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Stabilizing Machine Learning for Reproducible and Explainable Results: A Novel Validation Approach to Subject-Specific Insights
Gideon Vos, Liza van Eijk, Zoltan Sarnyai +1
Machine Learning is transforming medical research by improving diagnostic accuracy and personalizing treatments. General ML models trained on large datasets identify broad patterns…
The Effect of Acute Stress on the Interpretability and Generalization of Schizophrenia Predictive Machine Learning Models
Gideon Vos, Maryam Ebrahimpour, Liza van Eijk +2
Introduction Schizophrenia is a severe mental disorder, and early diagnosis is key to improving outcomes. Its complexity makes predicting onset and progression challenging. EEG has…
Machine Learning for Asymptomatic Ratoon Stunting Disease Detection With Freely Available Satellite Based Multispectral Imaging
Ethan Kane Waters, Carla Chia-ming Chen, Mostafa Rahimi Azghadi
Disease detection in sugarcane, particularly the identification of asymptomatic infectious diseases such as Ratoon Stunting Disease (RSD), is critical for effective crop management…
A Practical Approach to Underwater Depth and Surface Normals Estimation
Alzayat Saleh, Melanie Olsen, Bouchra Senadji +1
Monocular Depth and Surface Normals Estimation (MDSNE) is crucial for tasks such as 3D reconstruction, autonomous navigation, and underwater exploration. Current methods rely eithe…
Semi-Supervised Weed Detection for Rapid Deployment and Enhanced Efficiency
Alzayat Saleh, Alex Olsen, Jake Wood +2
Weeds present a significant challenge in agriculture, causing yield loss and requiring expensive control measures. Automatic weed detection using computer vision and deep learning…
Sugarcane Health Monitoring With Satellite Spectroscopy and Machine Learning: A Review
Ethan Kane Waters, Carla Chia-Ming Chen, Mostafa Rahimi Azghadi
Research into large-scale crop monitoring has flourished due to increased accessibility to satellite imagery. This review delves into previously unexplored and under-explored areas…