This presentation is useful to study about data structure and topic is Binary Tree Traversal. This is also useful to make a presentation about Binary Tree Traversal.
The document discusses binary trees and their representations and operations. It defines binary trees as trees where each node has at most two child nodes. It also defines complete binary trees as trees where every node has two children except leaf nodes. The document discusses array and linked representations of binary trees and various traversal operations like preorder, inorder and postorder traversals. It also provides code snippets for inserting and deleting nodes from a binary tree.
Traversal is a process to visit all the nodes of a tree and may print their values too. Because, all nodes are connected via edges (links) we always start from the root (head) node. That is, we cannot randomly access a node in a tree.
This document provides an overview of trees as a non-linear data structure. It begins by discussing how trees are used to represent hierarchical relationships and defines some key tree terminology like root, parent, child, leaf, and subtree. It then explains that a tree consists of nodes connected in a parent-child relationship, with one root node and nodes that may have any number of children. The document also covers tree traversal methods like preorder, inorder, and postorder traversal. It introduces binary trees and binary search trees, and discusses operations on BSTs like search, insert, and delete. Finally, it provides a brief overview of the Huffman algorithm for data compression.
This document discusses process synchronization and hardware solutions to critical section problems. It describes process synchronization as coordinating processes that share resources to maintain data consistency. Race conditions can occur if operations are not properly sequenced. Hardware solutions like locks, test-and-set instructions, and compare-and-swap can enforce mutual exclusion, progress, and bounded waiting to allow only one process in the critical section at a time.
This document defines key terminology used in tree data structures. It explains that a tree is a hierarchical data structure composed of nodes connected by edges. The root node is the top node, with child nodes connected below and leaf nodes at the bottom with no children. Other terms defined include parent, sibling, internal and external nodes, degree, level, height, depth, path, and subtree.
The Tiny Encryption Algorithm (TEA) is a symmetric key encryption algorithm created by David Wheeler and Roger Needham of Cambridge University. TEA is one of the fastest and most efficient cryptographic algorithms due to its minimal memory footprint and maximized speed. It is a Feistel cipher that achieves diffusion and confusion after only six rounds, though thirty-two rounds are recommended for security. TEA performs operations on 32-bit words and encrypts data in 64-bit blocks using a 128-bit key split into four 32-bit subkeys.
The document provides an overview of chapter 1 from the CCNA Routing and Switching Introduction to Networks course. It discusses how networks are used in everyday life and how they have changed the way people interact, learn, work and play. It also describes the basic components of networks, including end devices, intermediary devices, network media, and topologies. Additionally, it differentiates between local area networks (LANs), which span a small geographic area, and wide area networks (WANs), which interconnect LANs over a wider geographical area.
This PPT is all about the Tree basic on fundamentals of B and B+ Tree with it's Various (Search,Insert and Delete) Operations performed on it and their Examples...
Linked lists are linear data structures where elements are linked using pointers. The three main types are singly, doubly, and circular linked lists. Linked lists allow dynamic memory allocation and fast insertion/deletion compared to arrays but slower access. A linked list contains nodes, each with a data field and pointer to the next node. Basic operations on linked lists include insertion, deletion, traversal, and search. Doubly linked lists include pointers to both the next and previous nodes.
Linked lists are linear data structures where each node contains a data field and a pointer to the next node. There are two types: singly linked lists where each node has a single next pointer, and doubly linked lists where each node has next and previous pointers. Common operations on linked lists include insertion and deletion which have O(1) time complexity for singly linked lists but require changing multiple pointers for doubly linked lists. Linked lists are useful when the number of elements is dynamic as they allow efficient insertions and deletions without shifting elements unlike arrays.
Each node in a doubly linked list contains two pointers - one pointing to the next node and one pointing to the previous node. This allows traversal in both directions through the list. A doubly linked list supports common operations like insertion and deletion at both ends of the list as well as anywhere in the list by updating the relevant pointer fields of the nodes. While requiring more space per node, doubly linked lists allow easy traversal backwards through the list and reversing of the list.
This document discusses data structures and linked lists. It provides definitions and examples of different types of linked lists, including:
- Single linked lists, which contain nodes with a data field and a link to the next node.
- Circular linked lists, where the last node links back to the first node, forming a loop.
- Doubly linked lists, where each node contains links to both the previous and next nodes.
- Operations on linked lists such as insertion, deletion, traversal, and searching are also described.
The document discusses priority queues, which are data structures that allow elements to be inserted and removed based on priority. Elements with higher priority are served before those with lower priority. There are two main types - ascending order queues prioritize lower numbers, while descending order queues prioritize higher numbers. Priority queues can be implemented using linked lists, arrays, binary heaps, and binary search trees. Common applications include shortest path algorithms, heap sorting, and operating system processes.
Breadth First Search & Depth First SearchKevin Jadiya
The slides attached here describes how Breadth first search and Depth First Search technique is used in Traversing a graph/tree with Algorithm and simple code snippet.
This document discusses the implementation of a single linked list data structure. It describes the nodes that make up a linked list, which have an info field to store data and a next field pointing to the next node. The document outlines different ways to represent linked lists, including static arrays and dynamic pointers. It also provides algorithms for common linked list operations like traversing, inserting, and deleting nodes from the beginning, end, or a specified position within the list.
The document discusses graph traversal algorithms breadth-first search (BFS) and depth-first search (DFS). It provides examples of how BFS and DFS work, including pseudocode for algorithms. It also discusses applications of BFS such as finding shortest paths and detecting bipartitions. Applications of DFS include finding connected components and topological sorting.
Quicksort is a divide and conquer sorting algorithm that works by partitioning an array around a pivot value. It then recursively sorts the sub-arrays on each side. The key steps are: 1) Choose a pivot element to split the array into left and right halves, with all elements on the left being less than the pivot and all on the right being greater; 2) Recursively quicksort the left and right halves; 3) Combine the now-sorted left and right halves into a fully sorted array. The example demonstrates quicksorting an array of 6 elements by repeatedly partitioning around a pivot until the entire array is sorted.
This document discusses stacks and queues as linear data structures. It defines stacks as last-in, first-out (LIFO) collections where the last item added is the first removed. Queues are first-in, first-out (FIFO) collections where the first item added is the first removed. Common stack and queue operations like push, pop, insert, and remove are presented along with algorithms and examples. Applications of stacks and queues in areas like expression evaluation, string reversal, and scheduling are also covered.
Content of slide
Tree
Binary tree Implementation
Binary Search Tree
BST Operations
Traversal
Insertion
Deletion
Types of BST
Complexity in BST
Applications of BST
This document discusses data abstraction and abstract data types (ADTs). It defines an ADT as a collection of data along with a set of operations on that data. An ADT specifies what operations can be performed but not how they are implemented. This allows data structures to be developed independently from solutions and hides implementation details behind the ADT's operations. The document provides examples of list ADTs and an array-based implementation of a list ADT in C++.
The document discusses abstract data types (ADTs). It defines an ADT as a collection of data together with a set of operations on that data. An ADT specifies what data can be stored and what operations can be performed. Simple ADTs are predefined types like integers, while complex ADTs like lists are user-defined. Lists are presented as an example complex ADT, with elements ordered in a sequence and operations to insert, find, delete and traverse elements.
1) Trees are hierarchical data structures that store data in nodes connected by edges. They are useful for representing hierarchical relationships.
2) Binary search trees allow quick search, insertion, and deletion of nodes due to the organizational property that the value of each node is greater than all nodes in its left subtree and less than all nodes in its right subtree.
3) Common tree traversal algorithms include preorder, inorder, and postorder traversals, which visit nodes in different orders depending on whether the left/right children or root is visited first.
Normalization is a process used to organize data in a database. It involves breaking tables into smaller, more manageable pieces to reduce data redundancy and improve data integrity. There are several normal forms including 1NF, 2NF, 3NF, BCNF, 4NF and 5NF. The document provides examples of tables and how they can be decomposed into different normal forms to eliminate anomalies and redundancy through the creation of additional tables and establishing primary keys.
The document discusses different types of queues, including simple, circular, priority, and double-ended queues. It describes the basic queue operations of enqueue and dequeue, where new elements are added to the rear of the queue and existing elements are removed from the front. Circular queues are more memory efficient than linear queues by connecting the last queue element back to the first, forming a circle. Priority queues remove elements based on priority rather than order of insertion. Double-ended queues allow insertion and removal from both ends. Common applications of queues include CPU and disk scheduling, synchronization between asynchronous processes, and call center phone systems.
Binary trees can be implemented using two representations: linked and array. The linked representation stores each node as a structure with data, left child, and right child pointers. The array representation stores tree data in a 2D array with a root pointer variable. There are four basic tree operations: traversing, searching, inserting, and deleting. Traversing involves visiting nodes in different orders like preorder, inorder, and postorder.
This document discusses binary trees, including their basic definitions, traversal methods, node representations, and functions. It describes binary trees as having a root node that partitions the tree into two disjoint subsets, left and right subtrees. Traversal methods like preorder, inorder, and postorder are explained recursively. Applications of binary search trees for sorting and searching arrays are also covered.
Linked lists are linear data structures where elements are linked using pointers. The three main types are singly, doubly, and circular linked lists. Linked lists allow dynamic memory allocation and fast insertion/deletion compared to arrays but slower access. A linked list contains nodes, each with a data field and pointer to the next node. Basic operations on linked lists include insertion, deletion, traversal, and search. Doubly linked lists include pointers to both the next and previous nodes.
Linked lists are linear data structures where each node contains a data field and a pointer to the next node. There are two types: singly linked lists where each node has a single next pointer, and doubly linked lists where each node has next and previous pointers. Common operations on linked lists include insertion and deletion which have O(1) time complexity for singly linked lists but require changing multiple pointers for doubly linked lists. Linked lists are useful when the number of elements is dynamic as they allow efficient insertions and deletions without shifting elements unlike arrays.
Each node in a doubly linked list contains two pointers - one pointing to the next node and one pointing to the previous node. This allows traversal in both directions through the list. A doubly linked list supports common operations like insertion and deletion at both ends of the list as well as anywhere in the list by updating the relevant pointer fields of the nodes. While requiring more space per node, doubly linked lists allow easy traversal backwards through the list and reversing of the list.
This document discusses data structures and linked lists. It provides definitions and examples of different types of linked lists, including:
- Single linked lists, which contain nodes with a data field and a link to the next node.
- Circular linked lists, where the last node links back to the first node, forming a loop.
- Doubly linked lists, where each node contains links to both the previous and next nodes.
- Operations on linked lists such as insertion, deletion, traversal, and searching are also described.
The document discusses priority queues, which are data structures that allow elements to be inserted and removed based on priority. Elements with higher priority are served before those with lower priority. There are two main types - ascending order queues prioritize lower numbers, while descending order queues prioritize higher numbers. Priority queues can be implemented using linked lists, arrays, binary heaps, and binary search trees. Common applications include shortest path algorithms, heap sorting, and operating system processes.
Breadth First Search & Depth First SearchKevin Jadiya
The slides attached here describes how Breadth first search and Depth First Search technique is used in Traversing a graph/tree with Algorithm and simple code snippet.
This document discusses the implementation of a single linked list data structure. It describes the nodes that make up a linked list, which have an info field to store data and a next field pointing to the next node. The document outlines different ways to represent linked lists, including static arrays and dynamic pointers. It also provides algorithms for common linked list operations like traversing, inserting, and deleting nodes from the beginning, end, or a specified position within the list.
The document discusses graph traversal algorithms breadth-first search (BFS) and depth-first search (DFS). It provides examples of how BFS and DFS work, including pseudocode for algorithms. It also discusses applications of BFS such as finding shortest paths and detecting bipartitions. Applications of DFS include finding connected components and topological sorting.
Quicksort is a divide and conquer sorting algorithm that works by partitioning an array around a pivot value. It then recursively sorts the sub-arrays on each side. The key steps are: 1) Choose a pivot element to split the array into left and right halves, with all elements on the left being less than the pivot and all on the right being greater; 2) Recursively quicksort the left and right halves; 3) Combine the now-sorted left and right halves into a fully sorted array. The example demonstrates quicksorting an array of 6 elements by repeatedly partitioning around a pivot until the entire array is sorted.
This document discusses stacks and queues as linear data structures. It defines stacks as last-in, first-out (LIFO) collections where the last item added is the first removed. Queues are first-in, first-out (FIFO) collections where the first item added is the first removed. Common stack and queue operations like push, pop, insert, and remove are presented along with algorithms and examples. Applications of stacks and queues in areas like expression evaluation, string reversal, and scheduling are also covered.
Content of slide
Tree
Binary tree Implementation
Binary Search Tree
BST Operations
Traversal
Insertion
Deletion
Types of BST
Complexity in BST
Applications of BST
This document discusses data abstraction and abstract data types (ADTs). It defines an ADT as a collection of data along with a set of operations on that data. An ADT specifies what operations can be performed but not how they are implemented. This allows data structures to be developed independently from solutions and hides implementation details behind the ADT's operations. The document provides examples of list ADTs and an array-based implementation of a list ADT in C++.
The document discusses abstract data types (ADTs). It defines an ADT as a collection of data together with a set of operations on that data. An ADT specifies what data can be stored and what operations can be performed. Simple ADTs are predefined types like integers, while complex ADTs like lists are user-defined. Lists are presented as an example complex ADT, with elements ordered in a sequence and operations to insert, find, delete and traverse elements.
1) Trees are hierarchical data structures that store data in nodes connected by edges. They are useful for representing hierarchical relationships.
2) Binary search trees allow quick search, insertion, and deletion of nodes due to the organizational property that the value of each node is greater than all nodes in its left subtree and less than all nodes in its right subtree.
3) Common tree traversal algorithms include preorder, inorder, and postorder traversals, which visit nodes in different orders depending on whether the left/right children or root is visited first.
Normalization is a process used to organize data in a database. It involves breaking tables into smaller, more manageable pieces to reduce data redundancy and improve data integrity. There are several normal forms including 1NF, 2NF, 3NF, BCNF, 4NF and 5NF. The document provides examples of tables and how they can be decomposed into different normal forms to eliminate anomalies and redundancy through the creation of additional tables and establishing primary keys.
The document discusses different types of queues, including simple, circular, priority, and double-ended queues. It describes the basic queue operations of enqueue and dequeue, where new elements are added to the rear of the queue and existing elements are removed from the front. Circular queues are more memory efficient than linear queues by connecting the last queue element back to the first, forming a circle. Priority queues remove elements based on priority rather than order of insertion. Double-ended queues allow insertion and removal from both ends. Common applications of queues include CPU and disk scheduling, synchronization between asynchronous processes, and call center phone systems.
Binary trees can be implemented using two representations: linked and array. The linked representation stores each node as a structure with data, left child, and right child pointers. The array representation stores tree data in a 2D array with a root pointer variable. There are four basic tree operations: traversing, searching, inserting, and deleting. Traversing involves visiting nodes in different orders like preorder, inorder, and postorder.
This document discusses binary trees, including their basic definitions, traversal methods, node representations, and functions. It describes binary trees as having a root node that partitions the tree into two disjoint subsets, left and right subtrees. Traversal methods like preorder, inorder, and postorder are explained recursively. Applications of binary search trees for sorting and searching arrays are also covered.
This document provides information about Dream Valley College for Girls Centre for Educational Excellence. It includes an index and presentation on data structures covering topics like arrays, linked lists, queues, trees, and graphs. The presentation was presented by Harish Sir and includes definitions, examples, and diagrams to explain each data structure concept.
this presentation is made for the students who find data structure as a typical subject. the slide of this presentation is made in such a manner that student will find the subject topics such as stacks,queues,linklist,array, etc.. very easy to understand. in simple maner
this presentation is made by the student of BCA second sem: pooja,shaifali,rani,richa,trishla,pallavi,shivani
Tree and Binary search tree in data structure.
The complete explanation of working of trees and Binary Search Tree is given. It is discussed such a way that everyone can easily understand it. Trees have great role in the data structures.
The document contains questions and answers related to binary search trees and graphs. It discusses finding the minimum element in a binary search tree, different tree traversals like preorder, inorder and postorder, properties of binary search trees like the increasing order of inorder traversal and complexity, balance factors of binary trees, cut vertices in graphs, and properties of complete graphs.
This document discusses different types of tree traversals including pre-order, in-order, and post-order. It defines a binary tree and explains that traversal is the process of visiting every node once. The three techniques - pre-order, in-order, and post-order - are defined by the order in which the root and subtrees are visited. Pseudocode for functions implementing each type of traversal is also provided.
The document discusses binary tree representation and traversal methods. It provides two main representations of binary trees - array and linked representations. It also describes different types of binary trees like full, complete, left-skewed, and right-skewed trees. The document then explains three common traversal techniques for binary trees - inorder, preorder, and postorder traversals. Algorithms and code snippets are given for each traversal. Finally, applications of binary tree traversals are discussed along with expression trees and converting infix to postfix notation using a stack.
Slides cover definition of tree data structure with examples, related terminologies, accessors methods, query methods, generic methods, traversal algorithms (preorder, postorder, inorder) traversal, Binary tree, Binary tree implementation using linked list and array, Binary search
This document discusses binary trees and tree traversal algorithms. It begins with definitions of trees and binary trees. It then covers tree traversal methods including preorder, inorder, postorder and level order traversal. Implementation of these traversals using recursion is shown. Applications to arithmetic expressions and propositional logic are discussed. Node structures for representing binary trees and evaluating expressions are also presented.
This document discusses different types of tree data structures, including binary trees and binary search trees. It provides descriptions of key tree terminology like root, parent, child, leaf nodes, and different tree traversal methods. The document also covers operations on binary search trees like insertion, searching, and deletion. It provides pseudocode examples for these BST operations and discusses handling different cases that can occur during node deletion. Finally, it briefly introduces AVL trees and the concept of balance factors and rotations needed to maintain the balanced tree property.
The document discusses various tree data structures and algorithms related to binary trees. It begins with an introduction to different types of binary trees such as strict binary trees, complete binary trees, and extended binary trees. It then covers tree traversal algorithms including preorder, inorder and postorder traversal. The document also discusses representations of binary trees using arrays and linked lists. Finally, it explains algorithms for operations on binary search trees such as searching, insertion, deletion and rebalancing through rotations in AVL trees.
This document contains information about trees and binary trees. It begins with definitions of trees, tree terminology like root, child, parent and traversals like preorder, inorder and postorder. It then discusses properties of binary trees like complete and full binary trees. Various representations of trees like linked and sequential representations are described. Finally, it provides examples of using trees to represent expressions and evaluating them using traversals.
The document discusses different methods for traversing binary trees, including preorder, inorder, and postorder traversal. It provides pseudocode for algorithms to perform each type of traversal. Examples are given to illustrate the different traversal orders. The document also discusses how to reconstruct a binary tree from two given traversal sequences, such as inorder and preorder traversals.
The document discusses different methods for traversing binary trees, including preorder, inorder, and postorder traversal. It provides pseudocode for algorithms to perform each type of traversal. It also discusses how to reconstruct a binary tree from two given traversal sequences, such as inorder and preorder traversals. Given these two sequences, one can scan the preorder left to right using inorder to separate the tree into left and right subtrees, and recursively construct the tree in this manner.
The document discusses binary trees and various operations on them. It defines what a binary tree is composed of (nodes with values and pointers to left and right children). It describes tree traversals like preorder, inorder and postorder that output the nodes in different orders. It explains two common search strategies - depth-first search (DFS) and breadth-first search (BFS) - and provides examples of how they traverse a sample tree. It also briefly discusses operations like finding the minimum/maximum element, inserting a new element, and deleting an existing element from the binary search tree.
The document discusses tree data structures and binary trees. It provides definitions for key tree terminology like root, child, parent, leaf nodes, and discusses tree traversal methods like preorder, inorder, and postorder traversal. It also covers implementing binary trees using linked lists and building binary search trees with functions for insertion, searching, and deletion of nodes.
This document provides an overview of the this and static keywords in Java. It defines the this keyword as a reference variable that refers to the current object and lists six common uses. The static keyword is used for memory management and can be applied to variables, methods, blocks, and nested classes. Static variables and methods belong to the class rather than objects. The document includes examples and further explanation of static variables, methods, and blocks.
This Presentation is useful to make PPT on the topic "Servlet and Servlet Life Cycle" in Advanced Java. This Presentation is also useful to study this topic.
Dhrumil I. Panchal's document discusses Chomsky Normal Form (CNF) for context free grammars. It defines CNF as productions that are either of the form A->BC, where A, B, C are nonterminals, or A->a, where A is a nonterminal and a is a terminal. It provides the four steps to convert a context free grammar to CNF: 1) eliminate epsilon productions, 2) eliminate unit productions, 3) restrict productions to single terminals or pairs of nonterminals, and 4) shorten strings of nonterminals to length two. An example grammar is converted step-by-step to CNF.
Different Software Testing Types and CMM StandardDhrumil Panchal
This document discusses software engineering concepts including the CMM standard and different types of testing. It defines the five levels of the CMM standard for process maturity. It also describes various types of testing such as unit testing, integration testing, validation testing, system testing, and acceptance testing. For each type of testing it provides details about the goals, steps, and techniques involved.
This document provides information about Dhrumil I. Panchal, a 6th semester computer engineering student at seminar on web design issues. It discusses key topics in web design like display resolution, look and feel, and page layout and linking. Specifically, it notes the importance of display resolution in web design and provides options for addressing different resolutions. It also defines look and feel as the overall visual appearance of a website, including themes, typography, graphics, structure and navigation. Finally, it describes how page layout and linking are used to structure information and connect pages within a website.
Traditional Problems Associated with Computer CrimeDhrumil Panchal
Dhrumil I. Panchal's document discusses traditional problems associated with computer crime from a law enforcement perspective. Some key challenges include physical and jurisdictional concerns due to the intangible nature of digital evidence across borders, a lack of communication between law enforcement agencies, inconsistent laws and community standards, and the low cost and high benefit to perpetrators of computer crimes. Additionally, law enforcement faces resource constraints like limited budgets that impact their ability to acquire necessary training, personnel, hardware, software, and laboratories to effectively investigate computer crimes and compete with private cybersecurity industry.
This Presentation is useful to study about GSM means Global System for Mobile Communication. This Presentation is also useful to make PPT on this topic.
The third speaker at Process Mining Camp 2018 was Dinesh Das from Microsoft. Dinesh Das is the Data Science manager in Microsoft’s Core Services Engineering and Operations organization.
Machine learning and cognitive solutions give opportunities to reimagine digital processes every day. This goes beyond translating the process mining insights into improvements and into controlling the processes in real-time and being able to act on this with advanced analytics on future scenarios.
Dinesh sees process mining as a silver bullet to achieve this and he shared his learnings and experiences based on the proof of concept on the global trade process. This process from order to delivery is a collaboration between Microsoft and the distribution partners in the supply chain. Data of each transaction was captured and process mining was applied to understand the process and capture the business rules (for example setting the benchmark for the service level agreement). These business rules can then be operationalized as continuous measure fulfillment and create triggers to act using machine learning and AI.
Using the process mining insight, the main variants are translated into Visio process maps for monitoring. The tracking of the performance of this process happens in real-time to see when cases become too late. The next step is to predict in what situations cases are too late and to find alternative routes.
As an example, Dinesh showed how machine learning could be used in this scenario. A TradeChatBot was developed based on machine learning to answer questions about the process. Dinesh showed a demo of the bot that was able to answer questions about the process by chat interactions. For example: “Which cases need to be handled today or require special care as they are expected to be too late?”. In addition to the insights from the monitoring business rules, the bot was also able to answer questions about the expected sequences of particular cases. In order for the bot to answer these questions, the result of the process mining analysis was used as a basis for machine learning.
Lagos School of Programming Final Project Updated.pdfbenuju2016
A PowerPoint presentation for a project made using MySQL, Music stores are all over the world and music is generally accepted globally, so on this project the goal was to analyze for any errors and challenges the music stores might be facing globally and how to correct them while also giving quality information on how the music stores perform in different areas and parts of the world.
indonesia-gen-z-report-2024 Gen Z (born between 1997 and 2012) is currently t...disnakertransjabarda
Gen Z (born between 1997 and 2012) is currently the biggest generation group in Indonesia with 27.94% of the total population or. 74.93 million people.
Dr. Robert Krug - Expert In Artificial IntelligenceDr. Robert Krug
Dr. Robert Krug is a New York-based expert in artificial intelligence, with a Ph.D. in Computer Science from Columbia University. He serves as Chief Data Scientist at DataInnovate Solutions, where his work focuses on applying machine learning models to improve business performance and strengthen cybersecurity measures. With over 15 years of experience, Robert has a track record of delivering impactful results. Away from his professional endeavors, Robert enjoys the strategic thinking of chess and urban photography.
AI ------------------------------ W1L2.pptxAyeshaJalil6
This lecture provides a foundational understanding of Artificial Intelligence (AI), exploring its history, core concepts, and real-world applications. Students will learn about intelligent agents, machine learning, neural networks, natural language processing, and robotics. The lecture also covers ethical concerns and the future impact of AI on various industries. Designed for beginners, it uses simple language, engaging examples, and interactive discussions to make AI concepts accessible and exciting.
By the end of this lecture, students will have a clear understanding of what AI is, how it works, and where it's headed.
Oak Ridge National Laboratory (ORNL) is a leading science and technology laboratory under the direction of the Department of Energy.
Hilda Klasky is part of the R&D Staff of the Systems Modeling Group in the Computational Sciences & Engineering Division at ORNL. To prepare the data of the radiology process from the Veterans Affairs Corporate Data Warehouse for her process mining analysis, Hilda had to condense and pre-process the data in various ways. Step by step she shows the strategies that have worked for her to simplify the data to the level that was required to be able to analyze the process with domain experts.
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3. Binary Tree Traversal
Preorder
Inorder
Postorder
Example
4. The most common operations performed on tree
structure is that of traversal. This is a procedure by
which each node in the tree is processed exactly
once in a systematic manner.
There are three ways of traversing a binary tree.
1. Preorder Traversal
2. Inorder Traversal
3. Postorder Traversal
5. Preorder traversal : A B C D E F G
Inorder traversal : C B A E F D G
Postorder traversal : C B F E G D A
Converse Preorder traversal : A D G E F B C
Converse Inorder traversal : G D F E A B C
Converse Postorder traversal : G F E D C B A
A
B
C
D
E
F
G
6. Preorder traversal of a binary tree is defined as follow
Process the root node
Traverse the left subtree in preorder
Traverse the right subtree in preorder
If particular subtree is empty (i.e., node has no left or
right descendant) the traversal is performed by doing
nothing, In other words, a null subtree is considered to
be fully traversed when it is encountered.
The preorder traversal of a tree is given by
A B C D E F G
7. The Inorder traversal of a binary tree is given by
following steps,
Traverse the left subtree in Inorder
Process the root node
Traverse the right subtree in Inorder
• The Inorder traversal of a tree is given by
• C B A E F D G
8. The postorder traversal is given by
Traverse the left subtree in postorder
Traverse the right subtree in postorder
Process the root node
The Postorder traversal of a tree is given by
C B F E G D A
9. If we interchange left and right words in the
preceding definitions, we obtain three new traversal
orders which are called
Converse Preorder (A D G E F B C)
Converse Inorder (G D F E A B C)
Converse Postorder (G F E D C B A)
10. Given a binary tree whose root node address is
given by pointer variable root and whose node
structure is same as described below. This
procedure traverses the tree in preorder, in a
recursive manner.
ROOT LPTR RPTR
12. Given a binary tree whose root node address is
given by pointer variable “root” and whose node
structure is same as described below. This
procedure traverses the tree in inorder, in a
recursive manner.
LPTR ROOT RPTR
14. Given a binary tree whose root node address is
given by pointer variable “root” and whose node
structure is same as described below. This
procedure traverses the tree in postorder, in a
recursive manner.
LPTR RPTR ROOT
17. Inspiration from Prof. Keyur Suthar and Prof.
Rashmin Prajapati
Notes of DS
Textbook of DS
Image from Google images
Some my own knowledge