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

This work studies the impact of input machining parameters of Electrical Discharge Machining (EDM) on the machining process performance. Tool steel O1 was selected as the workpiece material, copper as the electrode material, and kerosene as the dielectric medium. Experimental runs have been carried out with a Design of Experiment (DOE) technique. Twenty tests are accomplished with the current range of (18 to 24 Ampere), a pulse duration range of (150 to 200 µs), and a pulse-off time range of (25 to 75 µs). Based upon the experimental study's output results, the EDM parameter's effect (voltage of power supply, discharge current, pulse duration, and pulse pause interval) on the responses of the process represented by sur

Staphylococcus Sp.is the most common type of bacteria found in contamination place, we design this
study to compare the contamination accident between two hospitals in Baghdad.One of them isthe Burns
Specialist Hospital in the Medical CityinRusafa and another one is Al-Karama Hospital in Karkh. The
samples were collected fromOperativeWard No1 (OW1), Operative Ward No2 (OW2), Consulting Pharmacy
(CP), Emergency Room (ER), Reception Room (RR), Women's Ward (WW) and Men's Ward (MW).The
samples were taken from inside each clinical unit, surfaces, food, and air. The results showed that the
number of samples containing Staphylococcus sp. bacteria is 81, including 45 belonging to Al-Karama Burns
Ward Ho

This paper aims to study the second-order geometric nonlinearity effects of P-Delta on the dynamic response of tall reinforced concrete buildings due to a wide range of earthquake ground motion forces, including minor earthquake up to moderate and strong earthquakes. The frequency domain dynamic analysis procedure was used for response assessment. Reinforced concrete building models with different heights up to 50 stories were analyzed. The finite element software ETABS (version 16.0.3) was used to analyze reinforced concrete building models.

The study reveals that the percentage increase in buildings' sway and drift due to P-Delta effects are nearly constant for specific building height irrespective of the seism