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

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

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

Background: The value of lateral cephalometric radiographs to evaluate the pharyngeal airway is limited because it provided 2-dimensional (2D) images of complex 3-dimensional (3D) anatomic structures. Three dimensional analyses of the airway volumes are required to understand oral and pharyngeal adaptations in mouth breathing and nasal breathing subjects. The aim of this study was to measure the pharyngeal airway volume and the size of the face, then compare between pharyngeal airway volume in mouth breathing and nasal breathing subjects and find the gender difference in each group, also to study the relation between pharyngeal airway volume and the size of the face. Material and Methods: Fifty patients including 28 males and 22 females wit

Watermelon is known to be infested by multiple insect pests both simultaneously and in sequence. Interactions by pests have been shown to have positive or negative, additive or non additive, compensatory or over compensatory effects on yields. Hardly has this sort of relationship been defined for watermelon vis-à-vis insect herbivores. A 2-year, 2-season (4 trials) field experiments were laid in the Research Farm of Federal University Wukari, to investigate the interactive effects of key insect pests of watermelon on fruit yield of Watermelon in 2016 and 2017 using natural infestations. The relationship between the dominant insect pests and fruit yield were determined by correlation (r) and linear regression (simple and multiple) analys

The current research aims to know the extent of the impact of performance evaluation in its dimensions as an explanatory variable in the behavioural and attitudinal work outputs with its dimensions as a response variable in order to reach appropriate solutions through which the University of Fallujah seeks to achieve its goal in the process of diagnosing the axes of strength and to benefit from them in the process of strengthening the status and sobriety of the academic position of the professor and the researcher relied on The descriptive and analytical approach in carrying out this study, and data was collected from university professors, including leaders, heads of departments and divisions, who numbered (97) teachers. And fie

Background: Poly cystic ovary syndrome is a common disorder in women of reproductive age, it is associated with disturbance of reproductive, endocrine and metabolic functions. The pathophysiology of PCOS appears to be multifactorial and polygenic. Leptin seems to play an important role in pathophysiology of PCOS especially in women with BMI ≥25kg/m2. Objectives: To assess leptin level in both PCOS and healthy women and explore the relation to their body weight and body mass index. Patient and Methods: A total of 120 women were enrolled in this study, 60 women (50%) had PCOS (study group) and the reminder 60 women (50%) were healthy women and considered as control group. BMI was calculated first. Both groups were further sub

Background: left ventricular hypertrophy is independent risk factor for cardiovascular morbidity and mortality. The presence of diabetic complications such as autonomic neuropathy and retinopathy may predict cardiac structural changes in diabetic patients. Objective: To explore the chance of occurrence of left ventricular hypertrophy in diabetic patients and whether it is related to the presence of other diabetic complications. Methods: ninty seven (97) normotensive diabetic patients (57) type II with mean duration of diabetes of (12±6 y) and forty (40) type I with mean duration of (8±6 y) were studied by echocardiography and compared with 41 patients as control. Results: The LVMI was significantly higher in type II diabetics compared