Google Scholar has published rankings of articles based on citation frequency, and FIT has celebrated several successes.

In July of this year, the well-known search engine for academic texts, Google Scholar, published a new ranking of journals and conferences based on citation frequency for the five-year period 2020–2024. The metrics for this service include all articles that Google Scholar had in its database as of July 2025. These include both journal articles and selected conference papers. On the other hand, journals with low publication activity (less than 100 articles for the five-year period) are excluded. What is important is that several works by researchers from our faculty have achieved significant success.

An important periodical in the Engineering & Computer Science category, subcategory Computer Hardware Design, is the journal IEEE Transactions on Very Large Scale Integration (VLSI) Systems. In this journal (which has a Web of Science Impact Factor of 3.1 and is therefore classified in the Q2 quartile), the article Improving the Accuracy and Hardware Efficiency of Neural Networks Using Approximate Multipliers (147 citations) – a significant part of its author team consists of members of VS Evolvable Hardware Lukáš Sekanina, Vojtěch Mrázek, and Zdeněk Vašíček. The same trio of authors is also behind another successful article, Libraries of Approximate Circuits: Automated Design and Application in CNN Accelerators, which was published in the IEEE Journal on Emerging and Selected Topics in Circuits and Systems and ranked 20th in terms of citations in the relevant comparison. Finally, the names of the same authors, joined by Filip Vaverka, appear under the text "TFApprox: Towards a Fast Emulation of DNN Approximate Hardware Accelerators on GPU", which ranked 31st in terms of citations in the journal Design, Automation & Test in Europe Conference & Exhibition.

The fact that we are not only successful in the Computer Hardware Design subcategory is evidenced, for example, by the success of the article Bayesian HMM clustering of x-vector sequences (VBx) in speaker diarization: Theory, implementation and analysis on standard tasks, co-authored by Lukáš Burget. The text ranked 6th among the most cited articles in the journal Computer Speech & Language, with 254 citations.

The complete Google Scholar rankings can be found here.

We congratulate our researchers on these achievements within the international scientific community.