Research Article
Image Clustering Using Exponential Regularized Discriminant Analysis
Issue:
Volume 12, Issue 1, February 2026
Pages:
1-12
Received:
16 September 2025
Accepted:
23 October 2025
Published:
26 January 2026
Abstract: When clustering images, the images are typically sampled as nonlinear manifolds. In this case, local learning-based image clustering models are used. Several proposed clustering models are based on linear discriminant analysis (LDA). In image clustering based on linear discriminant analysis (LDA), the problem of small-sample-size (SSS) is presented when the dimensionality of image data is larger than the number of samples. To solve this problem, various image clustering models based on local learning have been introduced. In the proposed clustering models, we added tuning parameters to deal with the small-sample-size (SSS) problem arising in linear discriminant analysis (LDA). In this paper, we propose an exponential regularized discriminant clustering model as an image clustering model based on local learning. In the proposed local exponentially regularized discriminant clustering (LERDC) model, the local scattering matrices of the regularized discriminant model are projected into the exponential domain to address the SSS problem of LDA. Compared with previous clustering methods based on local learning, k-nearest neighbors and regularization parameter λ in the local exponentially regularized discriminant clustering model are the tuning parameters for clustering. The experiments are concluded that the clustering performance of the proposed LERDC model is comparable to that of the clustering methods based on previous local learning.
Abstract: When clustering images, the images are typically sampled as nonlinear manifolds. In this case, local learning-based image clustering models are used. Several proposed clustering models are based on linear discriminant analysis (LDA). In image clustering based on linear discriminant analysis (LDA), the problem of small-sample-size (SSS) is presented...
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Research Article
Application of Linear Programming to Optimize Production and Profit (A Case Study of Oda Natural Spring Water Factory)
Ahmed Buseri Ashine*
,
Mideksa Tola Jiru
Issue:
Volume 12, Issue 1, February 2026
Pages:
13-23
Received:
6 November 2025
Accepted:
14 January 2026
Published:
31 January 2026
DOI:
10.11648/j.ijtam.20261201.12
Downloads:
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Abstract: This research focuses on optimizing production processes at Oda Natural Spring Water Factory in Ethiopia through the application of Linear Programming (LP). In the competitive bottled water industry, efficient resource management is crucial for profitability. Oda Natural Spring Water, known for its unique selenium-rich composition, faces challenges related to inefficient production practices and suboptimal resource allocation. This study develops a tailored LP model to determine the optimal daily production quantities for four bottled water sizes (35cl, 60cl, 100cl, and 200cl), with the objective of maximizing profit while adhering to constraints such as production time, cost budget, and market demand limits. Data collected over six months were analyzed using Excel Solver. The findings indicate that the factory can achieve a maximum daily profit of 150,143 Ethiopian Birr (ETB) by producing 1,100 packs of 35cl, 1,714 packs of 60cl, 1,441 packs of 100cl, and 976 packs of 200cl water. Sensitivity analysis reveals that the production cost constraint is binding, while significant production time remains unused. The study underscores LP as a practical decision-making tool in manufacturing, providing actionable strategies for improving resource allocation, reducing costs, and enhancing profitability. Recommendations include cost reduction initiatives and regular review of production plans in response to market dynamics.
Abstract: This research focuses on optimizing production processes at Oda Natural Spring Water Factory in Ethiopia through the application of Linear Programming (LP). In the competitive bottled water industry, efficient resource management is crucial for profitability. Oda Natural Spring Water, known for its unique selenium-rich composition, faces challenges...
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