Канал: Engineering Assignments by Dr Nitha C Velayudhan

System calls and Types of System calls|CST 206: OPERATING SYSTEMS

System calls and Types of System calls|CST 206: OPERATING SYSTEMS

Operating System operations, functions, Service |

Operating System operations, functions, Service |

Operating system overview |CST 206: OPERATING SYSTEMS

Operating system overview |CST 206: OPERATING SYSTEMS

Approaches to hazard mapping

Approaches to hazard mapping

Hazard types and hazard mapping

Hazard types and hazard mapping

Definition and meaning of key terms in Disaster Risk Reduction and Management -Part2

Definition and meaning of key terms in Disaster Risk Reduction and Management -Part2

Definition and meaning of key terms in Disaster Risk Reduction and Management -Part1

Definition and meaning of key terms in Disaster Risk Reduction and Management -Part1

hydrosphere & Biosphere

hydrosphere & Biosphere

Indian monsoon

Indian monsoon

Cyclones & Atmospheric Circulations

Cyclones & Atmospheric Circulations

Green House Effect, Weather & Temperature

Green House Effect, Weather & Temperature

Atmosphere-layers & ozone layer

Atmosphere-layers & ozone layer

COMPOSITION OF THE LITHOSPHERE |Rocks| Soil | Module 1 | DISASTER MANAGEMENT | KTU Syllabus | B.Tech

COMPOSITION OF THE LITHOSPHERE |Rocks| Soil | Module 1 | DISASTER MANAGEMENT | KTU Syllabus | B.Tech

Gradient Descent algorithm- part2 | Machine Learning | CST 383,395,CST 413

Gradient Descent algorithm- part2 | Machine Learning | CST 383,395,CST 413

Gradient Descent algorithm- part1 | Machine Learning | CST 383,395,CST 413

Gradient Descent algorithm- part1 | Machine Learning | CST 383,395,CST 413

Data preprocessing | data mining | CS402 | CST 466 | B.Tech

Data preprocessing | data mining | CS402 | CST 466 | B.Tech

Steps in design and analysis of algorithms || algorithms analysis and design || daa

Steps in design and analysis of algorithms || algorithms analysis and design || daa

RAM model || Random Access Machine Model || Algorithm analysis and design

RAM model || Random Access Machine Model || Algorithm analysis and design

Criteria for analysing the Algorithm || priori and posterior Analysis of algorithms

Criteria for analysing the Algorithm || priori and posterior Analysis of algorithms

solved example of MLE- part2 | Machine Learning | CST 383,395,CST 413

solved example of MLE- part2 | Machine Learning | CST 383,395,CST 413

solved example of MLE- part1 | Machine Learning | CST 383,395,CST 413

solved example of MLE- part1 | Machine Learning | CST 383,395,CST 413

Basics of parameter estimation - maximum likelihood estimation(MLE) | B.tech | CST 383,395,CST 413

Basics of parameter estimation - maximum likelihood estimation(MLE) | B.tech | CST 383,395,CST 413

semi-supervised Learning | difference b/w supervised & unsupervised learning | CST 383,395,CST 413

semi-supervised Learning | difference b/w supervised & unsupervised learning | CST 383,395,CST 413

Unsupervised Learning | Types of Learning| Machine learning paradigms | B.tech | CST 383,395,CST 413

Unsupervised Learning | Types of Learning| Machine learning paradigms | B.tech | CST 383,395,CST 413

Supervised Learning | Types of Learning| Machine learning paradigms | B.tech | CST 383,395,CST 413

Supervised Learning | Types of Learning| Machine learning paradigms | B.tech | CST 383,395,CST 413

How machines learn?| The Basic components of learning process | B.tech |CST 383,395,CST 413

How machines learn?| The Basic components of learning process | B.tech |CST 383,395,CST 413

Machine Learning definition | Traditional programming and ML | B.tech | CST 383,395,CST 413

Machine Learning definition | Traditional programming and ML | B.tech | CST 383,395,CST 413

OLAP Vs OLTP|CS402| data mining and warehousing | Module 1| B.Tech

OLAP Vs OLTP|CS402| data mining and warehousing | Module 1| B.Tech

Data Warehousing ,OLAP ,OLTP Vs DWH |CS402| data mining | Module 1| B.Tech

Data Warehousing ,OLAP ,OLTP Vs DWH |CS402| data mining | Module 1| B.Tech

Data Mining Models and Architecture |CS402| data mining and warehousing | Module 1| B.Tech

Data Mining Models and Architecture |CS402| data mining and warehousing | Module 1| B.Tech

Data Mining Concepts &Applications, Stages ,KDD process,|CS402| CST466data mining | Module 1| B.Tech

Data Mining Concepts &Applications, Stages ,KDD process,|CS402| CST466data mining | Module 1| B.Tech

Text mining | Datamining and warehousing | Module 6| KTU Syllabus | B.Tech

Text mining | Datamining and warehousing | Module 6| KTU Syllabus | B.Tech

Characteristics of Algorithms |Module 1 | DAA/ALGORITHM ANALYSIS & DESIGN | KTU Syllabus | B.Tech

Characteristics of Algorithms |Module 1 | DAA/ALGORITHM ANALYSIS & DESIGN | KTU Syllabus | B.Tech

MCN 301|KTU-Disaster Management |Module -5|Sendai Framework

MCN 301|KTU-Disaster Management |Module -5|Sendai Framework

MCN 301| module -5-The DMAct 2005 and INSTITUTIONAL ARRANGEMENTS FOR DISASTER MANAGEMENT IN INDIA

MCN 301| module -5-The DMAct 2005 and INSTITUTIONAL ARRANGEMENTS FOR DISASTER MANAGEMENT IN INDIA

MCN 301| Disaster Management |Module -5|LEGISLATIONS IN INDIA ON DISASTER MANAGEMENT & NDMPolicy

MCN 301| Disaster Management |Module -5|LEGISLATIONS IN INDIA ON DISASTER MANAGEMENT & NDMPolicy

MCN 301| Disaster Management |Module -5|COMMON DISASTER TYPES IN INDIA- part2

MCN 301| Disaster Management |Module -5|COMMON DISASTER TYPES IN INDIA- part2

MCN 301| Disaster Management |Module -5|COMMON DISASTER TYPES IN INDIA

MCN 301| Disaster Management |Module -5|COMMON DISASTER TYPES IN INDIA

MCN 301| Disaster Management |Module -3|Relief -international relief organizations

MCN 301| Disaster Management |Module -3|Relief -international relief organizations

MCN 301| Disaster Management |Module -3|Disaster Response

MCN 301| Disaster Management |Module -3|Disaster Response

MCN 301| Disaster Management |Module -3|Measures for Disaster Risk Reduction|prevention & mitigation

MCN 301| Disaster Management |Module -3|Measures for Disaster Risk Reduction|prevention & mitigation

MCN 301| Disaster Management |Module -3|Measures for Disaster Risk Reduction| Disaster Preparedness

MCN 301| Disaster Management |Module -3|Measures for Disaster Risk Reduction| Disaster Preparedness

MCN 301| Disaster Management|Module -4| capacity building |KTU B.tech

MCN 301| Disaster Management|Module -4| capacity building |KTU B.tech

MCN 301| Disaster Management|Module -4| Crisis Counselling |KTU B.tech

MCN 301| Disaster Management|Module -4| Crisis Counselling |KTU B.tech

MCN 301| Disaster Management|Module -4| Disaster communication |KTU B.tech

MCN 301| Disaster Management|Module -4| Disaster communication |KTU B.tech

MCN 301| Disaster Management|Module -4| Participatory stakeholder engagement-part3 |KTU B.tech

MCN 301| Disaster Management|Module -4| Participatory stakeholder engagement-part3 |KTU B.tech

MCN 301| Disaster Management|Module -4| Participatory stakeholder engagement-part2 |KTU B.tech

MCN 301| Disaster Management|Module -4| Participatory stakeholder engagement-part2 |KTU B.tech

MCN 301| Disaster Management|Module -4| Participatory stakeholder engagement-part1 |KTU B.tech

MCN 301| Disaster Management|Module -4| Participatory stakeholder engagement-part1 |KTU B.tech

MCN 301| Disaster Management|Module -3|Disaster Risk Management | disaster risk management cycle

MCN 301| Disaster Management|Module -3|Disaster Risk Management | disaster risk management cycle

Expectation-maximisation algorithm- PART2

Expectation-maximisation algorithm- PART2

Expectation-Maximization Algorithm PART-1

Expectation-Maximization Algorithm PART-1

Systems of earth(introduction) | Module-1 | KTU Syllabus | B.Tech

Systems of earth(introduction) | Module-1 | KTU Syllabus | B.Tech

Lithosphere| Module 1 | DISASTER MANAGEMENT | KTU Syllabus | B.Tech

Lithosphere| Module 1 | DISASTER MANAGEMENT | KTU Syllabus | B.Tech

Topological Sorting Algorithm| Module 2 |ALGORITHM ANALYSIS & DESIGN/DAA| KTU Syllabus | B.Tech

Topological Sorting Algorithm| Module 2 |ALGORITHM ANALYSIS & DESIGN/DAA| KTU Syllabus | B.Tech

Kruskal's Algorithm | Module 3 | Minimum Spanning Tree Algorithm | Design and Analysis of Algorithms

Kruskal's Algorithm | Module 3 | Minimum Spanning Tree Algorithm | Design and Analysis of Algorithms

B-tree deletion| Module 2 | Design and Analysis of Algorithms | KTU Syllabus | B.Tech

B-tree deletion| Module 2 | Design and Analysis of Algorithms | KTU Syllabus | B.Tech

Prim’s Algorithm | Module 3 | Minimum Spanning Tree Algorithm | Design and Analysis of Algorithms

Prim’s Algorithm | Module 3 | Minimum Spanning Tree Algorithm | Design and Analysis of Algorithms

Spanning tree | minimum spanning tree | Module 3 | Design and Analysis of Algorithms | KTU Syllabus

Spanning tree | minimum spanning tree | Module 3 | Design and Analysis of Algorithms | KTU Syllabus

DFS traversal| Module 2 |ALGORITHM ANALYSIS & DESIGN,DAA/ | KTU Syllabus | B.Tech

DFS traversal| Module 2 |ALGORITHM ANALYSIS & DESIGN,DAA/ | KTU Syllabus | B.Tech

BFS Analysis and Application | Module 2 |ALGORITHM ANALYSIS & DESIGN,DAA/ | KTU Syllabus | B.Tech

BFS Analysis and Application | Module 2 |ALGORITHM ANALYSIS & DESIGN,DAA/ | KTU Syllabus | B.Tech

BFS Numerical Explained | Module 2 |ALGORITHM ANALYSIS & DESIGN/DAA | KTU Syllabus | B.Tech

BFS Numerical Explained | Module 2 |ALGORITHM ANALYSIS & DESIGN/DAA | KTU Syllabus | B.Tech

Graph introduction | Module 2 |ALGORITHM ANALYSIS & DESIGN,DAA/ | KTU Syllabus | B.Tech

Graph introduction | Module 2 |ALGORITHM ANALYSIS & DESIGN,DAA/ | KTU Syllabus | B.Tech

C4 5 Decision Tree | Datamining and warehousing | Module 3 | KTU Syllabus | B.Tech

C4 5 Decision Tree | Datamining and warehousing | Module 3 | KTU Syllabus | B.Tech

Module 2-B-tree insertion |when order given is an odd number

Module 2-B-tree insertion |when order given is an odd number

MODULE 5-random forest

MODULE 5-random forest

MODULE 5- Ensemble learning -bagging and boosting

MODULE 5- Ensemble learning -bagging and boosting

Module 5- Ensemble learning -voting

Module 5- Ensemble learning -voting

module-5-Hidden Markov Model -part 2

module-5-Hidden Markov Model -part 2

module 5- Hidden Markov Model-introduction-(part1)

module 5- Hidden Markov Model-introduction-(part1)

module-5-kernal function| kernal trick

module-5-kernal function| kernal trick

module 5-multiclass svm

module 5-multiclass svm

module 6- edge detection methods

module 6- edge detection methods

Module-6-Spatial convolution and correlation | linear convolution

Module-6-Spatial convolution and correlation | linear convolution

module-6-measuring dissimilarity

module-6-measuring dissimilarity

module-6 -hierarchical clustering| Agglomerative clustering

module-6 -hierarchical clustering| Agglomerative clustering

module 6- relationship between the pixel (part-2)-digital path |region & boundary| distance measures

module 6- relationship between the pixel (part-2)-digital path |region & boundary| distance measures

Relationship Between Pixel Part 1 | Neighbor | Adjacency | Connectivity | Computer Graphics Module 6

Relationship Between Pixel Part 1 | Neighbor | Adjacency | Connectivity | Computer Graphics Module 6

Digital Image Representation | Computer Graphics | Module 6 | KTU Syllabus | B.Tech

Digital Image Representation | Computer Graphics | Module 6 | KTU Syllabus | B.Tech

Fundamental Steps in Image Processing | Computer Graphics | Module 6 | KTU Syllabus | B.Tech

Fundamental Steps in Image Processing | Computer Graphics | Module 6 | KTU Syllabus | B.Tech

Scanline method | Visible Surface Detection Method | Computer Graphics | Module 5 | KTU Syllabus

Scanline method | Visible Surface Detection Method | Computer Graphics | Module 5 | KTU Syllabus

A Buffer method | Visible Surface Detection Method | Module 5 | Computer Graphics | KTU Syllabus

A Buffer method | Visible Surface Detection Method | Module 5 | Computer Graphics | KTU Syllabus

module 6- digital signal processing introduction

module 6- digital signal processing introduction

module -4 -id3 algorithm

module -4 -id3 algorithm

module-4 :feature selection measures part2

module-4 :feature selection measures part2

module4-issues in decision tree| avoiding overfitting| Reduced Error Pruning |Missing Attribute

module4-issues in decision tree| avoiding overfitting| Reduced Error Pruning |Missing Attribute

module-5 -depth buffer /z buffer method| visible surface detection method

module-5 -depth buffer /z buffer method| visible surface detection method

module 5-backface detection method |visible surface detection methods

module 5-backface detection method |visible surface detection methods

module-4 back propagation algorithm

module-4 back propagation algorithm

module 4-backpropagation introduction and problem explained

module 4-backpropagation introduction and problem explained

module-4feature selection part1

module-4feature selection part1

module-4-decision tree introduction

module-4-decision tree introduction

module -4 -Bezier curve

module -4 -Bezier curve

module-5-visible surface detection algorithms- introduction

module-5-visible surface detection algorithms- introduction

module-4-blobby object/spline/convex hull/control graph

module-4-blobby object/spline/convex hull/control graph

module-4-parametric and geometric continuity condition

module-4-parametric and geometric continuity condition

module-4- Characteristics of artificial neural networks

module-4- Characteristics of artificial neural networks

module 4 :Introduction to artificial neural network | perceptron

module 4 :Introduction to artificial neural network | perceptron

Module-3-regression part3

Module-3-regression part3

module 3-regression- part 2

module 3-regression- part 2

Module-3:regression-part1

Module-3:regression-part1

module 3-Bayesian classifier/Naive bayes algorithm

module 3-Bayesian classifier/Naive bayes algorithm

module 4 -quadric surfaces

module 4 -quadric surfaces

module4 -3D object representation

module4 -3D object representation

module 3-ROC

module 3-ROC

module 4-3d object representation-polygon mesh and plane equation

module 4-3d object representation-polygon mesh and plane equation

module-4 -3d display method

module-4 -3d display method

Module-2 Polygon Filling Algorithm Boundary Fill & Flood Fill (Engineering Assignments)

Module-2 Polygon Filling Algorithm Boundary Fill & Flood Fill (Engineering Assignments)

module4-polygon clipping algorithms-Sutherland Hodgeman and Weiler Atherton algorithm

module4-polygon clipping algorithms-Sutherland Hodgeman and Weiler Atherton algorithm

module2-bresenham's circle drawning algorithm

module2-bresenham's circle drawning algorithm

module-2-midpoint circle drawing algorithm

module-2-midpoint circle drawing algorithm

module 2-bresenham's line drawing algorithm

module 2-bresenham's line drawing algorithm

module1-architecture of raster and random scan display

module1-architecture of raster and random scan display

module 1-interative graphics system -PART2

module 1-interative graphics system -PART2

module1- interactive graphics system -PART1

module1- interactive graphics system -PART1

module 1 -list of input and output devices

module 1 -list of input and output devices

module1-raster and random scan display

module1-raster and random scan display

module 1: flat panel display

module 1: flat panel display

module-1:color CRT-beam penetration,shadow mask techniques and DVST

module-1:color CRT-beam penetration,shadow mask techniques and DVST

module 3- Measuring classifier performance- Precision, recall -Confusion matrix

module 3- Measuring classifier performance- Precision, recall -Confusion matrix

module 3-composite transformation

module 3-composite transformation

module 3 -HOMOGENOUS COORDINATES

module 3 -HOMOGENOUS COORDINATES

Module3-2D TRANSFORMTIONS

Module3-2D TRANSFORMTIONS

module 3-cross validation and resampling

module 3-cross validation and resampling

module 2-principal component analysis

module 2-principal component analysis

module2-feature selection

module2-feature selection

module 2-Introduction to dimensionality reduction

module 2-Introduction to dimensionality reduction

module 2-Model selection

module 2-Model selection

module 2- noise in machine learning

module 2- noise in machine learning

module 2-learning multiple class

module 2-learning multiple class

module 2-generalization in machine learning

module 2-generalization in machine learning

module 1:Hypothesis space (part2 )and version space

module 1:Hypothesis space (part2 )and version space

module 1-supervised learning-input representation ,hypothesis space-PART 1

module 1-supervised learning-input representation ,hypothesis space-PART 1

module-1-Vapnik-Chervonenkis (VC)dimension

module-1-Vapnik-Chervonenkis (VC)dimension

module -1-Different types of learning

module -1-Different types of learning

module 1-part3-General classes/examples of machine learning applications-Regression

module 1-part3-General classes/examples of machine learning applications-Regression

module 3-window to view port transformation-windowing

module 3-window to view port transformation-windowing

module 1-part2-General classes / examples of machine learning applications-Classification

module 1-part2-General classes / examples of machine learning applications-Classification

module 1-part 1-General classes /examples of machine learning -learning association

module 1-part 1-General classes /examples of machine learning -learning association

module 1-Applications of machine learning and Understanding data

module 1-Applications of machine learning and Understanding data

module 3-midpoint subdivision algorithm

module 3-midpoint subdivision algorithm

module 3- cohen -sutherland line clipping algorithm

module 3- cohen -sutherland line clipping algorithm

module 3-introduction to cohen-sutherland line clipping algorithm

module 3-introduction to cohen-sutherland line clipping algorithm

module 2-DDA Line Drawing algorithm

module 2-DDA Line Drawing algorithm

module 3-clipping

module 3-clipping

module 3-windowing

module 3-windowing

module 3-2D -transformation-shear

module 3-2D -transformation-shear

module 3-2D-Transformation-Reflection

module 3-2D-Transformation-Reflection

module 2-PAC-PART 3

module 2-PAC-PART 3

module 2-PAC-PART 2

module 2-PAC-PART 2

module 2-PAC-PART1

module 2-PAC-PART1

module 1-working of CRT

module 1-working of CRT

module 1-introduction to CG part 3

module 1-introduction to CG part 3

module 1-introduction to cg part 2

module 1-introduction to cg part 2

module 1-introduction to ml part2

module 1-introduction to ml part2

module 1-introduction to ml part 1

module 1-introduction to ml part 1

Computer Graphics-Module 1-Basic concepts in Computer Graphics -part 1

Computer Graphics-Module 1-Basic concepts in Computer Graphics -part 1

Graph Mining

Graph Mining

Web Mining -part 2

Web Mining -part 2

Web Mining-PART1

Web Mining-PART1

Dynamic programming- matrix chain multiplication- solved problem

Dynamic programming- matrix chain multiplication- solved problem

Complexity Theory

Complexity Theory

Branch and Bound-Travelling Salesman Problem

Branch and Bound-Travelling Salesman Problem

Branch and Bound-Travelling Salesman Problem

Branch and Bound-Travelling Salesman Problem

Strassen's matrix multiplication -method 2 | Module 3 |AAD/DAA | KTU Syllabus | B.Tech

Strassen's matrix multiplication -method 2 | Module 3 |AAD/DAA | KTU Syllabus | B.Tech

0/1 Knapsack Problem

0/1 Knapsack Problem

N Queens Problem

N Queens Problem

Control abstraction- backtracking

Control abstraction- backtracking

OPTICS

OPTICS

Density-Based Clustering –DBSCAN

Density-Based Clustering –DBSCAN

Hierarchical Clustering method-BIRCH

Hierarchical Clustering method-BIRCH

The Partitioning method: K-Means and K-Medoid Clustering

The Partitioning method: K-Means and K-Medoid Clustering

Categorization of clustering methods

Categorization of clustering methods

FP Growth Algorithm

FP Growth Algorithm

Cluster Analysis Introduction

Cluster Analysis Introduction

APROIR ALGORITHM- Association Rule Mining Algorithm

APROIR ALGORITHM- Association Rule Mining Algorithm

Greedy Knapsack Algorithm with Example Problem

Greedy Knapsack Algorithm with Example Problem

Difference Between Greedy and Dynamic Programming

Difference Between Greedy and Dynamic Programming

Greedy Strategy- Control Abstraction| Module 3 |AAD/DAA| KTU Syllabus | B.Tech

Greedy Strategy- Control Abstraction| Module 3 |AAD/DAA| KTU Syllabus | B.Tech

Prediction

Prediction

Association Rule Mining Introduction

Association Rule Mining Introduction

Accuracy of Classifier

Accuracy of Classifier

Optimal matrix multiplication | DAA/ALGORITHM ANALYSIS & DESIGN | KTU Syllabus-2019 ,2015 | B.Tech

Optimal matrix multiplication | DAA/ALGORITHM ANALYSIS & DESIGN | KTU Syllabus-2019 ,2015 | B.Tech

Dynamic Programming- Principle of Optimality

Dynamic Programming- Principle of Optimality

Numerical on Strassen's Matrix | Module 3 |ALGORITHM ANALYSIS & DESIGN,DAA/ | KTU Syllabus | B.Tech

Numerical on Strassen's Matrix | Module 3 |ALGORITHM ANALYSIS & DESIGN,DAA/ | KTU Syllabus | B.Tech

Divide & Conquer, Strassen's Matrix Multiplication -Analysis |Module3 |AAD/DAA|KTU Syllabus|B.tech

Divide & Conquer, Strassen's Matrix Multiplication -Analysis |Module3 |AAD/DAA|KTU Syllabus|B.tech

Lazy learners- K nearest neighbor classification and worked out problem

Lazy learners- K nearest neighbor classification and worked out problem

Back Propagation Algorithm Explanation with worked out problem

Back Propagation Algorithm Explanation with worked out problem

Neural network-Back propagation (introduction)

Neural network-Back propagation (introduction)

Neural network- Multi-layer Feed Forward Neural Network

Neural network- Multi-layer Feed Forward Neural Network

Merge sort Algorithm & Problem | Module 3 |ALGORITHM ANALYSIS & DESIGN,DAA/ | KTU Syllabus | B.Tech

Merge sort Algorithm & Problem | Module 3 |ALGORITHM ANALYSIS & DESIGN,DAA/ | KTU Syllabus | B.Tech

Introduction to Merge Sort | Module 3 |ALGORITHM ANALYSIS & DESIGN/DAA | KTU Syllabus | B.Tech

Introduction to Merge Sort | Module 3 |ALGORITHM ANALYSIS & DESIGN/DAA | KTU Syllabus | B.Tech

Divide and Conquer| Module 3 |ALGORITHM ANALYSIS & DESIGN,DAA| KTU Syllabus | B.Tech

Divide and Conquer| Module 3 |ALGORITHM ANALYSIS & DESIGN,DAA| KTU Syllabus | B.Tech

module-6-k means clustering2

module-6-k means clustering2

module-6-K means clustering part 1

module-6-K means clustering part 1

module 6-Introduction to clustering in machine learning

module 6-Introduction to clustering in machine learning

module 5- Support Vector Machine

module 5- Support Vector Machine

module 6-Average linkage HAC

module 6-Average linkage HAC

module-6 measure of distance between group of data point| agglomerative Hierarchical Clustering

module-6 measure of distance between group of data point| agglomerative Hierarchical Clustering