Fractal geometry is receiving increase attention as a quantitative and qualitative model for natural phenomena description, which can establish an active classification technique when applied on satellite images. In this paper, a satellite image is used which was taken by Quick Bird that contains different visible classes. After pre-processing, this image passes through two stages: segmentation and classification. The segmentation carried out by hybrid two methods used to produce effective results; the two methods are Quadtree method that operated inside Horizontal-Vertical method. The hybrid method is segmented the image into two rectangular blocks, either horizontally or vertically depending on spectral uniformity criterion; otherwise the block is segmented by the quadtree. Then, supervised classification is carried out by means the Fractal Dimension. For each block in the image, the Fractal Dimension was determined and used to classify the target part of image. The supervised classification process delivered five deferent classes were clearly appeared in the target part of image. The supervised classification produced about 97% classification score, which ensures that the adopted fractal feature was able to recognize different classes found in the image with high accuracy level.
This work implements an Electroencephalogram (EEG) signal classifier. The implemented method uses Orthogonal Polynomials (OP) to convert the EEG signal samples to moments. A Sparse Filter (SF) reduces the number of converted moments to increase the classification accuracy. A Support Vector Machine (SVM) is used to classify the reduced moments between two classes. The proposed method’s performance is tested and compared with two methods by using two datasets. The datasets are divided into 80% for training and 20% for testing, with 5 -fold used for cross-validation. The results show that this method overcomes the accuracy of other methods. The proposed method’s best accuracy is 95.6% and 99.5%, respectively. Finally, from the results, it
... Show MoreThe economy is exceptionally reliant on agricultural productivity. Therefore, in domain of agriculture, plant infection discovery is a vital job because it gives promising advance towards the development of agricultural production. In this work, a framework for potato diseases classification based on feed foreword neural network is proposed. The objective of this work is presenting a system that can detect and classify four kinds of potato tubers diseases; black dot, common scab, potato virus Y and early blight based on their images. The presented PDCNN framework comprises three levels: the pre-processing is first level, which is based on K-means clustering algorithm to detect the infected area from potato image. The s
... Show MoreDisease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature
... Show MoreThis study examined the effects of water scarcity on rural household economy in El Fashir Rural Council / North Darfur State- western Sudan. Both quantitative and qualitative methods were used as to get a deeper understanding of the impact of water scarcity on the rural house economy in the study area. 174 households out of 2017 were selected from 45 villages which were distributed in eight village councils forming the study area. Statistical methods were used to manipulate the data of the study. The obtained results revealed that water scarcity negatively affected the rural household economy in the study area in many features. These include the followings: much family efforts and time were directed to fetch for water consequentl
... Show MoreThe heritage of the human peoples remains a beacon of data, after renewed awareness of them, open to their propositions, which are born from the womb of intertwined elements of customs, knowledge, arts, and literature ... Misleading the reception process, it may be something intriguing to identify with caution with the text sample selected by the current search procedures prepared according to the adoption of a descriptive therapist in a three-dimensional approach, as well as a comprehensive textual reference for that specific communication dimension within Anthology Andalusian - Moroccan poetry in the book of pleasure in the perception of the five senses Tahdib ibn Manzoor (d. 711 e) from the origin of Tivashi (d. 651 e) Tagged: Separat
... Show MoreThe current research dealt with the rapid development of industrial product design in recent times, and this development in the field of design led to the emergence of modern trends in many terms and theories to direct greater interest in the cognitive foundations of design and its relationship with the components of other natural sciences, and despite the impressive technological development, nature remains With its content of formative values and structural dimensions, it is the first source of inspiration and the source of all modern mathematical sciences and theories, as God made them tend towards organization to continue to provide us with endless inspiration. Hence, the fractional one, which is an important part of dedicating the d
... Show MoreMedical images play a crucial role in the classification of various diseases and conditions. One of the imaging modalities is X-rays which provide valuable visual information that helps in the identification and characterization of various medical conditions. Chest radiograph (CXR) images have long been used to examine and monitor numerous lung disorders, such as tuberculosis, pneumonia, atelectasis, and hernia. COVID-19 detection can be accomplished using CXR images as well. COVID-19, a virus that causes infections in the lungs and the airways of the upper respiratory tract, was first discovered in 2019 in Wuhan Province, China, and has since been thought to cause substantial airway damage, badly impacting the lungs of affected persons.
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