Mika Juuti

1.5k citations
5 papers · 536 indexed · 1 hit paper · h-index 5
Topics
Adversarial Robustness in Machine Learning (3 papers)Hate Speech and Cyberbullying Detection (2 papers)Cryptography and Data Security (1 paper)
Journals
NeurocomputingAaltodoc (Aalto University)
Partner nations
FinlandItalyCanada

In The Last Decade

Mika Juuti

5 papers receiving 515 citations

Hit Papers

Oblivious Neural Network Predictions via MiniONN Transfor...20172026202020232017100200300

Peers

Mika Juuti
Comparison fields: 5 of 56
  • Artificial Intelligence 482
  • Information Systems 97
  • Computer Vision and Pattern Recognition 71
  • Signal Processing 41
  • Computational Theory and Mathematics 31
Replace Qinmin Hu with:
Qinmin Hu Canada
Shady Shehata Canada
Yushun Dong United States
Xuanjing Huang China
Ashok C. Popat United States
Danilo Croce Italy
Luheng He United States
Yun-Cheng Wang United States
Piero Molino Italy
Mika Juuti relative to Qinmin Hu Canada Qinmin Hu's profile →
Citations per field
00.5×1.5×2.4×
Qinmin Hu · 1×
Citations per year

Countries citing papers authored by Mika Juuti

Since Specialization
Citations

This map shows the geographic impact of Mika Juuti's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mika Juuti with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mika Juuti more than expected).

Fields of papers citing papers by Mika Juuti

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mika Juuti. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mika Juuti. The network helps show where Mika Juuti may publish in the future.

Co-authorship network of co-authors of Mika Juuti

This figure shows the co-authorship network connecting the top 25 collaborators of Mika Juuti. A scholar is included among the top collaborators of Mika Juuti based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mika Juuti. Mika Juuti is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

5 of 5 papers shown
#WorkIndexed citations
1 12
2 7
3 109
4
Oblivious Neural Network Predictions via MiniONN Transformationsbreakdown →
389
5 19

About Mika Juuti

Mika Juuti is a scholar working on Signal Processing, Artificial Intelligence and Statistics and Probability, having authored 5 papers that have together received 536 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (3 papers), Hate Speech and Cyberbullying Detection (2 papers) and Cryptography and Data Security (1 paper). The work is most often cited by research in Artificial Intelligence (482 citations), Health Informatics (8 citations) and Information Systems (97 citations). Mika Juuti has collaborated with scholars based in Finland, Italy and Canada. Frequent co-authors include N. Asokan, Yao Lu, Jian Liu, Tommi Gröndahl, Mauro Conti, Luca Pajola, Prakash Shrestha, Nitesh Saxena, Francesco Corona and Juha Karhunen. Their work appears in journals such as Neurocomputing and Aaltodoc (Aalto University).

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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