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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  • 9.1 Introduction to AI and Intelligent Agent
  • 9.2 Problem Solving and Searching Techniques
  • 9.3 Knowledge Representation
  • 9.4 Expert Systems and Natural Language Processing
  • 9.5 Machine Learning
  • 9.6 Neural Networks

9. Artificial Intelligence and Neural Networks

9.1 Introduction to AI and Intelligent Agent

  • Concept of Artificial Intelligence (AI)

  • AI Perspectives

  • History of AI and Applications

  • Foundations of AI

  • Introduction to Agents

    • Structure of Intelligent Agent

    • Properties of Intelligent Agents

    • PEAS Description of Agents

  • Types of Agents:

    • Simple Reflexive, Model-Based, Goal-Based, Utility-Based

  • Environment Types:

    • Deterministic, Stochastic, Static, Dynamic, Observable, Semi-observable, Single Agent, Multi-Agent

9.2 Problem Solving and Searching Techniques

  • Problem Formulation and State Space Search

    • Well-defined Problems, Constraint Satisfaction Problem

  • Uninformed Search Techniques:

    • Depth First Search, Breadth First Search, Depth Limited Search, Iterative Deepening Search, Bidirectional Search

  • Informed Search:

    • Greedy Best First Search, A* Search, Hill Climbing, Simulated Annealing

  • Game Playing and Adversarial Search:

    • Mini-max Search, Alpha-Beta Pruning

9.3 Knowledge Representation

  • Knowledge Representation and Mappings

    • Approaches to Knowledge Representation

    • Issues in Knowledge Representation

  • Semantic Nets and Frames

  • Propositional Logic (PL):

    • Syntax, Semantics, Formal Logic-Connectives, Tautology, Validity, Well-Formed Formula, Inference Using Resolution

  • Predicate Logic (FOPL):

    • Syntax, Semantics, Quantification, Rules of Inference, Unification, Resolution Refutation System

  • Bayesian Networks:

    • Bayes' Rule, Reasoning in Belief Networks

9.4 Expert Systems and Natural Language Processing

  • Expert Systems:

    • Architecture of Expert Systems, Knowledge Acquisition, Declarative vs Procedural Knowledge, Development of Expert Systems

  • Natural Language Processing (NLP):

    • Terminology, Natural Language Understanding and Generation, Steps of NLP

    • Applications and Challenges of NLP

  • Machine Vision and Robotics:

    • Concepts, Stages of Machine Vision, Robotics

9.5 Machine Learning

  • Introduction to Machine Learning

  • Learning Concepts:

    • Supervised, Unsupervised, and Reinforcement Learning

  • Types of Learning:

    • Inductive Learning (Decision Tree), Statistical-Based Learning (Naive Bayes Model), Fuzzy Learning

  • Fuzzy Inference System and Methods

  • Genetic Algorithm:

    • Genetic Algorithm Operators, Encoding, Selection Algorithms, Fitness Function, and Genetic Algorithm Parameters

9.6 Neural Networks

  • Biological vs. Artificial Neural Networks (ANN)

  • McCulloch-Pitts Neuron and Mathematical Model of ANN

  • Activation Functions

  • Neural Network Architectures:

    • Perceptron, Learning Rate, Gradient Descent, Delta Rule, Hebbian Learning

    • Adaline Network, Multilayer Perceptron Neural Networks, Backpropagation Algorithm, Hopfield Neural Network

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