Example of Journal of Healthcare Engineering format
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Example of Journal of Healthcare Engineering format Example of Journal of Healthcare Engineering format Example of Journal of Healthcare Engineering format Example of Journal of Healthcare Engineering format Example of Journal of Healthcare Engineering format Example of Journal of Healthcare Engineering format Example of Journal of Healthcare Engineering format
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Example of Journal of Healthcare Engineering format Example of Journal of Healthcare Engineering format Example of Journal of Healthcare Engineering format Example of Journal of Healthcare Engineering format Example of Journal of Healthcare Engineering format Example of Journal of Healthcare Engineering format Example of Journal of Healthcare Engineering format
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This content is only for preview purposes. The original open access content can be found here.
open access Open Access ISSN: 20402295 e-ISSN: 20402309

Journal of Healthcare Engineering — Template for authors

Publisher: Hindawi
Categories Rank Trend in last 3 yrs
Surgery #56 of 422 up up by 230 ranks
Health Informatics #26 of 95 up up by 28 ranks
Biotechnology #101 of 282 up up by 95 ranks
Biomedical Engineering #90 of 229 up up by 77 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 643 Published Papers | 2945 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 02/06/2020
Insights & related journals
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FAQ

Journal Performance & Insights

  • Impact Factor
  • CiteRatio
  • SJR
  • SNIP

Impact factor determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

1.803

39% from 2018

Impact factor for Journal of Healthcare Engineering from 2016 - 2019
Year Value
2019 1.803
2018 1.295
2017 1.261
2016 0.965
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 39% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

CiteRatio is a measure of average citations received per peer-reviewed paper published in the journal.

4.6

77% from 2019

CiteRatio for Journal of Healthcare Engineering from 2016 - 2020
Year Value
2020 4.6
2019 2.6
2018 1.2
2017 0.8
2016 2.5
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 77% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR) measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

0.509

21% from 2019

SJR for Journal of Healthcare Engineering from 2016 - 2020
Year Value
2020 0.509
2019 0.42
2018 0.28
2017 0.28
2016 0.278
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has increased by 21% in last years.
  • This journal’s SJR is in the top 10 percentile category.

Source Normalized Impact per Paper (SNIP) measures actual citations received relative to citations expected for the journal's category.

1.422

35% from 2019

SNIP for Journal of Healthcare Engineering from 2016 - 2020
Year Value
2020 1.422
2019 1.052
2018 0.792
2017 0.535
2016 0.493
graph view Graph view
table view Table view

insights Insights

  • SNIP of this journal has increased by 35% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

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Journal of Healthcare Engineering

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Hindawi

Journal of Healthcare Engineering

The Journal of Healthcare Engineering is a peer-reviewed, Open Access journal publishing fundamental and applied research on all aspects of engineering involved in healthcare delivery processes and systems. It provides a vehicle for the exchange of advanced knowledge, emerging...... Read More

i
Last updated on
02 Jun 2020
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ISSN
2040-2295
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Impact Factor
Medium - 0.96
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Acceptance Rate
Not provided
i
Frequency
Not provided
i
Open Access
Yes
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
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Citation Type
Numbered
[25]
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Bibliography Example
C. W. J. Beenakker. “Specular andreev reflection in graphene”. Phys. Rev. Lett., vol. 97, no. 6, 067007, 2006.

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1155/2017/8783751
Development of the Arabic Voice Pathology Database and Its Evaluation by Using Speech Features and Machine Learning Algorithms

Abstract:

A voice disorder database is an essential element in doing research on automatic voice disorder detection and classification. Ethnicity affects the voice characteristics of a person, and so it is necessary to develop a database by collecting the voice samples of the targeted ethnic group. This will enhance the chances of arri... A voice disorder database is an essential element in doing research on automatic voice disorder detection and classification. Ethnicity affects the voice characteristics of a person, and so it is necessary to develop a database by collecting the voice samples of the targeted ethnic group. This will enhance the chances of arriving at a global solution for the accurate and reliable diagnosis of voice disorders by understanding the characteristics of a local group. Motivated by such idea, an Arabic voice pathology database (AVPD) is designed and developed in this study by recording three vowels, running speech, and isolated words. For each recorded samples, the perceptual severity is also provided which is a unique aspect of the AVPD. During the development of the AVPD, the shortcomings of different voice disorder databases were identified so that they could be avoided in the AVPD. In addition, the AVPD is evaluated by using six different types of speech features and four types of machine learning algorithms. The results of detection and classification of voice disorders obtained with the sustained vowel and the running speech are also compared with the results of an English-language disorder database, the Massachusetts Eye and Ear Infirmary (MEEI) database. read more read less

Topics:

Voice Disorder (52%)52% related to the paper
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191 Citations
open accessOpen access Journal Article DOI: 10.1155/2017/8314740
Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images.
QingZeng Song, Lei Zhao, XingKe Luo, XueChen Dou

Abstract:

Lung cancer is the most common cancer that cannot be ignored and cause death with late health care. Currently, CT can be used to help doctors detect the lung cancer in the early stages. In many cases, the diagnosis of identifying the lung cancer depends on the experience of doctors, which may ignore some patients and cause so... Lung cancer is the most common cancer that cannot be ignored and cause death with late health care. Currently, CT can be used to help doctors detect the lung cancer in the early stages. In many cases, the diagnosis of identifying the lung cancer depends on the experience of doctors, which may ignore some patients and cause some problems. Deep learning has been proved as a popular and powerful method in many medical imaging diagnosis areas. In this paper, three types of deep neural networks (e.g., CNN, DNN, and SAE) are designed for lung cancer calcification. Those networks are applied to the CT image classification task with some modification for the benign and malignant lung nodules. Those networks were evaluated on the LIDC-IDRI database. The experimental results show that the CNN network archived the best performance with an accuracy of 84.15%, sensitivity of 83.96%, and specificity of 84.32%, which has the best result among the three networks. read more read less

Topics:

Lung cancer (53%)53% related to the paper
View PDF
175 Citations
open accessOpen access Journal Article DOI: 10.1155/2019/4180949
An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare.
Okeke Stephen1, Mangal Sain1, Uchenna Joseph Maduh2, Do-Un Jeong1

Abstract:

This study proposes a convolutional neural network model trained from scratch to classify and detect the presence of pneumonia from a collection of chest X-ray image samples. Unlike other methods that rely solely on transfer learning approaches or traditional handcrafted techniques to achieve a remarkable classification perfo... This study proposes a convolutional neural network model trained from scratch to classify and detect the presence of pneumonia from a collection of chest X-ray image samples. Unlike other methods that rely solely on transfer learning approaches or traditional handcrafted techniques to achieve a remarkable classification performance, we constructed a convolutional neural network model from scratch to extract features from a given chest X-ray image and classify it to determine if a person is infected with pneumonia. This model could help mitigate the reliability and interpretability challenges often faced when dealing with medical imagery. Unlike other deep learning classification tasks with sufficient image repository, it is difficult to obtain a large amount of pneumonia dataset for this classification task; therefore, we deployed several data augmentation algorithms to improve the validation and classification accuracy of the CNN model and achieved remarkable validation accuracy. read more read less

Topics:

Deep learning (57%)57% related to the paper, Convolutional neural network (54%)54% related to the paper, Interpretability (50%)50% related to the paper
View PDF
172 Citations
open accessOpen access Journal Article DOI: 10.1260/2040-2295.1.2.197
Locomotor training in subjects with sensori-motor deficits: An overview of the robotic gait orthosis lokomat
Robert Riener1, Lars Lünenburger, Irin C. Maier2, Gery Colombo, Volker Dietz

Abstract:

It is known that improvement in walking function can be achieved in patients suffering a movement disorder after stroke or spinal cord injury by providing intensive locomotor training. Rehabilitation robots allow for a longer and more intensive training than that achieved by conventional therapies. Robot assisted treadmill tr... It is known that improvement in walking function can be achieved in patients suffering a movement disorder after stroke or spinal cord injury by providing intensive locomotor training. Rehabilitation robots allow for a longer and more intensive training than that achieved by conventional therapies. Robot assisted treadmill training also offers the ability to provide objective feedback within one training session and to monitor functional improvements over time. This article provides an overview of the technical features and reports the clinical data available for one of these systems known as "Lokomat". First, background information is given for the neural mechanisms of gait recovery. The basic technical approach of the Lokomat system is then described. Furthermore, new features are introduced including cooperative control strategies, assessment tools and augmented feedback. These features may be capable of further enhancing training intensity and patient participation. Findings from clinical studies are presented covering the feasibility as well as efficacy of Lokomat assisted treadmill training. read more read less

Topics:

Rehabilitation robotics (58%)58% related to the paper, Gait (human) (51%)51% related to the paper
View PDF
154 Citations
open accessOpen access Journal Article DOI: 10.1155/2017/3090343
A Review on Human Activity Recognition Using Vision-Based Method
Shugang Zhang1, Zhiqiang Wei1, Jie Nie2, Lei Huang1, Shuang Wang1, Zhen Li1

Abstract:

Human activity recognition (HAR) aims to recognize activities from a series of observations on the actions of subjects and the environmental conditions. The vision-based HAR research is the basis of many applications including video surveillance, health care, and human-computer interaction (HCI). This review highlights the ad... Human activity recognition (HAR) aims to recognize activities from a series of observations on the actions of subjects and the environmental conditions. The vision-based HAR research is the basis of many applications including video surveillance, health care, and human-computer interaction (HCI). This review highlights the advances of state-of-the-art activity recognition approaches, especially for the activity representation and classification methods. For the representation methods, we sort out a chronological research trajectory from global representations to local representations, and recent depth-based representations. For the classification methods, we conform to the categorization of template-based methods, discriminative models, and generative models and review several prevalent methods. Next, representative and available datasets are introduced. Aiming to provide an overview of those methods and a convenient way of comparing them, we classify existing literatures with a detailed taxonomy including representation and classification methods, as well as the datasets they used. Finally, we investigate the directions for future research. read more read less

Topics:

Activity recognition (55%)55% related to the paper, Taxonomy (general) (51%)51% related to the paper
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152 Citations
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With SciSpace, you do not need a word template for Journal of Healthcare Engineering.

It automatically formats your research paper to Hindawi formatting guidelines and citation style.

You can download a submission ready research paper in pdf, LaTeX and docx formats.

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Time taken to format a paper and Compliance with guidelines

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SciSpace allows imports from all reference managers like Mendeley, Zotero, Endnote, Google Scholar etc.

Frequently asked questions

Absolutely not! With our tool, you can freely write without having to focus on LaTeX. You can write your entire paper as per the Journal of Healthcare Engineering guidelines and autoformat it.

Yes. The template is fully compliant as per the guidelines of this journal. Our experts at SciSpace ensure that. Also, if there's any update in the journal format guidelines, we take care of it and include that in our algorithm.

Sure. We support all the top citation styles like APA style, MLA style, Vancouver style, Harvard style, Chicago style, etc. For example, in case of this journal, when you write your paper and hit autoformat, it will automatically update your article as per the Journal of Healthcare Engineering citation style.

You can avail our Free Trial for 7 days. I'm sure you'll find our features very helpful. Plus, it's quite inexpensive.

Yup. You can choose the right template, copy-paste the contents from the word doc and click on auto-format. You'll have a publish-ready paper that you can download at the end.

A matter of seconds. Besides that, our intuitive editor saves a load of your time in writing and formating your manuscript.

One little Google search can get you the Word template for any journal. However, why do you need a Word template when you can write your entire manuscript on SciSpace, autoformat it as per Journal of Healthcare Engineering's guidelines and download the same in Word, PDF and LaTeX formats? Try us out!.

Absolutely! You can do it using our intuitive editor. It's very easy. If you need help, you can always contact our support team.

SciSpace is an online tool for now. We'll soon release a desktop version. You can also request (or upvote) any feature that you think might be helpful for you and the research community in the feature request section once you sign-up with us.

Sure. You can request any template and we'll have it up and running within a matter of 3 working days. You can find the request box in the Journal Gallery on the right sidebar under the heading, "Couldn't find the format you were looking for?".

After you have written and autoformatted your paper, you can download it in multiple formats, viz., PDF, Docx and LaTeX.

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 those factors the review board, rejection rates, frequency of inclusion in indexes, Eigenfactor, etc. You must assess all the factors and then take the final call.

SHERPA/RoMEO Database

We have extracted this data from Sherpa Romeo to help our researchers understand the access level of this journal. The following table indicates the level of access a journal has as per Sherpa Romeo 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.

The 5 most common citation types in order of usage are:.

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

Our journal submission experts are skilled in submitting papers to various international journals.

After uploading your paper on SciSpace, you would see a button to request a journal submission service for Journal of Healthcare Engineering.

Each submission service is completed within 4 - 5 working days.

Yes. SciSpace provides this functionality.

After signing up, you would need to import your existing references from Word or .bib file.

SciSpace would allow download of your references in Journal of Healthcare Engineering Endnote style, according to hindawi guidelines.

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I spent hours with MS word for reformatting. It was frustrating - plain and simple. With SciSpace, I can draft my manuscripts and once it is finished I can just submit. In case, I have to submit to another journal it is really just a button click instead of an afternoon of reformatting.

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