Example of IEEE Transactions on Emerging Topics in Computing format
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Example of IEEE Transactions on Emerging Topics in Computing format Example of IEEE Transactions on Emerging Topics in Computing format Example of IEEE Transactions on Emerging Topics in Computing format Example of IEEE Transactions on Emerging Topics in Computing format Example of IEEE Transactions on Emerging Topics in Computing format Example of IEEE Transactions on Emerging Topics in Computing format Example of IEEE Transactions on Emerging Topics in Computing format
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Example of IEEE Transactions on Emerging Topics in Computing format Example of IEEE Transactions on Emerging Topics in Computing format Example of IEEE Transactions on Emerging Topics in Computing format Example of IEEE Transactions on Emerging Topics in Computing format Example of IEEE Transactions on Emerging Topics in Computing format Example of IEEE Transactions on Emerging Topics in Computing format Example of IEEE Transactions on Emerging Topics in Computing format
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IEEE Transactions on Emerging Topics in Computing — Template for authors

Publisher: IEEE
Categories Rank Trend in last 3 yrs
Computer Science (miscellaneous) #2 of 69 -
Computer Science Applications #41 of 693 down down by 14 ranks
Information Systems #20 of 329 down down by 1 rank
Human-Computer Interaction #9 of 120 down down by 3 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 213 Published Papers | 2239 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 20/07/2020
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Related Journals

open access Open Access
recommended Recommended

IEEE

Quality:  
High
CiteRatio: 23.3
SJR: 3.109
SNIP: 3.707
open access Open Access

Springer

Quality:  
Good
CiteRatio: 3.2
SJR: 0.325
SNIP: 0.865
open access Open Access

IEEE

Quality:  
High
CiteRatio: 6.4
SJR: 0.786
SNIP: 2.027
open access Open Access
recommended Recommended

Taylor and Francis

Quality:  
High
CiteRatio: 6.8
SJR: 1.321
SNIP: 1.764

Journal Performance & Insights

CiteRatio

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

A measure of average citations received per peer-reviewed paper published in the journal.

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

10.5

24% from 2019

CiteRatio for IEEE Transactions on Emerging Topics in Computing from 2016 - 2020
Year Value
2020 10.5
2019 8.5
2018 8.0
2017 8.4
2016 7.4
graph view Graph view
table view Table view

0.781

3% from 2019

SJR for IEEE Transactions on Emerging Topics in Computing from 2016 - 2020
Year Value
2020 0.781
2019 0.807
2018 0.659
2017 0.658
2016 0.821
graph view Graph view
table view Table view

2.019

14% from 2019

SNIP for IEEE Transactions on Emerging Topics in Computing from 2016 - 2020
Year Value
2020 2.019
2019 1.764
2018 1.955
2017 2.306
2016 2.644
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 24% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has decreased by 3% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 14% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

IEEE Transactions on Emerging Topics in Computing

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IEEE

IEEE Transactions on Emerging Topics in Computing

Approved by publishing and review experts on SciSpace, this template is built as per for IEEE Transactions on Emerging Topics in Computing formatting guidelines as mentioned in IEEE author instructions. The current version was created on 19 Jul 2020 and has been used by 892 authors to write and format their manuscripts to this journal.

Computer Science

i
Last updated on
19 Jul 2020
i
ISSN
2168-6750
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
IEEEtran
i
Citation Type
Numbered
[25]
i
Bibliography Example
C. W. J. Beenakker, “Specular andreev reflection in graphene,” Phys. Rev. Lett., vol. 97, no. 6, p.

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1109/TETC.2014.2330519
A Survey of Clustering Algorithms for Big Data: Taxonomy and Empirical Analysis

Abstract:

Clustering algorithms have emerged as an alternative powerful meta-learning tool to accurately analyze the massive volume of data generated by modern applications. In particular, their main goal is to categorize data into clusters such that objects are grouped in the same cluster when they are similar according to specific me... Clustering algorithms have emerged as an alternative powerful meta-learning tool to accurately analyze the massive volume of data generated by modern applications. In particular, their main goal is to categorize data into clusters such that objects are grouped in the same cluster when they are similar according to specific metrics. There is a vast body of knowledge in the area of clustering and there has been attempts to analyze and categorize them for a larger number of applications. However, one of the major issues in using clustering algorithms for big data that causes confusion amongst practitioners is the lack of consensus in the definition of their properties as well as a lack of formal categorization. With the intention of alleviating these problems, this paper introduces concepts and algorithms related to clustering, a concise survey of existing (clustering) algorithms as well as providing a comparison, both from a theoretical and an empirical perspective. From a theoretical perspective, we developed a categorizing framework based on the main properties pointed out in previous studies. Empirically, we conducted extensive experiments where we compared the most representative algorithm from each of the categories using a large number of real (big) data sets. The effectiveness of the candidate clustering algorithms is measured through a number of internal and external validity metrics, stability, runtime, and scalability tests. In addition, we highlighted the set of clustering algorithms that are the best performing for big data. read more read less

Topics:

Cluster analysis (70%)70% related to the paper, Correlation clustering (68%)68% related to the paper, Fuzzy clustering (66%)66% related to the paper, Data stream clustering (66%)66% related to the paper, Canopy clustering algorithm (66%)66% related to the paper
833 Citations
open accessOpen access Journal Article DOI: 10.1109/TETC.2016.2606384
A Comprehensive Study of Security of Internet-of-Things
Arsalan Mosenia1, Niraj K. Jha1

Abstract:

Internet of Things (IoT), also referred to as the Internet of Objects, is envisioned as a transformative approach for providing numerous services. Compact smart devices constitute an essential part of IoT. They range widely in use, size, energy capacity, and computation power. However, the integration of these smart things in... Internet of Things (IoT), also referred to as the Internet of Objects, is envisioned as a transformative approach for providing numerous services. Compact smart devices constitute an essential part of IoT. They range widely in use, size, energy capacity, and computation power. However, the integration of these smart things into the standard Internet introduces several security challenges because the majority of Internet technologies and communication protocols were not designed to support IoT. Moreover, commercialization of IoT has led to public security concerns, including personal privacy issues, threat of cyber attacks, and organized crime. In order to provide a guideline for those who want to investigate IoT security and contribute to its improvement, this survey attempts to provide a comprehensive list of vulnerabilities and countermeasures against them on the edge-side layer of IoT, which consists of three levels: (i) edge nodes, (ii) communication, and (iii) edge computing. To achieve this goal, we first briefly describe three widely-known IoT reference models and define security in the context of IoT. Second, we discuss the possible applications of IoT and potential motivations of the attackers who target this new paradigm. Third, we discuss different attacks and threats. Fourth, we describe possible countermeasures against these attacks. Finally, we introduce two emerging security challenges not yet explained in detail in previous literature. read more read less

Topics:

Internet security (60%)60% related to the paper, Security service (58%)58% related to the paper, The Internet (56%)56% related to the paper, Edge computing (51%)51% related to the paper
547 Citations
open accessOpen access Journal Article DOI: 10.1109/TETC.2015.2390034
The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey
Charith Perera1, Chi Harold Liu2, Srimal Jayawardena1

Abstract:

The Internet of Things (IoT) is a dynamic global information network consisting of Internet-connected objects, such as Radio frequency identifications, sensors, actuators, as well as other instruments and smart appliances that are becoming an integral component of the future Internet. Over the last decade, we have seen a larg... The Internet of Things (IoT) is a dynamic global information network consisting of Internet-connected objects, such as Radio frequency identifications, sensors, actuators, as well as other instruments and smart appliances that are becoming an integral component of the future Internet. Over the last decade, we have seen a large number of the IoT solutions developed by start-ups, small and medium enterprises, large corporations, academic research institutes (such as universities), and private and public research organizations making their way into the market. In this paper, we survey over one hundred IoT smart solutions in the marketplace and examine them closely in order to identify the technologies used, functionalities, and applications. Based on the application domain, we classify and discuss these solutions under five different categories: 1) smart wearable; 2) smart home; 3) smart city; 4) smart environment; and 5) smart enterprise. This survey is intended to serve as a guideline and a conceptual framework for future research in the IoT and to motivate and inspire further developments. It also provides a systematic exploration of existing research and suggests a number of potentially significant research directions. read more read less

Topics:

Smart city (62%)62% related to the paper, Smart environment (61%)61% related to the paper, Internet appliance (55%)55% related to the paper, The Internet (54%)54% related to the paper
View PDF
388 Citations
open accessOpen access Journal Article DOI: 10.1109/TETC.2016.2633228
A Two-Layer Dimension Reduction and Two-Tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks
Hamed Haddad Pajouh1, Reza Javidan1, Raouf Khayami1, Ali Dehghantanha2, Kim-Kwang Raymond Choo3

Abstract:

With increasing reliance on Internet of Things (IoT) devices and services, the capability to detect intrusions and malicious activities within IoT networks is critical for resilience of the network infrastructure. In this paper, we present a novel model for intrusion detection based on two-layer dimension reduction and two-ti... With increasing reliance on Internet of Things (IoT) devices and services, the capability to detect intrusions and malicious activities within IoT networks is critical for resilience of the network infrastructure. In this paper, we present a novel model for intrusion detection based on two-layer dimension reduction and two-tier classification module, designed to detect malicious activities such as User to Root (U2R) and Remote to Local (R2L) attacks. The proposed model is using component analysis and linear discriminate analysis of dimension reduction module to spate the high dimensional dataset to a lower one with lesser features. We then apply a two-tier classification module utilizing Naive Bayes and Certainty Factor version of K-Nearest Neighbor to identify suspicious behaviors. The experiment results using NSL-KDD dataset shows that our model outperforms previous models designed to detect U2R and R2L attacks. read more read less

Topics:

Anomaly-based intrusion detection system (62%)62% related to the paper, Intrusion detection system (58%)58% related to the paper, Naive Bayes classifier (50%)50% related to the paper
View PDF
356 Citations
open accessOpen access Journal Article DOI: 10.1109/TETC.2015.2508382
Cost Efficient Resource Management in Fog Computing Supported Medical Cyber-Physical System
Lin Gu1, Deze Zeng2, Song Guo3, Ahmed Barnawi4, Yong Xiang5

Abstract:

With the recent development in information and communication technology, more and more smart devices penetrate into people’s daily life to promote the life quality. As a growing healthcare trend, medical cyber-physical systems (MCPSs) enable seamless and intelligent interaction between the computational elements and the medic... With the recent development in information and communication technology, more and more smart devices penetrate into people’s daily life to promote the life quality. As a growing healthcare trend, medical cyber-physical systems (MCPSs) enable seamless and intelligent interaction between the computational elements and the medical devices. To support MCPSs, cloud resources are usually explored to process the sensing data from medical devices. However, the high quality-of-service of MCPS challenges the unstable and long-delay links between cloud data center and medical devices. To combat this issue, mobile edge cloud computing, or fog computing, which pushes the computation resources onto the network edge (e.g., cellular base stations), emerges as a promising solution. We are thus motivated to integrate fog computation and MCPS to build fog computing supported MCPS (FC-MCPS). In particular, we jointly investigate base station association, task distribution, and virtual machine placement toward cost-efficient FC-MCPS. We first formulate the problem into a mixed-integer non-linear linear program and then linearize it into a mixed integer linear programming (LP). To address the computation complexity, we further propose an LP-based two-phase heuristic algorithm. Extensive experiment results validate the high-cost efficiency of our algorithm by the fact that it produces near optimal solution and significantly outperforms a greedy algorithm. read more read less

Topics:

Mobile edge computing (60%)60% related to the paper, Cloud computing (59%)59% related to the paper, Edge device (53%)53% related to the paper, Cyber-physical system (53%)53% related to the paper, Greedy algorithm (53%)53% related to the paper
309 Citations
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IEEE Transactions on Emerging Topics in Computing format uses IEEEtran citation style.

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Frequently asked questions

1. Can I write IEEE Transactions on Emerging Topics in Computing in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the IEEE Transactions on Emerging Topics in Computing guidelines and auto format it.

2. Do you follow the IEEE Transactions on Emerging Topics in Computing guidelines?

Yes, the template is compliant with the IEEE Transactions on Emerging Topics in Computing guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in IEEE Transactions on Emerging Topics in Computing?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the IEEE Transactions on Emerging Topics in Computing citation style.

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Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for IEEE Transactions on Emerging Topics in Computing.

5. Can I use a manuscript in IEEE Transactions on Emerging Topics in Computing that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper IEEE Transactions on Emerging Topics in Computing that you can download at the end.

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7. Where can I find the template for the IEEE Transactions on Emerging Topics in Computing?

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Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

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SciSpace's IEEE Transactions on Emerging Topics in Computing is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

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12. Is IEEE Transactions on Emerging Topics in Computing's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for IEEE Transactions on Emerging Topics in Computing?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for IEEE Transactions on Emerging Topics in Computing. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In IEEE Transactions on Emerging Topics in Computing?

The 5 most common citation types in order of usage for IEEE Transactions on Emerging Topics in Computing are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the IEEE Transactions on Emerging Topics in Computing?

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16. Can I download IEEE Transactions on Emerging Topics in Computing in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in IEEE Transactions on Emerging Topics in Computing Endnote style according to Elsevier guidelines.

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