2. Knowledge Management
•Definition: Knowledge Management involves the creation,
organization, and application of an organization's collective
knowledge to achieve its goals.
•Purpose: Enhance decision-making, improve efficiency, foster
innovation, and promote learning.
3. Knowledge Management Systems
•Definition: KMS are tools, processes, and technologies designed to
facilitate knowledge management within an organization.
•Components: Capture, organize, store, retrieve, and share
information.
4. Types and Uses of Knowledge Management
Systems
•Document Management Systems (DMS)
• Description: Focus on capturing, storing, and organizing documents.
• Example: SharePoint, Google docs, Google Drive.
•Collaboration Platforms
• Description: Facilitate communication and collaboration among employees.
• Example: Slack, Microsoft Teams.
•Knowledge Repositories
• Description: Centralized storage for explicit knowledge.
• Example: Wikipedia, Confluence.
5. •Decision Support
• Description: Provide relevant information for better decision-making.
• Example: Business intelligence tools like Tableau.
•Training and Development
• Description: Support employee learning and development.
• Example: Learning Management Systems (LMS).
6. Expert Systems in Knowledge Management
•Expert Systems (ES) are computer programs that mimic the
decision-making ability of a human expert in a specific domain.
•Components:
•Knowledge Base: Contains expert knowledge and rules.
•Inference Engine: Processes information using predefined rules.
•User Interface: Allows interaction with users.
•Eg: Diagnostics and Project management
7. • Chatbots for Customer Support: Many companies employ advanced chatbots
using natural language processing and expert system techniques to handle
customer inquiries and provide assistance.
• Medical Diagnosis Apps: Various mobile applications leverage expert system
principles to assist users in self-diagnosing minor health issues based on
symptoms and medical knowledge.
• Financial Advisory Systems: Some financial institutions use expert systems to
provide personalized financial advice and investment recommendations to clients.
• AI in Drug Discovery
8. • Types of Expert Systems:
• Rule-Based Systems:
• Description: Utilize a set of predefined rules to make decisions.
• Example: Diagnostic systems in healthcare.
• Fuzzy Logic Systems:
• Description: Handle uncertainty and imprecision in decision-making.
• Example: Air conditioning systems that adjust settings based on fuzzy input.
• Neural Networks:
• Description: Learn from data patterns and improve decision-making over time.
• Example: Fraud detection systems in finance.
• Case-Based Reasoning:
• Description: Solve new problems by referring to solutions of similar past cases.
• Example: Customer support systems resolving issues based on historical cases.
9. Machine Learning
• Machine Learning (ML) is a branch of artificial intelligence that enables computers to
learn from data and improve their performance over time without explicit programming.
• Key Concepts:
• Training Data: ML models learn patterns from data provided during the training phase.
• Algorithms: Mathematical models and algorithms are employed to make predictions or
decisions.
• Iterative Improvement: ML models continuously refine their predictions as they are
exposed to more data.
Eg: Predicting chance of a disease in an area
10. Types of Machine Learning:
• Supervised Learning - learning from labeled data
• Unsupervised Learning- learning from unlabeled data and finding patterns
within it
• Reinforcement Learning- training agents to make sequential decisions by
interacting with an environment
Applications:
• Image and Speech Recognition
• Natural Language Processing
• Predictive Analytics
11. •Benefits of ML in Business:
• Automation: Streamlining processes and automating repetitive tasks.
• Data-Driven Decision Making: Using insights derived from ML to make
informed business decisions.
• Fraud Detection: Identifying and preventing fraudulent activities.
• Sales and Marketing: Customer segmentation, lead scoring, and personalized
marketing campaigns.
• Supply Chain Optimization: Demand forecasting, inventory management,
and logistics optimization.
• Customer Service: Chatbots, sentiment analysis, and automated ticket
routing.
• Finance: Credit scoring, risk assessment, and fraud detection.
12. Neural Networks:
• Neural networks are a type of artificial intelligence inspired by the human
brain's structure. They consist of interconnected nodes, or neurons,
organized in layers to process and analyze data.
• Structure:
i. Input Layer: Receives data.
ii. Hidden Layers: Process information.
iii. Output Layer: Produces the network's final output.
• Learning Mechanism: Neural networks learn from data through a process
of adjusting weights on connections based on feedback.
Eg: Face recognition and Sales forecasting
13. Neural Networks in Business
• Predictive Analytics:
• Identify trends and patterns in large datasets for better decision-making.
• Examples: Sales forecasting, customer behavior analysis.
• Customer Relationship Management (CRM):
• Enhance customer experience by predicting preferences and personalized
recommendations.
• Fraud Detection:
• Identify unusual patterns in financial transactions for early fraud detection.
• Supply Chain Optimization:
• Improve inventory management and demand forecasting for efficient supply chain
operations.
• Marketing:
• Targeted advertising, sentiment analysis, and customer segmentation.
14. Genetic Algorithms
• Genetic algorithms are optimization algorithms inspired by the process of natural selection and genetics.
Operation:
• Initialization: Create an initial population of potential solutions.
• Selection: Evaluate and select solutions based on their fitness.
• Crossover: Combine genetic material of selected solutions.
• Mutation: Introduce random changes to the solutions.
• Repeat: Iteratively evolve solutions over generations.
• Applications:
• Optimization problems in engineering, finance, scheduling, and artificial intelligence.
Eg: Imagine a group of individuals are tested for how well they solve the problem. We call this their "fitness." The better they are at
solving the problem, the higher their fitness –high fitness genes are selected – new generation of high performers
Project Scheduling – potential schedule as chromosome – each schedule fitness is checked – selection – crossover – mutation -
After many generations, the genetic algorithm may produce a schedule that minimizes the overall project completion time.
15. Neural Language
• Neural Language Processing involves the use of neural networks to
understand, interpret, and generate human language.
• Components:
• Embeddings: Convert words into numerical vectors.
• Recurrent Neural Networks (RNNs): Process sequences of words.
• Transformer Architectures: Facilitate parallel processing for improved efficiency.
• Attention Mechanism: Focus on relevant parts of input data.
• Applications:
• Machine Translation: Translate text between languages.
• Sentiment Analysis: Determine sentiment in textual data.
• Text Generation: Create human-like text, as seen in chatbots and content creation.
16. Genetic Algorithms and Neural Language
• Optimizing Neural Networks:
• Genetic algorithms can be employed to optimize hyperparameters in neural network architectures.
• Feature Selection:
• Genetic algorithms aid in selecting relevant features for natural language processing tasks.
• Improving Training Processes:
• Genetic algorithms optimize the training process of neural networks, enhancing efficiency.
• Hybrid Models:
• The combination of genetic algorithms and neural language processing can lead to powerful hybrid models for complex problem-solving.
• Emerging Research:
• Ongoing research explores innovative ways to leverage the synergy of these two powerful paradigms for enhanced performance in various
applications.
17. Robotics
• Robotics is the branch of technology that deals with the design,
construction, operation, and use of robots.
• Key Components:
• Actuators: Motors and joints allowing movement.
• Sensors: Collect information about the environment.
• Control System: Directs the robot's actions.
• Applications:
• Manufacturing, healthcare, space exploration, education, and more.
18. Intelligent Agents: AI in Action
• Intelligent agents are autonomous entities that perceive their environment and take actions to achieve goals.
Components:
• Perception: Gathering information from the environment.
• Reasoning: Analyzing information to make decisions.
• Action: Executing actions based on decisions.
Types:
• Simple Reflex Agents, Model-Based Reflex Agents, Goal-Based Agents, Utility-Based Agents.
Applications:
• Autonomous vehicles, virtual assistants, recommendation systems.
19. Robotics and Intelligent Agents Integration
• Enhancing Autonomy:
• Intelligent agents improve a robot's ability to perceive and make decisions autonomously.
• Adaptive Behavior:
• Integration of intelligent agents allows robots to adapt to changing environments and unforeseen circumstances.
• Efficient Task Execution:
• Intelligent agents enable robots to prioritize tasks based on goals and environmental factors, improving efficiency.
• Learning Capabilities:
• Incorporating machine learning into intelligent agents empowers robots to learn from experience, enhancing their performance over time.
• Emerging Applications:
• Ongoing research explores innovative ways to integrate intelligent agents into robotic systems for advanced applications in diverse fields.
20. Decision Making
•Definition of Decision Making:
•Decision making is the process of selecting a course of action from
multiple alternatives to achieve a desired result or solve a problem.
22. Types of Decision Making:
•Routine/Operational Decisions: Everyday decisions with well-defined
processes.
•Strategic Decisions: Long-term decisions that impact the organization
as a whole.
•Tactical Decisions: Mid-term decisions that bridge the gap between
strategic and operational decisions.
•Individual vs. Group Decision Making: Made by individuals or groups
with different dynamics.
23. Group Decision
• Group decision making involves the collaborative process of reaching a consensus or
making choices through the input and discussion of multiple individuals within a team or
organization
• Challenges:
• Conflict: Differences in opinions may lead to conflicts that need resolution.
• Decision Time: The process can be time-consuming compared to individual decision making.
• Conformity: Pressure to conform to the group's opinion may stifle dissenting views.
• Steps in Group Decision Making:
• Problem Identification: Clearly define the issue or decision to be made.
• Information Sharing: Members share relevant data and insights.
• Discussion: Open dialogue to explore various perspectives.
• Decision-Making: Choose the best course of action through consensus or a formal voting process.
• Implementation: Put the decision into action with assigned responsibilities.
24. Support Systems
• Support systems play a crucial role in information systems by providing tools and resources to
enhance operational efficiency and decision-making processes.
• Types of Support Systems:
• 1. Decision Support Systems (DSS):
• Functionality: Assist in decision-making processes by analyzing data and presenting insights.
• Example: Business executives using DSS for strategic planning and forecasting.
• 2. Customer Relationship Management (CRM) Systems:
• Functionality: Manage and analyze interactions with customers to improve relationships.
• Example: A company using CRM to track customer interactions and preferences.
25. • 3. Enterprise Resource Planning (ERP) Systems:
• Functionality: Integrate core business processes and functions across an
organization.
• Example: Implementation of ERP for streamlined financial, HR, and supply chain
management.
• 4. Knowledge Management Systems:
• Functionality: Capture, organize, and share organizational knowledge and
information.
• Example: Intranet systems facilitating the sharing of best practices and expertise.
26. Building Information System
• Building Information Systems (IS) is a strategic process that involves the development, implementation, and
maintenance of technology-driven solutions to meet organizational needs.
• Key Components:
• 1. Planning and Analysis:
• Objectives: Define the goals and scope of the information system.
• Activities: Conduct a thorough needs assessment and feasibility study.
• 2. Design and Development:
• Objectives: Create a blueprint for the system and develop the software.
• Activities: Design the user interface, database, and program functionality.
• 3. Implementation:
• Objectives: Integrate the new system into the organization's operations.
• Activities: Train users, migrate data, and deploy the system.
• 4. Testing and Quality Assurance:
• Objectives: Ensure the reliability and functionality of the system.
• Activities: Conduct thorough testing, identify and fix defects.
• 5. Deployment and Maintenance:
• Objectives: Launch the system and provide ongoing support.
• Activities: Monitor performance, address issues, and implement updates.
27. •Considerations while building Info. systems:
•User Involvement: Engage end-users throughout the development
process.
•Scalability: Design the system to accommodate future growth and
changes.
•Security: Implement measures to protect sensitive data.
28. • System Analysis and Design (SAD) is a disciplined process for developing or improving information systems to meet specified
requirements and maximize organizational efficiency.
Key Components:
• 1. System Analysis:
• Objective: Understand the existing system and identify improvements or new system requirements.
• Activities: Gather and analyze information, define system objectives, and document findings.
• 2. Requirements Specification:
• Objective: Clearly define the functional and non-functional requirements of the system.
• Activities: Document user needs, constraints, and system specifications.
• 3. System Design:
• Objective: Create a blueprint for the new system based on specified requirements.
• Activities: Architectural design, database design, user interface design, and system interface design.
• 4. Implementation:
• Objective: Transform the design into a working system.
• Activities: Coding, testing, debugging, and integrating components.
• 5. Maintenance and Review:
• Objective: Ensure the system remains effective over time.
• Activities: Monitor performance, address issues, and implement updates
29. •Methodologies:
•Waterfall Model: Sequential phases, each building upon the previous
one.
•Agile Model: Iterative and flexible, with frequent feedback and
adaptations.
•Prototyping: Build a prototype to gather user feedback early in the
development process.
30. Structured Methodology
•A systematic and organized approach to solving complex problems or
managing projects.
•Key Characteristics:Step-by-Step Approach:
• Breaks down tasks into well-defined steps.
•Documentation:
• Emphasizes clear and thorough documentation.
•Roles and Responsibilities:
• Defines clear roles for efficient collaboration.
•Repeatability:
• Designed to be repeatable for consistency.
31. Examples of Structured Methodologies
• 1. Waterfall Model:
• Description: Sequential and linear approach to software development.
• Characteristics:
• Each phase must be completed before moving to the next.
• 2. PRINCE2 (PRojects IN Controlled Environments):
• Description: Project management methodology providing a framework for
managing projects.
• Characteristics:
• Defined roles, processes, and stages.
• 3. Six Sigma:
• Description: Process improvement approach emphasizing data-driven
decision-making.
• Characteristics:
• Reduction of defects and continuous improvement.
32. Object-Oriented Development
• A programming paradigm that organizes software design around objects,
encapsulation, inheritance, and polymorphism.
• Key Concepts:
• Objects:
• Instances representing real-world entities.
• Classes:
• Blueprints or templates for creating objects.
• Encapsulation:
• Bundling data and methods that operate on the data.
• Inheritance:
• A mechanism for creating a new class based on an existing class.
• Polymorphism:
• The ability for objects to take on multiple forms.
33. Example: Library Management System
1. Objects and Classes:
• Objects:
• Book: Represents individual books in the library.
• Member: Represents library members who borrow books.
• Classes:
• BookClass: Blueprint for creating Book objects with properties like title, author, and availability.
• MemberClass: Blueprint for creating Member objects with properties like name, ID, and borrowed books.
2. Encapsulation:
• Each class encapsulates its data (e.g., book details, member information) and methods (e.g., borrowing, returning) to operate
on that data.
34. 3. Inheritance:
• Base Class: Item
• Represents common properties for various items (e.g., books, DVDs).
• Derived Class: Book
• Inherits from Item and adds specific properties (e.g., author, genre).
4. Polymorphism:
• Method Overriding:
• Both Book and Member classes may have a method displayInfo(), but each displays relevant information based on the object type.
5. Reusability:
• The Item class can be reused for other types of items in the library without rewriting common properties and methods.
6. Modularity:
• Separate classes for Book and Member provide modularity, making it easier to manage and update specific components of the system.
35. Computer-Aided Software Engineering
(CASE)
• The use of computer-based tools and methods to assist in software
development.
• Key Objectives:
• Automation:
• Automate repetitive tasks in software development.
• Consistency:
• Ensure consistency and standardization across development stages.
• Productivity:
• Improve productivity by reducing manual effort.
• Collaboration:
• Facilitate collaboration among development team members.
36. •Common CASE Tools:
•1. Diagramming Tools:
• Assist in creating visual representations of software structures.
•2. Code Generators:
• Generate code automatically based on design specifications.
•3. Version Control Systems:
• Manage and track changes in software versions.
37. System Development Life Cycle (SDLC)
Definition:
• A structured and systematic approach to developing information systems.
Key Stages:
• 1. Planning:
• Define project scope, goals, and resources.
• 2. Analysis:
• Gather and analyze user requirements.
• 3. Design:
• Create a blueprint for the system architecture.
• 4. Implementation:
• Develop and code the system based on the design.
• 5. Testing:
• Verify that the system functions as intended.
• 6. Deployment:
• Roll out the system for use by end-users.
• 7. Maintenance:
• Address issues, updates, and improvements over time.
38. Prototypes
•Prototypes are preliminary models or versions of a software product
developed to test ideas, gather feedback, and identify requirements.
•Key Objectives:
•Idea Exploration:
• Test and refine concepts before full development.
•User Feedback:
• Gather input from users for better alignment with requirements.
•Risk Reduction:
• Identify and address potential issues early in the development process.
39. End-User Development (EUD)
• Definition:
• End-User Development refers to the practice of allowing non-professional developers, typically
end-users, to create and modify software applications to meet their specific needs.
• Key Objectives:
• Empowerment:
• Empower end-users to create or customize software without extensive programming skills.
• Flexibility:
• Provide a flexible approach for users to adapt technology to their specific requirements.
• Rapid Solutions:
• Enable the quick development of solutions tailored to unique user needs.
• Examples of EUD Tools:
• Low-Code Platforms:
• Platforms allowing users to build applications with minimal hand-coding.
• Spreadsheet Software:
• Users create functional applications using spreadsheet functionalities.
40. Rapid Application Development (RAD) &
Agile Development
• Definition:
• Agile is an iterative and collaborative software development approach that prioritizes
flexibility, customer feedback, and continuous improvement.
• Key Principles:
• Iterative Development:
• Work is divided into small, manageable iterations or sprints.
• Customer Collaboration:
• Regular feedback and collaboration with customers throughout the process.
• Adaptability:
• Embraces changing requirements even late in the development.
• Agile Manifesto Values:
• Individuals and Interactions over Processes and Tools.
• Working Software over Comprehensive Documentation.
• Customer Collaboration over Contract Negotiation.
• Responding to Change over Following a Plan.
41. Rapid Application Development (RAD) &
Agile Development
•RAD is an agile development approach that emphasizes quick
development cycles, user feedback, and collaboration between
developers and end-users.
Key Principles:
• Iterative Prototyping:
• Develop prototypes quickly, gather feedback, and iterate.
• User Involvement:
• Encourage active participation of end-users throughout the development process.
• Collaborative Development:
• Foster close collaboration between developers, users, and stakeholders.
42. RAD Phases:
• Requirements Planning:
• Define project scope and requirements.
• Quick Design:
• Create initial prototypes and design.
• Build Prototypes:
• Develop prototypes for quick user evaluation.
• User Feedback:
• Gather feedback for rapid iterations.
• Finalization:
• Finalize and deploy the application.
