ABSTRACT
Many people are trading in the forex market during the COVID-19 pandemic with the hope of earning money, but they are experiencing shortages due to the lack of information and technology-based tools for existing daily data. Sometimes traders only use moving averages in trading data, even though this information needs to be processed again to get the right inflection point. The objective of this research is to find inflection points based on Forex trading database. Another algorithm can also be used to determine the inflection point between two points on a moving average. This can be supported by the Bisection method used because it can guarantee that convergence will occur. The results show that the points resulting from the bisection calculation on the moving average provide a fairly accurate decision support for the location where the inflection point is located. From 10,000 data there is a standard deviation of 0.71 points which is very small compared to an average of 20 pips (points used as the difference in price values in forex). The use of the bisection method provides an accuracy of the results in seeing the inflection point of 87%. © 2023,International Journal of Advanced Computer Science and Applications. All Rights Reserved.
ABSTRACT
This paper discusses the design and implementation of an Internet of Things (IoT)-based telemedicine health monitoring system (THMS) with an early warning scoring (EWS) function that reads, assesses, and logs physiological parameters of a patient such as body temperature, oxygen saturation level, systemic arterial pressure, breathing patterns, pulse (heart) rate, supplemental oxygen dependency, consciousness, and pain level using Particle Photon microcontrollers interfaced with biosensors and switches. The Mandami fuzzy inference-based medical decision support system (FI-MDSS) was also developed using MATLAB to assist medical professionals in evaluating a patient's health risk and deciding on the appropriate clinical intervention. The patient's physiological measurements, EWS, and health risk category are stored on the Particle cloud and Thing Speak cloud platforms and can be accessed remotely and in real-time via the Internet. Furthermore, a RESTful application programming interface (API) was developed using GO language and PostgreSQL database to enhance data presentation and accessibility. Based on the paired samples t-tests obtained from 6 sessions with 10 trials for each vital sign per session, there were no significant differences between the clinical data obtained from the designed prototype and the commercially sold medical equipment. The mean differences between the compared samples for each physiological data were not more than 0.40, the standard deviations were less than 2.3, and the p-values were greater than 0.05. With a 96.67% accuracy, the FI-MDSS predicted health risk levels that were comparable to conventional EWS techniques such as the Modified National Early Warning Score (m-NEWS) and NEWS2, which are used in the clinical decision-making process for managing patients with COVID-19 and other infectious illnesses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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The COVID-19 pandemic has highlighted how the success of the appropriation of various healthcare innovations was due to agile and responsive innovation ecosystems. However, some progress was deterred in various country and regional contexts due to the delay in ascertaining the right stakeholders to work collaboratively with for health promotion. What has become evident is that stakeholder management plays a central role in the agility of an innovation ecosystem. This paper proposes a decision support tool that includes assessing the innovation capabilities and sustainability related activities of stakeholders. The tool is evaluated by two subject matter experts and applied to a case study of the key innovation ecosystem actors participating in the development of South Africa's first mRNA technology transfer hub. The strategic technology hub management tool is of a generic and agile nature to ensure that it can be applied to any project context for stakeholder management and decision making across various industries. The overall aim being that of informing an organization that has the role of being an ecosystem builder on other tools that they can quickly utilize to assess stakeholders. © 2022 IEEE.
ABSTRACT
Mental health is an ever-growing issue of concern, especially in light of the COVID pandemic. In this context, we study big data from social media over a 7-year time span to gauge evolving perceptions of mental health, and discuss our research findings, potentially useful for decision support in healthcare. We deploy topic modeling and sentiment analysis to estimate public perceptions of mental health issues, focusing on Twitter as the social media site. We claim that it is important to consider polarity as well as subjectivity in sentiment analysis to comprehend two different aspects of sentiment, i.e. orientation in the emotion, and extent of fact vs. opinion. We assert that ranking via topic modeling is beneficial to fathom the relative importance of issues over the years. We harness tools/techniques from natural language processing and data mining to discover knowledge from big data on social media, related to mental health. Some of our findings reveal that the sentiment around mental health has remained positive overall, but has decreased since the beginning of the COVID pandemic. Major events, such as elections and the pandemic, greatly impact the conversation surrounding mental health. Some topics have remained consistent throughout the years. In other topics, the tone of the public discussions has shifted. The outcomes of our study would be useful to a variety of professionals, ranging from data scientists to epidemiologists and psychologists. This work impacts big healthcare data in general. © 2022 IEEE.
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This chapter is a short introduction in the contemporary approaches aimed at the multidimensional processing and analysis of various kinds of signals, investigated in related research works, which were presented at the Third International Workshop "New Approaches for Multidimensional Signal Processing”, (NAMSP), held at the Technical University of Sofia, Bulgaria in July 2022. Some of the works cover various topics, as: moving objects tracking in video sequences, automatic audio classification, representation of color video чpeз 2-level tensor spectrum pyramid, etc., and also introduce multiple applications of the kind: analysis of electromyography signals, diagnostics of COVID based on ECG, etc. Short descriptions are given for the main themes covered by the book, which comprises the following three sections: multidimensional signal processing;applications of multidimensional signal processing, and applications of blockchain and network technologies. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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This paper describes an integrated, data-driven operational pipeline based on national agent-based models to support federal and state-level pandemic planning and response. The pipeline consists of (i) an automatic semantic-aware scheduling method that coordinates jobs across two separate high performance computing systems;(ii) a data pipeline to collect, integrate and organize national and county-level disaggregated data for initialization and post-simulation analysis;(iii) a digital twin of national social contact networks made up of 288 Million individuals and 12.6 Billion time-varying interactions covering the US states and DC;(iv) an extension of a parallel agent-based simulation model to study epidemic dynamics and associated interventions. This pipeline can run 400 replicates of national runs in less than 33 h, and reduces the need for human intervention, resulting in faster turnaround times and higher reliability and accuracy of the results. Scientifically, the work has led to significant advances in real-time epidemic sciences. © The Author(s) 2022.
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The spread of the COVID-19 pandemic across the world has presented a unique problem to researchers and policymakers alike. In addition to uncertainty around the nature of the virus itself, the impact of rapidly changing policy decisions on the spread of the virus has been difficult to predict. Using an epidemiological Susceptible-Infected-Recovered-Dead (SIRD) model as a basis, this paper presents a methodology for modeling many uncertain factors impacting disease spread, ultimately to understand how a policy decision may impact the community long term. The COVID-19 Decision Support (CoviDeS) tool, utilizes an agent-based time simulation model that uses Bayesian networks to determine state changes of each individual. The model has a level of interpretability more extensive than many existing models, allowing for insights to be drawn regarding the relationships between various inputs and the transmission of the disease. Test cases are presented for different scenarios that demonstrate relative changes in transmission resulting from different policy decisions. Further, we will demonstrate the model's ability to support decisions for a smaller sub-community that is contained in a larger population center (e.g. a university within a city). Results of simulations for the city of Los Angeles are presented to demonstrate the use of the model for parametric analysis that could give insight to other real-world scenarios of interest. Though improvements can be made in the model's accuracy relative to real case data, the methods presented offer value for future use either as a predictive tool or as a decision-making tool for COVID-19 or future pandemic scenarios. © 2022 Probabilistic Safety Assessment and Management, PSAM 2022. All rights reserved.
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The COVID-19 pandemic emphasised the need for decision-support tools to assist urban designers in building resilient and smart cities. Therefore, a multi-disciplinary systematic review was conducted following the PRISMA guideline to identify papers relevant for selecting appropriate methodologies that can be applied to build decision-support tools for resilient cities. This paper presents a list of 109 key references, selected from 8,737 records found from the searches, and identified major research themes, fundamental design interventions, and computer modelling techniques. We extracted six groups of interventions categorised by different scales of action: from an individual, crowds (social distancing and travel-related interventions), to a building, a neighbourhood/district, and a city. In addition, there are three sorts of computational modelling approaches, i.e., computer simulation, statistical models, and AI algorithms. Most of the studies developed models for predictive purposes, and 28% of the modelling studies built models for descriptive purposes. This work intends to empower urban designers and planners to overcome and get prepared for unpredictable disasters in pursuit of resilient and smart cities, particularly in the post-pandemic world. This review enables them to quickly find relevant papers as well as suitable methodologies and tools for a particular research purpose. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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COVID-19 still represents one of the greatest global challenges of the last decades in terms of medical, coordination and management aspects, but also on the societal and economic level. Even after more than two years, the rapidly changing requirements that the emerging variations of the virus call for, show that Austria – as the majority of countries and organizations – is still struggling with a stringent and pertinent management approach. The call for a comprehensive, applicable and interoperable solution portfolio including evidence-based analysis of current processes/structures, tools and infrastructures as well as lessons learned from the current pandemic response, is evident. The enhanced “ROADS to Health”-approach, currently evaluated by national funding agencies, reflects this aim: a holistic solution set aiming at a technologically supported, lessons learned based system for the pandemic management for the future. ROADS focuses on a basis for optimized crisis management for future pandemics/epidemics from a holistic, user-centric perspective. The concrete goal is to create a basis for a technologically supported measure matching to current requirements for decision-makers and critical infrastructures. Thus, interventions or future mitigation measures for the management of a pandemic are matched with concrete and current requirements. This measure matching will build upon the existing "Portfolio of Solutions" (POS) platform developed by AIT. Relevant medical/epidemiological, social, economic and legal fundamentals and different types and characteristics of pandemics/epidemics will also be considered (infection routes, morbidity and mortality risks, affectedness: age, gender ...) as well as various needs, given resources and processes. International lessons learned from the COVID-19 crisis, knowledge and results from merging practical experiences from crisis management feed into a concept design to facilitate and initiate technological support for enhanced future pandemics/epidemics tackling and potentially for other crisis situations. This keynote paper will draft the frame of this model by presenting the underlying background and basis of the ROADS to Health-solution set and open the floor for a wider range of perspectives of optimization in pandemic and crisis management. © 2022 IDIMT. All rights reserved.
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Artificial intelligence has finally brought in a qualitative leap in health care, allowing for the exploration of medical data for decision support and prediction. Recent research has demonstrated that artificial intelligence and machine learning is being used to combat the COVID-19 infection. Prediction models can be incorporated and thus aid in designing better strategies and making effective decisions. These technologies analyze past events to make better predictions about what will happen in the future, which may aid in preparation for potential threats and consequences. This survey paper aimed to cover a group of research that uses artificial intelligence applications to predict COVID-19 disease. This survey systematically presented the research to extract data mainly related to the type of article, publication date, research objectives, study context, results, methodology, algorithm, and data set © 2022 IEEE.
ABSTRACT
A novel coronavirus disease is considered the most dangerous epidemic spread in the world recently. The world health organization (WHO) has named this epidemic a novel COVID-19. According to the literature review, the most important symptoms that confirm the infection with this epidemic are high temperature, coughing, shortness of breath, and chest pain. In general, these symptoms have become the actual cause of most deaths for people who have the novel COVID-19 epidemic. These symptoms adopted in this study as essential criteria. The conflict between criteria formatted a challenge in this study. This paper aims to propose a framework for diagnosing the disease symptoms based on multiple criteria using an analytical hierarchy process (AHP) method. Therefore, decision support methods most suitable to solve various criteria problems. The results reported the most important criterion at the mean (0.407) and ±SD (0.166) for the fever. The least important criterion of the chest pain at the mean (0.116) and ±SD (0.070). While the cough criterion at the mean (0.254) and ±SD (0.099) and the shortness of breath criterion at the mean (0.223) and ±SD (0.127), respectively. This study presented an optimal framework for physicians to immediately diagnose coronavirus symptoms for the person with this disease. © 2022, Springer Nature Switzerland AG.
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The Vida Decision Support System (Vida) is an application of the Environment-Vulnerability-Decision-Technology (EVDT) integrated modeling framework specifically aimed at COVID-19 impact and response analysis. The development of Vida has been an international collaboration involving multidisciplinary teams of academics, government officials (including public health, economics, environmental, and demographic data collection officials), and others from six states: Angola, Brazil, Chile, Indonesia, Mexico, and the United States. These collaborators have been involved with the identification of decision support needs, the surfacing and creation of relevant data products, and the evaluation of prototypes, with the vision of creating an openly available online platform that integrates earth observation instruments (Landsat, VIIRs, Planet Lab's PlanetScope, NASA's Socioeconomic Data and Applications Center, etc.) with in-situ data sources (COVID-19 case data, local demographic data, policy histories, mobile device-based mobility indices, etc.). Vida both visualizes historical data of relevance to decision-makers and simulates possible future scenarios. The modeling techniques used include system dynamics for public health, EO-based change detection and machine learning for environmental analysis, and discrete-event simulation of policy changes and impacts. In addition to the direct object of this collaboration (the development of Vida), collaborators have also benefited from sharing individual COVID-19-related insights with the network and from considering COVID-19 response in a more integrated fashion. This work outlines the Vida Decision Support System concept and the EVDT framework on which it is based. The international team is using Vida to evaluate the outcomes in several large cities regarding COVID cases, environmental changes, economic changes and policy decisions. It provides an overview of the overlapping and diverging needs and data sources of each of the collaborating teams, as well as how each of those teams have contributed to the development of Vida. The current state of the Vida prototypes and plans for future development will be presented. Additionally, this work will discuss the lessons learned from this development process and their relevance to other integrated applications. Copyright © 2021 by the International Astronautical Federation (IAF). All rights reserved.
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The abrupt rise in Coronavirus cases has led to shortage of rapid and highly sensitive reverse transcriptase polymerase chain reaction (RT-PCR) testing kits for the diagnosis of coronavirus disease 2019 (COVID-19). Radiologists have found X-ray images could be useful for diagnosis of COVID. In this work, Diagnostic Decision Support for Medical Imaging (DDSM)++ is introduced to detect the different abnormal conditions in lung including COVID. The scarcity of COVID dataset is handled by using various spatial transform augmentation techniques, such as power law transformation, Gaussian blur, and sharpening. Also, to get the benefit of inference accelerators, an android mobile application is developed which is quantized and optimized for ARM Mali GPU. The DDSM++ model is an extended version of DDSM model (inspired from Densenet-121), and the X-ray images are preprocessed with Contrast Limited Adaptive Histogram Equalization (CLAHE) to improve the quality of X-ray images. The COVID X-ray images are obtained from the open source and the proposed method has obtained almost 98.47% accuracy for COVID detection. Further, the model is quantized to FP-16 using TFLITE and is utilized to benchmark the inference acceleration on Edge devices with ARM Mali GPU. About 30% and 80% reduction in inference time was observed for FP-32 and FP-16 models when run on ARM Mali GPU. Post quantization, about 5% drop in accuracy is observed for COVID detection. © 2021 IEEE.
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Compared to the automotive sector, where automation is the rule, in many other less standardized sectors automation is still the exception. This could soon hurt the productivity of industrialized countries, where the unemployment is low and the population is aging. Phenomena like the recent downfall in productivity, due to lockdowns and social distancing for prevention of health hazards during the COVID19 pandemic, only add to the problem. For these reasons, the relevance, motivation and intention for more automation in less standardized sectors has probably never been higher. However, available statistics say that providers and users of technologies struggle to bring more automation into action in automation-unfriendly sectors. In this paper, we present a decision support method for investment in automation that tackles the problem: the STIC analysis. The method takes a holistic and quantitative approach tying together technological, context-related and economic input parameters and synthetizing them in a final economic indicator. Thanks to the modelling of such parameters, it is possible to gain sensibility on the technological and/or process adjustments that would have the highest impact on the efficiency of the automation, thereby delivering value for both technology users and technology providers. © 2021 IEEE.
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This paper reports on the development of a model of COVID-19 transmission dynamics that takes into account a comprehensive mitigation protocol. This is necessary for public health decision support and making actionable recommendations on COVID-19 response. The comprehensive mitigation protocol includes (1) personal protection and social distancing, (2) use of smart applications for symptom reporting and contact tracing, (3) targeted testing based on identification of individuals with possible exposure and/or infection via symptom reporting and contact tracing, (4) surveillance testing, and (5) shelter, quarantine and isolation procedures. The proposed model (1) extends a common epidemiological discrete dynamic model with the comprehensive mitigation protocol, (2) uses Bayesian probability analysis to estimate the conditional probabilities of being in non-circulating epidemiological sub-compartments as a function of the mitigation protocol parameters, based on which it (3) estimates transition ratios among the compartments, and (4) computes a range of key performance indicators including health outcomes, mitigation cost and productivity loss. The proposed model can serve as a critical component for COVID-19 mitigation decision support and recommender systems, as part of a broader effort to support urgent pandemic response. © 2021 IEEE.
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COVID-19 epidemic continues to threaten public health with the appearance of new, more severe mutations, and given the delay in the vaccination process, the situation becomes more complex. Thus, the implementation of rapid solutions for the early detection of this virus is an immediate priority. To this end, we provide a deep learning method called CovSeg-Unet to diagnose COVID-19 from chest CT images. The CovSeg-Unet method consists in the first time of preprocessing the CT images to eliminate the noise and make all images in the same standard. Then, CovSeg-Unet uses an end-to-end architecture to form the network. Since CT images are not balanced, we propose a loss function to balance the pixel distribution of infected/uninfected regions. CovSeg-Unet achieved high performances in localizing COVID-19 lung infections compared to others methods. We performed qualitative and quantitative assessments on two public datasets (Dataset-1 and Dataset-2) annotated by expert radiologists. The experimental results prove that our method is a real solution that can better help in the COVID-19 diagnosis process © 2022,International Journal of Advanced Computer Science and Applications.All Rights Reserved
ABSTRACT
For agent-based social simulations to be a powerful tool for policy makers and other decision makers in a given context (e.g. the current COVID-19 pandemic), they need to be socially realistic and thus, appropriately represent complex social concepts, such as social rules. In this paper, we focus on norms. Norms describe ‘normal’ behavior and aim at assuring the interests and values of groups or the society as a whole. People react differently to norms, and focus only on the parts that are relevant for them. Furthermore, norms are not only restrictions on behavior, but also trigger new behavior. Seeing a norm only as a restriction on certain behavior misses important aspects and leads to simulations that can be very misleading. Different perspectives need to be incorporated into the simulation to capture the variety of ways different stakeholders react to a norm and how this affects their interaction. We therefore present an approach to include these different perspectives on norms, and their consequences for different people and groups in decision support simulations. A perspective is specified by their goals, actions, effects of those actions, priorities in values, and social affordances. Through modeling perspectives we enable policy makers and other decision makers (the users) to be active in the modeling process and to tailor the simulation to their specific needs, by representing norms as modifiable objects, and providing textual and graphical representations of norms. This provides them with differentiated insights meaningful for the decisions they are faced with. We indicate the requirements for both the simulation platform as well as the agents that follow from our approach. Early explorations of our social simulation are showing the necessity of our approach. © 2022, Springer Nature Switzerland AG.
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The paper discusses the prospects of integrating the concepts of Digital Earth and Smart Cities to achieve synergy and improve global governance by harmonizing decision supports at different scales. Achieving this goal requires overcoming the scale-dependent differentiation of information systems and thus integration of Smart Cities with their geospatial context. A possible approach can be based on the unique property of spatial and temporal localizations – their invariance for all subjects. Spatial and temporal localizations are fundamentally different from classical "thematic" ontologies, inevitably contradictory and relative. Implementing the concept of "ontological pluralism" will allow the seamless integration of heterogeneous information into a single spatial and temporal volume. The urgency of practical implementation of such systems is demonstrated briefly against the background of the COVID-19 pandemic and the need to find new approaches to the processing and analyzing environmental factors. © 2021 Copyright for this paper by its authors.