Published by: Elsevier
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 344 | 45 | 68 | 17 |
Smart Health generally zeroes in on subjects such as Artificial intelligence, Machine learning, Human–computer interaction, Wearable technology and Physical medicine and rehabilitation. The journal explores topics in Artificial intelligence which can be helpful for research in disciplines like Computer vision and Pattern recognition. Decision tree and Support vector machine are among the concentrations of Machine learning that garnered much attention in it.
The featured Human–computer interaction works encompass concepts such as Usability and examines them in conjunction with Smartwatch. Studies in Wearable technology and Gait (human) are the key highlights in it.
The published articles aim to foster the development of research in Artificial intelligence, Machine learning, Deep learning, Health informatics and Personalization. The most cited articles facilitate discussions on Artificial intelligence that incorporate concepts from other fields like Crowds and Vulnerability (computing). Aside from discussions in Deep learning, the published papers also deal with the subject of Digital health which intersects with Data mining and Data science disciplines.
Smart Health focuses largely on the fields of Artificial intelligence, Real-time computing, Wearable technology, Bluetooth and Applied psychology. The Artificial intelligence works featured in the journal incorporate elements from Machine learning, Computer vision and Pattern recognition. It focuses on Machine learning but the discussions also offer insight into other areas such as Multi-source and Margin of error.
The concepts on Real-time computing presented in the journal can also apply to other research fields, including Biometrics, Default gateway, Throughput, Authentication and Sensor fusion. The work on Applied psychology tackled in the journal brings together disciplines like Quality (business) and Mood. Smart Health facilitates discussions on Deep learning that incorporate concepts from other fields like Disease management (health), Crowds, Type 2 diabetes and Image editing.
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in Smart Health (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in Smart Health (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 2.78% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 40.00% were posted by at least one author from the top 10 institutions publishing in the journal. Another 8.57% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 17.14% of all publications and 34.29% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
Given the growing interest and research into areas like Artificial intelligence, Machine learning, Human–computer interaction, Wearable technology, and other Smart Health topics, it opens up numerous career opportunities in the healthcare sector. For instance, with the understanding and usage of Machine Learning algorithms, one can establish a career in future-oriented instruction, like becoming a Math Teacher. Specifically, those residing in the Garden State might consider the pathway to become a middle school math teacher in New Jersey. This career allows one to help develop the future workforce with much-needed skills for a world increasingly defined by technology.
Beyond teaching, technical roles involving these explored Smart Health subjects are in high demand. Data scientists, analysts, software engineers, and AI specialists are some of the roles sought after in healthcare. With technological advancements rapidly transforming healthcare, it offers an opportunity for individuals trained in these areas to create a significant impact on how care is delivered and health outcomes are improved.
Whether in the form of teaching, research, or practice, professionals adept in these key areas of Smart Health are well-equipped to drive the future of healthcare and education.
Pranvera Kortoçi;Naser Hossein Motlagh;Martha Arbayani Zaidan;Pak Lun Fung
(2021)Shweta Ware;Chaoqun Yue;Reynaldo Morillo;Jin Lu
(2020)Woosub Jung;Hongyang Zhao;Minglong Sun;Gang Zhou
(2020)Amelie Gyrard;Amit Sheth
(2020)Ioannis Papavasileiou;Ioannis Papavasileiou;Zhi Qiao;Chenyu Zhang;Wenlong Zhang
(2021)Ada Dogrucu;Alex Perucic;Anabella Isaro;Damon Ball
(2020)Congyu Wu;Amanda N. Barczyk;R. Cameron Craddock;Gabriella M. Harari
(2021)Lahiru Wijayasingha;John A. Stankovic
(2021)Cong Shi;Li Lu;Jian Liu;Yan Wang
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