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 |
Title: |
Mining Academic and Research Collaboration Networks: The Mendeley, Scopus, and Google Scholar Case Study |
Authors: |
JRG Pulido, José Román Herrera-Morales and Armando Román Gallardo |
Source: |
International Journal of Latest Engineering Research and Applications, pp 26 - 32, Vol 11 - No. 01, 2026 |
Abstract: |
The exponential growth of indexed scientific publications has made the automatic identification of complete and accurate metadata increasingly challenging. In particular, inconsistencies in author name representation—such as the use of abbreviations in some sources and full names in others—often lead to ambiguity and misattribution. Consequently, precise identification of authors -their academic profiles, scientific output such as institutional affiliations, and collaboration relationships- is essential.By addressing these identification issues, it becomes possible to generate relevant insights through automated data mining processes. This paper presents a methodological framework that leverages information management services to identify authors and collaboration networks. Data mining techniques were applied to Mendeley, Scopus, and Scholar platforms, thenby extracting and consolidating bibliographic data from these repositories, a structured set of metadata was obtained.The results demonstrate that collaboration networks within scientific publications can be effectively identified through author-based data mining approaches. These networks can be used to generate precise quantitative indicators, which may serve as valuable metrics for evaluating academic influence, collaboration patterns, and the prestige of research communities. |
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Title: |
Clinical Open-Datasetlong-COVID Prediction Using Machine Learning |
Authors: |
J.R.G. Pulido, César Javier Ramírez Manzo, Erika Margarita Ramos Michel, Pedro Damian Reyes, Ricardo Acosta-Díaz |
Source: |
International Journal of Latest Engineering Research and Applications, pp 33 - 38, Vol 11 - No. 01, 2026 |
Abstract: |
The COVID-19 pandemic, declared by the World Health Organization (WHO, 2020) posed an unprecedented challenge to global health. Now, years after that critical period of time, there is growing concern about long-term covid sequelae in recovered patients not only in Europe and USA, but in other countries. This study focuses on developing machine learning tools to predict the severity of both acute symptoms and post-COVID sequelae, aiming to provide software tools for healthcare specialists, not only in Mexico but all over the world. Using a tailor-made dataset from a number of clinical open datasets, we trained decision tree models on 488 records featuring pre-existing conditions as variables -smoking, diabetes, hypertension. The models predicted symptom and sequelae severity -moderate, severe, critical- with average accuracies as high as 95%. A validation via normalized confusion matrices and ROC curves was also carried out. These results, first, confirm the feasibility of using interpretable AI models to support clinical prognosis and, second, highlight the need for more comprehensive datasets, particularly for long-covid critical cases. |
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DOI: |
10.56581/IJLERA.11.01.33-38 |