In this study, we made a comparison between LASSO & SCAD methods, which are two special methods for dealing with models in partial quantile regression. (Nadaraya & Watson Kernel) was used to estimate the non-parametric part ;in addition, the rule of thumb method was used to estimate the smoothing bandwidth (h). Penalty methods proved to be efficient in estimating the regression coefficients, but the SCAD method according to the mean squared error criterion (MSE) was the best after estimating the missing data using the mean imputation method

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

This paper deals with a Twin Rotor Aerodynamic System (TRAS). It is a Multi-Input Multi-Output (MIMO) system with high crosscoupling between its two channels. It proposes a hybrid design procedure that combines frequency response and root locus approaches. The proposed controller is designated as PID-Lead Compensator (PIDLC); the PID controller was designed in previous work using frequency response design specifications, while the lead compensator is proposed in this paper and is designed using the root locus method. A general explicit formula for angle computations in any of the four quadrants is also given. The lead compensator is designed by shifting the dominant closed-loop poles slightly to the left in the

In this paper, RBF-based multistage auto-encoders are used to detect IDS attacks. RBF has numerous applications in various actual life settings. The planned technique involves a two-part multistage auto-encoder and RBF. The multistage auto-encoder is applied to select top and sensitive features from input data. The selected features from the multistage auto-encoder is wired as input to the RBF and the RBF is trained to categorize the input data into two labels: attack or no attack. The experiment was realized using MATLAB2018 on a dataset comprising 175,341 case, each of which involves 42 features and is authenticated using 82,332 case. The developed approach here has been applied for the first time, to the knowledge of the authors, to dete

Finite Element Approach is employed in this research work to solve the governing differential equations related to seepage via its foundation's dam structure. The primary focus for this reason is the discretization of domain into finite elements through the placement of imaginary nodal points and the discretization of governing equations into an equation system; An equation for each nodal point or part, and unknown variables are solved. The SEEP / W software (program) is a sub-program of the Geo-Studio software, which is used by porous soil media to compensate for the problems of seepage. To achieve the research goals, a study was carried out on Hemrin dam, which located in the Diyala River 100 km northeast of Baghdad, Iraq. Thus, o

Permanent deformation in asphalt concrete pavements is pervasive distress [1], influenced by various factors such as environmental conditions, traffic loading, and mixture properties. A meticulous investigation into these factors has been conducted, yielding a robust dataset from uniaxial repeated load tests on 108 asphalt concrete samples. Each sample underwent systematic evaluation under varied test temperatures, loading conditions, and mixture properties, ensuring the data’s comprehensiveness and reliability. The materials used, sourced locally, were selected to enhance the study ʼs relevance to pavement constructions in hot climate areas, considering different asphalt cement grades and con- tents to understand material variability ef

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Background: Hyperfunction of the muscles of the upper lip is considered as the most common cause of excessive gingival display (EGD). The aim of this study was to demonstrate the effectiveness of botulinum toxin (BT) injection as a conservative treatment for EGD due to muscular hyperfunction and to compare the outcome of 2 injection methods. Material and methods: This study included 40 participants who were randomly assigned into 2 groups of 20 each, The first group received 2.5IU BT injection at 1 point per side (2-points group), while the second group received a total of 5 IU of BT at 2 points per side (4-points group). The outcome variables were the reduction in the central and lateral gingival display expressed as the difference between