This study proposes a bald eagle search (BES) algorithm, which is a novel, nature-inspired meta-heuristic optimisation algorithm that mimics the hunting strategy or intelligent social behaviour of ...bald eagles as they search for fish. Hunting by BES is divided into three stages. In the first stage (selecting space), an eagle selects the space with the most number of prey. In the second stage (searching in space), the eagle moves inside the selected space to search for prey. In the third stage (swooping), the eagle swings from the best position identified in the second stage and determines the best point to hunt. Swooping starts from the best point and all other movements are directed towards this point. BES is tested by adopting a three-part evaluation methodology that (1) describes the benchmarking of the optimisation problem to evaluate the algorithm performance, (2) compares the algorithm performance with that of other intelligent computation techniques and parameter settings and (3) evaluates the algorithm based on mean, standard deviation, best point and Wilcoxon signed-rank test statistic of the function values. Optimisation results and discussion confirm that the BES algorithm competes well with advanced meta-heuristic algorithms and conventional methods.
•Understanding sentiment analysis role and opinion mining in Covid-19 and other infectious diseases.•Literature’s categorization for sentiment analysis and infectious disease.•Academic challenges and ...motivations of sentiment analysis with infectious diseases.•Different applications for mitigating infectious diseases by sentiment analysis.
The COVID-19 pandemic caused by the novel coronavirus SARS-CoV-2 occurred unexpectedly in China in December 2019. Tens of millions of confirmed cases and more than hundreds of thousands of confirmed deaths are reported worldwide according to the World Health Organisation. News about the virus is spreading all over social media websites. Consequently, these social media outlets are experiencing and presenting different views, opinions and emotions during various outbreak-related incidents. For computer scientists and researchers, big data are valuable assets for understanding people’s sentiments regarding current events, especially those related to the pandemic. Therefore, analysing these sentiments will yield remarkable findings. To the best of our knowledge, previous related studies have focused on one kind of infectious disease. No previous study has examined multiple diseases via sentiment analysis. Accordingly, this research aimed to review and analyse articles about the occurrence of different types of infectious diseases, such as epidemics, pandemics, viruses or outbreaks, during the last 10 years, understand the application of sentiment analysis and obtain the most important literature findings. Articles on related topics were systematically searched in five major databases, namely, ScienceDirect, PubMed, Web of Science, IEEE Xplore and Scopus, from 1 January 2010 to 30 June 2020. These indices were considered sufficiently extensive and reliable to cover our scope of the literature. Articles were selected based on our inclusion and exclusion criteria for the systematic review, with a total of n = 28 articles selected. All these articles were formed into a coherent taxonomy to describe the corresponding current standpoints in the literature in accordance with four main categories: lexicon-based models, machine learning-based models, hybrid-based models and individuals. The obtained articles were categorised into motivations related to disease mitigation, data analysis and challenges faced by researchers with respect to data, social media platforms and community. Other aspects, such as the protocol being followed by the systematic review and demographic statistics of the literature distribution, were included in the review. Interesting patterns were observed in the literature, and the identified articles were grouped accordingly. This study emphasised the current standpoint and opportunities for research in this area and promoted additional efforts towards the understanding of this research field.
Various software packages offer a large number of customizable features to meet the specific needs of organizations. Improper selection of a software package may result in incorrect strategic ...decisions and subsequent economic loss of organizations. This paper presents a comparative study that aims to evaluate and select open-source electronic medical record (OS-EMR) software based on multiple-criteria decision-making (MCDM) techniques. A hands-on study is performed, and a set of OS-EMR software are implemented locally in separate virtual machines to closely examine the systems. Several measures as evaluation bases are specified, and systems are selected based on a set of metric outcomes by using AHP integrated with different MCDM techniques, namely, WPM, WSM, SAW, HAW, and TOPSIS. Paired sample t-test is then utilized to measure the correlations among different techniques on ranking scores and orders. Findings are as follows. (1) Significant differences exist among MCDM techniques on the basis of different integrations on ranking scores, whereas no significant differences exist among them when representing the ranking scores to the ranking orders in place of the technique scale. (2) The software GNUmed, OpenEMR, OpenMRS, and ZEPRS do not differ in ranking scores/orders of experiments for all MCDM techniques presented. On the contrary, discrepancies among the ranking scores/orders are more noticeable in other software. (3) GNUmed, OpenEMR, and OpenMRS software are the most promising candidates for providing a good basis on ranking scores/orders, whereas ZEPRS is not recommended because it records the worst ranking score/order in comparison with other OS-EMR software.
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•Significant differences exist among MCDM techniques based on different integration's on ranking scores.•There is no significant differences among MCDM techniques when the ranking scores are represented to the ranking orders.• The software GNUmed, OpenEMR, OpenMRS, and ZEPRS do not differ in ranking scores/orders for all MCDM techniques presented•GNUmed, OpenEMR, and OpenMRS software are the most promising candidates for providing a good basis ranking scores/orders.• ZEPRS is not recommended because it records the worst ranking score/order in comparison with other OS-EMR software.
The new and disruptive technology of smart home applications (hereafter referred to as apps) based on Internet of Things (IoT) is largely limited and scattered. To provide valuable insights into ...technological environments and support researchers, we must understand the available options and gaps in this line of research. Thus, in this study, a review is conducted to map the research landscape into a coherent taxonomy. We conduct a focused search for every article related to (1) smart homes, (2) apps, and (3) IoT in three major databases, namely, Web of Science, ScienceDirect, and IEEE Explore. These databases contain literature focusing on smart home apps using IoT. The final dataset resulting from the classification scheme includes 229 articles divided into four classes. The first class comprises review and survey articles related to smart home IoT applications. The second class includes papers on IoT applications and their use in smart home technology. The third class contains proposals of frameworks to develop and operate applications. The final class includes studies with actual attempts to develop smart home IoT applications. We then identify the basic characteristics of this emerging field in the following aspects: motivation of using IoT in smart home applications, open challenges hindering utilization, and recommendations to improve the acceptance and use of smart home applications in literature.
•Mapping the research landscape of smart home based on Internet of Things into a coherent taxonomy.•Figure out the motivation of using the internet of things in smart home.•Highlight the open challenges that hinder the utility Internet of Things in smart home.•Recommendations lists to improve the acceptance of used the Internet of Things in smart home in the literature.
Innovative technology on intelligent processes for smart home applications that utilize Internet of Things (IoT) is mainly limited and dispersed. The available trends and gaps were investigated in ...this study to provide valued visions for technical environments and researchers. Thus, a survey was conducted to create a coherent taxonomy on the research landscape. An extensive search was conducted for articles on (a) smart homes, (b) IoT and (c) applications. Three databases, namely, IEEE Explore, ScienceDirect and Web of Science, were used in the article search. These databases comprised comprehensive literature that concentrate on IoT-based smart home applications. Subsequently, filtering process was achieved on the basis of intelligent processes. The final classification scheme outcome of the dataset contained 40 articles that were classified into four classes. The first class includes the knowledge engineering process that examines data representation to identify the means of accomplishing a task for IoT applications and their utilisation in smart homes. The second class includes papers on the detection process that uses artificial intelligence (AI) techniques to capture the possible changes in IoT-based smart home applications. The third class comprises the analytical process that refers to the use of AI techniques to understand the underlying problems in smart homes by inferring new knowledge and suggesting appropriate solutions for the problem. The fourth class comprises the control process that describes the process of measuring and instructing the performance of IoT-based smart home applications against the specifications with the involvement of intelligent techniques. The basic features of this evolving approach were then identified in the aspects of motivation of intelligent process utilisation for IoT-based smart home applications and open-issue restriction utilisation. The recommendations for the approval and utilisation of intelligent process for IoT-based smart home applications were also determined from the literature.
•Mapping the research landscape of Fuzzy-TOPSIS into a coherent taxonomy.•Figure out the motivation of develop the Fuzzy-TOPSIS.•Highlight the open challenges that hinder the of develop the ...Fuzzy-TOPSIS.•Recommendations lists of develop the Fuzzy-TOPSIS in the literature.
A crucial topic in expert system and operations research is fuzzy multi-criteria decision making (FMCDM), which is used in different fields. Existing options and gaps in this topic must be understood to prepare valuable knowledge on FMCDM environments and assist scholars. This study maps the research landscape to provide a clear taxonomy. The authors focus on searching for articles related to (i) technique for order of preference by similarity to ideal solution (TOPSIS); (ii) development; and (iii) fuzzy sets in four primary databases, namely, IEEE Xplore, Web of Science, Elsevier ScienceDirect and Springer. These databases include literature that focuses on FMCDM. The resulting final set after the filtering process includes 170 articles, which are classified into four categories. The first, second, third and fourth categories include articles that used a type-1 fuzzy set with the TOPSIS method, a type-2 fuzzy set with the TOPSIS method, two fuzzy membership functions and a survey paper, respectively. The basic attributes of this topic include motivations for utilising FMCDM, open challenges and limitations that obstruct utilisation and recommendations to researchers for increasing the approval and application of FMCDM.
This study presents a systematic review of artificial intelligence (AI) techniques used in the detection and classification of coronavirus disease 2019 (COVID-19) medical images in terms of ...evaluation and benchmarking. Five reliable databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus were used to obtain relevant studies of the given topic. Several filtering and scanning stages were performed according to the inclusion/exclusion criteria to screen the 36 studies obtained; however, only 11 studies met the criteria. Taxonomy was performed, and the 11 studies were classified on the basis of two categories, namely, review and research studies. Then, a deep analysis and critical review were performed to highlight the challenges and critical gaps outlined in the academic literature of the given subject. Results showed that no relevant study evaluated and benchmarked AI techniques utilised in classification tasks (i.e. binary, multi-class, multi-labelled and hierarchical classifications) of COVID-19 medical images. In case evaluation and benchmarking will be conducted, three future challenges will be encountered, namely, multiple evaluation criteria within each classification task, trade-off amongst criteria and importance of these criteria. According to the discussed future challenges, the process of evaluation and benchmarking AI techniques used in the classification of COVID-19 medical images considered multi-complex attribute problems. Thus, adopting multi-criteria decision analysis (MCDA) is an essential and effective approach to tackle the problem complexity. Moreover, this study proposes a detailed methodology for the evaluation and benchmarking of AI techniques used in all classification tasks of COVID-19 medical images as future directions; such methodology is presented on the basis of three sequential phases. Firstly, the identification procedure for the construction of four decision matrices, namely, binary, multi-class, multi-labelled and hierarchical, is presented on the basis of the intersection of evaluation criteria of each classification task and AI classification techniques. Secondly, the development of the MCDA approach for benchmarking AI classification techniques is provided on the basis of the integrated analytic hierarchy process and VlseKriterijumska Optimizacija I Kompromisno Resenje methods. Lastly, objective and subjective validation procedures are described to validate the proposed benchmarking solutions.
•Improve the acceptance of mHealth apps among users and their efficacy.•Provides its intended protection a set of security checks and policies in mHealth apps.•Design a framework based SML that ...implements the security-check modules and SIL that interfaces SML to the Android OS.
Mobile Health (mHealth) applications are readily accessible to the average users of mobile devices, and despite the potential of mHealth applications to improve the availability, affordability and effectiveness of delivering healthcare services, they handle sensitive medical data, and as such, have also the potential to carry substantial risks to the security and privacy of their users. Developers of applications are usually unknown, and users are unaware of how their data are being managed and used. This is combined with the emergence of new threats due to the deficiency in mobile applications development or the design ambiguities of the current mobile operating systems. A number of mobile operating systems are available in the market, but the Android platform has gained the topmost popularity. However, Android security model is short of completely ensuring the privacy and security of users' data, including the data of mHealth applications. Despite the security mechanisms provided by Android such as permissions and sandboxing, mHealth applications are still plagued by serious privacy and security issues. These security issues need to be addressed in order to improve the acceptance of mHealth applications among users and the efficacy of mHealth applications in the healthcare systems. The focus of this research is on the security of mHealth applications, and the main objective is to propose a coherent, practical and efficient framework to improve the security of medical data associated with Android mHealth applications, as well as to protect the privacy of their users. The proposed framework provides its intended protection mainly through a set of security checks and policies that ensure protection against traditional as well as recently published threats to mHealth applications. The design of the framework comprises two layers: a Security Module Layer (SML) that implements the security-check modules, and a System Interface Layer (SIL) that interfaces SML to the Android OS. SML enforces security and privacy policies at different levels of Android platform through SIL. The proposed framework is validated via a prototypic implementation on actual Android devices to show its practicality and evaluate its performance. The framework is evaluated in terms of effectiveness and efficiency. Effectiveness is evaluated by demonstrating the performance of the framework against a selected set of attacks, while efficiency is evaluated by comparing the performance overhead in terms of energy consumption, memory and CPU utilization, with the performance of a mainline, stock version of Android. Results of the experimental evaluations showed that the proposed framework can successfully protect mHealth applications against a wide range of attacks with negligible overhead, so it is both effective and practical.
Blockchain in healthcare applications requires robust security and privacy mechanism for high-level authentication, interoperability and medical records sharing to comply with the strict legal ...requirements of the Health Insurance Portability and Accountability Act of 1996. Blockchain technology in the healthcare industry has received considerable research attention in recent years. This study conducts a review to substantially analyse and map the research landscape of current technologies, mainly the use of blockchain in healthcare applications, into a coherent taxonomy. The present study systematically searches all relevant research articles on blockchain in healthcare applications in three accessible databases, namely, ScienceDirect, IEEE and Web of Science, by using the defined keywords ‘blockchain’, ‘healthcare’ and ‘electronic health records’ and their variations. The final set of collected articles related to the use of blockchain in healthcare application is divided into three categories. The first category includes articles (i.e. 43/58 scientific articles) that attempted to develop and design healthcare applications integrating blockchain, particularly those on new architecture, system designs, framework, scheme, model, platform, approach, protocol and algorithm. The second category includes studies (i.e., 6/58 scientific articles) that attempted to evaluate and analyse the adoption of blockchain in the healthcare system. Finally, the third category comprises review and survey articles (i.e., 6/58 scientific articles) related to the integration of blockchain into healthcare applications. The final articles for review are discussed on the basis of five aspects: (1) year of publication, (2) nationality of authors, (3) publishing house or journal, (4) purpose of using blockchain in health applications and the corresponding contributions and
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problem types and proposed solutions. Additionally, this study provides identified motivations, open challenges and recommendations on the use of blockchain in healthcare applications. The current research contributes to the literature by providing a detailed review of feasible alternatives and identifying the research gaps. Accordingly, researchers and developers are provided with appealing opportunities to further develop decentralised healthcare applications through a comprehensive discussion of about the importance of blockchain and its integration into various healthcare applications.
Highlights • The categorization of literature articles suggests the lack of adequate information from an implementer perspective. • A more elaborated, hands-on study of individual OSS can reveal ...useful information to potential implementers, yielding better informed decisions. • OSS in healthcare informatics still lag behind established open source systems in several aspects, in particular security, usability and developers support.
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