Decision Support System - Management Information SystemNijaz N
Refers to class of system which supports in the process of decision making and does not always give a decision itself.
Decision Support Systems supply computerized support for the decision making process.
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
This presentation educates you about AI - Components and Acquisition of Expert Systems and those are Knowledge Base, Knowledge Base and User Interface, AI - Expert Systems Limitation.
For more topics stay tuned with Learnbay.
Decision Support System - Management Information SystemNijaz N
Refers to class of system which supports in the process of decision making and does not always give a decision itself.
Decision Support Systems supply computerized support for the decision making process.
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
This presentation educates you about AI - Components and Acquisition of Expert Systems and those are Knowledge Base, Knowledge Base and User Interface, AI - Expert Systems Limitation.
For more topics stay tuned with Learnbay.
Artificial Intelligence lecture notes. AI summarized notes for expert systems, inference mechanisms and so on, this is reading and may be for self-learning, I think.
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.
A study on “the impact of data analytics in covid 19 health care system”Dr. C.V. Suresh Babu
A Study on “The Impact of Data Analytics in COVID-19 Health Care System”, Presentation slides for International Conference on "Life Sciences: Acceptance of the New Normal", St. Aloysius' College, Jabalpur, Madhya Pradesh, India, 27-28 August, 2021
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
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
2. (CentreforKnowledgeTransfer)
institute
What are Expert Systems?
• The expert systems are the computer applications developed to solve
complex problems in a particular domain, at the level of extra-ordinary
human intelligence and expertise.
4. (CentreforKnowledgeTransfer)
institute
Capabilities of Expert Systems
The expert systems are capable of −
• Advising
• Instructing and assisting human in decision making
• Demonstrating
• Deriving a solution
• Diagnosing
• Explaining
• Interpreting input
• Predicting results
• Justifying the conclusion
• Suggesting alternative options to a problem
5. (CentreforKnowledgeTransfer)
institute
Why do you need Expert Systems?
Today’s world requires more and more experts in the ever-growing technological feats that humans are
achieving.The important thing here is to see if you can put the power of computing to good use. Expert
system in AI are the way computers replicate the knowledge and the skills of a person who’s an expert in
a field.
• Some of the biggest advantages that the expert systems provide us are these four aspects:
• Maximum efficiency
• Reliability
• High-level understandability
• Unbeatable performance
This process of taking an expert human’s knowledge and adding high amounts of computation power to
it has proved nothing but immensely beneficial in today’s world.
8. (CentreforKnowledgeTransfer)
institute
Knowledge Base
• It contains domain-specific and high-quality knowledge.
• Knowledge is required to exhibit intelligence. The success of any ES majorly
depends upon the collection of highly accurate and precise knowledge.
9. (CentreforKnowledgeTransfer)
institute
What is Knowledge?
• The data is collection of facts.The information is organized as data and facts
about the task domain. Data, information, and past experience combined
together are termed as knowledge.
10. (CentreforKnowledgeTransfer)
institute
Components of Knowledge Base
• The knowledge base of an ES is a store of both, factual and heuristic
knowledge.
• Factual Knowledge − It is the information widely accepted by the
Knowledge Engineers and scholars in the task domain.
• Heuristic Knowledge − It is about practice, accurate judgement, one’s
ability of evaluation, and guessing.
12. (CentreforKnowledgeTransfer)
institute
Knowledge Acquisition
• The success of any expert system majorly depends on the quality, completeness,
and accuracy of the information stored in the knowledge base.
• The knowledge base is formed by readings from various experts, scholars, and the
Knowledge Engineers.The knowledge engineer is a person with the qualities of
empathy, quick learning, and case analyzing skills.
• He acquires information from subject expert by recording, interviewing, and
observing him at work, etc. He then categorizes and organizes the information in a
meaningful way, in the form of IF-THEN-ELSE rules, to be used by interference
machine.The knowledge engineer also monitors the development of the ES.
13. (CentreforKnowledgeTransfer)
institute
Inference Engine
• Use of efficient procedures and rules by the Inference Engine is essential in deducting a correct, flawless solution.
• In case of knowledge-based ES, the Inference Engine acquires and manipulates the knowledge from the knowledge base
to arrive at a particular solution.
• In case of rule based ES, it −
• Applies rules repeatedly to the facts, which are obtained from earlier rule application.
• Adds new knowledge into the knowledge base if required.
• Resolves rules conflict when multiple rules are applicable to a particular case.
To recommend a solution, the Inference Engine uses the following strategies −
• Forward Chaining
• BackwardChaining
14. (CentreforKnowledgeTransfer)
institute
Forward Chaining
• It is a strategy of an expert system to answer the question, “What can
happen next?”
• Here, the Inference Engine follows the chain of conditions and derivations
and finally deduces the outcome. It considers all the facts and rules, and
sorts them before concluding to a solution.
• This strategy is followed for working on conclusion, result, or effect. For
example, prediction of share market status as an effect of changes in
interest rates.
15. (CentreforKnowledgeTransfer)
institute
Backward Chaining
• With this strategy, an expert system finds out the answer to the question,
“Why this happened?”
• On the basis of what has already happened, the Inference Engine tries to
find out which conditions could have happened in the past for this result.
This strategy is followed for finding out cause or reason. For example,
diagnosis of blood cancer in humans.
16. (CentreforKnowledgeTransfer)
institute
User Interface
• User interface provides interaction between user of the ES and the ES itself. It is generally
Natural Language Processing so as to be used by the user who is well-versed in the task
domain.The user of the ES need not be necessarily an expert in Artificial Intelligence.
• It explains how the ES has arrived at a particular recommendation.The explanation may
appear in the following forms −
• Natural language displayed on screen.
• Verbal narrations in natural language.
• Listing of rule numbers displayed on the screen.
• The user interface makes it easy to trace the credibility of the deductions.
17. (CentreforKnowledgeTransfer)
institute
Requirements of Efficient ES User
Interface
• It should help users to accomplish their goals in shortest possible way.
• It should be designed to work for user’s existing or desired work practices.
• Its technology should be adaptable to user’s requirements; not the other
way round.
• It should make efficient use of user input.
18. (CentreforKnowledgeTransfer)
institute
Expert Systems Limitations
No technology can offer easy and complete solution. Large systems are costly,
require significant development time, and computer resources. ESs have their
limitations which include −
• Limitations of the technology
• Difficult knowledge acquisition
• ES are difficult to maintain
• High development costs
19. (CentreforKnowledgeTransfer)
institute
Applications of Expert System
Application Description
Design Domain Camera lens design, automobile design.
Medical Domain
Diagnosis Systems to deduce cause of disease from observed data, conduction
medical operations on humans.
Monitoring Systems
Comparing data continuously with observed system or with prescribed
behavior such as leakage monitoring in long petroleum pipeline.
ProcessControl Systems Controlling a physical process based on monitoring.
Knowledge Domain Finding out faults in vehicles, computers.
Finance/Commerce
Detection of possible fraud, suspicious transactions, stock market trading,
Airline scheduling, cargo scheduling.
20. (CentreforKnowledgeTransfer)
institute
Applications of Expert System
• Information management
• Hospitals and medical facilities
• Help desks management
• Employee performance evaluation
• Loan analysis
• Virus detection
• Useful for repair and maintenance
projects
• Warehouse optimization
• Planning and scheduling
• The configuration of manufactured objects
• Financial decision making Knowledge publishing
• Process monitoring and control
• Supervise the operation of the plant and
controller
• Stock market trading
• Airline scheduling & cargo schedules
21. (CentreforKnowledgeTransfer)
institute
Expert SystemTechnology
• There are several levels of ES technologies available. Expert systems technologies include −
• Expert System Development Environment −The ES development environment includes hardware and tools.They are −
• Workstations, minicomputers, mainframes.
• High level Symbolic Programming Languages such as LISt Programming (LISP) and PROgrammation en LOGique
(PROLOG).
• Large databases.
• Tools −They reduce the effort and cost involved in developing an expert system to large extent.
• Powerful editors and debugging tools with multi-windows.
• They provide rapid prototyping
• Have Inbuilt definitions of model, knowledge representation, and inference design.
• Shells − A shell is nothing but an expert system without knowledge base. A shell provides the developers with knowledge
acquisition, inference engine, user interface, and explanation facility. For example, few shells are given below −
• Java Expert System Shell (JESS) that provides fully developed Java API for creating an expert system.
• Vidwan, a shell developed at the National Centre for SoftwareTechnology, Mumbai in 1993. It enables knowledge
22. (CentreforKnowledgeTransfer)
institute
Development of Expert Systems:
General Steps
The process of ES development is iterative. Steps in developing the ES include −
Identify Problem Domain
• The problem must be suitable for an expert system to solve it.
• Find the experts in task domain for the ES project.
• Establish cost-effectiveness of the system.
Design the System
• Identify the ESTechnology
• Know and establish the degree of integration with the other systems and databases.
• Realize how the concepts can represent the domain knowledge best.
Develop the Prototype
From Knowledge Base:The knowledge engineer works to −
• Acquire domain knowledge from the expert.
• Represent it in the form of If-THEN-ELSE rules.
Test and Refine the Prototype
• The knowledge engineer uses sample cases to test the prototype for any deficiencies in performance.
• End users test the prototypes of the ES.
Develop and Complete the ES
• Test and ensure the interaction of the ES with all elements of its environment, including end users, databases, and other
information systems.
• Document the ES project well.
• Train the user to use ES.
Maintain the System
• Keep the knowledge base up-to-date by regular review and update.
• Cater for new interfaces with other information systems, as those systems evolve.
23. (CentreforKnowledgeTransfer)
institute
Conventional System vs. Expert System
Conventional System Expert System
Knowledge and processing are combined in one
unit.
Knowledge database and the processing
mechanism are two separate components.
The programme does not make errors (Unless
error in programming).
The Expert System may make a mistake.
The system is operational only when fully
developed.
The expert system is optimized on an ongoing
basis and can be launched with a small number
of rules.
Step by step execution according to fixed
algorithms is required.
Execution is done logically & heuristically.
It needs full information.
It can be functional with sufficient or
insufficient information.
24. (CentreforKnowledgeTransfer)
institute
Human expert vs. Expert System
Human Expert Artificial Expertise
Perishable Permanent
Difficult toTransfer Transferable
Difficult to Document Easy to Document
Unpredictable Consistent
Expensive Cost effective System
25. (CentreforKnowledgeTransfer)
institute
Benefits of Expert Systems
• Availability −They are easily available due to mass production of software.
• Less Production Cost − Production cost is reasonable.This makes them affordable.
• Speed −They offer great speed.They reduce the amount of work an individual puts
in.
• Less Error Rate − Error rate is low as compared to human errors.
• Reducing Risk −They can work in the environment dangerous to humans.
• Steady response −They work steadily without getting motional, tensed or
fatigued.
26. (CentreforKnowledgeTransfer)
institute
Benefits of Expert Systems
• It improves the decision quality
• Cuts the expense of consulting experts for problem-solving
• It provides fast and efficient solutions to problems in a narrow area of specialization.
• It can gather scarce expertise and used it efficiently.
• Offers consistent answer for the repetitive problem
• Maintains a significant level of information
• Helps you to get fast and accurate answers
• A proper explanation of decision making
• Ability to solve complex and challenging issues
• Artificial Intelligence Expert Systems can steadily work without getting emotional, tensed or fatigued.
27. (CentreforKnowledgeTransfer)
institute
Limitations of the Expert System
• Unable to make a creative response in an extraordinary situation
• Errors in the knowledge base can lead to wrong decision
• The maintenance cost of an expert system is too expensive
• Each problem is different therefore the solution from a human expert can
also be different and more creative
28. (CentreforKnowledgeTransfer)
institute
Summary
• An Expert System is an interactive and reliable computer-based decision-making system which
uses both facts and heuristics to solve complex decision-making problem
• Key components of an Expert System are 1) User Interface, 2) Inference Engine, 3) Knowledge
Base
• Key participants in Artificial Intelligence Expert Systems Development are 1) Domain Expert 2)
Knowledge Engineer 3) End User
• Improved decision quality, reduce cost, consistency, reliability, speed are key benefits of an
Expert System
• An Expert system can not give creative solutions and can be costly to maintain.
• An Expert System can be used for broad applications like Stock Market,Warehouse, HR, etc