Spatial autocorrelation incorporated machine learning model for geotechnical subsurface modeling
HJ Kim, KBA Mawuntu, TW Park, HS Kim, JY Park… - Applied Sciences, 2023 - mdpi.com
Machine learning models for spatial prediction have been applied in various types of
research. However, spatial relation has not been fully considered in modeling, since the …
research. However, spatial relation has not been fully considered in modeling, since the …
[HTML][HTML] Machine learning regression tools for erosion prediction of WC-10Co4Cr thermal spray coating
The prediction of erosion in WC-10Co4Cr thermal spray coating is predicted using
regression machine learning technique. A pot tester helped to examine the erosion rate of …
regression machine learning technique. A pot tester helped to examine the erosion rate of …
Large-scale data-driven uniformity analysis and sensory prediction of commercial banana ripening process
Artificial intelligence (AI) and machine learning (ML) have found prominent yet mostly
academic applications in the food supply chain specifically to preserve and optimize the …
academic applications in the food supply chain specifically to preserve and optimize the …
Classification of cassava leaf diseases using deep Gaussian transfer learning model
Abstract In Sub‐Saharan Africa, experts visually examine the plants and look for disease
symptoms on the leaves to diagnose cassava diseases, a subjective method. Machine …
symptoms on the leaves to diagnose cassava diseases, a subjective method. Machine …
[HTML][HTML] Hybrid adaptive method for lane detection of degraded road surface condition
KH Almotairi - Journal of King Saud University-Computer and …, 2022 - Elsevier
Lane detection on roads is essential for autonomous vehicles. Most previous studies
detected the area of the road and all possible lanes built on it, whereas only the specific lane …
detected the area of the road and all possible lanes built on it, whereas only the specific lane …
CSI-Based Data-driven Localization Frameworking using Small-scale Training Datasets in Single-site MIMO Systems
This work presents a date-driven user localization framework for single-site massive Multiple-
Input-Multiple-Output (MIMO) systems. The framework is trained on a geo-tagged Channel …
Input-Multiple-Output (MIMO) systems. The framework is trained on a geo-tagged Channel …
A CSI-based Data-driven Localization Framework using Small-scale Training Datasets in Single-site MIMO Systems
This paper presents a new method for user localization in single-site massive Multiple-Input-
Multiple-Output (MIMO) systems, which circumvents the need for large labeled datasets …
Multiple-Output (MIMO) systems, which circumvents the need for large labeled datasets …
Optimizing Solar Energy Harvesting: Supervised Machine Learning-Driven Peak Power Point Tracking for Diverse Weather Conditions.
Solar Power is one of the significant prevalent forms of clean energy due to its perceived to
be pollution-free and easily accessible. The market for renewable energy was established …
be pollution-free and easily accessible. The market for renewable energy was established …
Roulette Wheel Variable Selection for High Dimensional Bayesian Optimization
L **, D Zhan - 2024 6th International Conference on Data …, 2024 - ieeexplore.ieee.org
Bayesian optimization (BO) is a widely used method for expensive black-box optimization.
The BO algorithm can effectively solve low-dimensional problems, but its performance of BO …
The BO algorithm can effectively solve low-dimensional problems, but its performance of BO …
Deep Gaussian convolutional neural network model in classification of cassava diseases using spectral data
Early disease identification in crops is critical for food security, especially in Sub-Saharan
Africa. To identify cassava diseases, professionals visually score the plants by looking for …
Africa. To identify cassava diseases, professionals visually score the plants by looking for …