computer-nec-license
  • NEC-Computer
  • 1. Concept of Basic Electrical and Electronics Engineering
    • 1.1 Basic Concepts
    • 1.2 Network Theorems
    • 1.3 Alternating Current Fundamentals
    • 1.4 Semiconductor Device
    • 1.5 Signal Generator
    • 1.6 Amplifiers
    • MCQs
      • MCQs On Basic Electrical
        • set-1
        • set-2
      • MCQs On Basic Electronics
        • set-1
        • set-2
  • 2. Digital Logic and Microprocessor
    • 2.1 Digital Logic
    • 2.2 Combinational & Arithmetic Circuit
    • 2.3 Sequential Logic Circuits
    • 2.4 Microprocessor
    • 2.5 Microprocessor System
    • 2.6 Interrupt Operations
    • MCQs
      • MCQs On Digital Logic
        • set-1
        • set-2
        • set-3
        • set-4
        • set-5
        • set-6
        • set-7
        • set-8
        • set-9
        • set-10
        • set-11
        • set-12
      • MCQs On Microprocessor
        • set-1
        • set-2
        • set-3
        • set-4
        • set-5
        • set-6
        • set-7
        • set-8
        • set-9
  • 3. Programming Language and Its Applications
    • 3.1 Introduction to C Programming
    • 3.2 Pointers, Structures, and Data Files
    • 3.3 C++ Language Constructs with Objects and Classes
    • 3.4 Features of Object-Oriented Programming
    • 3.5 Pure Virtual Functions and File Handling
    • 3.6 Generic Programming and Exception Handling
    • MCQs
      • set-1
      • set-2
      • set-3
      • set-4
      • set-5
  • 4. Computer Organization and Embedded System
    • 4.1 Control and CPU
    • 4.2 Computer Arithmetic and Memory System
    • 4.3 I/O Organization and Multiprocessor
    • 4.4 Embedded System Design
    • 4.5 Real-Time Operating and Control Systems
    • 4.6 Hardware Description Language (VHDL) and IC Technology
    • MCQs
      • set-1
      • set-2
      • set-3
      • set-4
      • set-5
      • set-6
      • set-7
      • set-8
      • set-9
      • set-10
      • set-11
  • 5. Concept of Computer Network and Network Security System
    • 5.1 Introduction to Computer Networks
    • 5.2 Data Link Layer
    • 5.3 Network Layer
    • 5.4 Transport Layer
    • 5.5 Application Layer
    • 5.6 Network Security
    • MCQs
      • Basic Networking
        • set-1
        • set-2
      • Advanced Networking
        • set-1
        • set-2
        • set-3
        • set-4
        • set-5
        • set-6
  • 6. Theory of Computation and Computer Graphics
    • 6.1 Introduction to Finite Automata
    • 6.2 Introduction to Context-Free Languages (CFL)
    • 6.3 Turing Machines (TM)
    • 6.4 Introduction to Computer Graphics
    • 6.5 Two-Dimensional Transformation
    • 6.6 Three-Dimensional Transformation
    • MCQs
      • MCQs on Theory of Computation
        • set-1
        • set-2
        • set-3
      • MCQs On Computer Graphics
        • set-1
        • set-2
        • set-3
        • set-4
        • set-5
        • set-6
  • 7. Data Structures and Algorithm, Database System and Operating System
    • 7.1 Introduction to Data Structures, Lists, Linked Lists, and Trees
    • 7.2 Sorting, Searching, Hashing and Graphs
    • 7.3 Introduction to Data Models, Normalization, and SQL
    • 7.4 Transaction Processing, Concurrency Control, and Crash Recovery
    • 7.5 Introduction to Operating System and Process Management
    • 7.6 Memory Management, File Systems, and System Administration
    • MCQs
      • MCQs ON DSA
        • set-1
        • set-2
        • set-3
        • set-4
        • set-5
        • set-6
      • MCQs On DBMS
        • set-1
        • set-2
      • MCQs On Operating System
        • set-1
        • set-2
        • set-3
        • set-4
        • set-5
        • set-6
        • set-7
        • set-8
        • set-9
        • set-10
        • set-11
        • set-12
  • 8. Software Engineering and Object-Oriented Analysis & Design
    • 8.1 Software Process and Requirements
    • 8.2 Software Design
    • 8.3 Software Testing, Cost Estimation, Quality Management, and Configuration Management
    • 8.4 Object-Oriented Fundamentals and Analysis
    • 8.5 Object-Oriented Design
    • 8.6 Object-Oriented Design Implementation
    • MCQs
      • set-1
      • set-2
      • set-3
      • set-4
      • set-5
      • set-6
      • set-7
      • set-8
      • set-9
  • 9. Artificial Intelligence and Neural Networks
    • 9.1 Introduction to AI and Intelligent Agents
    • 9.2 Problem Solving and Searching Techniques
    • 9.3 Knowledge Representation
    • 9.4 Expert System and Natural Language Processing
    • 9.5 Machine Learning
    • 9.6 Neural Networks
    • MCQs
      • set-1
      • set-2
      • set-3
      • set-4
      • set-5
      • set-6
      • set-7
      • set-8
      • set-9
  • 10. Project Planning, Design and Implementation
    • 10.1 Engineering Drawings and Its Concepts
    • 10.2 Engineering Economics
    • 10.3 Project Planning and Scheduling
    • 10.4 Project Management
    • 10.5 Engineering Professional Practice
    • 10.6 Engineering Regulatory Body
    • MCQs
      • MCQs On Engineering Drawing
        • set-1
        • set-2
      • MCQs On Engineering Economics
      • MCQs On Project Planning & Scheduling
      • MCQs On Project Mangement
      • MCQs On Engineering Professional Practice
      • MCQs On Engineering Regulatory Body
  • Questions Sets
    • Set 1 (Chaitra, 2080)
      • Short Questions (60*1=60 Marks)
      • Long Questions (20*2=40 Marks)
    • Set 2 (Aasadh, 2081)
      • Short Questions (60*1=60 Marks)
      • Long Questions (20*2=40 Marks)
    • Set 3 (Asojh, 2080)
      • Short Questions (60*1=60 Marks)
      • Long Questions (20*2=40 Marks)
    • Model Set - Computer Engineering By NEC
      • Short Questions (60*1=60 Marks)
      • Long Questions (20*2=40 Marks)
    • Model Set - Software Engineering By NEC
      • Short Questions (60*1=60 Marks)
      • Long Questions (20*2=40 Marks)
  • Tips & Tricks
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On this page
  • 1. Expert Systems
  • 2. Natural Language Processing (NLP)
  • 3. Machine Vision
  • 4. Robotics
  • Conclusion
  1. 9. Artificial Intelligence and Neural Networks

9.4 Expert System and Natural Language Processing

In AI, expert systems and natural language processing (NLP) are key fields that deal with mimicking human expertise and enabling machines to understand and generate human language. Below is a detailed breakdown of the concepts related to Expert Systems, NLP, Machine Vision, and Robotics.

1. Expert Systems

An Expert System is an AI program designed to simulate the decision-making ability of a human expert in a specific domain. Expert systems aim to solve complex problems by reasoning through bodies of knowledge, represented mostly as if-then rules, and providing explanations for their conclusions.

  • Definition: Expert systems are used for solving problems within a specialized domain by emulating the decision-making processes of a human expert.

  • Components of an Expert System:

    • Knowledge Base: Contains the domain knowledge, facts, and rules.

    • Inference Engine: The reasoning mechanism that processes the rules in the knowledge base to derive conclusions or make decisions.

    • User Interface: The interface through which users interact with the system to input data and receive output.

    • Explanation System: Provides the rationale for the conclusions made by the system, helping users understand how results were derived.

  • Types of Knowledge:

    • Declarative Knowledge: Describes "what" something is (e.g., facts, rules, relationships).

    • Procedural Knowledge: Describes "how" to do something (e.g., step-by-step instructions or methods).

  • Knowledge Acquisition: The process of gathering and organizing knowledge from human experts or other sources to build the system's knowledge base. Techniques include interviewing domain experts, using existing databases, or employing machine learning.

  • Development of Expert Systems: Expert systems are developed by gathering domain knowledge, designing a suitable knowledge representation (rules, facts, frames, etc.), building the inference engine, and integrating a user interface.

  • Examples of Expert Systems: Medical diagnosis systems, financial decision-making systems, and troubleshooting systems in engineering.

2. Natural Language Processing (NLP)

Natural Language Processing (NLP) is a field of AI that focuses on enabling computers to process, understand, and generate human language. NLP bridges the gap between human communication and machine understanding, making it easier for users to interact with systems in natural language.

  • Terminology in NLP:

    • Natural Language Understanding (NLU): The process of interpreting and understanding the meaning of human language. NLU involves tasks like syntactic parsing, semantic analysis, and named entity recognition (NER).

    • Natural Language Generation (NLG): The process of generating coherent and contextually appropriate text or speech from structured data or a knowledge base. NLG is often used in chatbots, report generation, and automated content creation.

  • Steps of Natural Language Processing:

    • Tokenization: Splitting text into individual words or tokens (e.g., breaking "I love programming" into ["I", "love", "programming"]).

    • Stop-word Removal: Removing common words like "is," "the," and "in" that do not contribute much meaning.

    • Stemming and Lemmatization: Reducing words to their base or root form (e.g., "running" becomes "run").

    • Part-of-Speech (POS) Tagging: Identifying the grammatical role of each word (e.g., noun, verb, adjective).

    • Syntactic Parsing: Analyzing the structure of sentences to understand how words relate to each other.

    • Named Entity Recognition (NER): Identifying entities like names, locations, dates, etc., in a text.

    • Sentiment Analysis: Determining the sentiment or emotion expressed in text (e.g., positive, negative, or neutral).

    • Semantic Analysis: Understanding the meaning of the words and how they contribute to the overall meaning of a sentence or text.

  • Applications of NLP:

    • Machine Translation: Translating text from one language to another (e.g., Google Translate).

    • Speech Recognition: Converting spoken language into text (e.g., Siri, Google Assistant).

    • Text Classification: Categorizing text into predefined categories (e.g., spam detection, sentiment analysis).

    • Chatbots and Virtual Assistants: Automated systems that interact with users via natural language (e.g., customer service bots).

    • Information Retrieval: Searching for relevant documents or data based on a user’s query (e.g., search engines).

  • Challenges in NLP:

    • Ambiguity: Words and phrases can have multiple meanings based on context.

    • Context: Understanding the context of a conversation is crucial, as meaning changes depending on the surrounding words.

    • Slang and Idioms: Informal language, slang, and idioms may not follow grammatical rules, posing difficulties for NLP systems.

    • Language Evolution: Languages change over time, and NLP systems must adapt to new slang, terminology, and usage patterns.

3. Machine Vision

Machine Vision refers to the field of AI that enables computers to interpret and make decisions based on visual data, such as images and videos. It involves the use of cameras and sensors to collect data and algorithms to process and understand that data.

  • Concepts in Machine Vision:

    • Image Processing: Techniques used to enhance and extract features from images, such as filtering, edge detection, and object recognition.

    • Feature Extraction: Identifying important features in images, such as shapes, patterns, textures, or specific objects.

    • Object Recognition: Identifying and classifying objects within an image.

    • Motion Detection: Analyzing the movement of objects within video frames.

  • Stages of Machine Vision:

    1. Image Acquisition: Capturing visual data using cameras or sensors.

    2. Preprocessing: Enhancing or cleaning the image to make it suitable for analysis (e.g., removing noise).

    3. Feature Extraction: Identifying key characteristics of objects in the image.

    4. Pattern Recognition: Classifying the objects based on the extracted features.

    5. Decision Making: Using the identified objects to make decisions or perform actions.

4. Robotics

Robotics is the branch of AI that involves designing, building, and controlling robots—machines that can perform tasks autonomously or semi-autonomously. Robotics combines AI, machine vision, and control systems to enable robots to interact with their environment and carry out complex tasks.

  • Components of a Robot:

    • Sensors: Used for gathering information about the environment (e.g., cameras, LIDAR, accelerometers).

    • Actuators: Motors or other devices used to move the robot and perform tasks (e.g., arms, wheels, grippers).

    • Control Systems: Algorithms and systems that control the robot's behavior and decision-making processes.

    • Processor: The computer that processes the sensory data and makes decisions for the robot.

  • Applications of Robotics:

    • Industrial Automation: Robots used in manufacturing and assembly lines (e.g., welding, packaging).

    • Medical Robots: Robots used in surgery, rehabilitation, and patient care (e.g., robotic surgery tools).

    • Autonomous Vehicles: Self-driving cars and drones that navigate without human intervention.

    • Service Robots: Robots used in customer service, logistics, and hospitality (e.g., delivery robots, reception robots).

Conclusion

Expert systems and natural language processing are two critical fields within AI that enable machines to mimic human-like decision-making and communication. While expert systems focus on automating the decision-making process in specific domains using a knowledge base, NLP focuses on enabling machines to understand and generate human language. Additionally, machine vision and robotics combine AI, sensor technologies, and control systems to enable machines to perceive the world and act autonomously. Together, these fields contribute significantly to advancements in intelligent systems across various applications.

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Last updated 4 months ago