Martin Boldt

1.1k total citations
48 papers, 712 citations indexed

About

Martin Boldt is a scholar working on Artificial Intelligence, Information Systems and Sociology and Political Science. According to data from OpenAlex, Martin Boldt has authored 48 papers receiving a total of 712 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Artificial Intelligence, 22 papers in Information Systems and 19 papers in Sociology and Political Science. Recurrent topics in Martin Boldt's work include Advanced Malware Detection Techniques (12 papers), Spam and Phishing Detection (9 papers) and Privacy, Security, and Data Protection (9 papers). Martin Boldt is often cited by papers focused on Advanced Malware Detection Techniques (12 papers), Spam and Phishing Detection (9 papers) and Privacy, Security, and Data Protection (9 papers). Martin Boldt collaborates with scholars based in Sweden, Bulgaria and United States. Martin Boldt's co-authors include Anton Borg, Bengt Carlsson, Andreas Jacobsson, Niklas Lavesson, Paul Davidsson, Dejan Baca, Henric Johnson, Veselka Boeva, Selim İckin and Jörgen Gustafsson and has published in prestigious journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and Future Generation Computer Systems.

In The Last Decade

Martin Boldt

42 papers receiving 669 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Martin Boldt Sweden 12 277 254 194 168 158 48 712
Zubair Shafiq United States 16 189 0.7× 311 1.2× 231 1.2× 213 1.3× 150 0.9× 67 782
Sujith Samuel Mathew United Arab Emirates 15 214 0.8× 200 0.8× 198 1.0× 187 1.1× 85 0.5× 46 654
Andreas Jacobsson Sweden 13 249 0.9× 143 0.6× 158 0.8× 279 1.7× 171 1.1× 46 738
Eva Onaindía Spain 15 310 1.1× 432 1.7× 171 0.9× 145 0.9× 67 0.4× 91 909
George Lawton Australia 17 321 1.2× 142 0.6× 96 0.5× 370 2.2× 113 0.7× 64 866
Christos Kalloniatis Greece 17 498 1.8× 269 1.1× 332 1.7× 154 0.9× 198 1.3× 77 850
Paul Haskell‐Dowland Australia 15 313 1.1× 173 0.7× 83 0.4× 276 1.6× 227 1.4× 71 714
Ian Fette United States 5 510 1.8× 373 1.5× 266 1.4× 291 1.7× 252 1.6× 8 906
Enrique Costa‐Montenegro Spain 15 420 1.5× 399 1.6× 152 0.8× 217 1.3× 45 0.3× 56 991
Anas Mahmoud United States 21 779 2.8× 287 1.1× 110 0.6× 177 1.1× 75 0.5× 60 1.1k

Countries citing papers authored by Martin Boldt

Since Specialization
Citations

This map shows the geographic impact of Martin Boldt'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 Martin Boldt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Martin Boldt more than expected).

Fields of papers citing papers by Martin Boldt

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Martin Boldt. 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 Martin Boldt. The network helps show where Martin Boldt may publish in the future.

Co-authorship network of co-authors of Martin Boldt

This figure shows the co-authorship network connecting the top 25 collaborators of Martin Boldt. A scholar is included among the top collaborators of Martin Boldt 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 Martin Boldt. Martin Boldt is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Borg, Anton, et al.. (2024). Predicting Public Violent Crime Using Register and OpenStreetMap Data: A Risk Terrain Modeling Approach Across Three Cities of Varying Size. Applied Spatial Analysis and Policy. 18(1). 3 indexed citations
3.
Lundberg, Lars, Martin Boldt, Anton Borg, & Håkan Grahn. (2024). Bibliometric Mining of Research Trends in Machine Learning. SHILAP Revista de lepidopterología. 5(1). 208–236. 7 indexed citations
4.
Boldt, Martin, et al.. (2024). Predicting B2B Customer Churn using a Time Series Approach. KTH Publication Database DiVA (KTH Royal Institute of Technology). 44–51.
5.
Boldt, Martin, et al.. (2024). Automated Generation of CCTV Camera Coverage Areas for Smart Cities Using Line-of-Sight Analysis. KTH Publication Database DiVA (KTH Royal Institute of Technology). 1–6.
6.
Nilsson, Gustav, Martin Boldt, & Sadi Alawadi. (2024). The Role of the Data Quality on Model Efficiency: An Exploratory Study on Centralised and Federated Learning. KTH Publication Database DiVA (KTH Royal Institute of Technology). 253–260. 3 indexed citations
7.
Boldt, Martin, et al.. (2023). Evaluation of Defense Methods Against the One-Pixel Attack on Deep Neural Networks. Linköping electronic conference proceedings. 199. 49–57. 1 indexed citations
8.
Boldt, Martin, et al.. (2021). Alarm Prediction in Cellular Base Stations Using Data-Driven Methods. IEEE Transactions on Network and Service Management. 18(2). 1925–1933. 11 indexed citations
9.
Dallora, Ana Luiza, Ola Kvist, Johan Berglund, et al.. (2020). Chronological Age Assessment in Young Individuals Using Bone Age Assessment Staging and Nonradiological Aspects: Machine Learning Multifactorial Approach. JMIR Medical Informatics. 8(9). e18846–e18846. 8 indexed citations
10.
Borg, Anton & Martin Boldt. (2020). Using VADER sentiment and SVM for predicting customer response sentiment. Expert Systems with Applications. 162. 113746–113746. 159 indexed citations
11.
Borg, Anton, et al.. (2020). Predicting e-Mail Response Time in Corporate Customer Support. KTH Publication Database DiVA (KTH Royal Institute of Technology). 305–314. 1 indexed citations
12.
Borg, Anton, et al.. (2020). E-mail classification with machine learning and word embeddings for improved customer support. Neural Computing and Applications. 33(6). 1881–1902. 16 indexed citations
13.
Boldt, Martin, et al.. (2015). Crawling Online Social Networks. KTH Publication Database DiVA (KTH Royal Institute of Technology). 15 indexed citations
14.
Baca, Dejan, Martin Boldt, Bengt Carlsson, & Andreas Jacobsson. (2015). A Novel Security-Enhanced Agile Software Development Process Applied in an Industrial Setting. KTH Publication Database DiVA (KTH Royal Institute of Technology). 11–19. 36 indexed citations
15.
Jacobsson, Andreas, Martin Boldt, & Bengt Carlsson. (2015). A risk analysis of a smart home automation system. Future Generation Computer Systems. 56. 719–733. 193 indexed citations
16.
Boldt, Martin. (2010). Privacy-Invasive Software. KTH Publication Database DiVA (KTH Royal Institute of Technology). 1 indexed citations
17.
Boldt, Martin, et al.. (2008). Preventing Privacy-Invasive Software using Online Reputations. KTH Publication Database DiVA (KTH Royal Institute of Technology). 1 indexed citations
18.
Boldt, Martin & Bengt Carlsson. (2006). Analysing Countermeasures Against Privacy-Invasive Software. International Conference on Software Engineering Advances. 61–61. 7 indexed citations
19.
Boldt, Martin & Bengt Carlsson. (2006). Analysing Privacy-Invasive Software Countermeasures. International Conference on Software Engineering Advances. 1 indexed citations
20.
Boldt, Martin, Bengt Carlsson, & Andreas Jacobsson. (2004). Exploring Spyware Effects. KTH Publication Database DiVA (KTH Royal Institute of Technology). 39–58. 16 indexed citations

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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