This paper presents a hierarchical two-stage outdoor scene classification method using multi-classes of Support Vector Machine (SVM). In this proposed method, the gist feature of all the images in the database is extracted first to obtain the feature vectors. The image of database is classified into eight outdoor scenes classes, four manmade scenes and four natural scenes. Second, a hierarchical classification is applied, where the first stage classifies all manmade scene classes against all natural scene classes, while the second stage of a hierarchical classification classifies the outputs of first stage into either one of the four manmade scene classes or natural scene classes. Binary SVM and multi-classes SVMs are employed in the first and second stage of a hierarchical classification respectively. The proposed method is designed also to compare and find the most suitable multi-classes SVMs approach and the kernel function for classification task, where their performances are analyzed based on experimental results. The multi-classes SVMs used in this paper are One-versus-All (OvA) and One-versus-One (OvO), while the kernel functions used are linear kernel, Radius Basis Function (RBF) kernel and Polynomial kernel. Experimental results indicate that OvO classifier provides better performance than OvA classifier. The results, also show that the Polynomial kernel function is superior to others kernel function.
Printed Arabic document image retrieval is a very important and needed system for many companies, governments and various users. In this paper, a printed Arabic document images retrieval system based on spotting the header words of official Arabic documents is proposed. The proposed system uses an efficient segmentation, preprocessing methods and an accurate proposed feature extraction method in order to prepare the document for classification process. Besides that, Support Vector Machine (SVM) is used for classification. The experiments show the system achieved best results of accuracy that is 96.8% by using polynomial kernel of SVM classifier.
Soil pH is one of the main factors to consider before undertaking any agricultural operation. Methods for measuring soil pH vary, but all traditional methods require time, effort, and expertise. This study aimed to determine, predict, and map the spatial distribution of soil pH based on data taken from 50 sites using the Kriging geostatistical tool in ArcGIS as a first step. In the second step, the Support Vector Machines (SVM) machine learning algorithm was used to predict the soil pH based on the CIE-L*a*b values taken from the optical fiber sensor. The standard deviation of the soil pH values was 0.42, which indicates a more reliable measurement and the data distribution is normal.
Let be any connected graph with vertices set and edges set . For any two distinct vertices and , the detour distance between and which is denoted by is a longest path between and in a graph . The detour polynomial of a connected graph is denoted by ; and is defined by . In this paper, the detour polynomial of the theta graph and the uniform theta graph will be computed.
In this paper we show that the function , () p fLI α ∈ ,0<p<1 where I=[-1,1] can be approximated by an algebraic polynomial with an error not exceeding , 1 ( , , ) kp ft n ϕ αω where
,
1 ( , , ) kp ft n ϕ αω is the Ditizian–Totik modules of smoothness of unbounded function in , () p LI
The aim of this paper is to estimate the concentrations of some heavy metals in Mohammed AL-Qassim Highway in Baghdad city for different distances by using the polynomial interpolation method for functions passing from the data, which is proposed by using the MATLAB software. The sample soil in this paper was taken from the surface layer (0-25 cm depth) at the two sides of the road with four distances (1.5, 10, 25 and 60 m) in each side of the road. Using this method, we can find the concentrations of heavy metals in the soil at any depth and time without using the laboratory, so this method reduces the time, effort and costs of conducting laboratory analyzes.
We will also derive practical solutions using predictive analytics. And this would include application making predictions with real world example from University of Faculty of Chariaa of Fez. As soon as student enrolled to the university, they will certainly encounter many difficulties and problems which discourage their motivation towards their courses and which pushes them to leave their university.
The aim of our article is to manage an investigation of the issue of dropping out their studies. This investigation actively integrates the benefits ofmachine learning. Hence, we will concentrate on two fundamental strategies which are KNN, which depends on the idea of likeness among data; and the famous strategy SVM, which can break the
Offline Arabic handwritten recognition lies in a major field of challenge due to the changing styles of writing from one individual to another. It is difficult to recognize the Arabic handwritten because of the same appearance of the different characters. In this paper a proposed method for Offline Arabic handwritten recognition. The proposed method for recognition hand-written Arabic word without segmentation to sub letters based on feature extraction scale invariant feature transform (SIFT) and support vector machines (SVMs) to enhance the recognition accuracy. The proposed method experimented using (AHDB) database. The experiment result show (99.08) recognition rate.
Let be any group with identity element (e) . A subgroup intersection graph of a subset is the Graph with V ( ) = - e and two separate peaks c and d contiguous for c and d if and only if , Where is a Periodic subset of resulting from . We find some topological indicators in this paper and Multi-border (Hosoya and Schultz) of , where , is aprime number.
Let be a connected graph with vertices set and edges set . The ordinary distance between any two vertices of is a mapping from into a nonnegative integer number such that is the length of a shortest path. The maximum distance between two subsets and of is the maximum distance between any two vertices and such that belong to and belong to . In this paper, we take a special case of maximum distance when consists of one vertex and consists of vertices, . This distance is defined by: where is the order of a graph .
In this paper, we defined – polynomials based on
... Show MoreIn this paper, a hybrid image compression technique is introduced that integrates discrete wavelet transform (DWT) and linear polynomial coding. In addition, the proposed technique improved the midtread quantizer scheme once by utilizing the block based and the selected factor value. The compression system performance showed the superiority in quality and compression ratio compared to traditional polynomial coding techniques.