A systematic literature review

Albuquerque, V., Dias, M. S., & Bacao, F. (2021). Machine learning approaches to bike-sharing systems: A systematic literature review. ISPRS International Journal of Geo-Information, 10(2), 1-25. [62]. https://doi.org/10.3390/ijgi10020062Cities are moving towards new mobility strategies to tackle smart cities’ challenges such as carbon emission reduction, urban transport multimodality and mitigation of pandemic hazards, emphasising on the implementation of shared modes, such as bike-sharing systems. This paper poses a research question and introduces a corresponding systematic literature review, focusing on machine learning techniques’ contributions applied to bike-sharing systems to improve cities’ mobility. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) method was adopted to identify specific factors that influence bike-sharing systems, resulting in an analysis of 35 papers published between 2015 and 2019, creating an outline for future research. By means of systematic literature review and bibliometric analysis, machine learning algorithms were identified in two groups: classification and prediction.publishersversionpublishe

An overview of bankruptcy prediction models for corporate firms: a systematic literature review

Purpose: The aim of this paper is to conduct a literature review of corporate bankruptcy prediction models, on the basis of the existing international academic literature in the corresponding area. It primarily attempts to provide a comprehensive overview of literature related to corporate bankruptcy prediction, to investigate and address the link between the different authors (co-authorship), and to address the primary models and methods that are used and studied by authors of this area in the past five decades. Design/methodology: A systematic literature review (SLR) has been conducted, using the Scopus database for identifying core international academic papers related to the established research topic from the year 1968 to 2017. Findings: It has been verified, firstly, that bankruptcy prediction in the corporate world is a field of growing interest, as the number of papers has increased significantly, especially after 2008 global financial crisis, which demonstrates the importance of this topic for corporate firms. Secondly, it should be mentioned that there is little co-authorship in this researching area, as researchers with great influence were barely working together during the last five decades. Thirdly, it has been identified that the two most frequently used and studied models in bankruptcy prediction area are Logistic Regression (Logit) and Neural Network. However, there are many other innovative methods as machine learning models applied in this field lately due to the emerging technology of computer science and artificial intelligence. Originality/value: We used an approach that allows a better view of the academic contribution related to the corporate bankruptcy prediction; this serves as the link among the different elements of the concept studied, and it demonstrates the growing interest in this area.Peer Reviewe

Diabetes Prediction using Machine learning : A Bibliometric Analysis

Diabetes Mellitus is a chronic disease which can be deadly if undetected for longer time. Artificial intelligence is helping in healthcare industry to a great extent by helping professionals to derive useful information and patterns from data available in various formats: Survey data, electronic health records, laboratory data.. Diabetes, if predicted at an early stage can help many people to save lives and cost for healthcare. Decision-making, diagnosing and predicting diabetes have become an increasing trend in recent years. There are numerous publications in diabetes prediction and yet it’s an ongoing research topic with availability of new data and methods. This study aims to provide a global picture on current status of diabetes prediction research field by analyzing trends in publications, authors, countries and institutions from Scopus and Web of Science database for the year 2009-2020. Further, to enhance the analysis, network inspection in terms of co-authorship, collaborative countries, citation analysis and keyword co-occurrences were explored. Tools like VoSviewer and tableau are used for analysis

Diabetes Prediction using Machine learning : A Bibliometric Analysis

Diabetes Mellitus is a chronic disease which can be deadly if undetected for longer time. Artificial intelligence is helping in healthcare industry to a great extent by helping professionals to derive useful information and patterns from data available in various formats: Survey data, electronic health records, laboratory data.. Diabetes, if predicted at an early stage can help many people to save lives and cost for healthcare. Decision-making, diagnosing and predicting diabetes have become an increasing trend in recent years. There are numerous publications in diabetes prediction and yet it’s an ongoing research topic with availability of new data and methods. This study aims to provide a global picture on current status of diabetes prediction research field by analyzing trends in publications, authors, countries and institutions from Scopus and Web of Science database for the year 2009-2020. Further, to enhance the analysis, network inspection in terms of co-authorship, collaborative countries, citation analysis and keyword co-occurrences were explored. Tools like VoSviewer and tableau are used for analysis

Examining the Trend of Literature on Classification Modelling: A Bibliometric Approach

This paper analyses and reports various types of published works related to classification or discriminant modelling. This paper adopted a bibliometric analysis based on the data obtained from the Scopus online database on 27th July 2019. Based on the ‘keywords’ search results, it yielded 2775 valid documents for further analysis. For data visualisation purposes, we employed VOSviewer. This paper reports the results using standard bibliometric indicators, particularly on the growth rate of publications, research productivity, analysis of the authors and citations. The outcomes revealed that there is an increased growth rate of classification literature over the years since 1968. A total of 2473 (89.12%) documents were from journals (n=1439; 51.86%) and conference proceedings (n=1034; 37.26%) contributed as the top publications in this classification topic. Meanwhile, 2578 (92.9%) documents are multi-authored with an average collaboration index of 3.34 authors per article. However, this classification research field found that the famous numbers of authors’ collaboration in a document are two (with n=758; 27.32%), three (n=752; 27.10%) and four (n=560; 20.18%) respectively. An analysis by country, China with 1146 (41.30%) published documents thus is ranked first in productivity. With respect to the frequency of citations, Bauer and Kohavi (1999)’s article emerged as the most cited article through 1414 total citations with an average of 70.7 citations per year. Overall, the increasing number of works on classification topics indicates a growing awareness of its importance and specific requirements in this research field

Trends and Patterns in Artificial Intelligence Research for Oil and Gas Industry: A Bibliometric Review

Purpose: This paper aims to outline a broad-spectrum perspective of the structure of research in artificial intelligence (AI), in the oil and gas industry (OGI) based on bibliometric and distance-based visualisation of similarities (VOS) analysis.   Theoretical framework: The OGI has been one of the major contributors to the world economy. With the increasing energy demand, it has become necessary for the industry to adopt the latest technologies to enhance efficiency, reduce costs, and improve safety. One such technology is AI, which has the potential to revolutionise OGI.   Design/methodology/approach: The paper uses the data from Scopus online database as of April 2023. Based on “key-terms” search results, 251 valid documents were obtained for further analysis using VOS viewer software and Harzing’s Publish or Perish for citation metrics and analysis.   Findings: The finding shows that the Journal of Petroleum Science and Engineering is the field's most relevant journal, with 14 (5.58) published Articles. The People's Republic of China is the most productive country regarding AI research in the OGI. El-Sebakhy's (2009) article is the most cited article, with 113 citations and an average of 8.07 citations per year.   Research, Practical & Social implications: AI could transform OGI. Thus, adopting AI technologies can increase efficiency, reduce costs, and improve safety, also may increase productivity and economic benefits in AI research-intensive countries.   Originality/value: This study provides a comprehensive analysis of the existing AI research in the OGI, utilising bibliometric data and graphical networks

Evaluating the state-of-the-art in mapping research spaces: a Brazilian case study

Scientific knowledge cannot be seen as a set of isolated fields, but as a highly connected network. Understanding how research areas are connected is of paramount importance for adequately allocating funding and human resources (e.g., assembling teams to tackle multidisciplinary problems). The relationship between disciplines can be drawn from data on the trajectory of individual scientists, as researchers often make contributions in a small set of interrelated areas. Two recent works propose methods for creating research maps from scientists' publication records: by using a frequentist approach to create a transition probability matrix; and by learning embeddings (vector representations). Surprisingly, these models were evaluated on different datasets and have never been compared in the literature. In this work, we compare both models in a systematic way, using a large dataset of publication records from Brazilian researchers. We evaluate these models' ability to predict whether a given entity (scientist, institution or region) will enter a new field w.r.t. the area under the ROC curve. Moreover, we analyze how sensitive each method is to the number of publications and the number of fields associated to one entity. Last, we conduct a case study to showcase how these models can be used to characterize science dynamics in the context of Brazil.Comment: 28 pages, 11 figure