifier. Just ordered face as veritable prompts open the entryway;in any case, the entryway is locked, and the framework sends a notice email to the home/clinical spot with identified face pictures and stores the recognized individual name and time data on the SQL data set. The near investigation of this work shows that the methodology accomplished 99.56% precision contrasted and a few different related techniques. Copyright © 2022, Anka Publishers. All rights reserved.
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For the last two years, the world has been fighting an invisible enemy: COVID-19 (coronavirus). The spreading of this virus caused an unprecedented pandemic, bringing out some critical health system issues due to overcrowded hospitals and undersized medical personnel compared to the number of infected. Indeed, in this context, health facilities have proven inadequate in treating COVID-19 patients who were in quarantine at home, leading to overcrowded hospitals. An efficient home monitoring system would have reserved hospital beds for patients in severe conditions while, at the same time, doctors would have followed up on patients who had mild disease symptoms remotely. Unfortunately, the development of telemedicine was not enough;devices were not user-friendly and had insufficient memory to guarantee daily data storage. ROH-BOT is an IoT device that allows real-Time contact with the doctor, tracking the patient's vital values, and registers the critical parameters that characterize the specific disease. It also allows quick contact with a trusted third person due to its association with a Telegram bot. Moreover, thanks to its user-friendly characteristics, ROH-BOT increases the growing digitalization in the medical field and the democratization of medical technology. In this way, ROH-BOT aims to solve the difficulties related to home monitoring relieved during the pandemic. © 2022 IEEE.
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The tried and tested way for effective Knowledge Retrieval is by posting questions and retrieving data from the huge information repository. In the recent past the prevalence of pandemics and the spread of COVID-19, has led people to rigorously question the various forms of epidemiology data available on different sources. In general, the amount of information gathered is proportionate to the questioning patterns by the knowledge seeker. Question answering (QA) system is useful during unexpected situations, especially during a pandemic. In this paper, we have proposed a Knowledge Retrieval Question Answering system (KRQA) for answering the queries of users related to COVID-19. The KRQA system is divided into two modules. The first module consists of preprocessing (tokenization, stemming, bag of words) of the question to produce a word vector. The second module involves building, training, and testing the data repository. Feedforward neural network is used to extract the most relevant answer from a repository of all possible answers. The volume and quality of information about the pandemic scenario around the world are increased at a tremendous rate. Hence our work focuses on effective knowledge retrieval using question and answering approach. Our experimental results are found to give better results based on Percentage closeness, precision, and recall parameters. KRQA has the novelty of retrieving more relevant answers with good quality. © 2022 IEEE.
ABSTRACT
When public health emergencies occur, a large amount of low-credibility information is widely disseminated by social bots, and public sentiment is easily manipulated by social bots, which may pose a potential threat to the public opinion ecology of social media. Therefore, exploring how social bots affect the mechanism of information diffusion in social networks is a key strategy for network governance. This study combines machine learning methods and causal regression methods to explore how social bots influence information diffusion in social networks with theoretical support. Specifically, combining stakeholder perspective and emotional contagion theory, we proposed several questions and hypotheses to investigate the influence of social bots. Then, the study obtained 144,314 pieces of public opinion data related to COVID-19 in J city from March 1, 2022, to April 18, 2022, on Weibo, and selected 185,782 pieces of data related to the outbreak of COVID-19 in X city from December 9, 2021, to January 10, 2022, as supplement and verification. A comparative analysis of different data sets revealed the following findings. Firstly, through the STM topic model, it is found that some topics posted by social bots are significantly different from those posted by humans, and social bots play an important role in certain topics. Secondly, based on regression analysis, the study found that social bots tend to transmit information with negative sentiments more than positive sentiments. Thirdly, the study verifies the specific distribution of social bots in sentimental transmission through network analysis and finds that social bots are weaker than human users in the ability to spread negative sentiments. Finally, the Granger causality test is used to confirm that the sentiments of humans and bots can predict each other in time series. The results provide practical suggestions for emergency management under sudden public opinion and provide a useful reference for the identification and analysis of social bots, which is conducive to the maintenance of network security and the stability of social order.
ABSTRACT
As a result of the COVID-19 epidemic, there is a growing demand for robots to perform various operations which include service bots, cleaning, and disinfection bots. Viral contamination has been one of the major causes of human fatality which has abruptly increased in this situation. Availability of existing technologies is always surpassed by an effective one so as is the UV-Bot developed in this project. This bot aims for a highly accurate percentage of up to 96.8% of germ clearance at pre-defined conditions which are user-friendly. Also, the robot is designed in a compact size and effective shape to achieve maximum efficiency. The robot is deployed in hospital pathway and rooms for disinfection whereas human detection and obstacle avoidance has been included with a custom-developed algorithm that supports autonomous navigation and corner tracking facility. The robot also supports live streaming of the disinfecting site with an emergency alarm and stop in human detection. This type of robot is highly capable of destroying viral infections at a particular point which is validated using Taguchi analysis and also the robot is 3D modelled and tested using static and dynamic obstacles. Thus UV-Bot is manually controllable or autonomous which uses the A* algorithm to store or retrieve the disinfecting site map which is recorded if used frequently.
ABSTRACT
Instagram is a social media platform that allows users to share content such as photos and videos. Fake and bot account problems constitute a significant obstacle to social networking. Since fake and bot accounts have purposes such as increasing the number of followers, creating a perception by using misinformation, deceiving people, detecting these fake and bot accounts plays an essential role in creating a secure social network. Fake account detection is beneficial to keeping people safe from misinformation and malicious profiles on Instagram, ensuring customers' safe accounts, and preventing fraud. From this point, we aim to classify Instagram user profiles into fake, bot, and real accounts with classification algorithms. Additionally, we present a publicly available dataset for the fake, bot, and real accounts detection on Instagram. For data collection, real accounts were determined from our circle of friends, fake accounts were accessed by manual scanning from Instagram, and bot accounts were accessed by purchasing from bot account websites and mobile applications. These accounts' features were collected via web scraping. We use the seven classifiers to train classification models in fake, bot, and real profile detection. Our results show that the Random Forest gives the highest prediction accuracy with 90.2%.