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![REFERENCES
[1] Snapp R., CS 295: Pattern Recognition, Course Notes, Department of
Computer Science, University of Vermont,
(http://www.cs.uvm.edu/~snapp/teaching/CS295PR/whatispr.html)
[2] Duda, R.O., Hart, P.E., and Stork D.G., (2001). Pattern Classification. (2nd
ed.). New York: Wiley-Interscience Publication.
[3] Gutierrez-Osuna R., Intelligent Sensor Systems, Course Notes,
Department of Computer Science, Wright State University,
(http://research.cs.tamu.edu/prism/lectures/iss/iss_l9.pdf)
[4] Olszewski R. T. (2001) Generalized Feature Extraction for Structural
Pattern Recognition in TimeSeries Data, PhD. Thesis at School of
Computer Science, Carnegie Mellon University, Pittsburgh.](https://image.slidesharecdn.com/vipra-180307175809/75/Neural-Networks-for-Pattern-Recognition-39-2048.jpg)
Uploaded byVipra Singh
Neural Networks for Pattern Recognition
- Neural networks are computing systems inspired by biological neural networks in the brain that can be used for pattern recognition. An artificial neuron receives multiple inputs and produces a single output. Neural networks are trained to recognize complex patterns and identify categories. - An important application of neural networks is pattern recognition, where a network is trained to associate input patterns with output categories. Recent advances include using neural networks for tasks like predicting student performance, medical diagnosis, and analyzing customer interactions. Neural networks are also being used increasingly in business for applications like predictive analytics and artificial intelligence.
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Neural Networks for Pattern Recognition
- 1. NEURAL NETWORKS FOR PATTERN RECOGNITION -BY VIPRASINGH (52457)
- 2. INSIGHTS • Introduction • NeuralNetwork • Pattern Recognition • Examples of Pattern • Application Examples • Approaches • Components of a Pattern Recognition System
- 3. Contd… • Bayesian DecisionMaking • Recent Advances in Neural Networks • Conclusion • Neural Networks in Future • References
- 4. INTRODUCTION • As thename suggests, neural networks are inspired by the brain. • “Neural“ is an adjective for neuron, and “network” denotes a graph like structure.
- 5. •An artificial neuronis a device with many inputs and one output. •The neuron has two modes of operation; •the training mode and •the using mode Contd…
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- 7. HISTORICAL BACKGROUND • Neuralnetwork simulations appear to be a recent development. However, this field was established before the advent of computers, and has survived at least one major setback and several eras. • The first artificial neuron was produced in 1943 by the neurophysiologist Warren McCulloch and the logician Walter Pits.
- 8. PATTERN RECOGNITION •An importantapplication of neural networks is pattern recognition. Pattern recognition can be implemented by using a feed-forward neural network that has been trained accordingly. •During training, the network is trained to associate outputs with input patterns. •When the network is used, it identifies the input pattern and tries to output the associated output pattern. •The power of neural networks comes to life when a pattern that has no output associated with it, is given as an input. In this case, the network gives the output that corresponds to a taught input pattern that is least different from the given pattern.
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- 21. COMPONENTS OF APATTERN RECOGNITION SYSTEM
- 23. TASK: To differentiatethe fish on the basis of their species
- 24. PATTERN RECOGNITION SYSTEM Sensing Theinput to a pattern recognition system is often some kind of a transducer, such as a camera or a microphone array. The difficulty of the problem may well depend on the characteristics and limitations of the transducer- its bandwidth, resolution, sensitivity, distortion, signal-to-noise ratio, latency, etc
- 25. Segmentation • In practice,the fish would often be overlapping, and our system would have to determine where one fish ends and the next begins-the individual patterns have to be segmented. • We need a way to know when we have switched from one model to another, or to know when we just have background or no category.
- 27. Post Processing A classifierrarely exists in a vacuum. Instead, it is generally to be used to recommend actions (put this fish in this bucket, put that fish in that bucket), each action having an associated cost. The post-processor uses the output of the classifier to decide on the recommended action.
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- 31. Contd… • The Bayesiandecision making is a fundamental statistical approach which allows to design the optimal classifier if complete statistical model is known. • Task: to design decision rule q which minimizes Bayesian risk • R(q) =∑∑p(x, y)W(q(x), y) • y∈ ∈Y x X Definition: Obsevations Hidden states Decisions X Y D A loss function A decision rule A joint probability
- 32. RECENT ADVANCES INNEURAL NETWORKS • Interactive Voice Response (IVR) with pattern recognition based on Neural Networks • The addition of voice pattern recognition in the authentication process can potentially further enhance the security level. • The developed system is fully compliant with landline phone system. The results are promising based on false accept and false reject criteria offering quick response time. • It can potentially play an effective role in the existing authentication techniques used for identity verification to access secured services through telephone or similar media.
- 33. Predicting Student PerformanceUsing Artificial Neural Network: in the Faculty of Engineering and Information Technology (2015) • A number of factors that may possibly influence the performance of a student were outlined. Such factors as high school score, score of subject such as Math I, Math II, Electrical Circuit I, and Electronics I taken during the student freshman year, number of credits passed, student cumulative grade point average of freshman year, types of high school attended and gender, among others, were then used as input variables for the ANN model.
- 34. IN BUSINESS LEXICON,ARTIFICIAL INTELLIGENCE(AI) TOUCHED AN UNPRECEDENTED HIGH • AI started picking up in 2015 and saw a remarkable spike from the latter half of 2016, which continued into 2017. Insights said AI was mentioned 791 times in just the third quarter of 2017. • The same report showed how the AI system at the Icahn School of Medicine at Mt. Sinai, New York, called Deep Patient, has analyzed electronic health records from 700,000 patients. Deep Patient, it said, taught itself to predict risk factors for 78 different diseases – and doctors now turn to the system to aid in diagnoses.
- 35. Contd… • IBM’s AI-basedplatform Watson offers over 25 different services including knowledge validation, voice synthesis, language modeling and visual analysis. • A recent Infosys report said enterprises are moving beyond the experimentation phase with AI, and are deploying AI technologies more broadly and realizing benefits across their business. • The report, Leadership in the Age of AI, surveyed more than 1,000 business and IT leaders with decision-making power over AI solutions or purchases at large organizations across seven countries. • The survey found 73% of respondents agreeing or strongly agreeing that their AI deployments have already transformed the way they do business, and 90% of C-level executives reporting measurable benefits from AI within their organization.
- 36. Artificial intelligence ismaking inroads into tourism sector
- 37. CONCLUSION • In itsbroadest sense pattern recognition is the heart of all scientific inquiry, including understanding ourselves and the real-world around us. • And the developing of pattern recognition is increasing very fast, the related fields and the application of pattern recognition became wider and wider. • In addition, it is an important trend to use pattern recognition on engineering; we should make efforts on this. • And pattern recognition scientists should pay attention to new technique of PR, and enlarge the application areas of PR.
- 38. IN FUTURE… • Robotsthat can see, feel, and predict the world around them • Improved stock prediction • Common usage of self-driving cars • Composition of music • Handwritten documents to be automatically transformed into formatted word processing documents • Trends found in the human genome to aid in the understanding of the data compiled by the Human Genome Project • Self-diagnosis of medical problems using neural networks • And much more!
- 39. REFERENCES [1] Snapp R.,CS 295: Pattern Recognition, Course Notes, Department of Computer Science, University of Vermont, (http://www.cs.uvm.edu/~snapp/teaching/CS295PR/whatispr.html) [2] Duda, R.O., Hart, P.E., and Stork D.G., (2001). Pattern Classification. (2nd ed.). New York: Wiley-Interscience Publication. [3] Gutierrez-Osuna R., Intelligent Sensor Systems, Course Notes, Department of Computer Science, Wright State University, (http://research.cs.tamu.edu/prism/lectures/iss/iss_l9.pdf) [4] Olszewski R. T. (2001) Generalized Feature Extraction for Structural Pattern Recognition in TimeSeries Data, PhD. Thesis at School of Computer Science, Carnegie Mellon University, Pittsburgh.
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