...Technology Rough Set Approach for Feature Reduction in Pattern Recognition through Unsupervised Artificial Neural Network A. G. Kothari A.G. Keskar A.P. Gokhale Rucha Pranjali Lecturer Professor Professor Deshpande Deshmukh agkothari72@re B.Tech Student B.Tech Student diffmail.com Department of Electronics & Computer Science Engineering, VNIT, Nagpur Abstract The Rough Set approach can be applied in pattern recognition at three different stages: pre-processing stage, training stage and in the architecture. This paper proposes the application of the Rough-Neuro Hybrid Approach in the pre-processing stage of pattern recognition. In this project, a training algorithm has been first developed based on Kohonen network. This is used as a benchmark to compare the results of the pure neural approach with the RoughNeuro hybrid approach and to prove that the efficiency of the latter is higher. Structural and statistical features have been extracted from the images for the training process. The number of attributes is reduced by calculating reducts and core from the original attribute set, which results into reduction in convergence time. Also, the above removal in redundancy increases speed of the process reduces hardware complexity and thus enhances the overall efficiency of the pattern recognition algorithm Keywords: core, dimensionality reduction, feature extraction, rough sets, reducts, unsupervised ANN as any type of ANN is the most general tool and can work well in noisy conditions...
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...SEGMENTATION WITH NEURAL NETWORK B.Prasanna Rahul Radhakrishnan Valliammai Engineering College Valliammai Engineering College prakrish_2001@yahoo.com krish_rahul_1812@yahoo.com Abstract: Our paper work is on Segmentation by Neural networks. Neural networks computation offers a wide range of different algorithms for both unsupervised clustering (UC) and supervised classification (SC). In this paper we approached an algorithmic method that aims to combine UC and SC, where the information obtained during UC is not discarded, but is used as an initial step toward subsequent SC. Thus, the power of both image analysis strategies can be combined in an integrative computational procedure. This is achieved by applying “Hyper-BF network”. Here we worked a different procedures for the training, preprocessing and vector quantization in the application to medical image segmentation and also present the segmentation results for multispectral 3D MRI data sets of the human brain with respect to the tissue classes “ Gray matter”, “ White matter” and “ Cerebrospinal fluid”. We correlate manual and semi automatic methods with the results. Keywords: Image analysis, Hebbian learning rule, Euclidean metric, multi spectral image segmentation, contour tracing. Introduction: Segmentation can be defined as the identification of meaningful image components. It is a fundamental task in image processing providing the basis for any kind of...
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...3006-NEURAL COMPUTING ASSIGNMENT 2 GROUP 09 NAME G.D.P.M. Perera A.A. Vidura H.P.S.S. Kumara G.M.T.C. Galahena INDEX No 10001352 10000909 10000552 10000437 TAKE HOME ASSISGNMENT GROUP 09 Part 1 Introduction In the part 1, we have to classify the given four English Characters into known four different classes. As the output classes are known we used Feed Forward Back Propagation method to train the artificial neural network. Here is a brief description of a supervised training network. Consider the network output Y which is usually denoted by NET, When the network is trained for a specific Target, this training process is called Supervised Training. In supervised training, when the network is being trained it is required to produce the expected target. Training is carried out in such a way that, weights of the network are trained (or adjusted or updated or modified) until the network produces the expected target. Basic steps of supervised training Weights are randomly set. Target (Expected or Desired output) is identified. Input is applied to the Network. Network Actual Output (Weighted Sum of Input) is calculated. Calculate the Error (Error = Target ~ Actual Output). Weights are updated until the Error is minimized 2 TAKE HOME ASSISGNMENT GROUP 09 Dalta Rule – Wnew - Wold η – Learning rate parameter δ – Error (target – actual output) x – input In a feed forward back propagation network the signals are traveled on...
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...outliers may indicate something sinister such as unauthorised system access or fraudulent activity, or may be a new and previously unidentified occurrence. Whatever the cause of these outliers, it is important they are detected so appropriate action can be taken to minimise their harm if malignant or to exploit a newly discovered opportunity. Chandola, Banerjee and Kumar (2007) conducted a comprehensive survey of outlier detection techniques, which highlighted the importance of detection across a wide variety of domains. Their survey described the categories of outlier detection, applications of detection and detection techniques. Chandola et al. identified three main categories of outlier detection - supervised, semi-supervised and unsupervised detection. Each category utilises different detection techniques such as classification, clustering, nearest neighbour and statistical. Each category and technique has several strengths and weaknesses compared with other outlier detection methods. This review provides initial information on data labelling and classification before examining some of the existing outlier detection techniques within each of the three categories. It then looks at the use of combining detection techniques before comparing and discussing the advantages and disadvantages of each method. Finally, a new classification technique is proposed using a new outlier detection algorithm, Isolation Forest. DATA LABELLING Datasets normally consist of many data...
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...A Review of ANN-based Short-Term Load Forecasting Models Y. Rui A.A. El-Keib Department of Electrical Engineering University of Alabama, Tuscaloosa, AL 35487 Abstract - Artificial Neural Networks (AAN) have recently been receiving considerable attention and a large number of publications concerning ANN-based short-term load forecasting (STLF) have appreared in the literature. An extensive survey of ANN-based load forecasting models is given in this paper. The six most important factors which affect the accuracy and efficiency of the load forecasters are presented and discussed. The paper also includes conclusions reached by the authors as a result of their research in this area. Keywords: artificial neural networks, short-term load forecasting models Introduction Accurate and robust load forecasting is of great importance for power system operation. It is the basis of economic dispatch, hydro-thermal coordination, unit commitment, transaction evaluation, and system security analysis among other functions. Because of its importance, load forecasting has been extensively researched and a large number of models were proposed during the past several decades, such as Box-Jenkins models, ARIMA models, Kalman filtering models, and the spectral expansion techniques-based models. Generally, the models are based on statistcal methods and work well under normal conditions, however, they show some deficiency in the presence of an abrupt change in environmental or sociological variables...
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...Prepared by: Lama R. Khreiss Advanced Quantitative Methods in Business – MGT 501 Neural Network Technique Outline * Overview ………………………………………………………….……… 4 * Definition …………………………………………………4 * The Basics of Neural Networks……………………………………………5 * Major Components of an Artificial Neuron………………………………..5 * Applications of Neural Networks ……………….9 * Advantages and Disadvantages of Neural Networks……………………...12 * Example……………………………………………………………………14 * Conclusion …………………………………………………………………14 Overview One of the most crucial and dominant subjects in management studies is finding more effective tools for complicated managerial problems, and due to the advancement of computer and communication technology, tools used in management decisions have undergone a massive change. Artificial Neural Networks (ANNs) is an example, knowing that it has become a critical component of business intelligence. The below article describes the basics of neural networks as well as some work done on the application of ANNs in management sciences. Definition of a Neural Network? The simplest definition of a neural network, particularly referred to as an 'artificial' neural network (ANN), is provided by the inventor of one of the first neurocomputers, Dr. Robert Hecht-Nielsen who defines a neural network as follows: "...a computing system made up of a number of simple, highly interconnected processing elements, which process information by...
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...memorize old patterns Human brain: face recognition Adaptive Resonance Theory (ART) ! ! ! Plasticity vs. Stability Dilemma Backpropagation ! ART Characteristics Goal: Design a neural network that preserves its previously learned knowledge while continuing to learn new things. Biologically plausible: ART has a selfregulating control structure that allows autonomous recognition and learning no supervisory control or algorithmic implementation. ! ! ! New patterns require retraining of the network No Stabilization Stabilization achieved by decreasing learning rate Decreasing learning rate reduces plasticity ! ! Kohonen maps (SOM) ! ! other Neural Networks ART Online learning Self-organizing (unsupervised) Maintains permanent plasticity Learn in approximate match phase Non-stationary world Other ANN (BP) Offline learning supervised Plasticity regulated externally Learn in mismatch phase (error based) Stationary world ART Terminology STM : Short term memory ! ! Refers to the dynamics of neural units (recognition, matching) Refers to the adaptation of weights (learning) control structure to activate/deactivate search and matching ! LTM : Long term memory ! ! Gain control : ! 1 ART Basic Architecture F2 gain + ! ART Basic Architecture F2 layer: ! ! ! STM F2 LTM + ! Each node represents a prototype (template) Competitive dynamics (winner takes all) Performs template selection Performs template...
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...Johnathan Melicia Thomas Riddle Eng. 111 July 2, 2015 Free Range Parenting In the article “ There’s Never Been a Safer Time to be a Kid in America,” by Christopher Ingraham, spring folds the spotlight parents are put on about letting their kids have free range while being unsupervised or having to be by their side while the child is wanting to go somewhere just down the road. This has parents contemplating on letting their child be alone even though there really is no reason to be dwelling over the worst that can happen. Marc Elrich, chairman of the Montgomery County Council’s Public Safety Committee, refers to Ingraham by stating “walked more than a mile on his own to school” and baffled that “All of our parents would have been in jail” if there was a problem with letting your kids walk safely down the road. Thus persuading the fact that kids should be able to be unsupervised because it is safer than it used to be; for example, child mortality rates have decreased, abduction rates have dropped, and children pedestrians being struck in traffic had fallen tremendously. The importance of imagery in this article is used to give the audience a sense of direction in order to see Ingraham’s point of view. He gives a clear analogy of how two siblings, 6 and 10, were walking home when they got picked up by the police and detain them in the car for three hours. Ingraham uses the words “searching for you frantically” to give the audience an image of what the parents would be doing...
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...shocking, all the more because over one third of all frauds are detected by ’chance’ means. The second best detection method is internal control. As a result, it would be advisable to search for improvement of internal control systems. Taking into consideration the promising success stories of companies selling data mining software, along with the positive results of research in this area, we evaluate the use of data mining techniques for the purpose of fraud detection. Are we talking about real success stories, or salesmanship? For answering this, first a theoretical background is given about fraud, internal control, data mining and supervised versus unsupervised learning. Starting from this background, it is interesting to investigate the use of data mining techniques for detection of asset misappropriation, starting from unsupervised data. In this study, procurement fraud stands as an example of asset misappropriation. Data are provided by an international service-sector company. After mapping out the purchasing process, ’hot spots’ are identified, resulting in a series of known frauds and unknown frauds as object of the study. 1 Introduction Fraud is a million dollar business and it is increasing every year. ”45% of companies worldwide have fallen victim to economic crime in 2004 and 2005. The average damage to the companies from tangible frauds (i.e. asset misappropriation, false pretences, and counterfeiting) was US$ 1.7 million.” according to the ’Global economic crime...
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...RE: Determination of Whether the Bridge School Owed Al-Fulani, its student, a Duty of Care I. Introduction Kahlil Al-Fulani (“Al-Fulani”) is a 20-year-old Lebanese-American student who currently attends the Bridge School. On September 21, 2001 Lou Gaines (“Gaines”), a fellow student, assaulted Al-Fulani in an unsupervised school lounge. This memorandum addresses whether the Bridge School owed a duty of care to prevent Gaines’s assault on Al-Fulani. A court will likely conclude that the Bridge School owed a duty of care to Al-Fulani because of its special relationship with him and because the harm was foreseeable. II. Question Presented Whether the Bridge School owed Al-Fulani a duty of care given the school’s supervisory responsibility over student conduct and surroundings, knowledge of Gaines’s targeted harassment of Al-Fulani and recent wave of national violence towards Arab-American students after the World Trade Center attack, and Al-Fulani’s age? III. Brief Answer A court will likely conclude that the Bridge School owed Al-Fulani a duty of care. The law imposes a duty of care on schools when (1) a special relationship exists between the school and student and (2) the harm is foreseeable. Leger v. Stockton Unified Sch. Dist., 202 Cal. App. 3d 1448, 1453-54 (1988). A special relationship between a school and student is created when a school assumes the responsibility to supervise the conduct and control the environment of students, who do not do behave...
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...arthritis. The results of the study were that of the 400 patients who received the questionnaire 204 returned it within 4 weeks. The other 196 were contacted by phone. In total 252 patients had responded, making about 63% of the 400. The non-responders were slightly older than the responders and were more often male however these did not impact the results. 201 out of the 252 (80%) people participated in some sort of physical activity or exercise. The more inactive people seemed to be male, less educated, and older. The active people 45(22%) participated in only supervised activities, 72 (36%) in unsupervised activities and 84(42%) combined supervised and unsupervised activities. Of the unsupervised activities cycling and walking were performed most often. Supervised group exercise was the most performed while under supervision. The majority preferred supervised exercise over unsupervised and they also preferred water-based exercise over land based. The most frequent mentioned barriers were lack of energy, presence of pain, no motivation, lack of information, and fear of joint damage. The conclusion is that the majorities of people with rheumatoid arthritis participate in some sort of physical activity or exercise and prefer to be supervised while exercising. The preferences for different types of exercise or physical activity were widely varied, showing the need for a variety of options for people who have rheumatoid arthritis. ...
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...T H E R O YA L A U S T R A L I A N A N D NEW ZEALAND COLLEGE OF OBSTETRICIANS AND GYNAECOLOGISTS FRANZCOG TRAINING LOGBOOK 2014 Da i l y Tra i ni ng Re co r d fo r Co r e and A dva nce d Tra ining The Royal Australian and New Zealand College of Obstetricians and Gynaecologists FRANZCOG TRAINING PROGRAM LOGBOOK 2014 D ail y Tra i n i n g Re co r d f or Cor e a n d A dva n ce d Tra ining RANZCOG College House 254 - 260 Albert St East Melbourne VIC 3002 tel +61 3 9417 1699 fax +61 3 9419 0672 web: www.ranzcog.edu.au Initial contact: Ms Kathryn Hertrick Training Services Co-ordinator Training Services Department tel +61 3 9412 2936 fax +61 3 9419 7817 email training@ranzcog.edu.au Published by RANZCOG Publications The Royal Australian and New Zealand College of Obstetricians and Gynaecologists 254-260 Albert Street, East Melbourne, Victoria 3002, Australia. ISSN 1443-4415 RANZCOG 2014 This work is copyright. Apart from any use as permitted under the Copyright Act 1968, no part may be reproduced by any process without written permission from The Royal Australian and New Zealand College of Obstetricians and Gynaecologists. Requests and enquiries concerning reproduction should be directed to the Chief Executive Officer, RANZCOG, 254-260 Albert Street, East Melbourne, Victoria 3002, Australia. This material is available on the RANZCOG website http://www.ranzcog.edu.au PERSONAL DETAILS Name: Address: Telephone: TRAINING DETAILS Training site: ...
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...The moral of the story is; don’t leave children unsupervised in a big house. This moral can make parents or babysitters scared of leaving children unsupervised in your house for too long. In the story, the man on the phone with the babysitter says to the babysitter “Have you checked the children…” from The Choking Doberman. That line could indicate that the children are in danger and should be brought to safety quickly considering there is a man upstairs. Next, the story of “Gravity Hill” has the fear for children by having said children dying on the bottom of the hill. “The children were in a school bus were killed at the bottom of the hill. These kids now push up whatever may try to go down the hill,” to prevent others from the same fate. This story causes fear for children by telling children or bus drivers to be aware that they may not know...
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...Top 10 data mining algorithms in plain English 1.1K Today, I’m going to explain in plain English the top 10 most influential data mining algorithms as voted on by 3 separate panels in this survey paper. Once you know what they are, how they work, what they do and where you can find them, my hope is you’ll have this blog post as a springboard to learn even more about data mining. What are we waiting for? Let’s get started! Contents [hide] 1. C4.5 2. k-means 3. Support vector machines 4. Apriori 5. EM 6. PageRank 7. AdaBoost 8. kNN 9. Naive Bayes 10. CART Interesting Resources Now it’s your turn… Update 16-May-2015: Thanks to Yuval Merhav and Oliver Keyes for their suggestions which I’ve incorporated into the post. Update 28-May-2015: Thanks to Dan Steinberg (yes, the CART expert!) for the suggested updates to the CART section which have now been added. 1. C4.5 What does it do? C4.5 constructs a classifier in the form of a decision tree. In order to do this, C4.5 is given a set of data representing things that are already classified. Wait, what’s a classifier? A classifier is a tool in data mining that takes a bunch of data representing things we want to classify and attempts to predict which class the new data belongs to. What’s an example of this? Sure, suppose a dataset contains a bunch of patients. We know various things about each patient like age, pulse...
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...1 name: Audrey Stinsonstudent id: JM1609527runaway and homeless youthIf you have ever live in a big city you have seen a good amountof homeless people and sadly some of those people are kids. There are an estimated 100 million homeless people in the world. Approximately 1.3 million of those are homeless youth living unsupervised in old abandoned buildings, on the streets, with friends and with strangers. One out seven of kids between 10-18 years old will runaway (75%are female). Kids between 12-17 years old are at more risk of homelessness than adults.CAUSES FOR RUNWAY AND HOMELESS YOUTH These kids come from rough homes where they are abused and not excepted for who they are. 17% are forced in to unwanted sexual activities. 46% of them are...
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