In this work, using GPS which has best accuracy that can be established set of GCPs, also two satellite images can be used, first with high resolution QuickBird, and second has low resolution Landsat image and topographic maps with 1:100,000 and 1:250,000 scales. The implementing of these factors (GPS, two satellite images, different scales for topographic maps, and set of GCPs) can be applying. In this study, must be divided this work into two parts geometric accuracy and informative accuracy investigation. The first part is showing geometric correction for two satellite images and maps.
The second part of the results is to demonstrate the features (how the features appearance) of topographic map or pictorial map (image map), Where i

The existing study aimed to assess four soil moisture sensors’ capacitive (WH51 and SKU: S EN0193) and resistive (Yl69 and IC Station) abilities, which are affordable and medium-priced for their accuracy in six common soil types in the central region of Iraq. The readings’ calibration for the soil moisture sensor devices continued through two gravimetric methods. The first depended on the protocols’ database, while the second was the traditional calibration method. The second method recorded the lowest analysis error compared with the first. The moderate-cost sensor WH51 showed the lowest standard error (SE), MAD , and RMSE and the highest R² in both methods. The performance accuracy of WH51 was close to readings shown by the manufac

Statistical learning theory serves as the foundational bedrock of Machine learning (ML), which in turn represents the backbone of artificial intelligence, ushering in innovative solutions for real-world challenges. Its origins can be linked to the point where statistics and the field of computing meet, evolving into a distinct scientific discipline. Machine learning can be distinguished by its fundamental branches, encompassing supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Within this tapestry, supervised learning takes center stage, divided in two fundamental forms: classification and regression. Regression is tailored for continuous outcomes, while classification specializes in c

Machine learning (ML) is a key component within the broader field of artificial intelligence (AI) that employs statistical methods to empower computers with the ability to learn and make decisions autonomously, without the need for explicit programming. It is founded on the concept that computers can acquire knowledge from data, identify patterns, and draw conclusions with minimal human intervention. The main categories of ML include supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning. Supervised learning involves training models using labelled datasets and comprises two primary forms: classification and regression. Regression is used for continuous output, while classification is employed

Background: This study was conducted to evaluate the surface roughness and dimensional accuracy of commercially obtainable alginate impression material in terms of imbibition after immersion in two different media. Materials and method: Two disinfecting agents, ethanol 70% and povidone-iodine 4%, were used to access the dimensional accuracy and surface roughness of alginate impression material. Weights of specimen discs of alginate impressions were measured before and immediately after immersion to gain a measure of imbibition. For surface roughness, disinfected specimens rectangle was examined before and after disinfection. Results: Minimal changes in weight were observed after disinfection, but a statistically non-significant differenc

This paper presents L₁-adaptive controller for controlling uncertain parameters and time-varying unknown parameters to control the position of a DC servomotor. For the purpose of comparison, the effectiveness of L₁-adaptive controller for position control of studied servomotor has been examined and compared with another adaptive controller; Model Reference Adaptive Controller (MRAC). Robustness of both L₁-adaptive controller and model reference adaptive controller to different input reference signals and different structures of uncertainty were studied. Three different types of input signals are taken into account; ramp, step and sinusoidal. The L₁-adaptive controller ensured uniformly bounded