Countries
Institutions
Fields
Rankings
Compare

Search institutions, scholars, countries and publications

Sankore

Named after the University of Sankore in Timbuktu — one of the world’s oldest centres of learning. Every figure here is computed from work-level records in our own graph, never copied from a source’s summary count.

Built from

OpenAlex — works, authorships, subject classification
ROR — institutional identity
ORCID — researcher identity
Crossref — publisher confirmation

Browse

Countries
Institutions
Fields
Rankings
What we cover

Coverage is partial and says so. A country appears with its research output only once its works have been swept; until then the portal shows whatever the graph happens to hold for it and labels it as exactly that.

Content-Based Spam Classification of Academic E-mails: A Machine Learning Approach

2023Lecture notes in networks and systemsBook Chapter

10.1007/978-981-19-9888-1_7 Full text1 citations

Authors

3 authors across 3 institutions in 1 country.

Iddrisu Wahab AbdulFirst authorCorresponding
- Ghana Communication Technology University (GH)
as printed: Wahab Abdul Iddrisu
Sylvester Kwasi Adjei-Gyabaa
- Sunyani Technical University (GH)
Isaac AkotoSenior author
- University of Energy and Natural Resources (GH)

Fields

Assignment is probabilistic — a work belongs to several fields in proportions.

Spam and Phishing Detectionlevel 3 · 1.00
Text and Document Classification Technologieslevel 3 · 1.00
Sentiment Analysis and Opinion Mininglevel 3 · 1.00

Sources

Several sources describing one work is the point — it means the record rests on more than one authority.

openalexseen September 5, 2026
2023 · Lecture notes in networks and systems · 1 citations
https://openalex.org/W4378695971

Content-Based Spam Classification of Academic E-mails: A Machine Learning Approach · Sankore