Mapping of technologies using thermal images to control epidemics

The quest to combat the spread of the new Corona Virus Pandemic is a battle experienced worldwide, more specifically in the year 2020 when it caused a tragedy in the lives of a large part of the world population. The current numbers of contaminated people and deaths are alarming. Transmitted through droplets expelled through the nose or mouth, it leads to fever, which is the most common symptom of COVID-19. A technique that uses thermal images to check dispersed heat is a thermography. These images are captured by thermal cameras or devices with temperature sensors. Thus, the purpose of this work was to map the deposits of patent applications in order to seek technologies related to the use of thermal images to control the pandemic. The search base chosen for this research characterized as exploratory quantitative was Espacenet, which returned a final result of 119 published patent documents. Of these 93 documents were worked on in this article which gave us a more discussed result, since the others were repeated. The research revealed that patent applications in this area were stable until the current year when a Corona Virus pandemic spread, forcing researchers to develop research in order to combat it. The increase in the number of patents in 2020 shows the tendency to increase to 2021 when new research should appear and, consequently, new patented documents may be exposed in the future

Big data and risk management in business processes: implications for corporate real estate

PurposeThe purpose of this paper is to improve understanding of the integration between big data (BD) and risk management (RM) in business processes (BPs), with special reference to corporate real estate (CRE).Design/methodology/approachThis conceptual study follows, methodologically, the structuring inter-textual coherence process – specifically, the synthesised coherence tactical approach. It draws heavily on theoretical evidence published, mainly, in the corporate finance and the business management literature.FindingsA new conceptual framework is presented for CRE to proactively develop insights into the potential benefits of using BD as a business strategy/instrument. The approach was found to strengthen decision-making processes and encourage better RM – with significant consequences, in particular, for business process management (BPM). Specifically, by recognising the potential uses of BD, it is also possible to redefine the processes with advantages in terms of RM.Originality/valueThis study contributes to the literature in the fields of real estate, RM, BPM and digital transformation. To the best knowledge of authors, although the literature has examined the concepts of BD, RM and BP, no prior studies have comprehensively examined these three elements and their conjoint contribution to CRE. In particular, the study highlights how the automation of data-intensive activities and the analysis of such data (in both structured and unstructured forms), as a means of supporting decision making, can lead to better efficiency in RM and optimisation of processes

Internet of Things Adoption for Saudi Healthcare Services

Background: Recent studies in information systems have predicted that applications of the Internet of Things (IoT) innovations will revolutionise various sectors including healthcare. Besides the issues and opportunities of IoT based innovations, existing studies have shown limitations to advance the adoption of IoT-understanding and relevant interventions to benefit researchers and healthcare practitioners. Method: In this context, a systematic literature review study was conducted to re-position a qualitative, phenomenological investigation that could offer useful insights into the factors affecting IoT-adoption in a developing country’s healthcare service. In addition to it, five participants who worked in hospitals and clinics in Jazan, Saudi Arabia, took part in the semi-structured interviews developed based on the diffusion of innovation theory. Results: The study explored the relevant literature and evaluated how the outcome is used to identify the key delivers of IoT in healthcare. Conclusions: According to the findings, the capacity of the Saudi healthcare sector to accept and implement a new IT with IoT technologies is increasing and its integrations remains a debated issue

Deep Forest Based Internet of Medical Things System for Diagnosis of Heart Disease

Due to advancement in internet of medical things, the conventional health-care systems are transformed into smart health-care systems. The medical emergence services can be significantly enhanced by integration of IoMT and data analytic techniques. These technologies also examine the unexplored area of medical services that are still unseen and provide opportunity for investigation. Moreover, the concept of smart cities is not achievable without providing a smart connected healthcare scheme. Hence, the main purpose of this research is to come up with a smart healthcare system based on IoMT, Cloud and Fog computing and intelligent data analytic technique. The major objective of the proposed healthcare system is to develop a diagnostic model capable for earlier treatment of heart disease. The suggested scheme consists of distinct phases such as data acquisition, feature extraction, FogBus based edge/fog computing environment, classification, and evaluation. In data acquisition, different IoMT such as wearables and sensors devices are considered to acquire the data related to heart disease and the various features related to signal and data are extracted. Further, the deep forest technique is integrated into the proposed system for classification task and effective diagnosis capabilities of heart issues. The performance of the suggested scheme is evaluated through set of well-defined parameters. Comparison with other healthcare model was conducted for the purpose of performance evaluation. It is concluded that the proposed model has a superiority over other all other models in different aspects namely, the sensitivity measure, accuracy measure, and specificity

Innovative Business Model for Smart Healthcare Insurance

Information revolution and technology growth have made a considerable contribution to restraining the cost expansion and empowering the customer. They disrupted most business models in different industries. The customer-centric business model has pervaded the different sectors. Smart healthcare has made an enormous shift in patient life and raised their expectations of healthcare services quality. Healthcare insurance is an essential business in the healthcare sector; patients expect a new business model to meet their needs and enhance their wellness. This research develops a holistic smart healthcare architecture based on the recent development of information and communications technology. Then develops a disruptive healthcare insurance business model that adapts to this architecture and classifies the patient according to their technology needs. Finally, and implementing a prototype of a system that matches and suits the healthcare recipient condition to the proper healthcare insurance policy by applying Web Ontology Language (OWL) and rule-based reasoning model using SWRL using Protég

AI and Blockchain-assisted diagnostics in resource-limited setting

Diseases, including communicable and noncommunicable diseases, have been one of the major causes of human morbidity and mortality since the beginning of our history. Although many diseases have become treatable or preventable, thanks to interventions including pharmaceutical and technological advances, many people die each year in developing countries and remote rural areas due to limited (or even no) access to medical facilities and expertise. An accurate, rapid, and reliable diagnostic test is vital to improved disease treatment and prevention. However, running diagnostic tests usually requires complex, expensive instruments, professionally trained operators, and a stable power supply. Unfortunately, these resources are generally limited or unavailable in many low-resource settings. Although there are countless limitations in running diagnostic tests in low-resource settings, various endeavours have been made to overcome the existing obstacles. One of the most important advances has been the development of point-of-care or point-of-need tests. These diagnostic assays can be delivered in convenient formats and have successfully reduced the cost of running diagnostics, so playing an essential role in disease management and lifesaving in low-income countries. One key aspect of diagnosis may be the interpretation of the test, which can either be done by an expert in the field or by communicating that data to a remote expert or a “smart” system to interpret the data. Accurately interpreting the test outcome can help the patients receive appropriate treatment timely. However, issues presented in data management during such communication, such as tampered and counterfeited test results and unsecured data sharing between end users (patients) and professionals (doctors, healthcare workers, researchers, etc.). Also, problems like unreliable electricity supply and internet connection were found during the field study conducted by our group previously, and those issues can also delay the diagnosis of the disease. In this PhD study, an AI-assisted platform for DNA-based malaria diagnostic tests was developed and tested in the field. This platform allows users to run a test with a low-cost portable heater and record the test information with an Android phone. It can be used to run LAMP-based malaria tests with a portable heater and read the test results automatically with 97.8% accuracy. And it only takes around 20 milliseconds to classify one image on an inexpensive (~£100) Android phone. When the internet connection is available, the test information can be safely kept in a Blockchain network for future use to inform treatment or surveillance activities. Expertise developed in the deep neural network was also used to train algorithms for the diagnosis of retinopathies, involving developing methods for retina vessel segmentation and classification, which explores the possibility of applying AI to diagnostics in low-resource settings. In such settings, accessing medical expertise can be challenging. It has been found that using only a convolutional neural network is not sufficient in identifying arteries and veins. Models were trained for performing vessel segmentation and classification tasks; for segmenting vessels from the background achieved over 95% accuracy and over 0.8 mean average over the union score (MIoU) on the DRIVE dataset, while for A/V classification tasks, the MIoU decreased to less than 0.7. However, combining it with the traditional approach has the potential to achieve good performance. In addition, research was conducted on the utilisation of digital technologies to assist other researchers and engage with the public. To assist researchers in determining the minimum required sample size, a web-based calculator was developed during the COVID-19 pandemic. Furthermore, a website was created containing 360-degree images to help individuals comprehend the challenges of diagnostics and healthcare in developing regions and to raise awareness about how infectious diseases spread