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International Journal of Applied Mathematics, Sciences, and Technology for National Defense
Published by Foundation of Advanced Education
ISSN : 29860776     EISSN : 29859352     DOI : https://doi.org/10.58524/app.sci.def
Core Subject : Science, Education,
Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Mathematics Physics
International Journal of Applied Mathematics, Sciences, and Technology for National Defense (App.Sci.Def) [e-ISSN: 2985-9352, p_ISSN: 2986-0776] is a journal published by the Foundation of Advanced Education. International Journal of Applied Mathematics, Sciences and Technology for National Defense (App.Sci.Def) is an Applied Mathematics, Science, and Technology in National Defense is an international journal dedicated to the publication of high quality, peer-reviewed articles on all aspects of mathematics in defense, complex strategy, modeling, optimization, cybersecurity, and special issues on topics of current interest. The scope of the journal is very broad and interdisciplinary with an integrated, qualitative and quantitative approach. Review papers with insightful, integrative, applicable and up-to-date major topic progress are also welcome. Authors are invited to submit defense-related articles that have not been previously published and are not being considered elsewhere. In addition, the App.Sci.Def editorial board is strongly committed to promoting current advances and interdisciplinary research in defense mathematics, Science, and Technology.
Arjuna Subject : Umum - Umum
Articles 56 Documents
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Hybrid random forest–catboost ensemble for heart disease prediction on imbalanced datasets: Toward applications in military health systems Mahyus Ihsan; Zahnur; Iftahul Fadlan; Ikhsan Maulidi
International Journal of Applied Mathematics, Sciences, and Technology for National Defense Vol. 4 No. 1 (2026): International Journal of Applied Mathematics, Sciences, and Technology for Nati
Publisher : FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/app.sci.def.v4i1.1148

Abstract

ackground: Heart disease is one of the main causes of death worldwide, with cases increasing every year. This situation highlights the urgent need for early detection systems that are not only fast but also accurate and reliable. In recent years, machine learning has emerged as a promising alternative approach for analyzing medical data, particularly for disease classification and risk prediction tasks. Aims: This study aims to develop a heart disease prediction model by integrating Random Forest and CatBoost in a hybrid ensemble framework and evaluating its performance on an imbalanced medical dataset. Method: This study employs a quantitative approach based on supervised learning using the Behavioral Risk Factor Surveillance System (BRFSS) 2021 dataset, which consists of more than 300,000 observations. Data preprocessing includes duplicate removal, BMI categorization, encoding of categorical variables, and exploratory analysis. To address class imbalance, the Borderline-SMOTE technique was applied before splitting the dataset using an 80:20 train-test split. Random Forest and CatBoost models were trained and combined using a soft voting ensemble. Result: The evaluation results indicate that Random Forest achieved the highest accuracy of 0.94, with well-balanced precision and recall across all classes. CatBoost demonstrated relatively stable performance with accuracy around 0.84. The ensemble approach achieved an accuracy of 0.91 with strong metric stability and good sensitivity to positive cases. Conclusion: The results indicate that Random Forest performs best for the dataset used in this study, while the ensemble model provides a balanced compromise between predictive performance and robustness. The analysis also shows that Age Category, General Health, and BMI are the most influential predictors of heart disease risk. This model can support early cardiovascular risk detection in military personnel, contributing to maintaining operational readiness in defense systems. Furthermore, the proposed approach provides a reliable decision-support tool for large-scale medical screening in resource-constrained healthcare environments.
Impact of SGLT2 inhibitors on heart failure risk and kidney function in type 2 diabetic mellitus patients: A literature review Chelsy Vanya Br. Meliala; Syahrul Tuba; Adi Priyono; Syed Azhar Syed Sulaiman; Budi Sumaryono; Endah Permata Sari
International Journal of Applied Mathematics, Sciences, and Technology for National Defense Vol. 4 No. 2 (2026): International Journal of Applied Mathematics, Sciences, and Technology for Nati
Publisher : FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/app.sci.def.v4i2.1134

Abstract

Background: Type 2 diabetes mellitus is known to be closely associated with an increased risk of heart failure and chronic kidney disease, both of which are major causes of illness and death in diabetic patients. In recent years, sodium-glucose cotransporter-2 inhibitors (SGLT2i), which were initially developed as blood glucose-lowering drugs, have been shown to provide additional benefits beyond glycemic control, particularly in protecting the heart and kidneys. Aims: This literature review aims to comprehensively evaluate the impact of SGLT2 inhibitor use on the risk of heart failure and renal function outcomes in patients with type 2 diabetes mellitus (T2DM). Method: A literature search was conducted through the PubMed, Scopus, and ScienceDirect databases for publications from 2015 to 2025, including randomized controlled trials (RCTs), meta-analyses, and systematic reviews using the keywords "SGLT2 inhibitors," "heart failure," "renal outcomes," and "type 2 diabetes." Results: from various large clinical trials and meta-analyses show that SGLT2 inhibitors consistently reduce the risk of hospitalization due to heart failure and cardiovascular death, with a hazard ratio generally below 0.80. In addition, this therapy also slows the decline in glomerular filtration rate (eGFR), thereby reducing deaths due to renal causes. Conclusion: SGLT-2i has been shown to provide strong cardio-renal protection in patients with T2DM, especially in individuals at high risk of heart failure or chronic kidney disease, in accordance with current clinical guidelines.
Lee-Carter–ARIMA hybrid approach and machine learning for mortality rate forecasting in the United States: Implications for national defense and population risk assessment Vita Nuarini; Mahmudi; Nina Fitriyati; Madona Yunita Wijaya; Irma Fauziah
International Journal of Applied Mathematics, Sciences, and Technology for National Defense Vol. 4 No. 2 (2026): International Journal of Applied Mathematics, Sciences, and Technology for Nati
Publisher : FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/rhy4zg18

Abstract

Background: The Accuracy of mortality rate forecasting plays an important role in various decision-making processes in the life insurance sector, including determining premium amounts. In addition, it also contributes to assessing the readiness of human resources to support national defense, as well as to conducting risk assessments aimed at maintaining demographic stability. The United States mortality data was selected as the study object due to the availability of comprehensive and high-quality. Aims: This study explores five hybrid approaches that combine stochastic models and machine learning, along with one non-hybrid approach to assess their potential to improve forecasting accuracy. Method: In this study, the Lee-Carter–ARIMA, Lee-Carter–Random Forest, Lee-Carter–ANN, Lee-Carter–ARIMA–Random Forest, Lee-Carter–ARIMA–ANN, and ANN models were evaluated. These models were applied to mortality rate data from nine divisions in the United States (US), stratified by gender, using training data from 1966 to 2005 and test data from 2006 to 2015. The best model is determined based on the smallest Mean Absolute Percentage Error (MAPE) value while also considering the interpretability of the model. Result: The study's results show that, across the number of divisions, the Lee-Carter–ARIMA–Random Forest model produces the smallest MAPE values most often. However, in terms of average MAPE, the Lee-Carter–ARIMA–ANN model performs better, with MAPEs of 9.66% for females and 9.28% for males. Furthermore, neither of these models yields a substantial improvement in predictive accuracy compared with the Lee-Carter–ARIMA model. Conclusion: Considering the relatively small decrease in MAPE and the difficulty of interpreting machine learning models due to their black box nature, the Lee-Carter–ARIMA model demonstrates the best overall performance relative to the other models. Nevertheless, the Lee-Carter–ARIMA–Random Forest and Lee-Carter–ARIMA–ANN models show potential as alternative approaches that merit further investigation and may contribute to national defense planning and support the maintenance of demographic stability.
Numerical design and optoelectronic simulation of a germanium-on-silicon PIN photodetector for high-speed optical communication systems Ashenafi Abera Gebre; Kedir Botamo Adem
International Journal of Applied Mathematics, Sciences, and Technology for National Defense Vol. 4 No. 2 (2026): International Journal of Applied Mathematics, Sciences, and Technology for Nati
Publisher : FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/aj9sjj17

Abstract

Background: The rising demand for fast optical communication systems in the telecommunications industry, data centers, aerospace, and defense applications has made it imperative to design photodetectors with high responsivity, minimum dark current, and broad bandwidth. Germanium on Silicon (Ge-on-Si) PIN photodetectors are potential contenders in this regard due to their high optical absorption capability at the telecommunication wavelengths as well as CMOS silicon photonics compatibility. Optimizing their optoelectronic performance is an important issue, however. Aims: In this research, we intend to numerically design, optimize, and evaluate a Ge-on-Si PIN photodetector via a coupled optoelectronic simulation technique for high optical absorption, high responsivity, low dark current, and high speed operation in advanced optical communication systems. Method: The designed photodetector was modeled and simulated by using Lumerical FDTD Solutions and Lumerical DEVICE software. A combined simulation method of optical-electrical was utilized to simulate optical field distribution, optical absorption, carrier generation, photocurrent, dark current, and 3dB-bandwidth of the device. Structural parameters were carefully optimized to enhance the performance of the device for telecommunication operation. Result: The optimized Ge-on-Si PIN photodetector resulted in the optical absorption of about 92% at 1550 nm wavelength, peak responsivity of 0.84 A/W, and the minimum value of the dark current was 0.9 μA at reverse bias voltage  V. The calculated 3-dB bandwidth was 45 GHz. The increase in dark current with temperature due to the generation of carriers from thermal energy was also observed through simulations. Conclusion: The designed Ge-on-Si PIN detector offers a well-balanced structure, providing excellent responsivity, low dark current, and large bandwidth, which makes it a very promising device for silicon photonics and high-speed optical communication in the future. Besides, there is a great possibility to apply this detector for defense and aerospace applications such as optical communication for military and satellites and other systems requiring high-performance optical connections.
Sparsity-based memory scalability analysis of association rule mining algorithms using e-commerce heterogeneous multi-datasets for decision support systems Esti Fadhilah; Diandra Chika Fransisca
International Journal of Applied Mathematics, Sciences, and Technology for National Defense Vol. 4 No. 2 (2026): International Journal of Applied Mathematics, Sciences, and Technology for Nati
Publisher : FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/n3xn8q19

Abstract

Background: Many studies have compared the Apriori, FP-Growth, and ECLAT algorithms. Most of the previous literature focuses on runtime evaluation (execution speed) on homogeneous datasets. Research specifically mapping the visualization of memory curves against the level of data sparsity on common e-commerce heterogeneous. Aims: This study aims to analyze the relationship between sparsity and memory usage of three classic Association Rule Mining algorithms such as Apriori, FP-Growth and ECLAT using a heterogeneous e-commerce dataset. Method: A quantitative approach using Association Rule Mining was applied to different raw datasets from 721 to 30000 transactions. Transactions focused on various item types. The Apriori, FP-Growth, and ECLAT algorithms are implemented in Python, using grid search for minimum support and confidence adjustment to generate frequent item sets and association rules for performance comparison. Result: The results show that the relationship between sparsity and memory usage differs significantly in the three classical association rule mining algorithms. The ECLAT algorithm shows low memory consumption because it uses a vertical TID-list, while the Apriori and FP-Growth algorithms show high memory consumption because they are related to their pattern search methods (Candidate Generation and FP-Tree). Conclusion: This study shows that memory usage is not only influenced by sparsity, but also by the characteristics of the dataset. Then the results of this analysis provide an overview of heterogeneous e-commerce datasets when used in the classical association rule mining algorithm.
A nonlinear multispecies fisheries model for Namibian coastal waters: Implications for food security and marine ecosystem resilience Philipus N. Nangolo; David I. Iiyambo; Adetayo S. Eegunjobi
International Journal of Applied Mathematics, Sciences, and Technology for National Defense Vol. 4 No. 2 (2026): International Journal of Applied Mathematics, Sciences, and Technology for Nati
Publisher : FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/td7hp091

Abstract

Background: The Benguela Current Large Marine Ecosystem supports major fisheries that are essential to Namibia's food security and economy. The dynamics of Cape hake, horse mackerel, and sardine are strongly influenced by ecological interactions and harvesting, highlighting the need for mathematically rigorous multispecies fisheries models. Aims: This study aims to develop an analytically tractable nonlinear multispecies fisheries model that describes the ecological interactions among Cape hake, horse mackerel, and sardine under harvesting pressure, while providing a rigorous mathematical framework for evaluating ecosystem stability and sustainable fisheries management. Method: The proposed model integrates logistic growth, predator–prey interactions, interspecific competition, and species-specific harvesting mortality. Qualitative analysis is performed using nonlinear dynamical systems theory to establish existence and uniqueness, positivity, boundedness, equilibrium conditions, and local and global stability. Local stability is analyzed using linearization and the Routh–Hurwitz criterion, whereas global stability is established through a Volterra-type Lyapunov function and LaSalle’s Invariance Principle. In addition, a harvesting-rate estimation framework based on fisheries catch, effort, and biomass data is formulated to facilitate future model calibration. Result: The mathematical analysis establishes the well-posedness of the proposed model and derives analytical conditions for biologically feasible coexistence and system stability. Numerical simulations validate the theoretical results by demonstrating convergence toward a stable coexistence equilibrium under biologically realistic parameter values. The simulations further show that increasing harvesting intensity reduces equilibrium biomass and ecosystem resilience, while excessive exploitation of sardine populations indirectly destabilizes higher trophic levels by reducing prey availability for predator species. Conclusion: The proposed model provides a mathematically rigorous framework for analyzing multispecies fisheries dynamics and offers quantitative insights into the ecological consequences of harvesting. The results support ecosystem-based fisheries management by providing a scientific basis for evaluating sustainable harvesting strategies, establishing science-based catch quotas, identifying precautionary harvesting thresholds, and informing evidence-based policies to strengthen the long-term resilience and sustainability of Namibia's marine fisheries.

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