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UID USING FLUTTER LAB–23IT53

 UID USING FLUTTER LAB List of Experiments:  Students need to implement the following experiments  1. a) Install Flutter and Dart SDK.      b) Write a simple Dart program to understand the language basics.  2. a) Explore various Flutter widgets (Text, Image, Container, etc.).      b) Implement different layout structures using Row, Column, and Stack widgets.  3. a) Design a responsive UI that adapts to different screen sizes.      b) Implement media queries and breakpoints for responsiveness.  4. a) Set up navigation between different screens using Navigator.      b) Implement navigation with named routes.  5. a) Learn about stateful and stateless widgets.      b) Implement state management using set State and Provider.  6. a) Create custom widgets for specific UI elements.      b) Apply styling using themes and custom styles.  7. a) Design a form with v...
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23CS07- COMPUTER NETWORKS

COMPUTER NETWORKS  UNIT I: Introduction: Network Types, LAN, MAN, WAN, Network Topologies Reference models- The OSI Reference Model- the TCP/IP Reference Model - A Comparison of the OSI and TCP/IP Reference Models Physical Layer –Introduction to Guided Media- Twisted-pair cable, Coaxial cable and Fiber optic cable and introduction about unguided media.  UNIT II: Data link layer: Design issues, Framing: fixed size framing, variable size framing, flow control, error control, error detection and correction codes, CRC, Checksum: idea, one’s complement internet checksum, services provided to Network Layer, Elementary Data Link Layer protocols: simplex protocol, Simplex stop and wait, Simplex protocol for Noisy Channel. Sliding window protocol: One bit, Go back N, Selective repeat-Stop and wait protocol  UNIT – III: Media Access Control: Random Access: ALOHA, Carrier sense multiple access (CSMA), CSMA with Collision Detection, CSMA with Collision Avoidance, Channelization: fr...
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23CS58-COMPUTER NETWORKS LAB

 List of Experiments:  1. Study of Network devices in detail and connect the computers in Local Area Network.  2. Write a Program to implement the data link layer farming methods such as  i) Character stuffing  ii) bit stuffing.  3. Write a Program to implement data link layer farming method checksum.  4. Write a program for Hamming Code generation for error detection and correction.  5. Write a Program to implement on a data set of characters the three CRC polynomials – CRC-12, CRC-16  6. Write a Program to implement Sliding window protocol for Goback N.  7. Write a Program to implement Sliding window protocol for Selective repeat.  8. Write a Program to implement Stop and Wait Protocol.  9. Write a program for congestion control using leaky bucket algorithm  10. Write a Program to implement Distance vector routing algorithm by obtaining routing table at each node (Take an example subnet graph with weights indicating dela...
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23CS59-CLOUD COMPUTING LAB

                                          23CS59-CLOUD COMPUTING LAB List of Experiments: 1. Lab on web services 2. Lab on IPC, messagaging, publish/subscribe 3. Install VirtualBox/VMware Workstation with different flavours of Linux or windows OS on top of windows8 or above. 4. Install a C compiler in the virtual machine created using VirtualBox and execute Simple Programs. 5. Create an Amazon EC2 instance and set up a web-server on the instance and associate an IP address with the instance. In the process, create a security group allowing access to port 80 on the instance.                               OR 6. Do the same with OpenStack 7. Install Google App Engine. Create a hello world app and other simple web applications using python/java. 8. Start a Docker container and se...
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23CS11- CLOUD COMPUTING

 CLOUD COMPUTING  UNIT -I: Introduction to Cloud Computing Fundamentals Cloud computing at a glance, defining a cloud, cloud computing reference model, types of services (IaaS, PaaS, SaaS), cloud deployment models (public, private, hybrid), utility computing, cloud computing characteristics and benefits, cloud service providers (Amazon Web Services, Microsoft Azure, Google AppEngine). UNIT-II: Cloud Enabling Technologies Ubiquitous Internet, parallel and distributed computing, elements of parallel computing, hardware architectures for parallel computing (SISD, SIMD, MISD, MIMD), elements of distributed computing, Inter-process communication, technologies for distributed computing, remote procedure calls (RPC), service-oriented architecture (SOA), Web services, virtualization. UNIT-III: Virtualization and Containers Characteristics of virtualized environments, taxonomy of virtualization techniques, virtualization and cloud Computing, pros and cons of virtualization, technology ...
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DATA SCIENCE

DATA SCIENCE  UNIT – I: Introduction to Data Science: Data Analysis Life Cycle Overview. Data analysis Discovery, Framing Problem, Developing Initial Hypothesis, Sources of Data, Process for Making Sense of Data, Data Preparation, Performing ETLT, Data Conditioning, Survey and Visualize, Common tools for Data Preparation Phase, Data Exploration and Variable Selection, Common tools for the Model Planning and Building Phase, Communicate Results, Operationalize  UNIT – II: Describing Data: Observations and Variables, Types of Variables, Central Tendency, Distribution of the Data, Confidence Intervals, Hypothesis Tests, Student t-test  UNIT – III: Preparing Data Tables: Cleaning the Data, Removing Observations and Variables, Generating Consistent Scales across Variables, New Frequency Distribution, Converting Text to Numbers, Converting Continuous Data to Categories, Combining Variables, Generating Groups, Preparing Unstructured Data UNIT - IV: Understanding Relationships...
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DEEP LEARNING

DEEP LEARNING UNIT-I: Mathematical foundations of Deep Learning Scalars, Vectors, Matrices and Tensors, Multiplying Matrices and Vectors, Identity and Inverse Matrices, Linear dependence and span, Norms, Special kinds of matrices and vectors, Trace operations, Eigen value decomposition.  UNIT-II: Fundamentals of Deep Learning Anatomy of Neural Networks: Layers, Models, Loss functions and optimizers Training Deep Networks: Cost Functions, Optimizers Types of Deep Neural Networks. UNIT-III: Convolutional Neural Networks Motivation, Convolution Operation, Types of layers, Pooling, LENET5 Architecture.  UNIT-IV: Recurrent Neural Networks Architecture of traditional RNN, Types and applications of RNN, Variants of RNNs, Word Embedding using Word2vec  UNIT-V: Regularization and Autoencoders Regularization for Deep Learning: L1 and L2, Dropout, Data Augmentation, Early Stopping, Case study on MNIST data Autoencoders: Architecture, Implementation, Denoising Autoencoders, Spars...
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