scholarly journals Migration Multiple? Big Data, Knowledge Practices and the Governability of Migration

2021 ◽  
pp. 113-138
Author(s):  
Laura Stielike
Keyword(s):  
Big Data ◽  
Research Papers ◽  
Production Of Knowledge ◽  
Knowledge Practices ◽  
Research Fields ◽  
Migration Research ◽  
Migration Narratives ◽  
Stark Contrast

AbstractThis chapter explores the big-data-based production of knowledge on migration. Following Mol (2002) and Scheel et al. (2019), it is analysed how migration and migrants are enacted through big-data-based research papers. The emerging sub-discipline of big-data-based migration research enacts migration and migrants in multiple ways that open up possibilities to rethink migration. However, this multiplicity of migration is held together by reference to three migration narratives—demography, integration and humanitarianism—which stand in stark contrast to these alternative enactments, as they all frame migration as something that needs to be governed. As the research papers aim at contributing to these research fields, they inscribe themselves into these migration narratives and thereby adopt the assumption of migration as an object of government.

scholarly journals A Survey on Leukemia Detection using Image Processing Techniques

2021 ◽  
Vol 309 ◽  
pp. 01111
Author(s):  
Mohammed Junaid Ahmed ◽  
Padmalaya Nayak
Keyword(s):  
Deep Learning ◽  
Low Cost ◽  
Research Papers ◽  
Survey Paper ◽  
Machine Learning Methods ◽  
Research Fields ◽  
Dataset Size ◽  
Detection And Diagnosis ◽  
Processing Techniques ◽  
Late Works

Leukemia detection and diagnosis by inspecting the blood cell images is an intriguing and dynamic exploration region in both the Artificial Intelligence and Medical research fields. There are numerous procedures created to look at blood tests to identify leukemia illness, these strategies are the customary methods and the deep learning (DL) strategy. This survey paper presents a review on the distinctive conventional strategies and Deep Learning and Machine Learning methods towards that have been utilized in leukemia illness diagnosis dependent on platelets images and to analyze between the two methodologies in nature of appraisal, exactness, cost and speed. This article covers 11 research papers, 9 of these examinations were in customary strategies which utilized image handling and AI (ML) calculations, such as, K-closest neighbor (KNN), K-means, SVM, Naïve Bayes, and 2 investigations in cutting edge procedures which utilized Deep Learning, especially Convolutional Neural Networks (CNNs) which is the most generally utilized in the field leukemia detection since it is profoundly precise, quick, and has the smallest expense. What's more, it dissects various late works that have been presented in the field including the dataset size, the pre-owned procedures, the acquired outcomes, and so forth. At last, in view of the led study, it very well may be reasoned that the proposed framework CNN was accomplishing immense triumphs in the field whether in regards to highlights extraction or classification time, precision and also a best low cost in the identification of leukemia.

scholarly journals Mapping the Interaction between Research and Education Fields of Sustainability in Architecture: A retrospective Timeline

2021 ◽  
Author(s):  
Ömer Özgenç ◽  
◽  
Nur Çağlar ◽  
Işıl Ruhi-Sipahioğlu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Research Output ◽  
Architectural Education ◽  
Related Topic ◽  
Bibliometric Data ◽  
Journal Articles ◽  
Research Areas ◽  
Research Fields ◽  
Research And Education

Global research output grows exponentially each year. This paper attempts to drive meaning out of this big data on two fields of research in architecture. It maps the interaction between the research fields of sustainability in architecture and architectural education through the perspective of bibliometric data analysis and its visualization. Based on the analysis of bibliometric data, it draws and juxtaposes two timelines for the field of sustainable architecture and the field of architectural education. The objective is to propose a retrospective method that can provide insight for a broader understanding of sustainability and its impacts on architectural education. It utilizes VOSviewer, CiteSpace, and Gephi to visualize bibliometric networks, along with Tableau to analyze the number of journal articles and publications published across years. The paper presents initial findings concerning the leading scholars, trends, and patterns of the research areas, milestone events, and dominant studies to point out the significance of the cooperation between research and education fields of the related topic.

scholarly journals SUPPORTING AIR TRANSPORT POLICIES USING BIG DATA ANALYTICS: A DESCRIPTIVE APPROACH BASED EMERGING TREND ANALYSIS

2017 ◽  
Vol 8 (1) ◽  
pp. 51-72
Author(s):  
Jin-seo Park
Keyword(s):  
Qualitative Research ◽  
Big Data ◽  
Text Mining ◽  
Research Methods ◽  
Qualitative Research Methods ◽  
Research Papers ◽  
Emerging Trends ◽  
The Future ◽  
Descriptive Approach ◽  
Core Issues

Qualitative research methods based on literature review or expert judgement have been used to find core issues, analyze emerging trends and discover promising areas for the future. Deriving results from large amounts of information under this approach is both costly and time consuming. Besides, there is a risk that the results may be influenced by the subjective opinion of experts. In order to make up for such weaknesses, the analysis paradigm for choosing future emerging trend is undergoing a shift toward mplementing qualitative research methods along with quantitative research methods like text mining in a mutually complementary manner. The hange used to implement recent studies is being witnessed in various areas such as the steel industry, the information and communications technology industry, the construction industry in architectural engineering and so on. This study focused on retrieving aviation-related core issues and the promising areas for the future from research papers pertaining to overall aviation areas through text mining method, which is one of the big data analysis techniques. This study has limitations in that its analysis for retrieving the aviation-related core issues and promising fields was restricted to research papers containing the keyword "aviation." However, it has significance in that it prepared a quantitative analysis model for continuously monitoring the derived core issues and emerging trends regarding the promising areas for the future in the aviation industry through the application of a big data-based descriptive approach.

From co-production of knowledge to a participatory governance concept: a research design focusing on knowledge practices in flood risk management and disaster risk reduction

2021 ◽  
Author(s):  
Ida Wallin
Keyword(s):  
Risk Management ◽  
Risk Reduction ◽  
Research Design ◽  
Flood Risk ◽  
Disaster Risk Reduction ◽  
Disaster Risk ◽  
Flood Risk Management ◽  
Participatory Governance ◽  
Production Of Knowledge ◽  
Knowledge Practices

<p>Knowledge has been shown to be more effectively implemented in practice when produced in collaboration between researchers and other stakeholders as the co-produced knowledge is more likely to be accepted and found relevant. Knowledge co-production processes have however been found guilty of depoliticizing and hiding political struggles to the end of reinforcing existing unequal power relations and prevent broad societal transformation from taking place. From this perspective, knowledge co-production can come into conflict with participatory governance that focuses on the empowerment and capacity building of actors, social justice and advocacy. In this presentation I take a closer look at this conflictual perspective and propose a research focus on knowledge practices for exploring and analyzing participatory governance options for flood risk management (FRM) and disaster risk reduction (DRR). I do this by exemplifying and presenting a research design developed within the newly started PARADeS-project.</p><p>The PARADeS-project is a research project led by German research institutions in close collaboration with partners in Ghana and with the overall aim to contribute to enhancing Ghana’s national flood risk and disaster management strategy. Co-production of knowledge is foreseen to take place in several workshops including collaborative modelling, scenario- and policy back-casting exercises. One of the planned project outputs is a concept of participatory governance in FRM and DRR based on the findings from a stakeholder analysis, a policy network analysis and a participatory assessment of different policy options.</p><p>In this project context a research focus on stakeholders’ knowledge practices can be used to inform and improve the participatory governance concept and facilitate its implementation process. Knowledge is used by stakeholders as a powerful resource in suggesting certain policy options and convincing others of their necessity. Knowledge practices entail how actors use knowledge to argue, convince and make decisions. Through knowledge practices, stakeholders decide what knowledge to base decisions on and how to convince others of their position using that knowledge. What knowledge becomes accepted as legitimate in such interactions - often deliberative settings - can be decisive for the acceptability of any policy option. It is therefore important to study not only the different types of stakeholders and technical options for FRM and DRR, but the interaction between stakeholders and how they use information and co-create knowledge - the knowledge practices.</p><p>Within the presentation I discuss the proposed research design for how to study knowledge practices and how to make use of these findings when going from research project and co-production of knowledge to a concept of participatory governance in flood risk management and disaster risk reduction in Ghana.</p>

Big Data Analysis for Cardiovascular Diseases

2018 ◽  
pp. 102-119
Author(s):  
Miguel A. Sánchez-Acevedo ◽  
Zaydi A. Acosta-Chí ◽  
Beatriz A. Sabino-Moxo ◽  
José A. Márquez-Domínguez ◽  
Rosa M. Canton-Croda
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Medical Devices ◽  
Clinical Analysis ◽  
Big Data Analysis ◽  
Health Issues ◽  
Research Papers ◽  
Healthcare Data ◽  
Common Risk Factors ◽  
The Common

In the healthcare field, plenty of clinical data is generated every day from patient records, surveys, research papers, medical devices, among others sources. These data can be exploited to discover new insights about health issues. For helping decision makers and healthcare data managers, a survey of research works and tools covering the process of handling big data in the healthcare field is included. A methodology for CVD prevention, detection and management through the use of tools for big data analysis is proposed. Also, it is important to maintain privacy of patients when handling healthcare data; therefore, a list of recommendations for maintaining privacy when handling healthcare data is presented. Specific clinical analysis are recommended on those regions where the incidence rate of CVD is high, but a weak relation with the common risk factors is observed according to historical data. Finally, challenges which need to be addressed are presented.

scholarly journals Erratum: Annual tendency of research papers used ICR mice as experimental animals in biomedical research fields

2017 ◽  
Vol 33 (4) ◽  
pp. 320
Author(s):  
Ji Eun Kim ◽  
Jung Hoon Nam ◽  
Joon Young Cho ◽  
Kil Soo Kim ◽  
Dae Youn Hwang
Keyword(s):  
Biomedical Research ◽  
Research Papers ◽  
Experimental Animals ◽  
Icr Mice ◽  
Research Fields

scholarly journals Can Big Data Analysis Make Cities Smarter? Emerging Issues and Stakeholder Implications in Smart Mobility

2017 ◽  
Vol 8 (1) ◽  
pp. 23 ◽  
Author(s):  
Asst. Prof. Dr. Serkan Gürsoy ◽  
Asst. Prof. Dr. Murat Yücelen
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Smart Cities ◽  
Academic Research ◽  
Big Data Analysis ◽  
Ongoing Debate ◽  
Research Fields ◽  
Emerging Issues ◽  
Smart Mobility ◽  
Applied Fields

This study deals with the challenges and bottlenecks with respect to the concept of smart cities which has largely been constructed on knowledge utilization issues and challenges. Despite the abundant existent literature in this field, the effective transformation of data into knowledge which can become a source of competitive advantage is still an ongoing debate, especially due to contemporary developments in big data analysis methods, approaches and strategies. As an emerging problem, the derivation of significant meaning from big data is among popular academic research fields, as well as being a crucial industrial and policy making engagement regarding value creating mechanisms in smart cities. Therefore in this study, limitations and challenges in translating big data into valuable knowledge in academia and industries are considered within the concept of smart mobility. In an attempt to propose researchers, business firms and governmental entities a collaborative approach, a perception about emerging issues is presented for clarifying some future constructs intersecting in relevant research and applied fields.

Big Data Analysis for Cardiovascular Diseases

2019 ◽  
pp. 60-77
Author(s):  
Miguel A. Sánchez-Acevedo ◽  
Zaydi A. Acosta-Chí ◽  
Beatriz A. Sabino-Moxo ◽  
José A. Márquez-Domínguez ◽  
Rosa M. Canton-Croda
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Medical Devices ◽  
Clinical Analysis ◽  
Big Data Analysis ◽  
Health Issues ◽  
Research Papers ◽  
Healthcare Data ◽  
Common Risk Factors ◽  
The Common

In the healthcare field, plenty of clinical data is generated every day from patient records, surveys, research papers, medical devices, among others sources. These data can be exploited to discover new insights about health issues. For helping decision makers and healthcare data managers, a survey of research works and tools covering the process of handling big data in the healthcare field is included. A methodology for CVD prevention, detection and management through the use of tools for big data analysis is proposed. Also, it is important to maintain privacy of patients when handling healthcare data; therefore, a list of recommendations for maintaining privacy when handling healthcare data is presented. Specific clinical analysis are recommended on those regions where the incidence rate of CVD is high, but a weak relation with the common risk factors is observed according to historical data. Finally, challenges which need to be addressed are presented.

A Bibliometric Analysis of Research and Services in Chinese Academic Libraries

2016 ◽  
pp. 253-262
Author(s):  
Suoling Zhu ◽  
Wen Shi
Keyword(s):  
Big Data ◽  
Academic Libraries ◽  
Information Science ◽  
Library And Information Science ◽  
Research Subjects ◽  
China National Knowledge Infrastructure ◽  
Research Papers ◽  
Knowledge Infrastructure ◽  
Information Research ◽  
Text Database

This paper analyzes the years of publication, authors and their institutions, journal titles, and keywords of research papers relevant to academic libraries published in the 18 core journals of library and information science, which were downloaded from the full-text database of the China National Knowledge Infrastructure (CNKI). The purpose of this paper is to discover the distribution of the research subjects, hot topics in library and information research, and development trends in the age of big data.

scholarly journals Google Books Ngrams Recompressed and Searchable

2012 ◽  
Vol 37 (4) ◽  
pp. 271-281 ◽  
Author(s):  
Szymon Grabowski ◽  
Jakub Swacha
Keyword(s):  
Big Data ◽  
Computational Linguistics ◽  
Compression Ratio ◽  
Web Tool ◽  
Large Dataset ◽  
Research Fields ◽  
Original Distribution ◽  
Google Books ◽  
N Gram

AbstractOne of the research fields significantly affected by the emergence of “big data” is computational linguistics. A prominent example of a large dataset targeting this domain is the collection of Google Books Ngrams, made freely available, for several languages, in July 2009. There are two problems with Google Books Ngrams; the textual format (compressed with Deflate) in which they are distributed is highly inefficient; we are not aware of any tool facilitating search over those data, apart from the Google viewer, which, as a Web tool, has seriously limited use. In this paper we present a simple preprocessing scheme for Google Books Ngrams, enabling also search for an arbitrary n-gram (i.e., its associated statistics) in average time below 0.2 ms. The obtained compression ratio, with Deflate (zip) left as the backend coder, is over 3 times higher than in the original distribution.

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