Browse Items (33 total)
- Collection: Vol 9 No 5 (2025)
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A Comparative Evaluation of YOLOv9 and DETR Models in Traffic Object Detection for Intelligent Surveillance Systems
Object detection plays a crucial role in traffic surveillance, particularly in urban environments characterized by high vehicle density, diverse weather conditions, and limited computational resources. Although YOLOv9 and DETR have demonstrated…
A Data-Driven Comparison of Linear Mixed Model and Mixed Effects Regression Tree Approaches for Dairy Productivity Analysis
Dairy productivity studies often involve hierarchical and longitudinal data structures that violate the assumptions of linearregression. This study compares two modeling approaches, Linear Mixed Model (LMM) and Mixed Effects Regression…
Addressing Class Imbalance in Oil Palm Disease and Micronutrient Deficiency Detection Using Meta-Learned Transfer Metric Learning
Class imbalance is a major challenge in oil palm disease and nutrient deficiency detection, where healthy samples dominate while diseased or deficient cases are underrepresented, often leading to biased models with high false-negative …
Application of Reinforcement Learning to Solve Rubrik’s Cube with Markov Decision Process
The Rubik's Cube is a tricky puzzle that can be arranged in countless ways, making it hard for both people and computers to figure out. While standard solving methods use fixed strategies, this research looks into using reinforcement learning (RL) to…
Application of YOLOv8 Algorithm for Coral Reef Disease Detection as an Effort to Prevent Marine Habitat Damage in Batam
Research in 2019 in Batam City showed that out of 19 coral reef fisheries support facilities, 16 were declared not good. Coral reef damage increased from 36.28% to 39.44%. This is due to the threat of coral reef damage due to international shipping…
Automated Ripeness Detection of Oil Palm Fruit Using a Hybrid GLCM-HSV-KNN Model
Accurately determining the ripeness of oil palm fruit is crucial for ensuring the quality of palm oil. However, traditional manual methods are often time-consuming and less accurate. This study aimed to develop an automated system for …
Breast Cancer Histopathological Image Classification with Convolutional Neural Networks Models
Early diagnosis and treatment can reduce mortality rates by preventing the progression of breast cancer. Owing to convolutional neural networks (CNN),breast cancer diagnosis can be performed faster and more objectively …
Breast Cancer Histopathological Image Classification with Convolutional Neural Networks Models
Early diagnosis and treatment can reduce mortality rates by preventing the progression of breast cancer. Owing to convolutional neural networks (CNN),breast cancer diagnosis can be performed faster and more objectively …
Comparing Data Preprocessing Strategy on T5 Architecture to Classify ICD-10 Diagnosis
Manual ICD-10 coding in healthcare systems remains time-consuming, error-prone, and inefficient, particularly in resource-constrained settings. This study investigates the effect of various preprocessing strategies on the performance of the…
Early Detection of Grasserie Disease in Silkworms Using Computer Vision and Machine Learning
One of the major challenges associated with the sericulture industry is silkworm diseases, as they are very difficult to detect in the early stages. Timely identification of infected silkworms is essential to curb the spread of disease …