ABSTRACT
Infectious diseases are always alarming for the survival of human life and are a key concern in the public health domain. Therefore, early diagnosis of these infectious diseases is a high demand for modern-era healthcare systems. Novel general infectious diseases such as coronavirus are infectious diseases that cause millions of human deaths across the globe in 2020. Therefore, early, robust recognition of general infectious diseases is the desirable requirement of modern intelligent healthcare systems. This systematic study is designed under Kitchenham guidelines and sets different RQs (research questions) for robust recognition of general infectious diseases. From 2018 to 2021, four electronic databases, IEEE, ACM, Springer, and ScienceDirect, are used for the extraction of research work. These extracted studies delivered different schemes for the accurate recognition of general infectious diseases through different machine learning techniques with the inclusion of deep learning and federated learning models. A framework is also introduced to share the process of detection of infectious diseases by using machine learning models. After the filtration process, 21 studies are extracted and mapped to defined RQs. In the future, early diagnosis of infectious diseases will be possible through wearable health monitoring cages. Moreover, these gages will help to reduce the time and death rate by detection of severe diseases at starting stage.
ABSTRACT
Infectious diseases are always alarming for the survival of human life and are a key concern in the public health domain. Therefore, early diagnosis of these infectious diseases is a high demand for modern-era healthcare systems. Novel general infectious diseases such as coronavirus are infectious diseases that cause millions of human deaths across the globe in 2020. Therefore, early, robust recognition of general infectious diseases is the desirable requirement of modern intelligent healthcare systems. This systematic study is designed under Kitchenham guidelines and sets different RQs (research questions) for robust recognition of general infectious diseases. From 2018 to 2021, four electronic databases, IEEE, ACM, Springer, and ScienceDirect, are used for the extraction of research work. These extracted studies delivered different schemes for the accurate recognition of general infectious diseases through different machine learning techniques with the inclusion of deep learning and federated learning models. A framework is also introduced to share the process of detection of infectious diseases by using machine learning models. After the filtration process, 21 studies are extracted and mapped to defined RQs. In the future, early diagnosis of infectious diseases will be possible through wearable health monitoring cages. Moreover, these gages will help to reduce the time and death rate by detection of severe diseases at starting stage.
Subject(s)
Communicable Diseases , Humans , Databases, Factual , Intelligence , Machine Learning , Recognition, PsychologyABSTRACT
Improvement in the requirements for engineering practices is needed in areas such as requirement elicitation, validation, prioritization, and negotiations between stakeholders to create successful projects for COVID-19 (coronavirus disease 2019) software. Many algorithms and techniques are used to create quality software projects, but they still need more improvement to work effectively for global pandemic COVID-19 software. By improving the reliability of requirement engineering practices using blockchain-based technology, the software will be reliable and will make it easier for the users working in a lockdown situation because of COVID-19. Therefore, our purpose is to identify the factors for reliable software engineering practices using blockchain-oriented technology for COVID-19 software. A systematic literature review is conducted to identify challenges and offer solutions. Through using blockchain-based technology for requirement engineering practices, the requirements will be gathered accurately and validated, and the conflicts between stakeholders will also be solved. It will improve the quality and reliability of COVID-19 software projects, which will help society work effectively from home. Improvement in the quality and reliability of COVID-19 software will improve users’ interest, and their working capacity will be increased.
ABSTRACT
In the higher education sector, there is a growing trend to offer academic information to users through websites. Contemporarily, the users (i.e., students/teachers, parents, and administrative staff) greatly rely on these websites to perform various academic tasks, including admission, access to learning management systems (LMS), and links to other relevant resources. These users vary from each other in terms of their technological competence, objectives, and frequency of use. Therefore, academic websites should be designed considering different dimensions, so that everybody can be accommodated. Knowing the different dimensions with respect to the usability of academic websites is a multi-criteria decision-making (MCDM) problem. The fuzzy analytic hierarchy process (FAHP) approach has been considered to be a significant method to deal with the uncertainty that is involved in subjective judgment. Although a wide range of usability factors for academic websites have already been identified, most of them are based on the judgment of experts who have never used these websites. This study identified important factors through a detailed literature review, classified them, and prioritized the most critical among them through the FAHP methodology, involving relevant users to propose a usability evaluation framework for academic websites. To validate the proposed framework, five websites of renowned higher educational institutes (HEIs) were evaluated and ranked according to the usability criteria. As the proposed framework was created methodically, the authors believe that it would be helpful for detecting real usability issues that currently exist in academic websites.