scholarly journals Literature Review on the Applications of Machine Learning and Blockchain Technology in Smart Healthcare Industry: A Bibliometric Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yang Li ◽  
Biaoan Shan ◽  
Beiwei Li ◽  
Xiaoju Liu ◽  
Yi Pu
Keyword(s):  
Machine Learning ◽  
Web Of Science ◽  
Future Research ◽  
Healthcare Industry ◽  
Research Subjects ◽  
Major Research ◽  
Research Areas ◽  
Blockchain Technology ◽  
Smart Healthcare ◽  
Applications Of Machine Learning

The emergence of machine learning (ML) and blockchain (BC) technology has greatly enriched the functions and services of healthcare, giving birth to the new field of “smart healthcare.” This study aims to review the application of ML and BC technology in the smart medical industry by Web of Science (WOS) using bibliometric visualization. Through our research, we identify the countries with the greatest output, the major research subjects, funding funds, and the research hotspots in this field. We also find out the key themes and future research areas in application of ML and BC technology in healthcare area. We reveal the different aspects of research under the two technologies and how they relate to each other around five themes.

scholarly journals Trials, Skills, and Future Standpoints of AI Based Research in Bioinformatics

2020 ◽  
Vol 9 (1) ◽  
pp. 968-972
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Future Research ◽  
Multidisciplinary Research ◽  
Major Research ◽  
Research Areas ◽  
Genomics And Proteomics ◽  
Drug Designing ◽  
Level Analysis

In recent times, computer field has entered in all types of business and industries. Recent advancements in the information technology field, has open up many possibilities in multidisciplinary research. Machine learning, deep learning, convolution neural network, etc. are recent development in computer fields which has change the way of development of algorithms. Such algorithms can learn over a period of time while in execution and improves its performance and continue learning. Bioinformatics is the recent example of the science which strives to use such recent technologies of computer science for betterment in its own field. This article reviews Artificial Intelligence subset such as Machine learning and Deep learning in the genomics and proteomics domain. This article provides profound insights of various AI techniques which can be incorporated in the field of bioinformatics. The paper also highlighted the future research potential of this field. Computational biology, genomics, proteomics, Drug designing, gene expression level analysis are the major research areas in bioinformatics. These areas are also discussed in the paper.

scholarly journals Blockchain Technology - Current Status and Future Research Opportunities in Various Areas of Healthcare Industry

2021 ◽  
pp. 130-150
Author(s):  
P. S. Aithal ◽  
Architha Aithal ◽  
Edwin Dias
Keyword(s):  
Systematic Review ◽  
Healthcare Services ◽  
Healthcare Sector ◽  
Current Status ◽  
Future Research ◽  
Healthcare Industry ◽  
Secondary Sources ◽  
Blockchain Technology ◽  
Smart Healthcare ◽  
Research Agendas

Purpose: Blockchain technology is one of the emerging Information Communication and Computation (ICCT) underlying technologies of the 21st century with potential applications in primary, secondary, tertiary, and quaternary industry sectors. In this paper, we have identified and analyzed some of the potential fields of the healthcare industry that can get benefit by means of using blockchain technology principles. Based on a systematic review on the development of blockchain technology and its application in healthcare sector to improve the quality of healthcare services, this paper identifies some of the application areas in the healthcare industry including Healthcare Security & Authentication aspects, Clinical Trials & Precision Medicine, Personalizing the Healthcare Services, Healthcare Data Management, Strengthening Public Health Surveillance, e-Healthcare to Customers, Healthcare Administration & Medicine Management, Telehealth & Telemedicine, Managing Medical Imaging, Developing Smart Healthcare System, and Healthcare Information System. The purpose also includes the analysis of the current implementation challenges of blockchain technology in healthcare industry services. Methodology: The study is descriptive and exploratory in nature. The related information is collected from various secondary sources for review. The secondary sources include published literature from various scholarly journals searched through Google scholar by means of identified keywords. Results/Findings: Based on a systematic review, we have identified the current status of the use of blockchain in several areas of healthcare sector, desired status called ideal status, and the research gap of use of blockchain technology in various application areas of the healthcare industry along with identification of various possible research agendas for future research. Originality/Value: It is found that blockchain technology facilitates for the improvement of quality services in the healthcare sector and various research agendas are proposed to carry out further research for patient satisfaction and comfortability. Type of the Paper: Review based research analysis.

Alignment for Advanced Machine Learning Systems

2020 ◽  
pp. 342-382
Author(s):  
Jessica Taylor ◽  
Eliezer Yudkowsky ◽  
Patrick LaVictoire ◽  
Andrew Critch
Keyword(s):  
Machine Learning ◽  
Side Effects ◽  
Objective Function ◽  
Unintended Consequences ◽  
Learning Systems ◽  
Large Impact ◽  
Future Research ◽  
Objective Functions ◽  
Research Areas ◽  
The Right

This chapter surveys eight research areas organized around one question: As learning systems become increasingly intelligent and autonomous, what design principles can best ensure that their behavior is aligned with the interests of the operators? The chapter focuses on two major technical obstacles to AI alignment: the challenge of specifying the right kind of objective functions and the challenge of designing AI systems that avoid unintended consequences and undesirable behavior even in cases where the objective function does not line up perfectly with the intentions of the designers. The questions surveyed include the following: How can we train reinforcement learners to take actions that are more amenable to meaningful assessment by intelligent overseers? What kinds of objective functions incentivize a system to “not have an overly large impact” or “not have many side effects”? The chapter discusses these questions, related work, and potential directions for future research, with the goal of highlighting relevant research topics in machine learning that appear tractable today.

scholarly journals Automated Reasoning for Explainable Artificial Intelligence

10.29007/4b7h ◽  
2018 ◽  
Author(s):  
Maria Paola Bonacina
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Automated Reasoning ◽  
Position Statement ◽  
Short Paper ◽  
Future Research ◽  
Grand Challenge ◽  
New Paradigm ◽  
Research Areas ◽  
Explainable Artificial Intelligence

Reasoning and learning have been considered fundamental features of intelligence ever since the dawn of the field of artificial intelligence, leading to the development of the research areas of automated reasoning and machine learning. This short paper is a non-technical position statement that aims at prompting a discussion of the relationship between automated reasoning and machine learning, and more generally between automated reasoning and artificial intelligence. We suggest that the emergence of the new paradigm of XAI, that stands for eXplainable Artificial Intelligence, is an opportunity for rethinking these relationships, and that XAI may offer a grand challenge for future research on automated reasoning.

scholarly journals Collaboration of Blockchain and Machine Learning in Healthcare Industry

2019 ◽  
Vol 9 (1) ◽  
pp. 2642-2645
Keyword(s):  
Machine Learning ◽  
Medical Data ◽  
Machine Learning Techniques ◽  
Insurance Companies ◽  
Healthcare Sector ◽  
Healthcare Industry ◽  
Sensitive Data ◽  
Medical Practitioners ◽  
Blockchain Technology ◽  
Learning Techniques

The purpose of this paper is to explore the applications of blockchain in the healthcare industry. Healthcare sector can become an application domain of blockchain as it can be used to securely store health records and maintain an immutable version of truth. Blockchain technology is originally built on Hyperledger, which is a decentralized platform to enable secure, unambiguous and swift transactions and usage of medical records for various purposes. The paper proposes to use blockchain technology to provide a common and secured platform through which medical data can be accessed by doctors, medical practitioners, pharma and insurance companies. In order to provide secured access to such sensitive data, blockchain ensures that any organization or person can only access data with consent of the patient. The Hyperledger Fabric architecture guarantees that the data is safe and private by permitting the patients to grant multi-level access to their data. Apart from blockchain technology, machine learning can be used in the healthcare sector to understand and analyze patterns and gain insights from data. As blockchain can be used to provide secured and authenticated data, machine learning can be used to analyze the provided data and establish new boundaries by applying various machine learning techniques on such real-time medical data.

scholarly journals Machine Learning for Mental Health in Social Media: Bibliometric Study (Preprint)

2020 ◽  
Author(s):  
Jina Kim ◽  
Daeun Lee ◽  
Eunil Park
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Social Media ◽  
Large Scale ◽  
Web Of Science ◽  
Research Area ◽  
Research Subjects ◽  
Comprehensive Overview ◽  
Time Saving ◽  
Continuous Growth

BACKGROUND Social media platforms provide an easily accessible and time-saving communication approach for individuals with mental disorders compared to face-to-face meetings with medical providers. Recently, machine learning (ML)-based mental health exploration using large-scale social media data has attracted significant attention. OBJECTIVE We aimed to provide a bibliometric analysis and discussion on research trends of ML for mental health in social media. METHODS Publications addressing social media and ML in the field of mental health were retrieved from the Scopus and Web of Science databases. We analyzed the publication distribution to measure productivity on sources, countries, institutions, authors, and research subjects, and visualized the trends in this field using a keyword co-occurrence network. The research methodologies of previous studies with high citations are also thoroughly described. RESULTS We obtained a total of 565 relevant papers published from 2015 to 2020. In the last 5 years, the number of publications has demonstrated continuous growth with <i>Lecture Notes in Computer Science</i> and <i>Journal of Medical Internet Research</i> as the two most productive sources based on Scopus and Web of Science records. In addition, notable methodological approaches with data resources presented in high-ranking publications were investigated. CONCLUSIONS The results of this study highlight continuous growth in this research area. Moreover, we retrieved three main discussion points from a comprehensive overview of highly cited publications that provide new in-depth directions for both researchers and practitioners.

scholarly journals Application of Machine Learning in Supply Chain Management: A Comprehensive Overview of the Main Areas

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Erfan Babaee Tirkolaee ◽  
Saeid Sadeghi ◽  
Farzaneh Mansoori Mooseloo ◽  
Hadi Rezaei Vandchali ◽  
Samira Aeini
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Inventory Management ◽  
Future Research ◽  
Comprehensive Overview ◽  
Chain Management ◽  
Efficiency And Effectiveness ◽  
Applications Of Machine Learning ◽  
Supply Chain Risks

In today’s complex and ever-changing world, concerns about the lack of enough data have been replaced by concerns about too much data for supply chain management (SCM). The volume of data generated from all parts of the supply chain has changed the nature of SCM analysis. By increasing the volume of data, the efficiency and effectiveness of the traditional methods have decreased. Limitations of these methods in analyzing and interpreting a large amount of data have led scholars to generate some methods that have high capability to analyze and interpret big data. Therefore, the main purpose of this paper is to identify the applications of machine learning (ML) in SCM as one of the most well-known artificial intelligence (AI) techniques. By developing a conceptual framework, this paper identifies the contributions of ML techniques in selecting and segmenting suppliers, predicting supply chain risks, and estimating demand and sales, production, inventory management, transportation and distribution, sustainable development (SD), and circular economy (CE). Finally, the implications of the study on the main limitations and challenges are discussed, and then managerial insights and future research directions are given.

scholarly journals A Survey on the Applications of Machine Learning in the Early Phases of Product Development

2019 ◽  
Vol 1 (1) ◽  
pp. 2437-2446 ◽  
Author(s):  
Seyed Sina Shabestari ◽  
Michael Herzog ◽  
Beate Bender
Keyword(s):  
Machine Learning ◽  
Product Development ◽  
Product Development Process ◽  
Future Research ◽  
Concept Design ◽  
Survey Techniques ◽  
Current State ◽  
State Of Research ◽  
Applications Of Machine Learning ◽  
Early Phases

AbstractMachine learning has shown its potential to support the knowledge extraction within the development processes and particularly in the early phases where critical decisions have to be made. However, the current state of the research in the applications of the machine learning in the product development are fragmented. A holistic overall view provides the opportunity to analyze the current state of research and is the basis for the strategic planning of future research and the actions needed. Hence, implementing the systematic literature survey techniques, the state of the applications of machine learning in the early phases of the product development process namely the Requirements, functional modelling and principal concept design is reviewed and discussed.

A systematic review of international franchising

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ilan Alon ◽  
Indri Dwi Apriliyanti ◽  
Massiel Carolina Henríquez Parodi
Keyword(s):  
Design Methodology ◽  
Systematic Approach ◽  
Web Of Science ◽  
Meta Analysis ◽  
Limited Resources ◽  
Future Research ◽  
Major Research ◽  
Content Type ◽  
Mode Of Entry ◽  
International Franchising

Purpose This paper aims to provide a bibliometric meta-analysis of the already substantial and growing literature on international franchising. Franchising is a model for businesses to achieve scale with limited resources. International franchising is a mode of entry that allows firms to develop new markets with relatively little risk but also little control. Design/methodology/approach Using a systematic approach, the paper identifies all articles in the ISI Web of Science from 1970 to 2018 that includes the term international franchising (in the title, the abstract or keywords) and finds 131 articles. This paper used HistCite software to analyze the bibliometric data. Findings Four major research clusters in the international franchising literature are identified. In addition, this study shows a change in research patterns regarding topics, theories and methodologies from the 1970s through 2018. The paper presents the most influential articles, authors and journals. Originality/value From the analyzes, this study develops a conceptual framework of international franchising and suggest avenues for future research.

Mapping customer experience: a taxonomical study using bibliometric visualization

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pranay Sindhu ◽  
Kumkum Bharti
Keyword(s):  
Web Of Science ◽  
Software Tool ◽  
Customer Experience ◽  
Research Field ◽  
Future Research ◽  
Asian Countries ◽  
Content Type ◽  
Research Areas ◽  
Practical Implications ◽  
Taxonomical Study

Purpose This study aims to examine the trends and themes in the field of customer experience using a bibliometric analysis between 1957 and 2017. Design/methodology/approach The paper analyses 1,767 papers selected from Web of Science (WoS) database using VOS Viewer software tool to create bibliometric networks. The results of the study were classified under the following bibliometric indicators: evaluation of the number of studies analyzed; most cited documents; most influential authors; and highly influential journals, institutions and countries with the highest productivity. Additionally, the paper also presents three co-citation studies analyzing most co-cited references, first authors and journals. Findings Authors and institutes from the American and European countries dominate the contribution to the development of the field. The presence of Asian countries signifies the rising importance being given to the research field. The findings establish the argument that most of the ideas that follow today in the development of the field are mostly sourced from the works published in highly reputed journals. Co-citation analysis indicates the presence of multi-disciplinarity in the research field with journals representing different research areas such as management, strategy and psychology. Research limitations/implications The papers analyzed in the study were retrieved only from the WoS. Furthermore, the precise number of clusters obtained during the analysis depends on the parameter set by the authors which is subjective. Researchers are encouraged to draw further insights by manipulating the parameter criteria. Practical implications The findings in the study can be used to enrich the understanding of customer experience and future research on the topic. Originality/value To the best of the authors’ knowledge, this paper is one of the first comprehensive papers offering a general overview of the leading trends in the field over a period of 60 years.

Sign in / Sign up

Export Citation Format

Share Document