Delhi University MCA 5th Semester Syllabus 2024 – MCA 3rd Year Subjects

Here the details of Delhi University MCA 5th Semester Syllabus 2024 – MBA 3rd Year. Delhi University (दिल्ली विश्वविद्यालय) provides chapter wise syllabus of MCA 5th semester. Candidates can download the MBA 5th semester syllabus pdf on this page. Students are required to follow this site for more updates about the Delhi University revised syllabus 2024. The University official website also provides the entire MCA 5th Semester Syllabus. To check the Delhi University MCA fifth Sem Syllabus,

About Delhi University

The University of Delhi is the premier university located in New Delhi, India. It has grown into one of the largest university in India. Five Departments namely Chemistry, Geology, Zoology, Sociology, and History have been awarded the status of the Centres of Advanced Studies. Du offers UG, PG and other certificate courses with the maximum number of specializations in courses. Various departments are located in the Faculty of Arts and the Faculty of Inter-disciplinary and Applied Sciences. The official website is

DU MCA Course Structure

MCA Course structure in Delhi University
MCA is 3 years under graduation course1st yearSemester 14 months Duration
Semester 24 months
2nd yearSemester 34 months
Semester 44 months
3rd yearSemester 54 months
Semester 64 months

Delhi University MCA Specializations

Master of Computer Applications is the three-year course divided into a number of semesters which are generally six in number with different specializations that are given below.

  • Application Software
  • Hardware Technology
  • Management Information Systems
  • Internet
  • Software Development
  • Systems Management
  • Systems Development
  • Systems Engineering
  • Troubleshooting

Subjects in MCA 5th Semester – Delhi University

The following are the subjects in MCA 5th Semester

  • Modeling & Simulation
  • Visual Programming
  • Data Mining
  • Computational Intelligence
  • Artificial Intelligence
  • Digital Image Processing & Multimedia
  • Neural Networks
  • Combinatorial Optimization
  • Software Quality Assurance & Testing
  • Machine Learning
  • Embedded Systems
  • Cryptography
  • Programming Paradigms
  • Database Systems and Implementation
  • Human Resource Management
  • XML and Databases
  • Satellite and Mobile Communication Networks

Download MCA 5th Semester DU Syllabus 2024

<< Download Delhi University MCA 5th Semester Syllabus >>

DU MCA 5th Sem Syllabus

Course No.TitleL – T – PCreditsTotal Marks
MCA 501Modeling & Simulation3 – 0 – 24100
MCA 502Visual Programming3 – 0 – 24100
MCA 503Data Mining3 – 0 – 24100
MCA 504Computational Intelligence3 – 0 – 24100
MCA 505Artificial Intelligence3 – 0 – 24100
MCA 506Digital Image Processing & Multimedia3 – 0 – 24100
MCA 507Neural Networks3 – 0 – 24100
MCA 508Combinatorial Optimization3 – 0 – 24100
MCA 509Software Quality Assurance & Testing3 – 0 – 24100
MCA 510Machine Learning3 – 0 – 24100
MCA 511Embedded Systems3 – 0 – 24100
MCA 512Cryptography3 – 0 – 24100
MCA 513Programming Paradigms3 – 0 – 24100
MCA 514Database Systems and Implementation3 – 0 – 24100
MCA 515Human Resource Management3 – 0 – 24100
MCA 516XML and Databases3 – 0 – 24100
MCA 517Satellite and Mobile Communication Networks3 – 0 – 24100
  • L – T – P: Lectures – Tutorials – Practical


  • Systems and environment: Concept of model and model building, model classification and
    representation, Use of simulation as a tool, steps in a simulation study.
  • Continuous-time and Discrete-time systems: Laplace transform, transfer functions, statespace models, order of systems, z-transform, feedback systems, stability, observability,
    controllability. Statistical Models in Simulation: Common discrete and continuous
    distributions, Poisson process, empirical distributions
  • Random Numbers: Properties of random numbers, generation of pseudo random numbers,
    techniques of random number generation, tests for randomness, random variate generation
    using inverse transformation, direct transformation, convolution method, acceptance-rejection
  • Design and Analysis of simulation experiments: Data collection, identifying distributions
    with data, parameter estimation, goodness of fit tests, selecting input models without data,
    multivariate an time series input models, verification and validation of models, static and
    dynamic simulation output analysis, steady-state simulation, terminating simulation,
    confidence interval estimation, Output analysis for steady state simulation, variance reduction
  • Queuing Models: Characteristics of queuing systems, notation, transient and steady-state
    behavior, performance, network of queues
  • Large Scale systems: Model reduction, hierarchical control, decentralized control, structural
    properties of large scale systems


  1. Introduction: Development in a visual programming environment to develop interactive
    programs using a graphical user interface, iconic systems and their specifications, messages
    and message passing/ events and event-handling in visual programming environment,
  2. Programming: Programming with graphics devices, interaction with the user in event-based
    graphical environment, implementation of visual systems, different components and controls of visual system. Elementary data base usage.
  3. Project: a programming project involving object-oriented design, user interface design and
    implementation, and coding to support the interface and database linkages. It can be an Internet
    application in a visual programming environment.


  • Overview: The process of knowledge discovery in databases, predictive and descriptive data
    mining techniques, supervised and unsupervised learning techniques.
  • Techniques of Data Mining: Link analysis, predictive modeling, database segmentation, score
    functions for data mining algorithms, Bayesian techniques in data mining.
  • Issues in Data Mining: Scalability and data management issues in data mining algorithms,
    parallel and distributed data mining, privacy, social, ethical issues in KDD and data mining,
    pitfalls of KDD and data mining.


  1. Fuzzy Logic Systems: Notion of fuzziness, fuzzy modeling, operations on fuzzy sets, T-norms
    and other aggregation operators, basics of approximate reasoning, compositional rule of
    inference, fuzzy rule based systems, (Takagi-Sugeno and Mamdani-Assilian models), schemes
    of fuzzification, inferencing, defuzzificatin, fuzzy clustering, fuzzy rule based classifier
  2. Genetic Algorithms: Genetic operators, building block hypothesis, evolution of structure,
    genetic algorithms based on tree and linear graphs, applications in science and engineering
  3. Artificial Neural Networks: The neuron as a simple computing element, the perceptron,
    multilayer neural networks, accelerated learning in multilayer neural networks
    Rough Sets: Information Systems, decision tables, indiscernibly relation, set approximation,
    approximation of family of sets, analysis of decision tables.


  • Introduction and Problem Solving: Various definitions of AI, Introduction to AI applications
    and AI techniques, Production systems, control strategies, reasoning – forward & backward
  • Intelligent Agents: Definitions of a rational agent, reflex, model-based, goal-based, and
    utility-based agents, the environment in which a particular agent operates
  • Search and Game Playing: Breadth first search, depth first search, iterative deepening,
    uniform cost search, hill climbing, simulated annealing, genetic algorithm search, heuristic
    search, Best first search, A* algorithm, AO* algorithm, Minmax & game trees, refining
    minmax, Alpha – Beta pruning, constraint satisfaction
  • Knowledge Representation: First order predicate calculus, resolution, unification, natural
    deduction system, refutation, logic programming, PROLOG, semantic networks, frame system,
    value inheritance, conceptual dependency, Ontologies
  • Planning: basic representation for planning, symbolic-centralized vs. reactive-distributed,
    partial order planning algorithm
  • Uncertainty: different types of uncertainty – degree of belief and degree of truth, various
    probability constructs – prior probability, conditional probability, probability axioms,
    probability distributions, and joint probability distributions, Bayes’ rule, other approaches to
    modeling uncertainty such as Dempster-Shafer theory and fuzzy sets/logic
  • Natural language processing: component steps of communication, contrast between formal
    and natural languages in the context of grammar, parsing, and semantics


  1. Fundamental Steps in Image Processing: Element of visual perception, a simple image
    model, sampling and quantization, some basic relationships between pixel, image geometry in
    2D, image enhancement in the spatial domain.
  2. Introduction to spatial and frequency methods: Basic gray level transformations, histogram equalization, local enhancement, image subtraction, image averaging, basic spatial, filtering, smoothing spatial filters, sharpening spatial filters.
  3. Introduction to the Fourier transformation: Discrete fourier transformation, fast fourier
    transformation, filtering in the frequency domain, correspondence between filtering in the
    spatial and frequency domain smoothing frequency-domain filters, sharpening frequencydomain
    filters, homomorphic filtering, dilation and erosion, opening and closing, hit-or-miss
  4. Some basic morphological algorithms: Line detection, edge detection, gradient operator,
    edge linking and boundary detection, thresholding, region-oriented segmentation,
    representation schemes like chain codes, polygonal approximations, boundary segments,
    skeleton of a region, recognition and interpretation patterns and pattern classes, decisiontheoretic
    methods, introduction to neural network.
  5. Introduction to Image Compression: JPEG, MPEG, Wavelets, operating system issues in
    multimedia, real time OS issues, interrupt latency etc., network management issues Like QOS
    guarantee, resource reservation, traffic specification etc., security issues like digital
    watermarking, partial encryption schemes for video stream encryption.
    Latest developments in field of multimedia like VOIP, video on demand and video


  • Introduction: Neuron as basic unit of Neurobiology, McCulloch-Pitts model, Hebbian
    Hypothesis; limitations of single-layered neural networks.
  • Supervised Learning: Single-layered neural networks, Hopfield-Little model, perceptron
    rules, Gradient-descent algorithms; Multi-layered neural networks: first order methods, back
    propagation algorithm, second order methods, RBF networks; Constructive algorithms: singlehidden layer algorithms, upstart algorithm, cascade correlation algorithm; Unsupervised
    Learning: competitive learning, competition through lateral inhibition.
  • Kernel methods and support vector machines: binary classification, multiclass
    classification, allowing for training errors: soft margin techniques; neural networks and
    temporal sequences: sequence recognition, sequence generation; applications.


  1. Introduction: Optimization problems, neighborhoods, local and global optima, convex sets
    and functions, simplex method, degeneracy; duality and dual simplex algorithm, computational
    considerations for the simplex and dual simplex algorithms-Dantzig-Wolfe algorithms.
  2. Integer Linear Programming: Cutting plane algorithms, branch and bound technique and
    approximation algorithms for traveling salesman problem.
  3. Graph Algorithms: Primal-Dual algorithm and its application to shortest path, Math-flow
    problems (Ford and Fulkerson labeling algorithms, Dijkstra’s algorithm, Ford-Warshall
    algorithms), networking labeling and digraph search, Max-flow problem, matching problem,
    bipartite matching algorithm, non-bipartite matching algorithms, weighted matching-hungarian
    method for the assignment problem, non-bipartite weighted matching problem, efficient
    spanning tree algorithms, algorithm for matroid intersection problem.


  • Introduction: Concept of Software quality, product and process quality, software quality
    metrics, quality control and total quality management, quality tools and techniques, quality
  • Designing software quality assurance system: Statistical methods in quality assurance,
    fundamentals of statistical process control, process capability, Six-sigma quality.
  • Testing: Test strategies, test planning, functional testing, stability testing and debugging
  • Reliability: Basic concepts, reliability measurements, predictions and management.


  1. Overview and Introduction to Bayes Decision Theory: Machine intelligence and
    applications, pattern recognition concepts classification, regression, feature selection,
    supervised learning class conditional probability distributions,Examples of classifiers bayes optimal classifier and error, learning classification approaches.
  2. Linear machines: General and linear discriminants, decision regions, single layer neural
    network, linear separability, general gradient descent, perceptron learning algorithm, mean
    square criterion and widrow-Hoff learning algorithm; multi-Layer perceptrons: two-layers
    universal approximators, backpropagation learning, on-line, off-line error surface, important
  3. Learning decision trees: Inference model, general domains, symbolic decision trees,
    consistency, learning trees from training examples entropy, mutual information, ID3 algorithm
    criterion, C4.5 algorithm continuous test nodes, confidence, pruning, learning with incomplete
  4. Instance-based Learning: Nearest neighbor classification, k-nearest neighbor, nearest
    neighbor error probability,
  5. Machine learning concepts and limitations: Learning theory, formal model of the learnable,sample complexity, learning in zero-bayes and realizable case, VC-dimension, fundamental algorithm independent concepts, hypothesis class, target class, inductive bias, occam’s razor, empirical risk, limitations of inference machines, approximation and estimation errors, Tradeoff.
  6. Machine learning assessment and Improvement: Statistical model selection, structural risk minimization, bootstrapping, bagging, boosting.Support Vector Machines: Margin of a classifier, dual perceptron algorithm, learning nonlinear hypotheses with perceptron kernel functions, implicit non-linear feature space, theory, zero-Bayes, realizable infinite hypothesis class, finite covering, margin-based bounds on risk, maximal margin classifier.


  • Introduction to Embedded Systems: Overview of embedded systems, features, requirements
    and applications of embedded systems, recent trends in the embedded system design, common
    architectures for the ES design, embedded software design issues, communication software,
    introduction to development and testing tools.
  • Embedded System Architecture: Basics of 8 – bit RISC microcontroller (PIC), block
    diagram, addressing modes, instruction set, timers, counters, stack operation, programming
    using PIC controller, basics of 32 – bit microprocessor (ARM), processor and memory
    organization, data operations, flow of control, pipelining in ARM, ARM bus (AMBA).
  • Embedded Software: Programming in embedded environment, Programming for
    microcontrollers such as Intel 8051 and PIC. Overview of Java 2 micro edition (J2ME),
    concept of a MIDLET, applications of J2ME in mobile communication.
  • Interfacing and Communication Links: Serial interfacing, real time clock, SPI / micro wire
    bus, I2C bus, CAN bus, PC parallel port, IRDA data link, PCI bus architecture.
    Operating Systems for Embedded Systems: OS Fundamentals, processes and threads,
    context switching, scheduling issues, inter task communication, introduction to memory
    management, evaluating OS performance, real time operating systems, popular RTOS and their
  • Applications of Embedded Systems: Industrial and control applications, networking and
    telecom applications, DSP and multimedia applications, applications in the area of consumer
    appliances, concept of smart home.


  1. Elementary number theory: Prime numbers, Fermat’s and Euler’s theorems, Testing for
    primality, Chinese remainder theorem, discrete logarithms.
  2. Finite fields: Review of groups, rings and fields; Modular Arithmetic, Euclidean Algorithms,
    Finite fields of the form GF(p), Polynomial Arithmetic, Finite fields of the form GF(2″).
  3. Data Encryption Techniques: Algorithms for block and stream ciphers, private key
    encryption – DES, AES, RC4; Algorithms for public key encryption – RSA, DH Key
    exchange, KERBEROS, elliptic curve cryptosystems.Message authentication and hash functions, Digital Signatures and authentication protocols, Public key infrastructure, Cryptanalysis of block and stream ciphers.


  • Overview: Overview of programming languages, programming paradigms and models.
    Imperative Language: Principles, data, flow of control, program, composition, examples of
    imperative languages.
  • Object-Oriented Paradigms: Principles, classes, inheritance, class hierarchies, polymorphism,
    dynamic binding reference semantics -and their implementation.
  • Functional Programming: Principles, functions, lists, types and polymorphisms, higher-order
    functions, lazy evaluation, equations and pattern matching, program development in LISP,
    implementation of -LISP.
  • Logic Programming: Principles, Horn clauses and their execution, logical variables, relation,
    data structures, controlling the search order.
  • Parallel Programming: Principles of Parallelism, co-routines, communication and
    synchronization, parallel procedural and logic programming concepts and their implementation


  1. Overview of Database Management Concepts and models: Data Definition Language, Data Control Language, Storage management, Query Processing, Transaction Processing,
    Relational Model, Object Oriented Model and Object-Relational model.
  2. Storage Management and Data Representation: Storage and access of data in secondary
    storage, Disk failures, Recovery from disk crashes-RAID levels 1 to 6. Representation of
    various data types, Fixed length/variable length data/record formats, Logical/physical
    addressing schemes, Pointer Swizzling, pinning/unpinning of records.
  3. Index Structures for Single Dimension searches: Primary and secondary indexes, dense and sparse indexes, B+tree indexes, Hash indexes-linear and extensible hash indexes.
    Index Structures for Multidimensional searches: Grid files, KD-trees, Quad trees, R-trees
  4. Query Processing and Optimization: Query parsing, Algorithms and cost estimation for
    various operation – select, project, cross product, join, union, intersection, difference, and
    aggregate operations. Equivalent relational algebra expressions, generation of query plans and
    choice of query plan for query execution. Cost based and Heuristic based query optimization.
  5. Transaction Processing and Concurrency Control: Concept of transaction, ACID
    properties, Serial and concurrent schedules, Serializability, testing for serializablity, Lockbased
    protocols, Timestamp based protocols, deadlock handling
  6. Recovery: Classification of failures, Log based recovery, shadow paging, buffer management


  • Human Resource Planning: How HRP Relates to Organizational Planning or Strategic
    Planning, The need for Human Resource Planning, The Steps in Human Resource Planning
    Process, Situation Analysis, Environmental Scanning and Strategic Planning, Forecasting
    Human Resource Demands.
  • Job Analysis and Job Design: Purpose and uses of Job Analysis, Job Analysis Technique, Job
    Analysis – Methods of Data Collection, Job Design Approaches, Job Characteristic Approach
    to Job Design.
  • The Recruitment Process: Environmental Factors Affecting Recruitment Process,
    Recruitment Methods, Evaluating the Recruitment Process.
  • The Selection Process: Step in Selection Process (Techniques of Selection Process), Ethical
    Standards of Testing, Types of Interviews, Evaluation of the Selection Program.
  • Training and Development: The Functions of Training, Assessing Training Needs, Types of
    Training, Evaluation of Training and Development.
  • Career Planning and Development: Career Development, Career Management.
  • Industrial Relations: Characteristics of Industrial Relations, Significance of Harmonious
    Industrial Relations, Approaches to Industrial Relations, Factors Affecting Industrial Relations
    Strategy, Causes of Poor Industrial Relations, Effects of Poor Industrial Relations.
  • Strategic Human Resource Management: Strategic Human Resource Management, Strategic Planning, Need for Strategic Management, Benefits of Strategic Management, Dysfunctions of Strategic Management.


  1. Introduction to XML: Representing data in XML, Element Content Model, Document Type
    Definition, XML schemas
  2. Presentation of XML documents on the web: HTML, XHTML, CSS, XSLT, XSL-FO,
    XLinks, XPointers, XForms, Xpath
  3. Database Concepts: Review of Relational, Object Relational and Object Oriented Database
  4. Type of Documents: Data-Centric Documents and Document Centric Documents
    Mappings between traditional Databases and XML documents: Mapping Document
    Schemas to Database Schemas -Table-Based Mapping and Object-Relational Mapping, use of
    DOM, SAX and web enabled databases
  5. Query Languages: Template Based Query languages and SQL Based Query Languages,
  6. Native XML databases: Native XML Database Architectures, Storing Data in a Native XML
    Database, Retrieving Data from Native databases, Security, Transactions, Locking and
    Concurrency, Round-tripping
  7. Applications of XML & Databases: Case Studies


  • Satellite Communication and Networks: Geosynchronous satellite, low orbit satellite
    networks, polling, ALOHA, FDMA TDMA, CDMA, low orbit satellite for mobile
    communication, VSAT networks.
  • Mobile Voice Communication and Networks: Global Systems for Mobile communication
    (GSM), Code Division Multiple Access (CDMA).
  • Mobile Data Communication and Networks: High-speed circuits switch data (HSCSD),
    GSM General Pocket Radio Service (GPRS), Third Generation Mobile Systems.

Students of MCA (Master of Computer Application) of 5th Semester can also download the previous year’s Syllabus as well. The links were also mentioned below for the scholars.

Also, read

FAQS About MCA 5th Semester Delhi University Syllabus 2024 Subjects

What is MCA 5th semester Delhi University syllabus?

The MCA 5th semester syllabus for Delhi University includes subjects like Computer Graphics, Artificial Intelligence, Network Security, Software Engineering, and Elective Courses.

What is Computer Graphics?

Computer Graphics is a subject that deals with the creation and manipulation of visual content using computer software. It involves techniques like modeling, rendering, animation, and visualization.

What is Artificial Intelligence?

Artificial Intelligence is the simulation of human intelligence processes by computer systems. It includes techniques like machine learning, natural language processing, and expert systems.

What are the subjects in the MCA 5th semester Delhi University syllabus?

The subjects in the MCA 5th semester Delhi University syllabus are Software Engineering, Computer Networks, Web Technologies, and Elective I (choose one of the following: Artificial Intelligence, Data Science, Cyber Security, or Blockchain Technology).

What is the duration of the MCA 5th semester in Delhi University?

The duration of the MCA 5th semester in Delhi University is generally around 4-5 months, depending on the academic calendar.

What is the marking scheme for MCA 5th semester exams in Delhi University?

The marking scheme for MCA 5th semester exams in Delhi University generally consists of a combination of internal assessment (IA) marks and end-semester examination marks. The weightage for IA marks and end-semester examination marks may vary depending on the subject.

What are the eligibility criteria for admission to the MCA program in Delhi University?

The eligibility criteria for admission to the MCA program in Delhi University are as follows: Candidates must have a bachelor’s degree in any discipline with at least 50% marks in aggregate and Mathematics as one of the subjects at the 10+2 level or at the graduation level. Additionally, candidates must have studied Computer Science / Information Technology as one of the subjects either at the 10+2 level or at the graduation level.

Is there any practical component in the MCA 5th semester Delhi University syllabus?

Yes, there is a practical component in the MCA 5th semester Delhi University syllabus. The practical component is included in subjects such as Software Engineering, Computer Networks, and Web Technologies, among others.

Here in this article, Delhi University MCA 5th Semester Syllabus 2024 – MBA 3rd Year is clearly given. Follow this article to know the complete syllabus of the DU MCA fifth Semester. In this page, you may also download DU MCA 5th Semester Syllabus 2024 in pdf format. Share this article with your friends who want Delhi University MCA 5th Sem Syllabus. For any queries or doubts regarding Delhi University MCA 5th Semester Syllabus – MBA 3rd Year Subjects, you may comment on the below comment box. We will answer them as soon as possible.

Leave a Reply

Your email address will not be published. Required fields are marked *