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International Journal of Recent Technology and Applied Science (IJORTAS)
Published by Lamintang Education & Training Centre
ISSN : 27212017     EISSN : 27217280     DOI : https://doi.org/10.36079/lamintang.ijortas
Core Subject : Science, Engineering,
Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology
The aim of this journal is to publish high-quality articles dedicated to all aspects of the latest outstanding developments in the field of Technology and Applied Science.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 64 Documents
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Utilization of GIS-Based UAV Systems for Mapping and Monitoring Agricultural Land Mahmud Uddin Haque; Mazid Mofazzal Islam; Mirza Hossain; Mohammad Abdul Alamgir
International Journal of Recent Technology and Applied Science (IJORTAS) Vol 8 No 1: March 2026
Publisher : Lamintang Education and Training (LET) Centre

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijortas-0801.997

Abstract

Bangladesh faces serious challenges in agricultural mapping and monitoring due to urbanization, climate change, and the limitations of manual methods. This research aims to utilize a Geographic Information System (GIS)-based UAV system for efficient and accurate agricultural mapping and monitoring. The system collects multispectral data on vegetation conditions, soil moisture, and crop growth, and identifies areas affected by drought or pest infestations. The UAV is designed to cover large areas difficult for humans to access, with data integrated into GIS for spatial analysis and precision agriculture-based recommendations. The research methodology employed a quantitative approach with experimental methods and GIS spatial analysis. It was conducted from September to December 2025 in three key agricultural districts: Gazipur, Rajshahi, and Khulna. The multirotor UAV was equipped with a multispectral camera, a high-resolution RGB camera, GPS, and autopilot for coordinate accuracy and automated flight path planning. Image acquisition was conducted two to three times during the growing season, supplemented by soil moisture measurements, weather data, visual observations, and respondent interviews for validation. The results demonstrated that the UAV-GIS system was capable of producing accurate maps of vegetation conditions, soil moisture, and crop stress areas. Field findings indicate that the integration of GIS spatial analysis can detect crop conditions with an accuracy of up to 92%, surpassing conventional methods, which only reach 70%. Furthermore, operational efficiency has increased, with work time reduced from 10 hours to just 2 hours per hectare, and costs reduced from $25 to $15. This system aligns with previous studies on the effectiveness of UAVs and GIS in monitoring hard-to-reach areas, supporting precision agriculture practices, and improving productivity and sustainability. Further research could explore fixed-wing UAVs for larger areas, the integration of LiDAR or hyperspectral sensors, the development of AI algorithms for predicting crop stress and disease, and sustainable business models for UAV-GIS adoption in developing countries.
A Framework for Sovereign AI Governance and Economic Growth in Cameroon Eyong Atem
International Journal of Recent Technology and Applied Science (IJORTAS) Vol 8 No 1: March 2026
Publisher : Lamintang Education and Training (LET) Centre

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijortas-0801.999

Abstract

Artificial Intelligence is no longer just a trend in technology. It has become a structural force that determines national competitiveness and economic resilience. While many advanced nations are already integrating AI into their core systems, most Sub-Saharan African states still lack the institutional frameworks needed to turn these innovations into sustainable development. This paper argues that Cameroon should not view AI simply as modernization. Instead, it must be treated as a sovereign strategy built on institutional economics, deliberate governance, and a solid blended finance architecture. Using comparative policy analysis and digital infrastructure modeling, the study proposes a three-layer framework tailored to Cameroon’s specific political economy. This model draws on international standards from the OECD, UNESCO, and the African Union, alongside the NIST Risk Management Framework. The findings show that with coordinated reform, AI could boost Cameroon’s long-term productivity by 1.5% to 2.8% annually. To fund this transition, the paper introduces a blended finance structure designed to attract multilateral banks and private venture capital. Further research is needed to explore the longitudinal impact of these AI deployments on local labor markets and the creation of indigenous datasets that reflect Cameroon’s unique linguistic diversity. Ultimately, this work contributes to the growing body of research on digital sovereignty and the political economy of AI in frontier markets.
A Low-Cost Arduino-Based Smart Parking Assistance System Using Distance Sensors for Real-Time Vehicle Guidance Harfian Rais; Normalisa
International Journal of Recent Technology and Applied Science (IJORTAS) Vol 8 No 2: September 2026
Publisher : Lamintang Education and Training (LET) Centre

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijortas-0802.996

Abstract

The growing number of vehicles has made parking increasingly challenging, particularly in areas with limited parking space. Conventional parking methods often rely on drivers' visual judgment, which may lead to inaccurate distance estimation and increase the risk of minor collisions. To address this issue, this study presents the design and implementation of a low-cost smart parking assistance system based on an Arduino Uno microcontroller integrated with an ultrasonic distance sensor and LED indicators that provide real-time visual guidance. The system was developed using the Arduino IDE and experimentally evaluated by comparing sensor measurements with actual distances ranging from 10 to 60 cm, while also examining the accuracy of distance detection and the reliability of LED responses. The experimental results showed stable and consistent performance, with a maximum measurement error of only ±1 cm across all test distances. In addition, the system achieved a 100% success rate in LED indicator activation, where the green, yellow, and red LEDs responded accurately according to the predefined safety thresholds without noticeable delay. These findings demonstrate that the proposed system can provide reliable real-time obstacle detection, helping drivers park more safely while reducing the risk of collisions. Owing to its simple design, low implementation cost, and dependable performance, the proposed system is well suited for residential garages, university campuses, and small commercial parking facilities. Future research should explore the integration of multi-sensor fusion, Internet of Things (IoT)-based remote monitoring, cloud-based data analytics, and artificial intelligence techniques to enable predictive parking occupancy analysis and intelligent parking management for large-scale smart city applications.
Interpretable Machine Learning Framework for Personalized Health Insurance Risk Prediction Mohammed Al-Mhadawi; Qahtan M. Yas
International Journal of Recent Technology and Applied Science (IJORTAS) Vol 8 No 2: September 2026
Publisher : Lamintang Education and Training (LET) Centre

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijortas-0802.1081

Abstract

In modern health insurance systems, accurate medical expenditure prediction is vital for financial solvency and equitable premium distribution. However, deployment is often hampered by the trade-off between predictive accuracy and model transparency. This study presents a unified, interpretable machine learning regression framework to evaluate personalized health insurance charges using structured tabular data. We benchmarked six predictive architectures (five tree ensembles and a multi-layer perceptron control) on a real-world dataset (n=1,338). Experimental results demonstrate that Gradient Boosting achieved superior performance with R2=0.8789, RMSE = $4,335.47, MAPE = 28.49%, and an operational model footprint of only 170 KB. In contrast, standard deep learning (MLP) failed catastrophically (R2 = −0.3947) due to severe right-skewness and nonlinear tabular feature interactions. Model interpretability via SHAP values identified smoker status (48.2% Gini importance) and BMI interactions as primary cost drivers. Future research will focus on evaluating hybrid TabNet-Boosting architectures and integrating longitudinal temporal claims to capture evolving risk profiles across multinational cohorts.

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