Definition And Application Of Pattern

Biometrics technology aims at utilizing major and distinctive characteristics such as behavioral or biological, for the sake of positively indentifying people. With the help of a combination of hardware and specific identifying sets of rules, a basic human attribute, automated biometric recognition mimics to distinguish and categorize other people as individual and unique. But the challenges surrounding biometrics are great as well.

What did you think of the two contrasting patterns in this chapter? Do you or someone you know fit one or the other of these patterns? Which pattern do you think is more likely to emphasize modesty over pride? Why? Which pattern do you think is likely to emphasize manners over honest self-expression? Why?

ABSTRACT- An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information [1]. Artificial Neural Networks (ANN) also called neuro-computing, or parallel distributed processing (PDP), provide an alternative approach to be applied to problems where the algorithmic and symbolic approaches are not well suited. The objective of the neural network is to transform the inputs into meaningful outputs. There are many researches which show that brain store information as pattern. Some of these patterns are very complicated and allows us to recognize from different angles. This paper gives a review of the artificial neural network and analyses the techniques in terms of performance.

In Paper 2, the main problem is that the current way of recording human activity is not accurate enough. Human Activity recognition is very important for health care monitoring which is one of the main reasons why accuracy is very crucial. The proposed solution for this problem is the Monkey Algorithm which is a classification algorithm. The goal of the solution is to use a combination between classification task and nature inspired computing techniques to classify human activity patterns based on smartphone data. The proposed solution allows for nature inspired computing to study human behavior based on wireless devices and the new age of modern technology. After data was taken from the Monkey

Based on Chapter 2, Neural Network Method (NN) will be chosen for voice-based command recognition method because it can handle bigger databased. For Neural Network to implement pattern recognition is quite common, and beneficial to use is backpropagation. Supervised learning that starts by inputting the training data through the network is a form of this method. When the data is put in the network, it will generate propagation output activations and then propagated backwards through the neural network, and generating a delta value for all hidden and output neuron. The weights of the network are then update by calculated delta values that generate by neural network, which increase the speech and quality of the learning process.

Table 2 illustrates that the second, third and fourth kernel parameter gives the highest accuracy. The performance of the classifier can be evaluated by using Accuracy. The equation (1) can be used to calculate the accuracy. Any of the kernel parameters can be used for further classification to

We have used support vector machine (SVM) for classification task. We have used RBF kernel for training the classifier. 10 fold cross-validation is used for determining cost parameter C and best kernel width for RBF kernel function. If we perform classification without any feature selection or feature extraction then the accuracy is 48.99% and 65.82% for AVIRIS and HYDICE image respectively which is very poor and it highly motivates us to apply feature reduction technique. In table II we have shown the classification accuracy for each of the pair of class for PCA, MI and PCA-QMI.

Mammography is a popular technique but it has its limitations especially in younger women and in denser breasts. The Computer-Aided-Diagnosis has been proposed for the medical prognosis [7-9]. The fuzzy logic and Artificial Neural Network form the basis of the intelligent systems. There are several instances where the artificial intelligence is used for the diagnosis of the breast cancer. The methods have included many Artificial Neural Networks architectures such as Convolution Neural Network [10], Radial Basis Network [11], General Regression Neural Network [11], Probabilistic Neural Network [11], Resilient Back propagation Neural Network [12], and hybrid with Fuzzy Logic [13]. In this paper [7] a supervised artificial neural network [14-16] was used to help classify the breast lesions into malignant and benign classes by processing the computer cytology images. The accuracy of the trained neural network was found to be 82.21%. The ANN has been established as a robust system for the diagnosis of breast cancer [18].There is a complex relationship between different biomarkers which were identified for the diagnosis of this cancer [19], the MLP neural network was simulated for the diagnosis using four biomarkers

As we listen we are placed in space we can figure out the space we are in and what other noise factors are in our surroundings. we use patterns to help distinguish what is going on around us. Such as listening for cues, like listening for our name to be called up to even tuning other noises out to be

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.

I think that your analysis is very good. However, if I were to test pattern preferences, I would use the method or preferential looking. According to the textbook, Life-Span Human Development, 7th edition, by Carol Sigelman, on page 175, preferential looking has "researchers that present an infant with two stimuli and measure the length of time the infant spends looking at each. A preference for one over the other indicates that the infant discriminates between the two stimuli." This method is good to use because if you place a blank color and a pattern next to eachother, the infant will tend to look at the pattern instead, meaning that they fix their eyes on the pattern. This was also discussed in class on Tuesday about why a infant chooses

To discuss knowledge questions raised by the idea of “Humans are pattern seeking animals and we are adept at finding patterns whether they exist or not”, we must understand the statement or the claim. Pattern seeking can be describe as something that we look for, or in other words repetitive events, saying, actions and so on, which can be obtained from the language of our society and the emotion we feel. From my own understanding of the statement, it is saying that humans tend to believe in the things that they see other people do and believed that if that person did it, then so will anyone that is almost the same as them or closely related will do the same. The idea of “humans are pattern seeking animals” will be explore through looking through human history and ethics, what we came to believe and how.

Our world is replete with patterns, and these can be found in all areas of knowledge and ways of knowing with various applications ranging from architectural tessellations, weather predictions, creating rhythm, and dictating a logical syntax for this very sentence. As individuals perceive the world, the connections they have constructed from their experiences provides them a sense of predictability, and perhaps even aesthetic appeal and uniformity. That aside, these patterns would not always reflect our reality as they could also be projected according to our schematic biases. From Shermer’s How We Believe (2000), he explored how humans have a tendency, called ‘patternicity’, to seek for “meaningful patterns behind meaningless noise”. This

The solutions provided in this research work are more compatible for retrieving images from natural and photographic image databases and use an amalgamation of image processing and machine learning algorithms to perform retrieval in a fast manner while improving both the fraction of retrieved images that are relevant to the find and fraction of the images that are relevant to the query image that are successfully retrieved.

Abstract— Process of selecting relevant features from available dataset is known as features selection. Feature selection is use to remove or reduce redundant and irrelevant features. Various feature selection algorithms such as CFS (correlation feature selection), FCBF (Fast Correlation Based Filter) and CMIM (Conditional Mutual Information Maximization) are used to remove redundant and irrelevant features. To determine efficiency and effectiveness is the aim of feature selection algorithm. Time factor is denoted by efficiency and quality factor is denoted by effectiveness of subset of features. Problem of feature selection algorithm is accuracy is not guaranteed, computational complexity is large, ineffective at removing redundant features. To overcome these problems Fast Clustering based feature selection algorithm (FAST) is used. Removal of irrelevant features, construction of MST (Minimum Spanning Tree) from relative one and partition of MST and selecting representative features using kruskal’s method are the three steps used by FAST algorithm.