scholarly journals A Scientometric Analysis of Publications Related to Predictive Medicine

2020 ◽  
pp. paper81-1-paper81-12
Author(s):  
Aida Khakimova ◽  
Dongxiao Gu ◽  
Oleg Zolotarev ◽  
Maria Berberova ◽  
Michael Charnine
Keyword(s):  
Noun Phrases ◽  
Subject Area ◽  
Research Trends ◽  
Free Software ◽  
Scientometric Analysis ◽  
Research Topics ◽  
Pubmed Database ◽  
Preventive And Personalized Medicine ◽  
New Research ◽  
And Personalized Medicine

Due to the increasing popularity of new research in medicine thisstudy was conducted to determine recent research trends of predictive, preventive and personalized medicine (PPM). We identified the terms relevant to PPM using own search engine based on neural network processing in PubMed database. We extracted initially about 15000 articles. Then we carried out the statistical analysis for identifying research trends. The article presents the results of solving the problem of evaluating research topics at the level of thematic clusters in a separate subject area. An approach based on the analysis of article titles has been implemented. Identification of terms, connections between them and thematic clustering were carried out using the free software VOSViewer, which allows to extract terms in the form of noun phrases, as well as to cluster them.

scholarly journals How are topics born? Understanding the research dynamics preceding the emergence of new areas

2016 ◽  
Author(s):  
Angelo A Salatino ◽  
Francesco Osborne ◽  
Enrico Motta
Keyword(s):  
Early Detection ◽  
State Of The Art ◽  
Early Stage ◽  
Research Area ◽  
Research Trends ◽  
Research Topics ◽  
Related Research ◽  
Research Areas ◽  
Institutional Funding ◽  
New Research

The ability to recognise new research trends early is strategic for many stakeholders, such as academics, institutional funding bodies, academic publishers and companies. While the state of the art presents several works on the identification of novel research topics, detecting the emergence of a new research area at a very early stage, i.e., when the area has not been even explicitly labelled and is associated with very few publications, is still an open challenge. This limitation hinders the ability of the aforementioned stakeholders to timely react to the emergence of new areas in the research landscape. In this paper, we address this issue by hypothesising the existence of an embryonic stage for research topics and by suggesting that topics in this phase can actually be detected by analysing diachronically the co-occurrence graph of already established topics. To confirm our hypothesis, we performed a study of the dynamics preceding the creation of novel topics. This analysis showed that the emergence of new topics is actually anticipated by a significant increase of the pace of collaboration and density in the co-occurrence graphs of related research areas. These findings are very relevant to a number of research communities and stakeholders. Firstly, they confirm the existence of an embryonic phase in the development of research topics and suggest that it might be possible to perform very early detection of research topics by taking into account the aforementioned dynamics. Secondly, they bring new empirical evidence to related theories in Philosophy of Science. Finally, they suggest that significant new topics tend to emerge in an environment in which previously less interconnected research areas start cross-fertilising.

scholarly journals How are topics born? Understanding the research dynamics preceding the emergence of new areas

2016 ◽  
Author(s):  
Angelo A Salatino ◽  
Francesco Osborne ◽  
Enrico Motta
Keyword(s):  
Early Detection ◽  
State Of The Art ◽  
Early Stage ◽  
Research Area ◽  
Research Trends ◽  
Research Topics ◽  
Related Research ◽  
Research Areas ◽  
Institutional Funding ◽  
New Research

The ability to recognise new research trends early is strategic for many stakeholders, such as academics, institutional funding bodies, academic publishers and companies. While the state of the art presents several works on the identification of novel research topics, detecting the emergence of a new research area at a very early stage, i.e., when the area has not been even explicitly labelled and is associated with very few publications, is still an open challenge. This limitation hinders the ability of the aforementioned stakeholders to timely react to the emergence of new areas in the research landscape. In this paper, we address this issue by hypothesising the existence of an embryonic stage for research topics and by suggesting that topics in this phase can actually be detected by analysing diachronically the co-occurrence graph of already established topics. To confirm our hypothesis, we performed a study of the dynamics preceding the creation of novel topics. This analysis showed that the emergence of new topics is actually anticipated by a significant increase of the pace of collaboration and density in the co-occurrence graphs of related research areas. These findings are very relevant to a number of research communities and stakeholders. Firstly, they confirm the existence of an embryonic phase in the development of research topics and suggest that it might be possible to perform very early detection of research topics by taking into account the aforementioned dynamics. Secondly, they bring new empirical evidence to related theories in Philosophy of Science. Finally, they suggest that significant new topics tend to emerge in an environment in which previously less interconnected research areas start cross-fertilising.

scholarly journals How are topics born? Understanding the research dynamics preceding the emergence of new areas

2016 ◽  
Author(s):  
Angelo A Salatino ◽  
Francesco Osborne ◽  
Enrico Motta
Keyword(s):  
Early Detection ◽  
State Of The Art ◽  
Early Stage ◽  
Research Area ◽  
Research Trends ◽  
Research Topics ◽  
Related Research ◽  
Research Areas ◽  
Institutional Funding ◽  
New Research

The ability to recognise new research trends early is strategic for many stakeholders, such as academics, institutional funding bodies, academic publishers and companies. While the state of the art presents several works on the identification of novel research topics, detecting the emergence of a new research area at a very early stage, i.e., when the area has not been even explicitly labelled and is associated with very few publications, is still an open challenge. This limitation hinders the ability of the aforementioned stakeholders to timely react to the emergence of new areas in the research landscape. In this paper, we address this issue by hypothesising the existence of an embryonic stage for research topics and by suggesting that topics in this phase can actually be detected by analysing diachronically the co-occurrence graph of already established topics. To confirm our hypothesis, we performed a study of the dynamics preceding the creation of novel topics. This analysis showed that the emergence of new topics is actually anticipated by a significant increase of the pace of collaboration and density in the co-occurrence graphs of related research areas. These findings are very relevant to a number of research communities and stakeholders. Firstly, they confirm the existence of an embryonic phase in the development of research topics and suggest that it might be possible to perform very early detection of research topics by taking into account the aforementioned dynamics. Secondly, they bring new empirical evidence to related theories in Philosophy of Science. Finally, they suggest that significant new topics tend to emerge in an environment in which previously less interconnected research areas start cross-fertilising.

Robust Control and Stability Analysis for Stochastic Systems with Markov Jump

2014 ◽  
Vol 631-632 ◽  
pp. 684-687
Author(s):  
Cheng Wang
Keyword(s):  
Stability Analysis ◽  
Robust Control ◽  
Markov Process ◽  
Stochastic Systems ◽  
Research Trends ◽  
Information Control ◽  
Research Topics ◽  
Markov Jump ◽  
New Type ◽  
New Research

Stochastic systems with Markov jump is a new type of stochastic system in recent years, which is a new field integrated by information, control and Markov process. This paper introduces the research history and the newest research trends of stochastic systems with Markov jump, and presents many widespread theoretical and application problems. Moreover, some new research topics and directions related to stochastic systems with Markov jump are proposed.

scholarly journals How are topics born? Understanding the research dynamics preceding the emergence of new areas

2016 ◽  
Author(s):  
Angelo A Salatino ◽  
Francesco Osborne ◽  
Enrico Motta
Keyword(s):  
Early Detection ◽  
State Of The Art ◽  
Early Stage ◽  
Research Area ◽  
Research Trends ◽  
Research Topics ◽  
Related Research ◽  
Research Areas ◽  
Institutional Funding ◽  
New Research

The ability to recognise new research trends early is strategic for many stakeholders, such as academics, institutional funding bodies, academic publishers and companies. While the state of the art presents several works on the identification of novel research topics, detecting the emergence of a new research area at a very early stage, i.e., when the area has not been even explicitly labelled and is associated with very few publications, is still an open challenge. This limitation hinders the ability of the aforementioned stakeholders to timely react to the emergence of new areas in the research landscape. In this paper, we address this issue by hypothesising the existence of an embryonic stage for research topics and by suggesting that topics in this phase can actually be detected by analysing diachronically the co-occurrence graph of already established topics. To confirm our hypothesis, we performed a study of the dynamics preceding the creation of novel topics. This analysis showed that the emergence of new topics is actually anticipated by a significant increase of the pace of collaboration and density in the co-occurrence graphs of related research areas. These findings are very relevant to a number of research communities and stakeholders. Firstly, they confirm the existence of an embryonic phase in the development of research topics and suggest that it might be possible to perform very early detection of research topics by taking into account the aforementioned dynamics. Secondly, they bring new empirical evidence to related theories in Philosophy of Science. Finally, they suggest that significant new topics tend to emerge in an environment in which previously less interconnected research areas start cross-fertilising.

scholarly journals How are topics born? Understanding the research dynamics preceding the emergence of new areas

2017 ◽  
Vol 3 ◽  
pp. e119 ◽  
Author(s):  
Angelo A. Salatino ◽  
Francesco Osborne ◽  
Enrico Motta
Keyword(s):  
Research Area ◽  
Research Trends ◽  
Embryonic Stage ◽  
Relevant Research ◽  
Research Topics ◽  
Research Areas ◽  
Institutional Funding ◽  
Community Of Researchers ◽  
Number Of Publications ◽  
New Research

The ability to promptly recognise new research trends is strategic for many stakeholders, including universities, institutional funding bodies, academic publishers and companies. While the literature describes several approaches which aim to identify the emergence of new research topics early in their lifecycle, these rely on the assumption that the topic in question is already associated with a number of publications and consistently referred to by a community of researchers. Hence, detecting the emergence of a new research area at an embryonic stage, i.e., before the topic has been consistently labelled by a community of researchers and associated with a number of publications, is still an open challenge. In this paper, we begin to address this challenge by performing a study of the dynamics preceding the creation of new topics. This study indicates that the emergence of a new topic is anticipated by a significant increase in the pace of collaboration between relevant research areas, which can be seen as the ‘parents’ of the new topic. These initial findings (i) confirm our hypothesis that it is possible in principle to detect the emergence of a new topic at the embryonic stage, (ii) provide new empirical evidence supporting relevant theories in Philosophy of Science, and also (iii) suggest that new topics tend to emerge in an environment in which weakly interconnected research areas begin to cross-fertilise.

scholarly journals CSO Classifier 3.0: a scalable unsupervised method for classifying documents in terms of research topics

2021 ◽  
Author(s):  
Angelo Salatino ◽  
Francesco Osborne ◽  
Enrico Motta
Keyword(s):  
Computer Science ◽  
Digital Libraries ◽  
Gold Standard ◽  
Research Trends ◽  
Alternative Methods ◽  
Relevant Research ◽  
Research Topics ◽  
Research Papers ◽  
Research Areas ◽  
Unsupervised Approach

AbstractClassifying scientific articles, patents, and other documents according to the relevant research topics is an important task, which enables a variety of functionalities, such as categorising documents in digital libraries, monitoring and predicting research trends, and recommending papers relevant to one or more topics. In this paper, we present the latest version of the CSO Classifier (v3.0), an unsupervised approach for automatically classifying research papers according to the Computer Science Ontology (CSO), a comprehensive taxonomy of research areas in the field of Computer Science. The CSO Classifier takes as input the textual components of a research paper (usually title, abstract, and keywords) and returns a set of research topics drawn from the ontology. This new version includes a new component for discarding outlier topics and offers improved scalability. We evaluated the CSO Classifier on a gold standard of manually annotated articles, demonstrating a significant improvement over alternative methods. We also present an overview of applications adopting the CSO Classifier and describe how it can be adapted to other fields.

scholarly journals Swarm Robotic Interactions in an Open and Cluttered Environment: A Survey

Designs ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 37
Author(s):  
Maxime Vaidis ◽  
Martin J.-D. Otis
Keyword(s):  
Literature Review ◽  
Research Trends ◽  
Cluttered Environment ◽  
Advantages And Disadvantages ◽  
Population Migrations ◽  
New Research ◽  
Interaction Strategy ◽  
Crowded Environment

Recent population migrations have led to numerous accidents and deaths. Little research has been done to help migrants in their journey. For this reason, a literature review of the latest research conducted in previous years is required to identify new research trends in human-swarm interaction. This article presents a review of techniques that can be used in a robots swarm to find, locate, protect and help migrants in hazardous environment such as militarized zone. The paper presents a swarm interaction taxonomy including a detailed study on the control of swarm with and without interaction. As the interaction mainly occurs in cluttered or crowded environment (with obstacles) the paper discussed the algorithms related to navigation that can be included with an interaction strategy. It focused on comparing algorithms and their advantages and disadvantages.

A topic modeling analysis of Korea’s T&I research trends in the 2010s

Babel ◽  
2021 ◽  
Author(s):  
Changsoo Lee
Keyword(s):  
Topic Modeling ◽  
Research Output ◽  
Research Trends ◽  
Korean Language ◽  
Modeling Analysis ◽  
Trends Over Time ◽  
New Research ◽  
Topic Structure ◽  
Manual Classification

Abstract The present study aims to demonstrate the relevance of topic modeling as a new research tool for analyzing research trends in the T&I field. Until now, most efforts to this end have relied on manual classification based on pre-established typologies. This method is time- and labor-consuming, prone to subjective biases, and limited in describing a vast amount of research output. As a key component of text mining, topic modeling offers an efficient way of summarizing topic structure and trends over time in a collection of documents while being able to describe the entire system without having to rely on sampling. As a case study, the present paper applies the technique to analyzing a collection of abstracts from four Korean Language T&I journals for the 2010s decade (from 2010 to 2019). The analysis proves the technique to be highly successful in uncovering hidden topical structure and trends in the abstract corpus. The results are discussed along with implications of the technique for the T&I field.

scholarly journals Research trends and solutions for secure traffic management of SDN

2020 ◽  
Vol 2 (3) ◽  
pp. 97-105
Author(s):  
Ravi Shankar Pandey ◽  
Vivek Srivastava ◽  
Lal Babu Yadav
Keyword(s):  
Traffic Management ◽  
Single Point ◽  
Research Trends ◽  
Security Requirements ◽  
New Paradigm ◽  
Research Papers ◽  
Malicious Attack ◽  
Security Issues ◽  
Free Environment ◽  
New Research

Software Defined Network (SDN) decouples the responsibilities of route management and datatransmission of network devices present in network infrastructure. It integrates the control responsibility at thecentralized software component which is known as controller. This centralized aggregation of responsibilities mayresult the single point of failure in the case malicious attack at the controller side. These attacks may also affect thetraffic flow and network devices. The security issues due to such malicious attacks in SDN are dominating challengesin the implementation and utilization of opportunities provided by this new paradigm. In this paper we haveinvestigated the several research papers related to proposal of new research trends for security and suggestionswhich fulfil the security requirements like confidentiality, integrity, availability, authenticity, authorization,nonrepudiation, consistency, fast responsiveness and adaptation. We have also investigated the new future researchfor creating the attack free environment for implementing the SDN.

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