Title: |
Secure Fair Domination in the Join of Two Graphs |
Authors: |
Apple Kate A. Ambray, Enrico L. Enriquez |
Source: |
International Journal of Latest Engineering Research and Applications, pp 01 - 09, Vol 11 - No. 01, 2026 |
Abstract: |
Let G be a connected simple graph. A dominating set S⊂V(G) is a fair dominating set in G if S=V(G) or if S≠V(G) and all vertices not in S are dominated by the same number of vertices from S, that is, |N u ∩S|=|N v ∩S|>0 for every two vertices u,v∈V G ∖S.A fair dominating set S of V(G) is a secure fair dominating set of G if for each u∈V G ∖S, there exists v∈S such that uv∈E(G) and the set S∖ v ∪{u} is a fair dominating set of G. The minimum cardinality of a secure fair dominating set of G, denoted by γsfd(G), is called the secure fair domination number of G. In this paper, we give some results on the secure fair domination in the join of two nontrivial connected graphs. |
Kaywords: |
Keywords: dominating set, secure dominating set, fair dominating set, secure fair dominating set, join of two graphs |
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DOI: |
10.56581/IJLERA.11.01.01-09 |
Title: |
Machine Learning for Real-Time Credit Risk Assessment in Moroccan SME Lending |
Authors: |
Rachid Maghniwi |
Source: |
International Journal of Latest Engineering Research and Applications, pp 10 - 25, Vol 11 - No. 01, 2026 |
Abstract: |
Small and Medium Enterprises (SMEs) constitute the backbone of Morocco's economy, representing 99.6% of the country's economic fabric according to the Moroccan MSME Observatory (OMTPME). Despite their critical role in generating 40% of GDP and employing 73% of the declared workforce, these enterprises face persistent barriers to credit access. With only 21% of Moroccan SMEs having access to a line of credit and a staggering financing gap estimated at $14 billion (13.5% of GDP) by the International Finance Corporation, traditional credit assessment methods have proven insufficient. This research proposes an advanced machine learning framework specifically tailored to the Moroccan SME context, leveraging alternative data sources and real-time risk assessment to address information asymmetry challenges while reducing bias in lending decisions. |
Kaywords: |
machine learning, SMEs, Credit Risk, Morocco, finance |
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DOI: |
10.56581/IJLERA.11.01.10-25 |
