Following topics are discussed in this presentation:What is Soft Computing?
What is Hard Computing?
What is Fuzzy Logic Models?
What is Neural Networks (NN)?
What is Genetic Algorithms or Evaluation Programming?
What is probabilistic reasoning?
Difference between fuzziness and probability
AI and Soft Computing
Future of Soft Computing
This presentation discusses the following Fuzzy logic concepts:
Introduction
Crisp Variables
Fuzzy Variables
Fuzzy Logic Operators
Fuzzy Control
Case Study
This presentation discusses the following ANN concepts:
Introduction
Characteristics
Learning methods
Taxonomy
Evolution of neural networks
Basic models
Important technologies
Applications
This presentation discusses the following topics:What is Genetic Algorithms?
Introduction to Genetic Algorithm
Classes of Search Techniques
Components of a GA
Components of a GA
Simple Genetic Algorithm
GA Cycle of Reproduction
Population
Reproduction
Chromosome Modification: Mutation, Crossover, Evaluation, Deletion
Example
GA Technology
Issues for GA Practitioners
Benefits of Genetic Algorithms
GA Application Types
This presentation discusses about the following topics:
Hybrid Systems
Hybridization
Combinations
Comparison of Expert Systems, Fuzzy Systems, Neural Networks and Genetic Algorithms
Current Progress
Primary Components
MultiComponents
Degree of Integration
Transformational, hierarchial and integrated
Stand Alone Models
Integrated – Fused Architectures
Generalized Fused Framework
System Types for Hybridization
This presentation discusses the following Fuzzy logic concepts:
Introduction
Crisp Variables
Fuzzy Variables
Fuzzy Logic Operators
Fuzzy Control
Case Study
This presentation discusses the following ANN concepts:
Introduction
Characteristics
Learning methods
Taxonomy
Evolution of neural networks
Basic models
Important technologies
Applications
This presentation discusses the following topics:What is Genetic Algorithms?
Introduction to Genetic Algorithm
Classes of Search Techniques
Components of a GA
Components of a GA
Simple Genetic Algorithm
GA Cycle of Reproduction
Population
Reproduction
Chromosome Modification: Mutation, Crossover, Evaluation, Deletion
Example
GA Technology
Issues for GA Practitioners
Benefits of Genetic Algorithms
GA Application Types
This presentation discusses about the following topics:
Hybrid Systems
Hybridization
Combinations
Comparison of Expert Systems, Fuzzy Systems, Neural Networks and Genetic Algorithms
Current Progress
Primary Components
MultiComponents
Degree of Integration
Transformational, hierarchial and integrated
Stand Alone Models
Integrated – Fused Architectures
Generalized Fused Framework
System Types for Hybridization
How can you deal with Fuzzy Logic. Fuzzy logic is a form of many-valued logic; it deals with reasoning that is approximate rather than fixed and exact. In contrast with traditional logic theory, where binary sets have two-valued logic: true or false, fuzzy logic variables may have a truth value that ranges in degree
between 0 and 1
This Presentation discusses about the following topics:
Introduction to Intelligent Systems
Expert Systems
Neural Networks
Fuzzy Logic
Intelligent Agents
This presentation discusses about the following topics:
Truth values and tables,
Fuzzy propositions,
Formation of rules decomposition of rules,
Aggregation of fuzzy rules,
Fuzzy reasoning‐fuzzy inference systems
Overview of fuzzy expert system‐
Fuzzy decision making.
In the field of artificial intelligence (AI), planning refers to the process of developing a sequence of actions or steps that an intelligent agent should take to achieve a specific goal or solve a particular problem. AI planning is a fundamental component of many AI systems and has applications in various domains, including robotics, autonomous systems, scheduling, logistics, and more. Here are some key aspects of planning in AI:
Definition of Planning: Planning involves defining a problem, specifying the initial state, setting a goal state, and finding a sequence of actions or a plan that transforms the initial state into the desired goal state while adhering to certain constraints.
State-Space Representation: In AI planning, the problem is often represented as a state-space, where each state represents a snapshot of the system, and actions transform one state into another. The goal is to find a path through this state-space from the initial state to the goal state.
Search Algorithms: AI planning typically relies on search algorithms to explore the state-space efficiently. Uninformed search algorithms, such as depth-first search and breadth-first search, can be used, as well as informed search algorithms, like A* search, which incorporates heuristics to guide the search.
Heuristics: Heuristics are used in planning to estimate the cost or distance from a state to the goal. Heuristic functions help inform the search algorithms by providing an estimate of how close a state is to the solution. Good heuristics can significantly improve the efficiency of the search.
Plan Execution: Once a plan is generated, the next step is plan execution, where the agent carries out the actions in the plan to achieve the desired goal. This often requires monitoring the environment to ensure that the actions are executed as planned.
Temporal and Hierarchical Planning: In more complex scenarios, temporal planning deals with actions that have temporal constraints, and hierarchical planning involves creating plans at multiple levels of abstraction, making planning more manageable in complex domains.
Partial and Incremental Planning: Sometimes, it may not be necessary to create a complete plan from scratch. Partial and incremental planning allows agents to adapt and modify existing plans to respond to changing circumstances.
Applications: Planning is used in a wide range of applications, from manufacturing and logistics (e.g., scheduling production and delivery) to robotics (e.g., path planning for robots) and game playing (e.g., chess and video games).
Challenges: Challenges in AI planning include dealing with large search spaces, handling uncertainty, addressing resource constraints, and optimizing plans for efficiency and performance.
AI planning is a critical component in creating intelligent systems that can autonomously make decisions and solve complex problems.
---TABLE OF CONTENT---
Introduction
Differences between crisp sets & Fuzzy sets
Operations on Fuzzy Sets
Properties
MF formulation and parameterization
Fuzzy rules and Fuzzy reasoning
Fuzzy interface systems
Introduction to genetic algorithm
Computational Intelligence: concepts and applications using AthenaPedro Almir
Computational Intelligence (CI) is a sub-branch of Artificial Intelligence (AI) and is concentrated in the study of adaptive mechanisms to enable or facilitate intelligent behavior
in complex and changing environments. This presentation presents the key concepts of this area and how to use Athena to create intelligent systems. Athena is a visual tool developed aiming at offering a simple approach to the development of CI-based software systems, by dragging and dropping components in a visual environment, creating a new concept, that we call CI as a Service (CIaaS).
How can you deal with Fuzzy Logic. Fuzzy logic is a form of many-valued logic; it deals with reasoning that is approximate rather than fixed and exact. In contrast with traditional logic theory, where binary sets have two-valued logic: true or false, fuzzy logic variables may have a truth value that ranges in degree
between 0 and 1
This Presentation discusses about the following topics:
Introduction to Intelligent Systems
Expert Systems
Neural Networks
Fuzzy Logic
Intelligent Agents
This presentation discusses about the following topics:
Truth values and tables,
Fuzzy propositions,
Formation of rules decomposition of rules,
Aggregation of fuzzy rules,
Fuzzy reasoning‐fuzzy inference systems
Overview of fuzzy expert system‐
Fuzzy decision making.
In the field of artificial intelligence (AI), planning refers to the process of developing a sequence of actions or steps that an intelligent agent should take to achieve a specific goal or solve a particular problem. AI planning is a fundamental component of many AI systems and has applications in various domains, including robotics, autonomous systems, scheduling, logistics, and more. Here are some key aspects of planning in AI:
Definition of Planning: Planning involves defining a problem, specifying the initial state, setting a goal state, and finding a sequence of actions or a plan that transforms the initial state into the desired goal state while adhering to certain constraints.
State-Space Representation: In AI planning, the problem is often represented as a state-space, where each state represents a snapshot of the system, and actions transform one state into another. The goal is to find a path through this state-space from the initial state to the goal state.
Search Algorithms: AI planning typically relies on search algorithms to explore the state-space efficiently. Uninformed search algorithms, such as depth-first search and breadth-first search, can be used, as well as informed search algorithms, like A* search, which incorporates heuristics to guide the search.
Heuristics: Heuristics are used in planning to estimate the cost or distance from a state to the goal. Heuristic functions help inform the search algorithms by providing an estimate of how close a state is to the solution. Good heuristics can significantly improve the efficiency of the search.
Plan Execution: Once a plan is generated, the next step is plan execution, where the agent carries out the actions in the plan to achieve the desired goal. This often requires monitoring the environment to ensure that the actions are executed as planned.
Temporal and Hierarchical Planning: In more complex scenarios, temporal planning deals with actions that have temporal constraints, and hierarchical planning involves creating plans at multiple levels of abstraction, making planning more manageable in complex domains.
Partial and Incremental Planning: Sometimes, it may not be necessary to create a complete plan from scratch. Partial and incremental planning allows agents to adapt and modify existing plans to respond to changing circumstances.
Applications: Planning is used in a wide range of applications, from manufacturing and logistics (e.g., scheduling production and delivery) to robotics (e.g., path planning for robots) and game playing (e.g., chess and video games).
Challenges: Challenges in AI planning include dealing with large search spaces, handling uncertainty, addressing resource constraints, and optimizing plans for efficiency and performance.
AI planning is a critical component in creating intelligent systems that can autonomously make decisions and solve complex problems.
---TABLE OF CONTENT---
Introduction
Differences between crisp sets & Fuzzy sets
Operations on Fuzzy Sets
Properties
MF formulation and parameterization
Fuzzy rules and Fuzzy reasoning
Fuzzy interface systems
Introduction to genetic algorithm
Computational Intelligence: concepts and applications using AthenaPedro Almir
Computational Intelligence (CI) is a sub-branch of Artificial Intelligence (AI) and is concentrated in the study of adaptive mechanisms to enable or facilitate intelligent behavior
in complex and changing environments. This presentation presents the key concepts of this area and how to use Athena to create intelligent systems. Athena is a visual tool developed aiming at offering a simple approach to the development of CI-based software systems, by dragging and dropping components in a visual environment, creating a new concept, that we call CI as a Service (CIaaS).
This presentation is actually an orientation about the "computer science" branch.This presentation includes 2 videos.....
(i)Evolutions
(ii)Influential persons in history of computer
This presentation discusses the following topics:
Basic features of R
Exploring R GUI
Data Frames & Lists
Handling Data in R Workspace
Reading Data Sets & Exporting Data from R
Manipulating & Processing Data in R
A study on “Diagnosis Test of Diabetics and Hypertension by AI”, Presentation slides for International Conference on "Life Sciences: Acceptance of the New Normal", St. Aloysius' College, Jabalpur, Madhya Pradesh, India, 27-28 August, 2021
A study on “impact of artificial intelligence in covid19 diagnosis”Dr. C.V. Suresh Babu
A study on “Impact of Artificial Intelligence in COVID-19 Diagnosis”, Presentation slides for International Conference on "Life Sciences: Acceptance of the New Normal", St. Aloysius' College, Jabalpur, Madhya Pradesh, India, 27-28 August, 2021
A study on “impact of artificial intelligence in covid19 diagnosis”Dr. C.V. Suresh Babu
Although the lungs are one of the most vital organs in the body, they are vulnerable to infection and injury. COVID-19 has put the entire world in an unprecedented difficult situation, bringing life to a halt and claiming thousands of lives all across the world. Medical imaging, such as X-rays and computed tomography (CT), is essential in the global fight against COVID-19, and newly emerging artificial intelligence (AI) technologies are boosting the power of imaging tools and assisting medical specialists. AI can improve job efficiency by precisely identifying infections in X-ray and CT images and allowing further measurement. We focus on the integration of AI with X-ray and CT, both of which are routinely used in frontline hospitals, to reflect the most recent progress in medical imaging and radiology combating COVID-19.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
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For more information, visit-www.vavaclasses.com
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
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Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdf
Introduction to soft computing V 1.0
1. Department of Information Technology 1Soft Computing (ITC4256 )
Introduction to
Soft Computing
Dr. C.V. Suresh Babu
Professor
Department of IT
Hindustan Institute of Science & Technology
2. Department of Information Technology 2Soft Computing (ITC4256 )
Discussion Topics
• What is Soft Computing?
• What is Hard Computing?
• What is Fuzzy Logic Models?
• What is Neural Networks (NN)?
• What is Genetic Algorithms or Evaluation Programming?
• What is probabilistic reasoning?
• Difference between fuzziness and probability
• AI and Soft Computing
• Future of Soft Computing
3. Department of Information Technology 3Soft Computing (ITC4256 )
Soft computing differs from conventional (hard)
computing in that, unlike hard computing, it is tolerant
of imprecision, uncertainty, partial truth, and
approximation. In effect, the role model for soft
computing is the human mind.
What is Soft Computing ?
4. Department of Information Technology 4Soft Computing (ITC4256 )
Hard computing, i.e., conventional computing, requires a precisely stated analytical
model and often a lot of computation time.
• Many analytical models are valid for ideal cases.
• Real world problems exist in a non-ideal environment.
• Premises and guiding principles of Hard Computing are
- Precision, Certainty, and Rigor.
• Many contemporary problems do not lend themselves to precise solutions such as
• Recognition problems (handwriting, speech, objects, images, texts)
• Mobile robot coordination, forecasting, combinatorial problems etc.
• Reasoning on natural languages
What is Hard Computing ?
5. Department of Information Technology 5Soft Computing (ITC4256 )
What is SC?
Another possible definition of soft computing is to consider it as an anti-thesis
to the concept of computer we now have, which can be described with all the adjectives such as hard,
crisp, rigid, inflexible and stupid. Along this track,
one may see soft computing as an attempt to mimic natural creatures: plants, animals, human beings,
which are soft, flexible, adaptive and clever. In this sense soft computing is the name of a family of
problem-solving methods that have analogy with biological reasoning and problem solving (sometimes
referred to as cognitive computing).
The basic methods included in cognitive computing are
• fuzzy logic (FL)
• neural networks (NN)
• genetic algorithms (GA)
Note: this methods which do not derive from classical theories.
6. Department of Information Technology 6Soft Computing (ITC4256 )
Soft computing employs ANN, EC, FL etc, in a
complementary rather than a competitive way.
• One example of a particularly effective combination is
"neurofuzzy systems.”
• Such systems are becoming increasingly visible as
consumer products ranging from air conditioners and
washing machines to photocopiers, camcorders and
many industrial applications.
Implications of Soft Computing
7. Department of Information Technology 7Soft Computing (ITC4256 )
The principal constituents, i.e., tools, techniques, of Soft
Computing (SC) are
• Fuzzy Logic (FL),
• Artificial Neural Networks (ANN),
• Evolutionary Computation (EC),
• Swarm Intelligence (i.e. Ant colony optimization and
Particle swarm optimization, )
• Additionally Some Machine Learning (ML) and
Probabilistic Reasoning (PR) areas.
Tools & Techniques of Soft Computing
8. Department of Information Technology 8Soft Computing (ITC4256 )
Properties of Soft computing
• Learning from experimental data generalization
• Soft computing techniques derive their power of generalization
from approximating or interpolating to produce outputs from
previously unseen inputs by using outputs from previous learned
inputs
• Generalization is usually done in a high dimensional space.
9. Department of Information Technology 9Soft Computing (ITC4256 )
• Handwriting recognition
• Automotive systems and manufacturing
• Image processing and data compression
• Architecture
• Decision-support systems
• Data Mining
• Power systems
• Control Systems
Recent Applications using Soft Computing
10. Department of Information Technology 10Soft Computing (ITC4256 )
Single absolute truth is exist:
Absolute truths are not exist
Need for Soft Computing
11. Department of Information Technology 11Soft Computing (ITC4256 )
Different representations of concepts by different
persons
Blue
sky sea
Jeans
Blue
diamond
homosexualitysky
12. Department of Information Technology 12Soft Computing (ITC4256 )
Different representations of concepts in different
languages
• Blue
– Pale blue one word in Russian
– Dark blue one another word in Russian
• Pigmy has many single words for description of forest:
• Forest under rain
• Forest after rain
• Forest in hot season
• Forest in morning
• Forest in evening
• and so on
13. Department of Information Technology 13Soft Computing (ITC4256 )
The tools for soft computing
• Fuzzy logic models
• Neural networks
• Genetic algorithms
• Probabilistic reasoning
14. Department of Information Technology 14Soft Computing (ITC4256 )
What is Fuzzy Logic Models?
FUZZY LOGIC is defined as a many-valued logic form which may have truth values of variables
in any real number between 0 and 1. It is the handle concept of partial truth. In real life, we
may come across a situation where we can't decide whether the statement is true or false. At
that time, fuzzy logic offers very valuable flexibility for reasoning.
Fuzzy logic algorithm helps to solve a problem after considering
all available data. Then it takes the best possible decision for
the given the input. The FL method imitates the way of decision
making in a human which consider all the possibilities between
digital values T and F.
15. Department of Information Technology 15Soft Computing (ITC4256 )
Examples of tasks solving by Fuzzy models
• Control of clothes washer
• Making of decision in diagnostic systems (expert systems in medicine,
for example)
• Making of decision in business planning
May be used knowledge such as:
If temperature is high then diagnose is grippe with confidence 80%
If speed is slow then increase transfer of fuel
16. Department of Information Technology 16Soft Computing (ITC4256 )
What is Neural Networks (NN)?
A neural network is a series of algorithms that endeavors to recognize underlying
relationships in a set of data through a process that mimics the way the human brain
operates. In this sense, neural networks refer to systems of neurons, either organic or
artificial in nature
• NN consists of many number of simple elements (neurons) connected between
them in system
• Whole system is able to solve of complex tasks and to learn for it like a natural brain
• For user NN is black box with Input vector (source data) and Output vector (result)
Examples of tasks:
• Recognition of images (visual, speech and so on)
• Prediction of situations (cost of actions,
currency)
• Classification and clusterization of images (for
example, in diagnostic systems)
18. Department of Information Technology 18Soft Computing (ITC4256 )
What is Genetic Algorithms or Evaluation Programming?
Genetic Algorithm (GA) is a search-based optimization
technique based on the principles of Genetics and Natural
Selection. It is frequently used to find optimal or near-optimal
solutions to difficult problems which otherwise would take a
lifetime to solve.
Examples of application:
• Finding of optimal (suitable) path,
• Finding of better structure of neural network
• Finding of configuration of robot
• Optimal cutting
19. Department of Information Technology 19Soft Computing (ITC4256 )
What is probabilistic reasoning?
• Probabilistic reasoning is a method of representation of
knowledge where the concept of probability is applied to
indicate the uncertainty in knowledge. Probabilistic reasoning
is used in AI: When we are unsure of the predicates
For example,
20. Department of Information Technology 20Soft Computing (ITC4256 )
Examples of applications of probabilistic reasoning
• Recognition of speech
• Navigation of mobile robots
• And so on
21. Department of Information Technology 21Soft Computing (ITC4256 )
Difference between fuzziness and probability (from
modeling of world)
• Probability deal with unknown entity (time, property before any
event). After any event the entity become known.
• Fuzziness is own property of any entity or (concept or object or
property). It may be more or less but not disappears practically.
• May be fuzzy probability and probability of fuzziness
• Probability may be use for simulation of fuzziness
22. Department of Information Technology 22Soft Computing (ITC4256 )
AI and Soft Computing: A Different Perspective
• AI: predicate logic and symbol manipulation techniques
UserInterface
Inference
Engine
Explanation
Facility
Knowledge
Acquisition
KB:•Fact
•rules
Global
Database
Knowledge
Engineer
Human
Expert
Question
Response
Expert Systems
User
23. Department of Information Technology 23Soft Computing (ITC4256 )
AI and Soft Computing
ANN
Learning and
adaptation
Fuzzy Set Theory
Knowledge representation
Via
Fuzzy if-then RULE
Genetic Algorithms
Systematic
Random Search
24. Department of Information Technology 24Soft Computing (ITC4256 )
AI and Soft Computing
ANN
Learning and
adaptation
Fuzzy Set Theory
Knowledge representation
Via
Fuzzy if-then RULE
Genetic Algorithms
Systematic
Random Search
AI
Symbolic
Manipulation
25. Department of Information Technology 25Soft Computing (ITC4256 )
AI and Soft Computing
cat
cut
knowledge
Animal? cat
Neural character
recognition
26. Department of Information Technology 26Soft Computing (ITC4256 )
• Soft computing is likely to play an especially
important role in science and engineering, but
eventually its influence may extend much
farther.
• Soft computing represents a significant paradigm shift in the aims
of computing
•A shift which reflects the fact that the human mind, unlike present day computers,
possesses a remarkable ability to store and process information which is pervasively
imprecise, uncertain and lacking in categoricity.
Future of Soft Computing