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B. B. Zaidan

Bio: B. B. Zaidan is an academic researcher from Sultan Idris University of Education. The author has contributed to research in topics: Computer science & Steganography. The author has an hindex of 49, co-authored 210 publications receiving 7117 citations. Previous affiliations of B. B. Zaidan include National Yunlin University of Science and Technology & Multimedia University.


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TL;DR: A review is conducted to map the research landscape of smart home based on Internet of Things into a coherent taxonomy and identifies 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 smartHome IoT applications in literature.

413 citations

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TL;DR: Optimisation results and discussion confirm that the BES algorithm competes well with advanced meta-heuristic algorithms and conventional methods.
Abstract: 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.

281 citations

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TL;DR: This study 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.
Abstract: 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.

210 citations

Journal ArticleDOI
TL;DR: An overview of this critical virus, address the limitations of utilising data mining and ML algorithms, and provide the health sector with the benefits of this technique, as well as reviewing the state-of-the-art techniques for CoV prediction algorithms based on datamining and ML assessment.
Abstract: Coronaviruses (CoVs) are a large family of viruses that are common in many animal species, including camels, cattle, cats and bats. Animal CoVs, such as Middle East respiratory syndrome-CoV, severe acute respiratory syndrome (SARS)-CoV, and the new virus named SARS-CoV-2, rarely infect and spread among humans. On January 30, 2020, the International Health Regulations Emergency Committee of the World Health Organisation declared the outbreak of the resulting disease from this new CoV called 'COVID-19', as a 'public health emergency of international concern'. This global pandemic has affected almost the whole planet and caused the death of more than 315,131 patients as of the date of this article. In this context, publishers, journals and researchers are urged to research different domains and stop the spread of this deadly virus. The increasing interest in developing artificial intelligence (AI) applications has addressed several medical problems. However, such applications remain insufficient given the high potential threat posed by this virus to global public health. This systematic review addresses automated AI applications based on data mining and machine learning (ML) algorithms for detecting and diagnosing COVID-19. We aimed to obtain an overview of this critical virus, address the limitations of utilising data mining and ML algorithms, and provide the health sector with the benefits of this technique. We used five databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus and performed three sequences of search queries between 2010 and 2020. Accurate exclusion criteria and selection strategy were applied to screen the obtained 1305 articles. Only eight articles were fully evaluated and included in this review, and this number only emphasised the insufficiency of research in this important area. After analysing all included studies, the results were distributed following the year of publication and the commonly used data mining and ML algorithms. The results found in all papers were discussed to find the gaps in all reviewed papers. Characteristics, such as motivations, challenges, limitations, recommendations, case studies, and features and classes used, were analysed in detail. This study reviewed the state-of-the-art techniques for CoV prediction algorithms based on data mining and ML assessment. The reliability and acceptability of extracted information and datasets from implemented technologies in the literature were considered. Findings showed that researchers must proceed with insights they gain, focus on identifying solutions for CoV problems, and introduce new improvements. The growing emphasis on data mining and ML techniques in medical fields can provide the right environment for change and improvement.

197 citations

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TL;DR: 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, and presents the development of the MCDA approach for benchmarking AI classification techniques on the basis of the integrated analytic hierarchy process.

188 citations


Cited by
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Tamar Frankel1
TL;DR: The Essay concludes that practitioners theorize, and theorists practice, use these intellectual tools differently because the goals and orientations of theorists and practitioners, and the constraints under which they act, differ.
Abstract: Much has been written about theory and practice in the law, and the tension between practitioners and theorists. Judges do not cite theoretical articles often; they rarely "apply" theories to particular cases. These arguments are not revisited. Instead the Essay explores the working and interaction of theory and practice, practitioners and theorists. The Essay starts with a story about solving a legal issue using our intellectual tools - theory, practice, and their progenies: experience and "gut." Next the Essay elaborates on the nature of theory, practice, experience and "gut." The third part of the Essay discusses theories that are helpful to practitioners and those that are less helpful. The Essay concludes that practitioners theorize, and theorists practice. They use these intellectual tools differently because the goals and orientations of theorists and practitioners, and the constraints under which they act, differ. Theory, practice, experience and "gut" help us think, remember, decide and create. They complement each other like the two sides of the same coin: distinct but inseparable.

2,077 citations

Journal ArticleDOI
TL;DR: Managing the Flow of Technology: Technology Transfer and the Dissemination of Technological Information Within the R&D Organization is summarized, showing how human and organizational systems could be restructured to bring about improved productivity and better person-to-person contact.
Abstract: The original edition of this book summarized more than a decade of work on communications flow in science and engineering organizations, showing how human and organizational systems could be restructured to bring about improved productivity and better person-to-person contact. While many studies have been done since then, few of them invalidate the general conclusions and recommendations Allen offers. In a new preface he points out new developments, noting areas that need some modification, elaboration, or extension, and directing readers to the appropriate journal articles where the findings, are reported. The first three chapters provide an overview of the communication system in technology, present the author's research methods, and describe differences in the career paths and goals of engineers and scientists that cause special problems for organizations. The book then discusses how technological information is acquired by the R & D organization, shows how critical technical communication within the laboratory is for R & D performance, and originates the idea of the \"gatekeeper,\" the person who links his or her organization to the world at large. Concluding chapters take up the influence of formal and informal organization and of architecture and office layouts on communication. Many of these ideas have been successfully incorporated by architects and managers in the design of new R & D facilities and complexes. Suggested Citation Handle: RePEc:mtp:titles:0262510278 Download reference as HTML Thomas J. Allen, 1984. \"Managing the Flow of Technology: Technology Transfer and the Dissemination of Technological Information Within the R&D Organization,\" MIT Press Books, The MIT Press, edition 1, volume 1, number 0262510278, November. More services and features MyIDEAS Follow serials, authors, keywords & more New papers by email Subscribe to new additions to RePEc Author registration Public profiles for Economics researchers MPRA Upload your paper to be listed on RePEc and IDEAS EconAcademics Blog aggregator for economics research Plagiarism Cases of plagiarism in Economics Rankings Various rankings of research in Economics & related fields RePEc Genealogy Who was a student of whom, using RePEc RePEc Biblio Curated articles & papers on various economics topics Job market papers RePEc working paper series dedicated to the job market Fantasy league Pretend you are at the helm of an economics department Services from the StL Fed Data, research, apps & more from the St. Louis Fed IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.

597 citations

Journal ArticleDOI
TL;DR: It is found that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases, which is a major weakness, given the urgency with which validated COVID-19 models are needed.
Abstract: Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts. Many machine learning-based approaches have been developed for the prognosis and diagnosis of COVID-19 from medical images and this Analysis identifies over 2,200 relevant published papers and preprints in this area. After initial screening, 62 studies are analysed and the authors find they all have methodological flaws standing in the way of clinical utility. The authors have several recommendations to address these issues.

581 citations