| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 145 | 279 | 315 | 32 |
The journal covers a variety of subjects, including Artificial intelligence, Machine learning, Data mining, Information retrieval and World Wide Web. ACM Transactions on Intelligent Systems and Technology addresses concerns in Artificial intelligence which are intertwined with other disciplines, such as Natural language processing, Computer vision and Pattern recognition. The studies tackled, which mainly focus on Machine learning, apply to Task (project management) as well.
In addition to Data mining research, it aims to explore topics under Scalability and Cluster analysis. Recommender system is a major topic of Information retrieval research presented in ACM Transactions on Intelligent Systems and Technology. It is focused mainly on Recommender system, particularly Collaborative filtering.
Studies on World Wide Web discussed in the journal link to the field of Data science.
Artificial intelligence, Machine learning, Data mining, World Wide Web and Recommender system are the main subjects of interest in the published papers. The works on Artificial intelligence tackled in the journal publications bring together disciplines like Focus (computing), Computer vision and Pattern recognition. The most cited publications about Machine learning cover related areas such as Discriminative model and also touches on topics like Phone.
ACM Transactions on Intelligent Systems and Technology investigates studies in Artificial intelligence, Task (project management), Natural language processing, Deep learning and Topic model. Artificial intelligence research featured in ACM Transactions on Intelligent Systems and Technology incorporates concerns from various other topics such as Machine learning, Key (cryptography), Computer vision and Pattern recognition. The tackled Machine learning research is interrelated with Meta learning (computer science) which concerns subjects like Urban computing.
Aside from investigating topics in Information extraction and Relationship extraction under Natural language processing, it also explores concepts in Universal relation, Intervention (counseling) and Extraction methods. ACM Transactions on Intelligent Systems and Technology focuses on Deep learning but the discussions also offer insight into other areas such as Classifier (UML), Human machine interaction, Text corpus, Transfer of learning and Semantics. It facilitates discussions on Topic model that incorporate concepts from other fields like Vector quantization, Variety (cybernetics), Information privacy and Self supervised learning.
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 ACM Transactions on Intelligent Systems and Technology (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 ACM Transactions on Intelligent Systems and Technology (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, 2.13% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 21.74% were posted by at least one author from the top 10 institutions publishing in the journal. Another 6.52% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 19.57% of all publications and 52.17% 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.
The knowledge and skills obtained from studying the subjects and research areas discussed in ACM Transactions on Intelligent Systems and Technology can pave the way for numerous professional opportunities. One such career path is becoming a Preschool Teacher Assistant, especially in the state of Delaware.
The expertise in Artificial Intelligence and Machine Learning that one acquires through these studies can be particularly valuable in this role. For instance, AI-based educational tools can be effectively utilized to enhance the learning experience for Preschool students.
Furthermore, skills in Data Mining and Information Retrieval can be beneficial in the development of learning materials, assessment of student performance, and improvement of teaching methods.
To learn more about how you can leverage these competencies in Delaware's education sector, you can refer to this comprehensive guide on how to become a preschool teacher assistant in Delaware and understand the income prospects by exploring data on preschool teacher assistant salary in Delaware.
. Make the most of the research subjects highlighted in ACM Transactions on Intelligent Systems and Technology, and build a rewarding career in the field of your choice.
Garrett Wilson;Diane J. Cook
(2020)Wei Emma Zhang;Quan Z. Sheng;Ahoud Alhazmi;Chenliang Li
(2020)Unknown
(2022)Jindong Wang;Yiqiang Chen;Wenjie Feng;Han Yu
(2020)Rui Yao;Guosheng Lin;Shixiong Xia;Jiaqi Zhao
(2020)Shuo Zhang;Krisztian Balog
(2020)Zhiwei Liu;Liangwei Yang;Ziwei Fan;Hao Peng
(2021)For those interested in advancing their education efficiently, exploring the shortest PhD programs online can be a strategic choice. These programs provide a fast track to terminal degrees without compromising on quality, allowing graduates to enter specialized roles more quickly.
Similarly, online one year masters programs offer an accelerated path to gaining advanced skills in computer science and related fields. Choosing from the online one year masters programs can help professionals upskill without putting their careers on hold.
When selecting a degree, it’s important to balance duration with return on investment. Many students opt for online programs that pay well, ensuring their time and effort translate into competitive salaries.
Ultimately, aligning your study choices with your career goals is key. Reviewing the college majors that lead to high-demand roles can help you make informed decisions about your educational pathway and future employment in the tech industry.
