1556-6013
Published by: IEEE
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 36 | 653 | 1200 | 69 |
The journal facilitates discussions on Artificial intelligence, Pattern recognition, Computer security, Computer vision and Feature extraction. Studies on Artificial intelligence discussed in it link to the field of Machine learning. While IEEE Transactions on Information Forensics and Security focused on Pattern recognition, it was also able to explore topics like Steganography, Steganalysis and Fingerprint.
The Computer security works featured in it incorporate elements from Computer network and Cloud computing. While Computer network is the key highlight in IEEE Transactions on Information Forensics and Security, it also covered some subjects on Secrecy and Communication channel. Digital image, Pixel and Digital watermarking are among the areas of Computer vision tackled.
The in-depth study on Feature extraction also explores topics in the intersecting field of Convolutional neural network. The subject of Theoretical computer science, which is connected to the field of Algorithm, serves as the foundation of the Cryptography research featured in it. Biometrics research is concerned with Iris recognition in particular.
The journal publications primarily focus on research topics in Artificial intelligence, Computer vision, Pattern recognition, Feature extraction and Computer security. The most cited articles connects the study in Artificial intelligence with the closely related areas of Machine learning. The studies on Computer security discussed at the most cited papers can also contribute to research in the domains of Computer network and Cloud computing.
IEEE Transactions on Information Forensics and Security focuses on Artificial intelligence, Feature extraction, Pattern recognition, Computer network and Machine learning. Most of the Artificial intelligence studies addressed also intersect with Computer vision. IEEE Transactions on Information Forensics and Security addresses concerns in Feature extraction which are intertwined with other disciplines, such as Artificial neural network and Feature (computer vision).
It explores research in Pattern recognition and the adjacent study of Robustness (computer science). The presented research on Computer network deals specifically with Communication channel but it also addresses topics in Physical layer, Secrecy and Algorithm. The journal addresses concerns in the field of Facial recognition system by exploring it in line with topics in Biometrics which intersect with Authentication subjects.
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 Information Forensics and Security (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 Information Forensics and Security (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, 6.76% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 26.09% were posted by at least one author from the top 10 institutions publishing in the journal. Another 14.78% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 16.23% of all publications and 42.90% 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.
While this journal provides expansive knowledge on various research topics such as Artificial intelligence, Computer security, and Pattern recognition, it's important to note that these studies also have a direct application in our education system. Advanced technologies today enable the development of smart and adaptive learning tools, providing a more interactive and personalized education. For instance, Artificial intelligence is being incorporated into interactive learning systems aimed to understand learning habits of students and adapt to individual learning pace. Pattern recognition, meanwhile, is applicable in personalized assessment systems to recognize recurring patterns in a student’s performance and provide accurate evaluations as well as necessary assistance. Moreover, computer security plays a significant role in maintaining the integrity of online learning platforms and safeguarding student data. With the shift towards online learning, developing secure platforms has become more important than ever. One such field that can benefit from these advanced technologies is the teaching profession. With equipped knowledge on AI, computer security, and more, educators can effectively utilize and develop e-learning tools to enhance their teaching methods. In Florida, for instance, there are several teaching credential programs that aim to educate aspiring teachers on these vital technologies. You can find a list of the best teaching credential programs in Florida which not only offer affordable options, but are also recognized for integrating these modern technologies in their curriculum. This establishes the fact that the subjects being explored in IEEE Transactions on Information Forensics and Security are not limited to scientific and technological growth. They also hold significant potential in shaping the future of education.
Kang Wei;Jun Li;Ming Ding;Chuan Ma
(2020)Guowen Xu;Hongwei Li;Sen Liu;Kan Yang
(2020)Davide Cozzolino;Luisa Verdoliva
(2020)Xiao Chen;Chaoran Li;Derui Wang;Sheng Wen
(2020)Mang Ye;Xiangyuan Lan;Zheng Wang;Pong C. Yuen
(2020)Nguyen Binh Truong;Kai Sun;Gyu Myoung Lee;Yike Guo
(2020)Anjith George;Zohreh Mostaani;David Geissenbuhler;Olegs Nikisins
(2020)Qin Zou;Yanling Wang;Qian Wang;Yi Zhao
(2020)Mang Ye;Jianbing Shen;Ling Shao
(2021)Jianhua Yang;Danyang Ruan;Jiwu Huang;Xiangui Kang
(2020)Exploring students’ options in Computer Science extends beyond traditional campus programs, with many turning to online degrees for flexibility and affordability. Considering the cheapest online college bachelor degree options can ease financial burden while still providing quality education. This approach is particularly appealing when combined with programs that offer accelerated timelines.
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Finally, selecting programs aligned with the most profitable majors ensures strong career prospects post-graduation. Computer Science consistently ranks high for salary potential, making it a smart investment for students planning their career pathways.
