The problem of steady, laminar, natural convective flow in an square enclosure with and without partitions is considered for Rayleigh number (103-106) and Prandtl number (0.7). Vertical walls were maintained isothermal at different temperatures while horizontal walls and the partitions were insulated. The length of partition was taken constant. The number of partitions were placed on horizontal surface in staggered arrangement from (1– 3) and ratio of partition thickness (H/L= 0.033, 0.083, 0.124). The problem is formulated in terms of the vorticity-stream function procedure. A numerical solution based on a program in Fortran 90 with the finite difference method is obtained. Representative results illustrating the effects of the thickn

The objective of the study: To diagnose the reality of the relationship between the fluctuations in world oil prices and their reflection on the trends of government spending on the various economic sectors.

The research found: that public expenditures contribute to the increase of national consumption through the purchase of consumer goods by the state for the performance of the state's duties or the payment of wages to employees in the public sector and thus have a direct impact on national consumption

The results of the standard tests showed that there is no common integration between the oil price fluctuations and the government expenditure on the security sector through the A

The researchers of the present study have conducted a genre analysis of two political debates between American presidential nominees in the 2016 and 2020 elections. The current study seeks to analyze the cognitive construction of political debates to evaluate the typical moves and strategies politicians use to express their communicative intentions and to reveal the language manifestations of those moves and strategies. To achieve the study’s aims, the researchers adopt Bhatia’s (1993) framework of cognitive construction supported by van Emeren’s (2010) pragma-dialectic framework. The study demonstrates that both presidents adhere to this genre structuring to further their political agendas. For a positive and promising image

APDBN Rashid, International Journal of Humanities and Social Sciences/ RIMAK, 2023

In this work, a deep computational study has been conducted to assign several qualities for the graph ⁠. Furthermore, determine the amount of the dihedral subgroups in the Held simple group He through utilizing the attributes of gamma.

The support vector machine, also known as SVM, is a type of supervised learning model that can be used for classification or regression depending on the datasets. SVM is used to classify data points by determining the best hyperplane between two or more groups. Working with enormous datasets, on the other hand, might result in a variety of issues, including inefficient accuracy and time-consuming. SVM was updated in this research by applying some non-linear kernel transformations, which are: linear, polynomial, radial basis, and multi-layer kernels. The non-linear SVM classification model was illustrated and summarized in an algorithm using kernel tricks. The proposed method was examined using three simulation datasets with different sample

Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the bes

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob