How to leverage intelligent agents and complex event processing to improve patient monitoring

This paper describes an intelligent ecosystem that can continuously monitor patients' health conditions, whether at home, at work or during recreational activities, by leveraging a creative blend of wearable medical devices, intelligent agents (IA) and complex event processing (CEP). With the help of a smart application, linking wearable devices and the power of IA and CEP, patients will be constantly and actively supervised during their daily activities. This can even save their lives in case they experience sudden or gradual problems. Thanks to our system, patients with chronic illnesses that are not serious but potentially unstable will no longer overburden first aid services. This is also helpful in containing the spread of COVID-19. Specifically, in this paper, we focus on automatic monitoring of vital parameters, electrocardiogram analysis and psoriasis detection. Experimental results conducted on real patients show how promising our approach is.

Agent-Based Social Simulation for Policy Making

In agent-based social simulations (ABSS), an artificial population of intelligent agents that imitate human behavior is used to investigate complex phenomena within social systems. This is particularly useful for decision makers, where ABSS can provide a sandpit for investigating the effects of policies prior to their implementation. During the Covid-19 pandemic, for instance, sophisticated models of human behavior enable the investigation of the effects different interventions can have and even allow for analyzing why a certain situation occurred or why a specific behavior can be observed. In contrast to other applications of simulation, the use for policy making significantly alters the process of model building and assessment, and requires the modelers to follow different paradigms. In this chapter, we report on a tutorial that was organized as part of the ACAI 2021 summer school on AI in Berlin, with the goal of introducing agent-based social simulation as a method for facilitating policy making. The tutorial pursued six Intended Learning Outcomes (ILOs), which are accomplished by three sessions, each of which consists of both a conceptual and a practical part. We observed that the PhD students participating in this tutorial came from a variety of different disciplines, where ABSS is mostly applied as a research method. Thus, they do often not have the possibility to discuss their approaches with ABSS experts. Tutorials like this one provide them with a valuable platform to discuss their approaches, to get feedback on their models and architectures, and to get impulses for further research. © 2023, Springer Nature Switzerland AG.

Compliance to "unpleasant" actions of crisis management: Some remarks from a management control perspective

In managing the Covid-16 pandemic, policy makers took actions which require the cooperation of individual citizens to succeed while the actions partially come at remarkable costs for individuals. The brief paper employs a thought experiment to identify factors which affect individuals' propensity to cooperate in the public goods game. These factors reasonably comprise, for example, risk perception and attitude towards risk, embeddedness in a social network or the desire for social approval and may differ remarkably among the individuals of a collective. The paper adopts a management control perspective which appears to be particularly helpful to identify how to implement policy makers' actions with respect to the diverse individuals in a collective. In order to predict the overall outcome of "unpleasant" actions, an approach is required which allows to capture the heterogeneity of individuals within a collective which makes agent-based modelling a promising candidate. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

An Intelligent Collaborative Image-Sensing System for Disease Detection

With the growth of smart medical devices and applications in smart hospitals, home care facilities, nursing, and the Internet of Medical Things (IoMT) are becoming more ubiquitous. It uses smart medical devices and cloud computing services, and basic Internet of Things (IoT) technology, to detect key body indicators, monitor health situations, and generate multivariate data to provide just-in-time healthcare services. In this article, we present a novel collaborative disease detection system based on IoMT amalgamated with captured image data. The system can be based on intelligent agents, where every agent explores the interaction between different medical data obtained by smart sensor devices using reinforcement learning as well as targets to detect diseases. The agents then collaborate to make a reliable conclusion about the detected diseases. Intensive experiments were conducted using medical data. The results show the importance of using intelligent agents for disease detection in healthcare decision-making. Moreover, collaboration increases the detection rate, with numerical results showing the superiority of the proposed framework compared with baseline solutions for disease detection. © 2001-2012 IEEE.

Optimization of Coronavirus Pandemic Model Through Artificial Intelligence

Artificial intelligence is demonstrated by machines, unlike the natural intelligence displayed by animals, including humans. Artificial intelligence research has been defined as the field of study of intelligent agents,which refers to any system that perceives its environment and takes actions that maximize its chance of achieving its goals. The techniques of intelligent computing solve many applications of mathematical modeling. The researchworkwas designed via a particularmethod of artificial neural networks to solve the mathematical model of coronavirus. The representation of the mathematical model is made via systems of nonlinear ordinary differential equations. These differential equations are established by collecting the susceptible, the exposed, the symptomatic, super spreaders, infection with asymptomatic, hospitalized, recovery, and fatality classes. The generation of the coronavirus model's dataset is exploited by the strength of the explicit Runge Kutta method for different countries like India, Pakistan, Italy, and many more. The generated dataset is approximately used for training, validation, and testing processes for each cyclic update in Bayesian Regularization Backpropagation for the numerical treatment of the dynamics of the desired model. The performance and effectiveness of the designed methodology are checked through mean squared error, error histograms, numerical solutions, absolute error, and regression analysis. © 2023 Tech Science Press. All rights reserved.

AN APPROACH TO SEMANTIC EDUCATIONAL CONTENT MINING USING NLP

The COVID-19 epidemic had caused one of the most significant disruptions to the global education system. Many educational institutions faced sudden pressure to switch from face-to-face to online delivery of courses. The conventional classes are no longer the primary means of delivery;instead, online education and resources have become the prominent approach. With the increasing demand for supplementary course materials to fulfill the needs of each area of study, students began to use search engines and online resources that contain discussions, practical demonstrations, and tutorial videos to aid students in their studies and course work. This study addresses the underlying challenges of retrieving relevant online educational materials by introducing an intelligent agent for semantic data mining. It works as middleware infrastructure that allow context-aware data processing and mining. YouTube was used to assess the consistency of the proposed model since it returns a large number of results in its search pool. The results showed that using the extraction of topics method, the similarities scores with the proposed model provided favorable results. Furthermore, an improvement in video ranking and sorting was realized. According to the findings, using this method provided users with a more productive and reliable study experience. © Proceedings of the International Conference on E-Learning 2022, EL 2022 - Part of the Multi Conference on Computer Science and Information Systems 2022, MCCSIS 2022. All rights reserved.

A Smart Ecosystem to improve Patient Monitoring using Wearables, Intelligent Agents, Complex Event Processing and Image Processing

Our work describes a smart-ecosystem able to mon-itor patients' health condition, even at home or at work, by ex-ploiting a creative blend of Medical Wearables, Intelligent Agents, Complex Event Processing and Image Processing. With the help of a smart application, that links together the Wearables and the power of Artificial Intelligence, patients will be continuously and actively supervised during their daily activities. This can even save their lives, in case sudden or gradual issues should occur. Using our system, patients with non-severe though potentially unstable chronic diseases will no longer overburden first aid services. This is also useful for containing the spread of COVID-19. Specifically, in this paper we focus on automated vitals monitoring, electrocardiogram (ECG) analysis, and Psoriasis detection. © 2022 IEEE.

City-Level Structured Prediction and Simulation Model of COVID-19

It is important for social public security and urban management to explore the spread of infectious diseases. A city-level structured prediction and simulation model for COVID-19 is proposed. This model is consisted of SEIR and social network model on the basis of latest infectious disease dynamics theory and real geographic networks. The prediction region is divided into multiple levels. Specifically, a bipartite network is applied to simulate the relationship between public facilities and community nodes at the macro level, and a modified SEIR is applied to simulate the infection within nodes at the micro level. Besides, intelligent agent is applied to track the individual transmission process. The contrast experimental results based on the confirmed and cursed cases of Wuhan and Beijing in 2020 published by National Health Commission, show that the proposed model has better flexibility and higher accuracy, and reflects the distribution and movement of people more directly. © 2022 Institute of Computing Technology. All rights reserved.

An Intelligent Ecosystem to improve Patient Monitoring using Wearables and Artificial Intelligence

Our work describes a smart-ecosystem able to monitor patients' health condition, even at home or at work, by exploiting a creative blend of Medical Wearables, Intelligent Agents, Complex Event Processing and Image Processing. With the help of a smart application, that links together the Wearables and the power of Artificial Intelligence, patients will be continuously and actively supervised during their daily activities. This can even save their lives, in case sudden or gradual issues should occur. Thanks to our system, patients with non-severe though potentially unstable chronic diseases will no longer overburden first aid services. This is also useful for containing the spread of COVID-19. Specifically, in this paper we focus on automated vitals monitoring, electrocardiogram (ECG) analysis, and Psoriasis detection. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

Intelligent Agents and Complex Event Processing to enhance Patient Monitoring

This paper describes a smart ecosystem able to continuously monitor patients' health condition, at home or at work or during recreational activities, by exploiting a creative blend of Medical Wearables, Intelligent Agents (IA) and Complex Event Processing (CEP). With the help of a smart application, that links together the Wearables and the power of IA and CEP, patients will be continuously and actively supervised during their daily activities. This can even save their lives, in case sudden or gradual issues should occur. Thanks to our system, patients with non-severe though potentially unstable chronic diseases will no longer overburden first-aid services. This is also useful for containing the spread of COVID-19. Specifically, in this paper we focus on automated vitals monitoring, electrocardiogram (ECG) analysis, and psoriasis detection. Experimental results carried out on real patients show how promising our approach is. © 2022 Copyright for this paper by its authors.

Analysis and simulation of social behavior during the COVID-19 pandemic in Argentina, using intelligent agents

The present work is an innovative educational strategy that uses a Final Integrative Work (FIW) as a method of evaluation of subjects of the Computer Engineering degree where students learn different subjects such as Artificial Intelligence and Databases, through real world problems related to COVID-19. The evaluation process through the FIW is based on several skills acquisition and by measuring the way in which students apply concepts of Databases and intelligent agents by means of numerical simulations that involves social behavior in times of the COVID-19 pandemic in the province of Tucuman, in the northwest of Argentina. The students carried out simulations of a multiagent system through the tool Netlogo, applying rules with a high impact factor for tackling a decision making problem. The results observed suggest that a paradigm shift in the degree evaluation processes is possible and necessary.

Medical Chatbot using AI and NLP

The use of chatbots has grown rapidly across industries, including marketing, assistive systems, education, healthcare, cultural heritage, and entertainment. This paper discusses the incentives for using chatbots and explains how useful chatbots are in various contexts. As intelligent software and hardware, also known as intelligent agents, are developed and analyzed, Artificial Intelligence (AI) is becoming more and more integrated into daily lives. From manual labor to complex procedures, intelligent agents are capable of performing a wide range of tasks. One of the simplest and most common forms of intelligent human-computer interaction is the chatbot, which is a classic example of an artificial intelligence Human-Computer Interaction (HCI) system. A chatbot is described as "a computer program designed to simulate interaction with human users, particularly over the Internet." In addition to chatbots, it also called smart bots, interactive agents, digital assistants, and intelligent conversational objects. In the midst of the COVID-19 pandemic, going to the doctor is no longer an indulgence. A chatbot is a Natural Language Processing (NLP) based chatbot to help with basic medical questions. Only the best knowledge of a chatbot can be used to answer medical questions.

A novel transfer deep learning model with reinforcement-learning-based hyperparameter optimization for short-term load forecasting during the COVID-19 pandemic

The coronavirus disease 2019 pandemic has significantly impacted the world. The sudden decline in electricity load demand caused by strict social distancing restrictions has made it difficult for traditional models to forecast the load demand during the pandemic. Therefore, in this study, a novel transfer deep learning model with reinforcement-learning-based hyperparameter optimization is proposed for short-term load forecasting during the pandemic. First, a knowledge base containing mobility data is constructed, which can reflect the changes in visitor volume in different regions and buildings based on mobile services. Therefore, the sudden decline in load can be analyzed according to the socioeconomic behavior changes during the pandemic. Furthermore, a new transfer deep learning model is proposed to address the problem of limited mobility data associated with the pandemic. Moreover, reinforcement learning is employed to optimize the hyperparameters of the proposed model automatically, which avoids the manual adjustment of the hyperparameters, thereby maximizing the forecasting accuracy. To enhance the hyperparameter optimization efficiency of the reinforcement-learning agents, a new advance forecasting method is proposed to forecast the state-action values of the state space that have not been traversed. The experimental results on 12 real-world datasets covering different countries and cities demonstrate that the proposed model achieves high forecasting accuracy during the coronavirus disease 2019 pandemic. © 2022 - IOS Press. All rights reserved.

Science gateway adoption using plug-in middleware for evidence-based healthcare data management

There is a growing need for next-generation science gateways to increase the accessibility of emerging large-scale datasets for data consumers (e.g., clinicians, researchers) who aim to combat COVID-19-related challenges. Such science gateways that enable access to distributed computing resources for large-scale data management need to be made more programmable, extensible, and scalable. In this article, we propose a novel socio-technical approach for developing a next-generation healthcare science gateway, namely, OnTimeEvidence that addresses data consumer challenges surrounding the COVID-19 pandemic related data analytics. OnTimeEvidence implements an intelligent agent, namely, Vidura Advisor that integrates an evidence-based filtering method to transform manual practices and improve scalability of data analytics. It also features a plug-in management middleware that improves the programmability and extensibility of the science gateway capabilities using microservices. Lastly, we present a usability study that shows the important factors from data consumers' perspective to adopt OnTimeEvidence with chatbot-assisted middleware support to increase their productivity and collaborations to access vast publication archives for rapid knowledge discovery tasks. © 2022 John Wiley & Sons, Ltd.

Optimal Resource Allocation during Epidemics using Reinforcement Learning

Epidemics can prove to be disastrous, which has been further emphasized by the recent COVID-19 pandemic, and several countries like India lack sufficient resources to meet the population's needs. It is therefore important that the limited testing and protective resources are utilized such that the disease spread is minimized and their reach to the most vulnerable demographic is maximized. This paper studies the scope of intelligent agents in aiding authorities with such policy-making decisions. This is done by exploring the performance of various action selection methods on custom environments dealing with socio-economic groups and Indian states. Experiments using multi-armed bandit techniques provide greater insight into administrative decisions surrounding resource allocation and their future potential for greater use in similar scenarios. © 2022 IEEE.

Formation of the Structure of Multilayer Polyagent Functionals

The processes of intellectual monitoring in emergencies are studied. The intelligent monitoring system is an environment for creating and using intelligent agents to provide knowledge of decision-making processes. In emergencies, objects acquire new properties quickly, and the informativeness of the results of previous observations decreases. To increase the power of data mining tools, monitoring agents are combined into agent functionalities with a multi-tier structure. The paper presents the results of research on the processes of formation of multi-echelon polyagent functionals. The efficiency of construction of a multi-echelon polyagent functional in solving the problem of predicting the incidence of the population of Ukraine on Covid-19 in conditions of low informativeness of the results of observations has been experimentally confirmed. © 2021 Copyright for this paper by its authors

Multi-Agent simulation modeling of Corona Virus Disease 2019 transmission based on various age groups

Multi-Agent System (MAS) is an important branch of artificial intelligence research. This study uses the bottom-up characteristics of multi-agents to construct multi-agent simulation models for Corona Virus Disease 2019 (COVID-19) virus prevention and control based on different age groups. The development of the epidemic and the infection of residents of all ages under different prevention and control measures issued by the government were studied. The simulation results proved that the multi-agent modeling method could effectively capture the emergence of complex systems. Its experimental conclusions provided a basis for predicting the development of the epidemic and provide scientific support for government decision-making. © 2021 IEEE.

Expert Decision Support System for Donor Identification Using Multi-Agent System

Convalescent Plasma (CP) therapy is an efficient method in the treatment of COVID-19 patients who either have a weak immune system or who are early in their illness. The notable setback for the implementation of the CP therapy lies in understanding the availability and spatial distribution of plasma donors. A multi-agent-based expert system is proposed in this paper to identify a suitable plasma donor in a short span and also in an efficient manner. Moreover, the issues with blood banks are twofold in connection with uneven intra-state and interstate distribution and lacuna of necessary facilities like the Component Blood Separation Units (CBSU) and Apheresis. The proposed expert system would remove the barriers of non-uniform distribution of blood banks and facilities across the country, and will provide a suitable solution to overcome the pandemic using multi-agent systems if implemented systematically. © 2021 IEEE.

A Smart Framework for Automatically Analyzing Electrocardiograms

In the last months, due to the pandemic, telemedicine has been emerging more and more as a vital technology for providing medical care to patients, while also attempting to minimize COVID-19 transmission among patients, families, and medical doctors. This involves developing and exploiting virtual platforms enabling clinicians to remotely monitor patients' vitals, such as the blood pressure or the electrocardiogram (ECG). In this context, this paper aims at defining a smart framework for automatically analyzing electrocardiograms, to be used at the patient's home or at the entrance of First Aids, allowing to: (i) efficiently and effectively discover normal and anomalous situations in patient's ECGs;(ii) automatically collect ECGs from commercial and effective ECG devices;(iii) be integrated into a smart app, supported by intelligent agents, which promptly provides patients with feedback about their health status. © 2021 IEEE.

Ubiquitous learning a new challenge of ubiquitous computing: State of the art

New technological developments made interaction possible with computer systems and applications anywhere and anytime. These applications must be capable to be adapted to the user as a person and his current situation. In 2021, following the pandemic COVID-19, learners across the country have had the opportunity to experience online modes of learning. Therefore, in the current study, an attempt has been carried out to develop a new paradigm of learning, known as ubiquitous learning or U-learning (UL), which is supported by ubiquitous computing technologies. Ubiquitous learning has become an economic and scientific issue, especially with the COVID-19 pandemic. Furthermore, the paper also aims to provide fundamental information related to U-learning as the definition and characteristics of U-learning and the areas of the most common applications in our daily lives. Finally, U-learning applications in the different domains are explained and compared to further improve the understanding of the concept of U-learning and the importance of using UL in our lives. © 2021 IEEE.