Title: |
Weakly Convex Restrained Domination on Graphs |
Authors: |
Jessavelle S. Baret, Enrico L. Enriquez |
Source: |
International Journal of Latest Engineering Research and Applications, pp 01 - 08, Vol 11 - No. 05, 2026 |
Abstract: |
Let G be a connected simple graph. A dominating set S⊆V(G) is a restrained dominating set if every vertex not in S is adjacent to a vertex inSand to a vertex in V G ∖S. Alternatively, a dominating setS⊆V(G) is a restrained dominating set if N S =V(G) and V G ∖S has no isolated vertices. A restrained dominating setSis called a weakly convex restrained dominating setif for every two vertices u,v∈S, there exists a u−v geodesic whose vertices belong to S. The minimum cardinality of a weakly convex restrained dominating set of G, denoted by γwcr(G), is called the weakly convex restrained domination number of G. This paper initiates the study of weakly convex restrained domination in graphs and determines the weakly convex restrained domination number of some special graphs. Furthermore, it presents a characterization of the weakly convex restrained dominating set in the join and corona of two nontrivial connected graphs. |
Kaywords: |
dominating set, restrained dominating set, weakly convex dominating set, weakly convex restrained dominating set |
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DOI: |
10.56581/IJLERA.11.05.01-08 |
Title: |
Aspect-Based Sentiment Analysis in the Education |
Authors: |
Dr. Megha Wankhade, Abhishek Shelar, Sayali Sherkar |
Source: |
International Journal of Latest Engineering Research and Applications, pp 09 - 12, Vol 11 - No. 05, 2026 |
Abstract: |
In the current digital era, the education sector generates a large amount of feedback data from students in the form of surveys, reviews, and textual responses. This data contains valuable information about student experiences, satisfaction levels, and expectations from educational institutions. However, traditional methods of analyzing such data are often limited to basic statistical summaries and fail to capture deeper insights. These methods do not provide a clear understanding of how students feel about specific aspects such as teaching quality, curriculum structure, infrastructure, or placement opportunities. To overcome these limitations, this study focuses on aspect-based sentiment analysis, which enables a more detailed and fine grained evaluation of student feedback. In this research, a structured survey consisting of multiple questions was designed to capture both quantitative and qualitative responses from students. The collected responses were processed using natural language processing techniques to extract meaningful patterns and sentiments. A transformer-based language model was used to analyze the textual responses and classify them into sentiment and emotion categories. The analyzed data was then visualized using an interactive dashboard, which provides a clear and user-friendly representation of insights through graphs, charts, and key performance indicators. The results of this study show that aspect-based sentiment analysis provides a more comprehensive understanding of student feedback compared to traditional approaches. It helps in identifying strengths and weaknesses in different areas of education, thereby enabling institutions to take data driven decisions for improvement. |
Kaywords: |
Aspect Based Sentiment Analysis, Education Analytics, Sentiment Classification, Text Mining |
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