| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 70 | 254 | 393 | 52 |
IEEE Transactions on Affective Computing is organized to address concerns in the fields of Artificial intelligence, Feature extraction, Affective computing, Speech recognition and Pattern recognition. It explores topics in Artificial intelligence which can be helpful for research in disciplines like Machine learning and Natural language processing. The journal explores issues in Feature extraction which can be linked to other research areas like Feature (machine learning), Support vector machine, Task analysis, Visualization and Feature selection.
The concepts on Affective computing presented in the journal can also apply to other research fields, including Cognitive psychology, Cognition and Affect (psychology). It focuses on Cognitive psychology but the discussions also offer insight into other areas such as Big Five personality traits, Social psychology, Personality and Perception. Topics in Speech recognition were tackled in line with various other fields like Valence (psychology) and Emotion classification.
Research on Valence (psychology) addressed in it frequently intersections with the field of Arousal. The journal holds forums on Pattern recognition that merges themes from other disciplines such as Artificial neural network and Electroencephalography. Studies in Facial expression and Expression (mathematics) are the key highlights in it.
The published articles aim to foster the development of research in Artificial intelligence, Feature extraction, Affective computing, Speech recognition and Facial expression. While the journal articles focused on Artificial intelligence, they were also able to explore topics like Machine learning, Computer vision and Natural language processing. The journal publications address concerns in Affective computing which are intertwined with other disciplines, such as Cognitive psychology, Social psychology, Affect (psychology), Database and Cognition.
The journal mainly tackles studies in Artificial intelligence, Affective computing, Feature extraction, Pattern recognition and Task analysis. The close relationship between Natural language processing and Feature learning is one of the points of interest dissected in Artificial intelligence research. While the primary focus in it is Affective computing, it also dissects topics surrounding Cognitive psychology and Affect (psychology) and Perception as a whole.
The Feature extraction works featured in the journal incorporate elements from Feature (machine learning), Speech recognition, Facial recognition system, Visualization and Discriminative model. Speech recognition and Valence (psychology) are closely related fields of research discussed in the journal. The tackled Pattern recognition research is interrelated with Electroencephalography which concerns subjects like Stimulus (physiology) and Audiology.
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 IEEE Transactions on Affective Computing (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 IEEE Transactions on Affective Computing (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, 13.51% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 19.53% were posted by at least one author from the top 10 institutions publishing in the journal. Another 10.16% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 17.97% of all publications and 52.34% 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.
For those fascinated by the multi-disciplinary field of Affective Computing, there are a variety of career opportunities available. One potential avenue for career development would be becoming a high school history teacher focused on developing ways to integrate technology within the curriculum. This role could involve teaching students to leverage Affective Computing concepts in the study of historical trends, societal changes, and human behavior over time. In terms of remuneration, the earning potential can vary depending on a variety of factors such as location, level of experience, and the specific institution one is affiliated with. For example, in the state of Connecticut, you may want to check out how much a high school history teacher makes. This link provides an in-depth guide for aspiring teachers, including the potential earnings one could expect in this specific state. Whilst a career as a teacher can be immensely rewarding, it is by no means the only option for individuals with expertise in Affective Computing. Other potential career paths could include roles in research and development, programmers in AI firms, or strategic roles in tech policy organizations. The interdisciplinary nature of Affective Computing ensures versatility, opening pathways in academia, industry, and consultancy. Thus, whether teaching future generations, pioneering innovative technologies, or influencing policy, individuals with a background in Affective Computing are set to play a significant role in shaping the technological landscape of the future.
Shan Li;Weihong Deng
(2020)Tengfei Song;Wenming Zheng;Peng Song;Zhen Cui
(2020)Peixiang Zhong;Di Wang;Chunyan Miao
(2020)Juan Abdon Miranda-Correa;Mojtaba Khomami Abadi;Nicu Sebe;Ioannis Patras
(2021)Wei Tao;Chang Li;Rencheng Song;Juan Cheng
(2020)Fatemeh Noroozi;Ciprian Adrian Corneanu;Dorota Kaminska;Tomasz Sapinski
(2021)Yang Li;Wenming Zheng;Yuan Zong;Zhen Cui
(2021)Sara Taylor;Natasha Jaques;Ehimwenma Nosakhare;Akane Sano
(2020)Semiu Salawu;Yulan He;Joanna Lumsden
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