Valentine Fontama

467 citations
8 papers · 298 indexed · h-index 6
Topics
Big Data and Business Intelligence (2 papers)Heat Transfer and Optimization (2 papers)Neural Networks and Applications (2 papers)
Journals
Water ResearchInternational Journal of Heat and Mass TransferCERN Document Server (European Organization for Nuclear Research)

In The Last Decade

Valentine Fontama

8 papers receiving 271 citations

Peers

Valentine Fontama
Comparison fields: 5 of 77
  • Mechanical Engineering 98
  • Artificial Intelligence 54
  • Ecology 47
  • Nature and Landscape Conservation 46
  • Computational Mechanics 41
Replace Qihong Yang with:
Qihong Yang China
Juliana Andrade Campos United States
Piotr Szymak Poland
Chengqian Zhang China
D.R. Blidberg United States
Ye Chen China
Mustafa Kaya Türkiye
Yin Wu China
Ningning Zhang China
John Mashford Australia
Valentine Fontama relative to Qihong Yang China Qihong Yang's profile →
Citations per field
00.5×10×14×
Qihong Yang · 1×
Citations per year

Countries citing papers authored by Valentine Fontama

Since Specialization
Citations

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

Fields of papers citing papers by Valentine Fontama

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Valentine Fontama

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

All Works

8 of 8 papers shown
#WorkIndexed citations
1 50
2
Predictive Analytics with Microsoft Azure Machine Learning: Build and Deploy Actionable Solutions in Minutes
5
3 36
4 8
5
New approaches to river quality classification based upon Artificial Intelligence.
18
6 50
7 3
8 128

About Valentine Fontama

Valentine Fontama is a scholar working on Management Information Systems, Water Science and Technology and Environmental Engineering, having authored 8 papers that have together received 298 indexed citations. Recurring topics across this work include Big Data and Business Intelligence (2 papers), Heat Transfer and Optimization (2 papers) and Neural Networks and Applications (2 papers). The work is most often cited by research in Nature and Landscape Conservation (46 citations), Mechanical Engineering (98 citations) and Ecological Modeling (10 citations). Valentine Fontama has collaborated with scholars based in United Kingdom, Finland and United States. Frequent co-authors include K. Jambunathan, S. Ashforth-Frost, William J. Walley, Roger Barga and Wee Hyong Tok. Their work appears in journals such as Water Research, International Journal of Heat and Mass Transfer and CERN Document Server (European Organization for Nuclear Research).

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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