This is an AI based project which bagged me “Best innovative project” where I built an expert system using JESS (Java expert system shell) , python and embedded it on to Raspberry pi. I used rain, soil moisture sensor to detect precipitation and servo motors to close or open windows. This project was inspired by 2015 IEEE paper “Design and implementation of rule-based uncertainty reasoning in Smart House” which gave me the idea to predict precipitation using Bayesian Network even when one of the sensors fail.
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
Control Strategies
Control Strategy in Artificial Intelligence
scenario is a technique or strategy, tells us about which rule has to be applied next while searching for the solution of a problem within problem space.
It helps us to decide which rule has to apply next without getting stuck at any point.
Characteristics of Control Strategies
A good Control strategy has two main
characteristics:
Control Strategy should cause Motion
Control strategy should be Systematic
Co ntrol Strategy should cause Motion
Each rule or strategy applied should cause the motion because if there will be no motion than such control strategy will never lead to a solution. Motion states about the change of state and if a state will not change then there be no movement from an initial state and we would never solve the problem.
Co ntrol Strategy should be Systematic
Though the strategy applied should create the
motion but if do not follow some systematic
strategy than we are likely to reach the same state
number of times before reaching the solution
which increases the number of steps. Taking care of only first strategy we may go through particular useless sequences of operators several times. Control Strategy should be systematic implies a need for global motion as well as for local motion.
Introduction to Expert Systems {Artificial Intelligence}FellowBuddy.com
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
Control Strategies
Control Strategy in Artificial Intelligence
scenario is a technique or strategy, tells us about which rule has to be applied next while searching for the solution of a problem within problem space.
It helps us to decide which rule has to apply next without getting stuck at any point.
Characteristics of Control Strategies
A good Control strategy has two main
characteristics:
Control Strategy should cause Motion
Control strategy should be Systematic
Co ntrol Strategy should cause Motion
Each rule or strategy applied should cause the motion because if there will be no motion than such control strategy will never lead to a solution. Motion states about the change of state and if a state will not change then there be no movement from an initial state and we would never solve the problem.
Co ntrol Strategy should be Systematic
Though the strategy applied should create the
motion but if do not follow some systematic
strategy than we are likely to reach the same state
number of times before reaching the solution
which increases the number of steps. Taking care of only first strategy we may go through particular useless sequences of operators several times. Control Strategy should be systematic implies a need for global motion as well as for local motion.
Introduction to Expert Systems {Artificial Intelligence}FellowBuddy.com
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
AI and expert system
What is TMS?
Enforcing logical relations among beliefs.
Generating explanations for conclusions.
Finding solutions to search problems
Supporting default reasoning.
Identifying causes for failure and recover from inconsistencies.
TMS applications
This presentation contains information about the AI reasoning in uncertain situations. It also includes discussion regarding the types of uncertainty, predicate logic, non-monotonic logic, truth maintenance system and reasoning with fuzzy sets.
I.INFORMED SEARCH IN ARTIFICIAL INTELLIGENCE II. HEURISTIC FUNCTION IN AI III...vikas dhakane
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Uncertain Knowledge and Reasoning in Artificial IntelligenceExperfy
Learn how to take informed decisions based on probabilities and expert knowledge
Understand and explore one of the most exciting advances in AI in the last decades.
Many hands-on examples, including Python code.
Check it out: https://www.experfy.com/training/courses/uncertain-knowledge-and-reasoning-in-artificial-intelligence
Rulebased system presentation under uncertainty using Bayesian networksAbhishek Kori
Rule based expert system using Bayesian networks under uncertainty.
As a part of our final year presentation we built a rule based expert system for home automation.
AI and expert system
What is TMS?
Enforcing logical relations among beliefs.
Generating explanations for conclusions.
Finding solutions to search problems
Supporting default reasoning.
Identifying causes for failure and recover from inconsistencies.
TMS applications
This presentation contains information about the AI reasoning in uncertain situations. It also includes discussion regarding the types of uncertainty, predicate logic, non-monotonic logic, truth maintenance system and reasoning with fuzzy sets.
I.INFORMED SEARCH IN ARTIFICIAL INTELLIGENCE II. HEURISTIC FUNCTION IN AI III...vikas dhakane
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Uncertain Knowledge and Reasoning in Artificial IntelligenceExperfy
Learn how to take informed decisions based on probabilities and expert knowledge
Understand and explore one of the most exciting advances in AI in the last decades.
Many hands-on examples, including Python code.
Check it out: https://www.experfy.com/training/courses/uncertain-knowledge-and-reasoning-in-artificial-intelligence
Rulebased system presentation under uncertainty using Bayesian networksAbhishek Kori
Rule based expert system using Bayesian networks under uncertainty.
As a part of our final year presentation we built a rule based expert system for home automation.
Rule-based Real-Time Activity Recognition in a Smart Home EnvironmentGeorge Baryannis
This presentation outlines a rule-based approach for both offline and real-time recognition of Activities of Daily Living (ADL), leveraging events produced by a non-intrusive multi-modal sensor infrastructure deployed in a residential environment. Novel aspects of the approach include: the ability to recognise arbitrary scenarios of complex activities
using bottom-up multi-level reasoning, starting from sensor events at the lowest level; an effective heuristics-based method for distinguishing between actual and ghost images in video data; and a highly accurate indoor localisation approach that fuses different sources of location information. The proposed approach is implemented as a rule-based system
using Jess and is evaluated using data collected in a smart home environment. Experimental results show high levels of accuracy and performance,proving the effectiveness of the approach in real world setups.
A practical look at how to build & run IoT business logicVeselin Pizurica
Automation is what takes IoT projects further than visualisation dashboards and offline analysis into real-world actions that drive results. Rule engines are automation frameworks that enable companies to accelerate application development and support the complexity and scale that IoT automation requires.
We will have a practical look at how you can evaluate any rules engine by immediately matching your unique business logic requirements with the necessary rules engine capabilities.
1 Object tracking using sensor network Orla SahiSilvaGraf83
1
Object tracking using sensor network
Orla Sahithi Reddy, email:[email protected]
Abstract—With the help of sensor networks we can keep
track on the events using low and tiny powered devices.
In the paper, we are going to analyze and compare
multiple object tracking methods. Instead of using a
single sensor we use multiple sensors and space them, so
it gives us information. Wireless sensor networks has
node with sensor capabilities and place in object
proximity for detecting them. Sensor networks are
applicable in many fields. Depending on the object
tracking in sensor network ranging from defense and
military applications to earth sciences and
environmental, habitat monitoring, traffic monitoring,
surveillance and military reconnaissance and cross-
border which involves habitat monitoring, infiltration
and other commercial applications.
Index Terms—energy efficient object tracking, object
tracking, quality of tracking, wireless sensor networks,
multi target tracking, routing
I. INTRODUCTION
We Need to have a gathering of frameworks which
cooperate to follow an item rather than a solitary
sensor. Due to this strength, ability and productivity of
the arrangement. Various sensors mitigate the issue of
single purpose of disappointment. A Single costly
sensor expands the danger of disappointment over the
zone of intrigue. Every sensor hub has a sensor ready,
a processor and a remote handset. Normally, a
following application research can be ordered in two
different ways. In recent years, Wireless sensor
network is one of the rapidly growing area[1]. To
begin with, the issue of precisely evaluating the area
of article and second being in organize information
preparing and information conglomeration model for
following item. Article can be situated out commonly
by two activities; by update from the sensors or
questioning the sensor for information to find the item.
Checking of articles would require less time than
following of new item.
Regularly, a remote sensor organize comprises of
enormous number of sensor hubs and is wanted to find
an item in the sensor arrange by playing out a routine
occasionally. This included following the article and
assembling data.
This is a term paper submitted for course requirement fulfillment of
“Advanced Wireless Networks”.
Sahithi Reddy Orla is current student in Wright State University
Computer Science and Engineering Department, Fairborn, OH
45324, USA (e-mail: [email protected], UID: U00916256).
We have to have a particular calculation to process or
track the area of the article with the assistance of
information There are different sorts of item following
strategies which can be looked at and broke down. In
remote sensor systems we have sensor hubs to find an
item in the system. This procedure is done
occasionally including gathering information from
sensor hubs.
There are two sign ...
Embedded system projects for final year BangaloreAidell2583
Final Year Engineering Projects For ECE in Bangalore. Embedded Innovation Lab is the right place for best real time final year engineering projects. EIL is not a training institute its a embedded company.You can learn new ideas and insdustrial working experience. BE/B.Tech student projects in bangalore,mechanical final year project in bangalore
CompTIA exam study guide presentations by instructor Brian Ferrill, PACE-IT (Progressive, Accelerated Certifications for Employment in Information Technology)
"Funded by the Department of Labor, Employment and Training Administration, Grant #TC-23745-12-60-A-53"
Learn more about the PACE-IT Online program: www.edcc.edu/pace-it
Data, Big Data and real time analytics for Connected DevicesSrinath Perera
Internet of things paints a vivid picture of a possible reality that is both fascinating and imposing. However, few talk about the sensing and decision making infrastructure--the brain--that must be present with those devices. Underline decision framework needs to collect data, analyze them, compare and contrast with all data, and draw conclusions and arrive at decisions before humans at the other end notice the lag.
In talk will start with IoT reference architecture and will discuss Complex Event Processing (CEP) coupled with Lambda architecture as a underline decision framework for underline IoT scenario while drawing examples from several real-world scenarios. You will learn about design choices in building an IoT architecture, CEP, Hive, and Lambda architecture.
Topics to be covered:
The relationship between IoT and data, big data, and real-time analytics
Design choices in building an IoT architecture, CEP, Hive, and Lambda architecture
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
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.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
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!
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
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.
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.
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/
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
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
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
1. Rule Based Expert System with uncertainty
management in Smart Homes
Team Members
o Abhay R Dixit - 1DS13CS003
o Abhishek Kori - 1DS13CS007
o Diksha Kushwaha - 1DS13CS034
o Varshini B. K. – 1DS13CS112
Under the guidance of
● Prof. Rashmi S R
2. Smart home
● A home equipped with lighting, heating, and electronic devices that can be controlled
remotely by smartphone or computer.
● Falls in the domain of IOT
● Sensor placed around the house for to sense the outer and inner environment
● All the sensors and electronic components are controlled by central processing and
control unit
3. Need for smart homes
● All the electronic devices are getting smarter and connected to the internet
● Advancement and digitalization of real estate sector
● Security and comfortable living
● Saves energy and cost of running house activities
● Personalised home living
4. Expert system
● A piece of software which uses databases of expert knowledge to offer advice or make
decisions
● It mimics a human expert
● Examples: Medical , Agriculture field
5. Rule based expert system
● A rule-based system is a set of "if-then" statements that uses a set of assertions, to
which rules on how to act upon those assertions are created.
Components
● Domain specific knowledge base
● Inference engine
● User Interface
6. Uncertainty
● The sensors and electronic appliances are exposed to nature and human interaction. They
are more likely to get damaged due and give wrong input
● The situations like sensor getting damaged , devices not able to connect to the internet
etc
● These situations collectively contribute to uncertainty situations
Need to solve uncertain situations
● The likelihood the uncertain situations is high
● Customers lose trust over the system
● Wrong decisions or no decisions may cause discomfort and in some cases it might be life
threatening
7. Problem statement
To build an smart system in a regular home which will react and take appropriate decisions to
changing environment and also counter uncertainty situations.
It should be
● Safe
● Dynamic
● Smart
● Adaptable
● Real time
8. Literature Review
“ Explicit Knowledge-based Reasoning for Visual Question Answering “ [1]
Pros:Recognising the question and answering it with reason.
Cons:The problem is that the amount of prior information that can be encoded within a LSTM system is very
limited
“Integration of Rule based and Case based Reasoning System to Support Decision Making “ [2]
Pros: Case Based Reasoning system. An approach has been made to develop a decision support system which will
take decision under complex environment.
Cons: The integration of raw facts and knowledge into a linking case base knowledge to enhance the potential of
the candidate system and to improve the probability of finding the correct measure.
“Detecting Inconsistencies in Rule-Based Reasoning for Ambient Intelligence” [3]
Pros: The purpose of this approach is to facilitate the creation of an information system for AmI using ontological
model and rule-based reasoning, in order to reduce the errors and encourage a more flexible prototyping process
Cons: little refactoring exists in models and rules, and expert-users often find themselves with redundancy between
the various rules’ content
9. “A Case-Based Reasoning System to Control Traffic at Signalized Intersections” [4]
Pros: Monitors , record and measure the traffic flow and regulates the signal
Cons: Initial knowledge is not available and and do not specify the correct source of knowledge
“A Rule-Based Tracking System for Video Surveillance Applications” [5]
Pros: Rule based object identification to help in monitoring video captured by security cameras
Cons: It’s very domain specific and can only focus on certain kind of situations
“Rule-Based Solution for Context-Aware Reasoning on Mobile Devices” [6]
Pros: Mobile computing devices which has even the knowledge of the context which it's currently present in
Cons: Modeling , representing and classifying context is a big challenge
“Considerations on uncertain spatio-temporal reasoning in smart home systems” [7]
Pros: Helps in assisting the elderly inside and promote over all healthy lifestyle
Cons: Inference and measurement of the environment by the hardware is still in its early stage
10. Implementation
Rule base and factbase
● Rule base and fact base are implemented in Java expert system shell (SHELL)
● What is JESS?
JESS execution Cycle
1. Match
2. Conflict
3. Act
JESS syntax
● (deftemplate <template-name> (slot <var-name>) (slot <var-name>) (slot <var-name>))
● (defrule <rule-name>
"<Brief description>"
(<Conditon or LHS>)
=>
(<Action or RHS>)
12. Bayesian Network
● is a probabilistic graphical model that represents a set of random variables and their
conditional dependencies via a directed acyclic graph
● Example: Rain , sprinkler, grass wet
13. Conditional probability
● Conditional probability is a measure of the probability of an event given that (by
assumption, presumption, assertion or evidence) another event has occurred.
Example taking from conditional probability table
● With conditional probability and table we can answer questions like
“What is the probability that it is raining, given the grass is wet?”
14. Overall process flow
1. Send sensor input to event detection module
2. Sense for any event (rain or no rain)
3. If detected assert the fact in fact base
4. Inference engine checks the rules for matching rule with fact in the fact base
5. If a rule is matched then RHS part of that rule gets executed
6. If there is uncertain condition go to Bayesian network , conditional probability module
to calculate the probability of rain
7. If the probability is more than the pre set threshold then assert its rain true else set false
in the fact base
8. Inference engine again scans through the rules for matching rules and executes the
respective RHS
9. Depending on the RHS part the action is sent to electromechanical unit
15. Rain Detection Sensor
● If there is no rain, the resistance between the wires will be very high and there will
be no conduction between the wires in the sensor.
● If there is rain, the water drops will fall on the rain sensor which will decrease the
resistance between the wires and wires on the sensor board will conduct
16. Soil Moisture Sensor
● The Soil Moisture Sensor uses capacitance to measure dielectric permittivity of the
surrounding medium.
● In soil, dielectric permittivity is a function of the water content.
● The sensor creates a voltage proportional to the dielectric permittivity, and therefore
the water content of the soil.
17. Raspberry Pi
● The Raspberry Pi is a low cost, single-board computer that is capable of doing
everything a desktop computer can to do
● It is very easy to interface hardware with raspberrry pi.
18. Servo Motors
● Servos are controlled by sending an electrical pulse of variable width, or pulse
width modulation (PWM), through the control wire.
● The PWM sent to the motor determines position of the shaft, and based on the
duration of the pulse sent via the control wire; the rotor will turn to the desired
position.
● For example, a 1.5ms pulse will make the motor turn to the 90° position. Shorter
than 1.5ms moves it in the counter clockwise direction toward the 0° position, and
any longer than 1.5ms will turn the servo in a clockwise direction toward the 180°
position.
20. Video stream : Video stream is a combination of various frames moving at a certain
speed.
Frame Processing , Key frames : All the frame are separated and and processed
separately, the important frames (key frames) are generated for the video.
Video Processing System: Object identification and annotation in the key frames are
done and their actions are noted down.
Facts Generation : Facts are generated depending on the object annotation.
Video Inference System : Inference is derived by checking the facts against the rules and
an action is decided upon.
Hybrid Knowledge Base : A theoretical framework capturing all the knowledge of the
system.
21. LabelMe - Annotation Tool
● The LabelMe annotation tool
provides a means for users to
contribute to the project.
● The tool can be accessed
anonymously or by logging in to
a free account.
● To access the tool, users must
have a compatible web browser
with javascript support.
● An XML code can be generate
that contain the data about the
annotated image.