Stacking Classifier-based Automated Insurance Fraud Detection System

During the Covid-19 pandemic, the insurance industry's digital shift quickened, resulting in a surge in insurance fraud. To combat insurance fraud, a system that securely manages and monitors insurance processes must be built by combining a machine learning classification framework with a web application. Examining and identifying fraudulent features is a frequent method of detecting fraud, but it takes a long time and can result in false results. One of these issues is addressed by the proposed solution. By digitalizing the paper-based workflow in insurance firms, this paper intends to improve the efficiency of the existing approach. This method also aimed to improve the present approach's data management by integrating a web application with a machine learning stacking classifier framework experimented on a linear regression-based iterative imputed data for detecting fraud claims and making the entire claim processing and documentation process more robust and agile. © 2022 IEEE.

Managing and assembling population-scale data streams, tools and workflows to plan for future pandemics within the INFORM-Africa Consortium

The article focuses on the establishment of the INFORM-Africa Research Hub, which aims to use big data from South Africa and Nigeria to improve pandemic preparedness. Topics include the investigation of SARS-CoV-2 transmission pathways, the impact of movement-based restrictions on COVID-19 risk factors, and the interplay between SARS-CoV-2 and HIV in Africa.

Public Health Data of the People, by the People, for the People: A Public Health of Consequence, June 2023

The author discusses a study by Krieger and colleagues, published within the issue which presents information on the alarming decreases in response rates across six national U.S. surveys in 2020 compared with those in 2019. Topics include people who were more likely to complete surveys than those who did, importance of the application of an equity-focused lens to data collection, and the impact of the COVID-19 pandemic on response rates.

The Ethics of Location Tracking During the COVID-19 Pandemic and Beyond: Building Awareness and Consensus

This article highlights the relatively limited but growing discussion surrounding ethical guidelines for the use of location tracking technology. After a review of recent literature related to location data and geoprivacy, this article is divided into two sections: The first highlights views of public officials and location tracking experts over the potential misuse of location data, especially in the context of the COVID-19 pandemic. The data come from available transcripts of the Location Tech Task Force organized in 2020 by the American Geographical Society as part of its EthicalGEO initiative. The second section documents various institutional approaches to elevate the dialogue and inform governance of location-based data and technology, including the development of the Locus Charter, an emerging international framework on the ethical use of location data. In conclusion, we urge the professional and academic geographic communities to engage with the elaboration and dissemination of ethical frameworks to guide the use and management of data from location tracking technology. (English) [ FROM AUTHOR] La reciente erudición geográfica feminista ha urgido a los geógrafos a distanciarse de los enfoques androcéntricos y eurocéntricos, y a abrir la disciplina a perspectivas diversas. En tanto que numerosos estudios se han enfocado a diversificar y descolonizar la geografía por medio de prácticas de reclutamiento, tutoría y producción de conocimiento, solo muy pocos han analizado cómo se traduce la diversidad en las prácticas de enseñanza, en particular en contextos donde la diversidad está relativamente bien establecida entre el personal. Basado en una encuesta por cuestionario entre el personal docente, en un análisis del contenido de los programas de los cursos y un análisis cuantitativo de los datos de los empleados del departamento, este artículo explora hasta qué punto la diversidad dentro del departamento conduce a la diversidad en las prácticas de la enseñanza. Desarrollando un marco de los espacios de la diversidad, analizamos tres espacios que potencialmente permiten practicar la diversidad en la enseñanza: El espacio académico del departamento promueve la libre elección de los tópicos de investigación y enseñanza, y las condiciones flexibles del trabajo;el espacio del departamento permite a los individuos asumir compromisos en la configuración de la enseñanza geográfica;y el espacio del conocimiento promueve la diversidad como un ideal. Sin embargo, encontramos que practicar la diversidad en geografía implica enfrentar los retos de las estructuras universitarias tradicionales y neoliberales y de las jerarquías formales y percibidas. Aún más, existe una necesidad de prácticas concretas sobre diversidad a niveles individuales e institucionales para llevar activamente las diversas perspectivas al salón de clase. (Spanish) [ FROM AUTHOR] 女权地理学的最新研究, 敦促地理学者远离以男性和欧洲为核心的方法, 接受不同的观点。许多研究都侧重通过招聘、指导和知识生产, 去实现地理学的多样化和去殖民化。只有少数研究分析了多样性如何转化为教学实践（尤其是在教职员工多样性相对稳定的情况下）。基于教师问卷调查、课程大纲内容分析以及对地理系员工数据的定量分析, 本文探讨了地理系的多样性在多大程度上导致教学实践的多样性。我们建立了一个多样性的空间框架, 分析了可能实现教学多样性的三个空间："学术空间"促进对研究课题、课程题目和灵活工作条件的自由选择, "地理系空间"使个人能够参与地理教学的建设, "知识空间"促进理想的多样性。然而, 传统的和新自由主义的大学体系以及严格的等级制度, 是实现地理多样性的挑战。此外, 还需要在个人和体制层面采取切实的多样性实践, 积极地将不同观点带入课堂。 (Chinese) [ FROM AUTHOR] Copyright of Professional Geographer is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Digitalization of the supply chain: transformation factors

PurposeThere is an increasing interest in the supply chain's digitalization, yet the topic is still in the preliminary stages of academic research. The academic literature has no consensus and is still limited to research assessing the supply chain's digitalization of organizations. This study aims to explore the supply chain digitalization drivers to understand the emerging phenomena. More specifically, the authors devised from the literature the most common factors in assessing the readiness in scaling supply chain digitalization.Design/methodology/approachThis study followed a five-phased systematic literature review (SLR) methodology in this research: designing, analyzing, conducting, writing and assessing the quality of the review. The SLR is beneficial for justifying future research regardless of the complex process that requires dealing with high-level databases, information filtering and relevancies of the content. Through analysis of 347 titles and s and 40 full papers, the authors showed and discussed the supply chain digitalization: transformation factors.FindingsThe results generated three main themes: technology, people and processes. The study also generated ten subthemes/primary drivers for assessing the readiness for supply chain digitalization in organizations: IT infrastructure, cybersecurity systems, digitalization reskilling and upskilling, digitalization culture, top management support, digitalization and innovation strategy, integrated supply chain, digital innovation management, big data management and data analytics and government regulations. The importance of each factor was discussed, and future research agenda was presented.Research limitations/implicationsWhile the key drivers of the supply chain digitalization were identified, there is still a need to study the statistical correlation to confirm the interrelationships among factors. This study is also limited by the articles available in the databases and content extraction.Practical implicationsThis study supports decision-makers in understanding the critical drivers in digitalizing the supply chain. Once these factors are studied and comprehended, managers and decision-makers could better anticipate and allocate the proper resources to embark on the digitalization journey and make informed decisions.Originality/valueThe digitalization of the supply chain is more critical nowadays due to the global disruptions caused by the Coronavirus (COVID-19) pandemic and the surge of organizations moving toward the digital economy. There is a gap between the digital transformation pilot studies and implementation. The themes and factors unearthed in this study will serve as a foundation and guidelines for further theoretical research and practical implications.

A global federated real-world data and analytics platform for research.

Objective: This article describes a scalable, performant, sustainable global network of electronic health record data for biomedical and clinical research. Materials and Methods: TriNetX has created a technology platform characterized by a conservative security and governance model that facilitates collaboration and cooperation between industry participants, such as pharmaceutical companies and contract research organizations, and academic and community-based healthcare organizations (HCOs). HCOs participate on the network in return for access to a suite of analytics capabilities, large networks of de-identified data, and more sponsored trial opportunities. Industry participants provide the financial resources to support, expand, and improve the technology platform in return for access to network data, which provides increased efficiencies in clinical trial design and deployment. Results: TriNetX is a growing global network, expanding from 55 HCOs and 7 countries in 2017 to over 220 HCOs and 30 countries in 2022. Over 19 000 sponsored clinical trial opportunities have been initiated through the TriNetX network. There have been over 350 peer-reviewed scientific publications based on the network's data. Conclusions: The continued growth of the TriNetX network and its yield of clinical trial collaborations and published studies indicates that this academic-industry structure is a safe, proven, sustainable path for building and maintaining research-centric data networks.

Artificial Intelligence-enabled Internet of Medical Things for COVID-19 pandemic data management

The dreaded coronavirus (COVID-19) disease traceable to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2) has killed thousands of people worldwide, and the World Health Organization (WHO) has proclaimed the viral respiratory disease a human pandemic. The adverse flare of COVID-19 and its variants has triggered collaborative research interests across all disciplines, especially in medicine and healthcare delivery. Complex healthcare data collected from patients via sensors and devices are transmitted to the cloud for analysis and sharing. However, it is pretty difficult to achieve rapid and intelligent decisions on the processed information due to the heterogeneity and complexity of the data. Artificial intelligence (AI) has recently appeared as a promising paradigm to address this issue. The introduction of AI to the Internet of Medical Things (IoMT) births the era of AI of Medical Things (AIoMT). The AIoMT enables the autonomous operation of sensors and devices to provide a favourable and secure environmental landscape to healthcare personnel and patients. AIoMT finds successful applications in natural language processing (NLP), speech recognition, and computer vision. In the current emergency, medical-related records comprising blood pressure, heart rate, oxygen level, temperature, and more are collected to examine the medical conditions of patients. However, the power usage of the low-power sensor nodes employed for data transmission to the remote data centres poses significant limitations. Currently, sensitive medical information is transmitted over open wireless channels, which are highly susceptible to malicious attacks, posing a significant security risk. An insightful privacy-aware energy-efficient architecture using AIoMT for COVID-19 pandemic data handling is presented in this chapter. The goal is to secure sensitive medical records of patients and other stakeholders in the healthcare domain. Additionally, this chapter presents an elaborate discussion on improving energy efficiency and minimizing the communication cost to improve healthcare information security. Finally, the chapter highlights the open research issues and possible lines of future research in AIoMT.

Resilient reverse logistics with blockchain technology in sustainable food supply chain management during COVID‐19

COVID‐19, which is a global problem, affects the all supply chains throughout the world. One of the supply chains most affected by COVID‐19 is food supply chains. Since the sustainable food supply chain processes are complex and vulnerable in terms of product variety, it has been negatively affected by the operational effects of COVID‐19. While the problems experienced in the supply chain processes and raw material constraints caused stops in production, the importance of new business models and production approaches came to the fore. One of the issues of increasing importance is the adoption of reverse logistics activities in sustainable food supply chains and increasing the resilience of food supply chains by integrating blockchain technology into processes. However, adapting blockchain technology to increase the resilience of reverse logistics activities in the food supply chain has advantages as well as risks that need to be considered. Therefore, it is aimed to determine these risks by using fuzzy synthetic evaluation method for eliminating the risks of blockchain adaptation for flexible reverse logistics in food supply chains to increase resiliency. The novelty of this study is that besides discussing about the benefits of BC‐T, it is to identify the risks it can create, to eliminate these risks and to guide the establishment of resilience in reverse logistics activities of SFSCs. According to results, the risks with the highest value among the subrisks are determined as data security risks. Data management risks are calculated as the risk with the highest value.

Design and Implementation of Epidemic Big Data Visualization Platform Based on Vue + ECharts

Data visualization technology makes massive data more intuitive and easy to analyze. Based on the epidemic data from the National Bureau of Statistics of China, with the help of ECharts chart, elementUI component library and Vue technology, the data are visualized by using visualization technology and map integration. Through node. JS, Express The framework and MySQL technology realize the annual data management, regional data management and user management of the epidemic situation, display the epidemic situation of each region from multiple perspectives, and provide users with a reliable and convenient understanding channel and data management platform. It provides convenience for people to understand the data of the new coronavirus epidemic, analyze the development trend of the epidemic and manage the big data of the epidemic. © 2023 IEEE.

Analyzing the Evolution of Disinformation Content on Facebook - a Pilot Study

Disinformation spread on social media generates a truly massive amount of content on a daily basis, much of it not quite duplicated but repetitive and related. In this paper, we present an approach for clustering social media posts based on topic modeling in order to identify and formalize an underlying structure in all the noise. This would be of great benefit for tracking evolving trends, analyzing large-scale campaigns, and focusing efforts on debunking or community outreach. The steps we took in particular include harvesting through CrowdTangle huge collection of Facebook posts explicitly identified as containing disinformation by debunking experts, following those links back to the people, pages and groups where they were shared then collecting all posts shared on those channels over an extended period of time. This generated a very large textual dataset which was used in the topic modeling experiments attempting to identify the larger trends in the available data. Finally, the results were transformed and collected in a Knowledge Graph for further study and analysis. Our main goal is to investigate different trends and common patterns in disinformation campaigns, and whether there exist some correlations between some of them. For instance, for some of the most recent social media posts related to COVID-19 and political situation in Ukraine. © 2022 Copyright for this paper by its authors.

Big Data Management Algorithms, Deep Learning-Based Object Detection Technologies, and Geospatial Simulation and Sensor Fusion Tools in the Internet of Robotic Things

The objective of this systematic review was to analyze the recently published literature on the Internet of Robotic Things (IoRT) and integrate the insights it articulates on big data management algorithms, deep learning-based object detection technologies, and geospatial simulation and sensor fusion tools. The research problems were whether computer vision techniques, geospatial data mining, simulation-based digital twins, and real-time monitoring technology optimize remote sensing robots. Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines were leveraged by a Shiny app to obtain the flow diagram comprising evidence-based collected and managed data (the search results and screening procedures). Throughout January and July 2022, a quantitative literature review of ProQuest, Scopus, and the Web of Science databases was performed, with search terms comprising "Internet of Robotic Things" + "big data management algorithms", "deep learning-based object detection technologies", and "geospatial simulation and sensor fusion tools". As the analyzed research was published between 2017 and 2022, only 379 sources fulfilled the eligibility standards. A total of 105, chiefly empirical, sources have been selected after removing full-text papers that were out of scope, did not have sufficient details, or had limited rigor For screening and quality evaluation so as to attain sound outcomes and correlations, we deployed AMSTAR (Assessing the Methodological Quality of Systematic Reviews), AXIS (Appraisal tool for Cross-Sectional Studies), MMAT (Mixed Methods Appraisal Tool), and ROBIS (to assess bias risk in systematic reviews). Dimensions was leveraged as regards initial bibliometric mapping (data visualization) and VOSviewer was harnessed in terms of layout algorithms.

DESIGN AND DEVELOPMENT OF ROBO MEDICAL ASSISTANT

The history of the medical robot is not very far from the first experiment in the 1980s. Nowadays robot in the medical sector plays a vital role in monitoring patient's health condition from distance. This paper aimed at developing an auxiliary medical solution that could provide a wide range of non-invasive diagnoses carried out by an automated robot whose motion can also be controlled manually using either a mobile application or voice command. The authors also incorporate modern features of video conferences and automated patient data management systems using the Internet of Things (IoT) which eventually facilitate medical practitioners in proper investigation from distance. The results of the clinical trial among 6 persons indicated that the robot could measure different health parameters properly using the proposed non-invasive method. The non-invasive results are verified by standard testing equipment and conventional clinical investigation and are also presented in this paper. The developed medical robot having a wide range of functionality could play a significant role in reducing human workload and ensuring timely medical assistance during a challenging crisis pandemic period like COVID-19. © 2023 IEEE.

Online Examination System with Measures for Prevention of Cheating along with Rapid Assessment and Automatic Grading

The requirement for remote examination had emerged along with remote learning during the COVID-19 pandemic as the unprecedented situation had brought the world to halt. The pandemic had forced many educational institutions to move towards the online mode of assessment to assess the caliber of the students. This paper focuses on the ways that an online examination system can be prepared and can be used for conducting exams remotely in a secure way. It also emphasizes on various test cases that are essential for an efficient and useful examination system that can benefit both students and faculty by saving them time and effort. Due to the challenges in the existing mode of online assessment such as the use of digital forms that are usually used for conducting surveys, scanning and uploading answer sheets using phone with poor camera quality, the problem of engaging in the different kinds of misconduct, it was important to understand the user requirements at an examiner and examinee level and prepare a web application that addresses them and makes it convenient to conduct and attempt. We propose different methodologies that can be implemented in a Python based web application with the help of JavaScript such as switching the browser window to full-screen in order to restrict access to other applications, limited exits from full-screen, easy management of examiner and candidate data along with visualization of exam data that help to better understand and draw quick conclusions at the time of exam. It is also focused on the continuously evolving distance education system and finding the best software solution possible for online examinations. Additionally, an automated grading system may help to reduce human error and declare results easily reducing fatigue. © 2022 IEEE.

A Tool for Automating IT Management in Small Schools

The outbreak of COVID-2019 has resulted in the adaptation of the teaching and learning style in schools to become more online, conducting teaching and learning from any places without classroom meeting. Systems such as School Management, Online Meeting, and Online library, have been deployed to support all school members including students, teachers, parents, and administrators. These systems need to be properly managed. For business enterprises, this job falls on the shoulders of the IT department, which is usually well-staffed and well-equipped as companies realize their competitive edge depends on it. For educational institutions, especially in small schools, only 1 or 2 "computer specialists"assume the responsibility of the whole IT department. This can be overwhelming for them and, when IT tasks are poorly managed, dissatisfaction and productivity loss among school members ensue. This paper describes a system that we have designed and developed called Admin Task Management Center (ATMC). It aims to significantly reduce the manual workload of IT staff in small schools in document management, system monitoring, and other IT-related tasks. Our ATMC is currently being deployed at Satit Kaset IP (Kasetsart University Laboratory School, Center for Educational Research and Development, International Program). Our evaluation shows that the ATMC considerably raises the productivity level of IT staff, as well as other members of the school. We have released version 1 of our ATMC tool as open-source software. It is available on Github. © 2022 IEEE.

Digital Technology in Community Engagement: Impacts and Implications

Health professionals are increasingly using digital technology as a strategy to maximize community engagement and effectively implement health interventions, a phenomenon evidenced by the COVID-19 pandemic. While technology has improved health information dissemination, communication, and data management, it cannot replace the human-based interactions offered by traditional grassroots outreach that can influence long-term health behavior change, particularly for underserved communities. Digital community engagement can be part of the digital divide, often widening disparities by excluding those without access or limited access to technology. It may hinder the accurate collection of contextual and comprehensive data needed to analyze social determinants of health, thereby widening the equity gap. This commentary explores the challenges of using digital technology and justifies leveraging it to complement traditional community engagement rather than as a replacement.

Antecedents and consequences of blockchain adoption in supply chains: a systematic literature review

PurposeDespite the availability of several published reviews on the adoption of blockchain (BC) in supply chain (SC), at present, the literature lacks a comprehensive review incorporating the antecedents and consequences of BC adoption. Moreover, the complex adoption of BC in SC, explained with the mediating and moderating relationships, is not fully consolidated. Thus, the aim of this study was to conduct a systematic literature review (SLR) on BC technology adoption (BCTA) in SC by integrating its antecedents and consequences.Design/methodology/approachKeyword searches were performed in multiple databases resulting 382 articles for evaluation and verification. After careful screening with respect to the purpose of the study and systematic processing of the retrieved articles, a total of 211 peer-reviewed articles were included in this study for review.FindingsVarious technological, organisational, individual, social, environmental, operational and economic factors were found as the antecedents of BCTA in SC. In addition, numerous applications of BC Technology (BCT) were identified, including asset management, identity management, transaction management, data management and operations management. Finally, the consequences of BCTA were categorised as operational, risk management, economic and sustainability outcomes.Practical implicationsThis study can assist relevant decision-makers in managing the factors influencing BCTA and the potential uses of the technology to enhance SC performance.Originality/value By integrating the antecedents, applications and consequences of BCTA in SC, including the mediators and moderators, an integrated framework was developed that can potentially assist researchers to develop theoretical models. Further, the results of this SLR provide future directions for studying BCTA in supply chain management (SCM).

Data-driven predictive control for smart HVAC system in IoT-integrated buildings with time-series forecasting and reinforcement learning

Optimising HVAC operations towards human wellness and energy efficiency is a major challenge for smart facilities management, especially amid COVID situations. Although IoT sensors and deep learning were applied to support HVAC operations, the loss of forecasting accuracy in recursive prediction largely hinders their applications. This study presents a data-driven predictive control method with time-series forecasting (TSF) and reinforcement learning (RL), to examine various sensor metadata for HVAC system optimisation. This involves the development and validation of 16 Long Short-Term Memory (LSTM) based architectures with bi-directional processing, convolution, and attention mechanisms. The TSF models are comprehensively evaluated under independent, short-term recursive, and long-term recursive prediction scenarios. The optimal TSF models are integrated with a Soft Actor-Critic RL agent to analyse sensor metadata and optimise HVAC operations, achieving 17.4% energy savings and 16.9% thermal comfort improvement in the surrogate environment. The results show that recursive prediction leads to a significant reduction in model accuracy, and the effect is more pronounced in the temperature-humidity prediction model. The attention mechanism significantly improves prediction performance in both recursive and independent prediction scenarios. This study contributes new data-driven methods for smart HVAC operations in IoT-enabled intelligent buildings towards a human-centric built environment. © 2023 The Authors

National approaches for citizen data management in response to COVID-19: An overview and implications of contact tracing apps in 21 countries

The COVID-19 pandemic brings the topic of citizen data management (CDAMA) into the public eye. This study is one of the first attempts to analyze the national approaches for CDAMA applied by governments of different countries and continents in public sectors. The study first conducts a systematic overview of the representative contact tracing apps in 21 countries of four continents, collecting information of the four aspects of the CDAMA system. It then summarizes and analyzes the various governments' approaches to the CDAMA system applied by different countries and continents based on the app overview. We found that governments' priority between national safety (i.e., public health in this study) and citizen privacy is different in terms of their national approaches for CDAMA. For example, governments of Asian countries are more intrusive and hold a stricter attitude in their national CDAMA approach than countries elsewhere. Our study has contributions both theoretically and practically. Theoretically, it fills the literature gap about data management by discussing the data management in governments;practically, the study provides the background information as well as implications for future debates and discussions on governments' data management system and citizen data use.

Data is the New Protein: How the Commonwealth of Virginia Built Digital Resilience Muscle and Rebounded from Opioid and COVID Shocks

During shocks, residents and businesses rely upon the government to ensure health, safety, and the continuity of services. The government's ability to respond depends upon how well it utilizes its data resources and builds digital resilience. Yet governments often fail to integrate data from different agencies to respond effectively to shocks. We conceptualize digital resilience as a dynamic capability (DC). Although the DC framework provides a theoretical basis, it is unclear what actions managers can take to build DC. Through process tracing, we examine how the Commonwealth of Virginia (COVA) built DCs and rebounded from two shocks-the opioid crisis and the COVID-19 pandemic. COVA managers leveraged statewide data assets, built routines to disseminate data, and reconfigured operational capabilities to build three DCs-relationship building, intelligence creation, and value extraction. Data functioned as the "protein" to build the digital resilience "muscle." We found that the relationship building DC leveraged the operational capabilities of data management, integration, and governance structure to foster data sharing, the intelligence creation DC leveraged analytics, and the value extraction DC converted analytics into cost savings, revenue generation, and new services. Whereas COVA built robust digital resilience by facilitating data sharing, the agencies exploited data assets to develop scalable solutions.

A topic-based hierarchical publish/subscribe messaging middleware for COVID-19 detection in X-ray image and its metadata.

Putting real-time medical data processing applications into practice comes with some challenges such as scalability and performance. Processing medical images from different collaborators is an example of such applications, in which chest X-ray data are processed to extract knowledge. It is not easy to process data and get the required information in real time using central processing techniques when data get very large in size. In this paper, real-time data are filtered and forwarded to the right processing node by using the proposed topic-based hierarchical publish/subscribe messaging middleware in the distributed scalable network of collaborating computation nodes instead of classical approaches of centralized computation. This enables processing streaming medical data in near real time and makes a warning system possible. End users have the capability of filtering/searching. The returned search results can be images (COVID-19 or non-COVID-19) and their meta-data are gender and age. Here, COVID-19 is detected using a novel capsule network-based model from chest X-ray images. This middleware allows for a smaller search space as well as shorter times for obtaining search results.