Publication: Optimization Seaweed Drying Efficiency Using Hybrid Solar Dryers And Sparse Robust Regression Models
| dc.contributor.author | Afouna, Nour Hamad Suleiman Abu | |
| dc.date.accessioned | 2026-06-04T07:04:40Z | |
| dc.date.available | 2026-06-04T07:04:40Z | |
| dc.date.issued | 2025-04 | |
| dc.description.abstract | Data analytics in statistics is vital for extracting insights, identifying patterns, and guiding decisions. In precision farming, particularly post-harvest management, challenges arise from iot sensor dependency, system complexity, and variable interactions, leading to issues like variability, multicollinearity, and sensitivity to outliers. Addressing these challenges requires improved data inclusivity, robust data management, and cross-sector collaboration to unlock the full potential of analytics. Variability in agricultural systems impacts crop yield and post-harvest processes. Heterogeneity in sensors, data collection methods, and transmission protocols complicates agricultural drying. Multicollinearity, where independent variables are highly correlated, creates difficulties in post-harvest monitoring as overlapping environmental data from multiple sensors obscures the impact of individual variables. Fluctuations due to environmental changes, sensor errors, and human interventions further complicate modeling, requiring robust statistical methods capable of handling noise and outliers. | |
| dc.identifier.uri | https://erepo.usm.my/handle/123456789/24311 | |
| dc.language.iso | en | |
| dc.subject | Optimization Seaweed Drying Efficiency Using Hybrid | |
| dc.title | Optimization Seaweed Drying Efficiency Using Hybrid Solar Dryers And Sparse Robust Regression Models | |
| dc.type | Resource Types::text::thesis::doctoral thesis | |
| dspace.entity.type | Publication | |
| oairecerif.author.affiliation | Universiti Sains Malaysia |