Nt1310 Unit 3 Test Report

Find and correct the mistakes in the test items below 1. Jane prefers doing nothing than working 2. Computers cannot do anything without program correctly 3. It took her a long time to get used to wear contact lenses 4. He would rather to upgrade her old PC than buy a new one 5.

Thank you for being my substitute today. Make sure that students are sitting in their assigned seats and take attendance for every period and turn it into the office. If you have any problems do not hesitate to call the front office at *500 and have the student removed. Let me know what happened so I can take care of it on Friday when I return.

This is Jeanicot Pierre and I am one of your students. I have just spoke with you right after the class regarding of my score on the test #3, in which I have a low score, and I think perhaps it could have been an error while you were correcting the test by different key because I had test B. I would really appreciate, if you could possibly double check for me because this is my last semester and I majoring in Nursing, therefore, I am worried about my GPA. Thank you for your consideration.

Thus our proposed optimal feature subset selection based on multi-level feature subset selection produced better results based on number of subset feature produced and classifier performance. The future scope of the work is to use these features to annotate the image regions, so that the image retrieval system can retrieve relevant images based on image semantics.

The training data contained both labeled data D_la={〖x_i,y_i}〗_(i=1)^kl and unlabeled data D_un= {〖x_j}〗_(j=kl+1)^(kl+u) where x_(i ) is the feature descriptor of image I and y_i={1,…,k} is its label .k is the number of categories. l is the number of labeled data in each category, and u is the number of unlabeled data. Our method aims to learn a high-level image representation S by exploiting the few labeled data D_land great quantities of unlabeled ones, which is then fed into different classifiers to obtain final classification results. The procedure of semisupervised feature learning by SSEP is shown in Fig. 1. First, a new sampling algorithm based on GNA [19] is proposed to produce T WT sets P^t={(〖s_i^t,c_i^t)}〗_(i=1)^kp , t ∈{1,…..,T}

Feature extraction is most important step in the process of CBIR. Feature extraction is a method of transforming input data into set of features [2]. The different feature extraction is colour, texture and shape. Features are classified as low level and high level. The low level feature contains colour, texture and high level feature contains shape. The various feature extraction are describing in below:

Algorithms for selecting useful texture features were developed by using stepwise discriminant analysis. They developed four models i.e. HSI_39, HSI_15, HS_10 and I_11. Classification was done by minimum distance classifier. The model using 15 selected HSI texture features got the best classification accuracy (95.6%), which recommended that it would be best to use a reduced hue, saturation and intensity texture feature set to differentiate orange diseases. The HSI_15 model, I_11 and HSI_39 models achieved classification accuracy 95.6%, 81.11% and 95.6% respectively.

Through this routine of advanced technology analysis, it has been established to increase the results and have hastened the procedure of identifying suspects of crimes. Facial recognition is also necessary for public involvement and observation as it also aids law enforcement officials to more easily zone in on possible suspects of a crimes being caught. With the use of facial recognition, it constantly has been proven quite an effective method with the incorporation of this technique.

Antenna pattern. This gives a distribution of radiated power as a function of direction in space. Generally, planar sections of the radiation pattern will be shown instead of the complete three-dimensional surface. The most important views are those of the principal E-plane and H-plane patterns. The E-plane pattern contains the plane in which the electric field lies. Similarly, the H-plane pattern is a sectional view in which the magnetic field lies. An example of each type is shown in Figure 4.1, which gives the antenna pattern for a half-wave dipole orientated in the z-direction.

For the fast and cost effective production of patient diagnosis, various image processing techniques or software has been developed to get desired information from medical images. Acute Lymphoblastic Leukemia (ALL) is a type of leukemia which is more common in children. The term ‘Acute‘means that leukemia can progress quickly and if not treated may lead to fatal death within few months. Due to its non specific nature of the symptoms and signs of ALL leads wrong diagnosis. Even hematologist finds it difficult to classify the leukemia cells, there manual classification of blood cells is not only time consuming but also inaccurate. Therefore, early identification of leukemia yields in providing the appropriate treatment to the patient. As a solution

Therefore, the original image space is highly redundant, and sample vectors could be projected to a low dimensional subspace when only the face pattern are of interest. A variety of subspace analysis methods, such as Eigen Face~\cite{turk1991eigenfaces}, Fisherface~\cite{belhumeur1997eigenfaces}, and Bayesian method~\cite{moghaddam2000bayesian}, have been widely used for solving these problems. One of the most useful methods is Mutual Subspace Method (MSM)~\cite{yamaguchi1998face}.

Abstract—Automatic recognition of people is a challenging problem which has received much attention during recent years due to its many applications in different fields. Face recognition is one of those challenging problems and up to date, there is no technique that provides a robust solution to all situations. This paper presents a technique for human face recognition. A self-organizing program is used to identify if the subject in the input image is “present” or “not present” in the image database. Face recognition with Eigen values is carried out by classifying Eigen values in both images. The main advantage of this technique is its high-speed processing capability and low computational requirements, in terms of both speed, accuracy and memory utilization. The goal is to implement the system for a particular face and distinguish it

Image classification and analysis processes are for digitally identify and classify pixels in the data. It performed on a multi-channel dataset. This process allocate each pixel and image to a particular class or theme and depend on statistical features of brightness value pixel. There are various approaches for digital classification:

To construct an optimal hyperplane, SVM employs an iterative training algorithm, which is used to minimize an error function. According to the form of the error function, SVM models can be classified into four distinct groups:

This representation of Haar-Like Feature was used to find and select features according to pixel intensities and not by pixels values. Haar-like feature can be calculated using the scalar product between the input image and some Haar-like templates [14].