This document discusses pattern recognition and its applications. It begins with an introduction to pattern recognition as a branch of artificial intelligence that allows machines to perceive, process, and predict patterns. It then defines what a pattern is and provides examples. The rest of the document discusses pattern recognition systems and models, various applications of pattern recognition such as character, speech, and face recognition, and concludes with a discussion of different pattern recognition approaches.
A brief introduction to Pattern Recognition. Slides were used for a Seminar at the Interactive Art PhD at School of Arts of the UCP, Porto, Portugal (http://artes.ucp.pt)
A brief introduction to Pattern Recognition. Slides were used for a Seminar at the Interactive Art PhD at School of Arts of the UCP, Porto, Portugal (http://artes.ucp.pt)
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
This Machine Learning Algorithms presentation will help you learn you what machine learning is, and the various ways in which you can use machine learning to solve a problem. At the end, you will see a demo on linear regression, logistic regression, decision tree and random forest. This Machine Learning Algorithms presentation is designed for beginners to make them understand how to implement the different Machine Learning Algorithms.
Below topics are covered in this Machine Learning Algorithms Presentation:
1. Real world applications of Machine Learning
2. What is Machine Learning?
3. Processes involved in Machine Learning
4. Type of Machine Learning Algorithms
5. Popular Algorithms with a hands-on demo
- Linear regression
- Logistic regression
- Decision tree and Random forest
- N Nearest neighbor
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
This Machine Learning Algorithms presentation will help you learn you what machine learning is, and the various ways in which you can use machine learning to solve a problem. At the end, you will see a demo on linear regression, logistic regression, decision tree and random forest. This Machine Learning Algorithms presentation is designed for beginners to make them understand how to implement the different Machine Learning Algorithms.
Below topics are covered in this Machine Learning Algorithms Presentation:
1. Real world applications of Machine Learning
2. What is Machine Learning?
3. Processes involved in Machine Learning
4. Type of Machine Learning Algorithms
5. Popular Algorithms with a hands-on demo
- Linear regression
- Logistic regression
- Decision tree and Random forest
- N Nearest neighbor
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
What is data science ?
Data rules the world we live in, and in fact, has been dubbed the “oil” of the 21st century. In the past few years, the world has witnessed a steep and continuing upsurge in data. Thanks to the growth of social media, smartphones, and the Internet of Things, the amount of data at our disposal today is beyond imagination. As Alphabet’s Eric Schmidt claims, every 48 hours, we generate the amount of data humanity produced since the dawn of civilization until 15 years ago. So, how then, are we able to make sense of such massive amounts of data?
To put in simple terms, Data Science is a combination of mathematics, programming, statistics, data analysis, and machine learning. By combining all these, Data Science uses advanced algorithms and scientific methods to extract information and insights from large datasets – both structured and unstructured. The advent of Big Data and Machine Learning has further fuelled the growth of Data Science. Today, Data Science is being used across all parallels of various industries, including business, healthcare, finance, and education.
While the IoT is already a reality that connects smart devices, in the future, we might be looking forward to being a part of an Intelligent Digital Mesh – a connected hub of apps, devices, and people working together in sync.
Product marketing and customer service will be revolutionized by advanced chatbots, Virtual Reality (VR), and Augmented Reality (AR). We might be looking forward to a time when personalized customer experience will include live simulations, interactive demos, visualization of proposed solutions.
Blockchain might just go mainstream – it will not only be limited to the finance sector, but blockchain will apply to healthcare, banking, insurance and other industries.
Automated ML systems and Augmented Analytics together will transform Predictive Analytics and take it to the next level. Predictive Analytics will further help change the face of healthcare.
The job title of a ‘Data Scientist’ will undergo a massive transformation to include an array of diverse roles. As technology, Data Science, and AI continue to advance, Data Scientists will have to evolve to keep pace with the dynamic learning curve of Data Science.
This is a presentation on data science in this presentation machine learning algorithems are explained with a brief description of artificial intellignece
[DSC Europe 22] Bridging the gap between AI Research and Human-Centered Produ...DataScienceConferenc1
AI creates a tone of value to business, society, and humanity. With fast-paced AI research, we see continues improvements in the benchmark data and stunning results which redefine possible. On the other hand, most of these systems are not widely deployed in real life. One of the challenges we face today in industry across domains is: how to move proof-of-concept AI to human-centered products. In this talk, I will share a perspective on this topic, including examples and learnings from my work in Microsoft when it comes to productization of SOTA AI that works for people.
Machine Learning The Powerhouse of AI Explained.pdfCIO Look Magazine
Artificial Intelligence (AI) and Machine Learning (ML) are two terms that have revolutionized the technology landscape, becoming integral in various sectors.
Fog computing based on face identification in internetummeHani43
It figures out the fog computing techniques basically used through internet.for the face regonisation in crime areas.and also used for the face detection and identification.
This slide is a brief about the rising technologies in data Communication.I hope this slide might help user to match up to the expectations of what they are actually looking for.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
1. Amity School of Engineering & Technology
PATTERN RECOGNITION
1
ABHIJITH MENON
BALINI MANOJ KUMAR
SUDHANVI VELLALA
MAAZ HASAN
PRIYANKA YADAV
2. Amity School of Engineering & Technology
CONTENTS
INTRODUCTION
PATTERN
PATTERN RECOGNITION
PATTERN RECOGNITION SYSTEM
PATTERN RECOGNITION MODEL
APPLICATION OF PATTERN RECOGNITION
CONCLUSION
2
3. Amity School of Engineering & Technology
3
INTRODUCTION
Pattern Recognition is a branch of Artificial
Intelligence.
Humans can recognize the faces without worrying
about the varying illuminations. When implementing
such recognition artificially ,it becomes a very complex
task.
The field of Artificial Intelligence has made this
complex task possible.
4. Amity School of Engineering & Technology
4
PATTERN
A pattern is a set of objects or phenomena or
concepts where the elements of the set are similar to
one another in certain ways or aspects.
A pattern is an entity , that could be given a name .
Example : Fingerprint Image, handwritten word ,
human face , speech signal , DNA sequence etc.
6. Amity School of Engineering & Technology
6
PATTERN RECOGNITION
Pattern recognition is the procedure of processing and analizing diverse
infornation ( numerical , literal, logical ) characterizing the objects or phenomenon ,
so as to provide descriptions ,identifications , classifications and interpretations for
them .
“ Perceive + Process + Prediction ” – It is the study of how machine can
Perceive: Observe the environment (i.e. Interact with the real –world) .
Process: Learn to distinguish patterns of interest from their background.
Prediction: Make sound and reasonable decision s about the categories of the
pattern.
7. Amity School of Engineering & Technology
7
PATTERN RECOGNITION SYSTEM
Design model of a pattern recognition system essentially involves the following 4
steps:-
Data acquisition and pre-processing
Data Representation
Feature extraction
Decision making
8. Amity School of Engineering & Technology
8
PATTERN RECOGNITION PROCESS
Data acquisition and sensing:
Measurements of physical variables.
Important issues: bandwidth, resolution , etc.
Pre-processing:
Removal of noise in data.
Isolation of patterns of interest from the
background.
Feature extraction:
Finding a new representation in terms of
features.
Classification
Using features and learned models to assign a
pattern to a category.
Post-processing
Evaluation of confidence in decisions.
9. Amity School of Engineering & Technology
9
PATTERN RECOGNITION MODEL
Statistical model: Pattern recognition systems are based on statistics
and probabilities.
Syntactic model: Structural models for pattern recognition and are
based on the relation between features. Here the patterns are represented by
structures .
Template matching model: In this model, a template or a
prototype of the pattern to be recognized is available.
Neural network model: An artificial neural network (ANN) is a self-
adaptive trainable process that is able to learn and resolve complex problems
based on available knowledge.
10. Amity School of Engineering & Technology
PATTERN CLASS
10
A Pattern class is a set of patterns sharing
common attributes .
A collection of “Similar” ( not necessarily
identical ) objects.
During recognition given objects are assigned
to prescribed classes.
11. Amity School of Engineering & Technology
11
CLASSIFICATION
SUPERVISED TRAINING/LEARNING:
12. Amity School of Engineering & Technology
12
CLASSIFICATION
UNSUPERVISED TRAINING/LEARNING:
13. Amity School of Engineering & Technology
13
CAD- Computer Aided Diaganosis
APPLICATIONS OF PATTERN RECOGNITION
14. Amity School of Engineering & Technology
14
CAD- Computer Aided Design
APPLICATIONS OF PATTERN RECOGNITION
15. Amity School of Engineering & Technology
15
APPLICATIONS OF PATTERN RECOGNITION
Pattern Recognition is used in any area of
science and engineering that studies the
structure of observations.
It is now frequently used in many
applications in manufacturing industry, health
care and military.
16. Amity School of Engineering & Technology
16
APPLICATIONS OF PATTERN RECOGNITION
Input: Images with characters (normally contaminated with noise)
Output: The identified character string
Useful in scenarios such as automatic license plate recognition (ALPR), optical
character recognition(OCR) ,etc.
CHARACTER RECOGNITION
17. Amity School of Engineering & Technology
17
APPLICATIONS OF PATTERN RECOGNITION
Input: Documents , web pages, etc
Output: Category of the text , such as political , economic , military , sports etc
Useful in scenarios such as information retrieval , document organization, etc.
TEXT CHARACTERIZATION
18. Amity School of Engineering & Technology
18
APPLICATIONS OF PATTERN RECOGNITION
Input: Acoustic signal (Sound waves etc)
Output: Contents of the speech
Useful in scenarios such as speech-to-text (STT), voice command and control etc.
SPEECH RECOGNITION
19. Amity School of Engineering & Technology
19
APPLICATIONS OF PATTERN RECOGNITION
FINGERPRINT RECOGNITION
Input: Fingerprint of some person
Output: The persons identity.
Useful in scenarios such as computerized access control , criminal pursuit, etc.
20. Amity School of Engineering & Technology
20
APPLICATIONS OF PATTERN RECOGNITION
Input: Signature of some person (Sequence of dots)
Output: The signatory’s identity
Useful in scenarios such as digital signature verification, credit card anti-fraud ,etc.
SIGNATURE RECOGNITION
21. Amity School of Engineering & Technology
21
APPLICATIONS OF PATTERN RECOGNITION
Input: Images with SEVERAL PEOPLE
Output: Locations of the peoples’ faces in the image.
FACE DETECTION
22. Amity School of Engineering & Technology
22
APPLICATIONS OF PATTERN
RECOGNITION
• Used in the detection and diagnosis of
Diseases.
• Electrocardiodiagram (ECG) waveforms
are sent as input and types of cardiac
condition and classes of brain condition is
analysed accordingly.
• Is of great use to the paramedical industry.
23. Amity School of Engineering & Technology
23
APPLICATIONS OF PATTERN
RECOGNITION
Brands use facing recognition to
transform marketing.
facial recognition and simulation has
been widely used for virtual makeovers
and virtual product try-
ons. Eg.VOGUE’s Makeup Simulation
application, which recently launched in
Japan.
facial detection and simulation is letting
consumers interact with beauty
products and brands on a more
personal level.
24. Amity School of Engineering & Technology
24
APPLICATIONS OF PATTERN
RECOGNITION
The impact of facial recognition and modeling on
finance may not be very clear, and so far there are
very few examples to show. One recent example
that garnered significant media and customer
interest was Merrill Edge’s Face
Retirement application, which was created to
entice customers to save for retirement.
The basis of the app was a study from Stanford
University that argued that if people were shown a
photo of their older selves, they would be more
likely to think about their retirement. As you can see
in the photo above, Merrill Edge uses facial
recognition and modeling to take a user’s photo to
show them how they would look at 50, 60, 70, and
all the way to 100.
Although this is a relatively newer marketing
campaign, early indications suggest it has been
very successful in its quest to highlight the need to
save for retirement.
25. Amity School of Engineering & Technology
25
APPLICATIONS OF PATTERN RECOGNITION
26. Amity School of Engineering & Technology
26
Template matching is simple to implement but the
template size must be small to decrease
computational delay.
Statistical methods highly depends on the
assumption of distribution.
Neural networks can adaptively refine the
classifier and the decision surface in principle can
be arbitrarily implemented .
Syntactic methods concerned structural sense to
encode but additional process to define primitives
are required.
CONCLUSION
27. Amity School of Engineering & Technology
27
FUTURE WORKS
Frequency domain or Wavelet domain
Image compression method to face
recognition
Video-based face recognition
Adding color factor into face recognition