The document discusses data structures and provides details about various types of data structures:
1) It describes linear and non-linear data structures, and lists arrays, stacks, queues, trees and graphs as examples.
2) It explains that primitive data structures like integers and characters are basic types directly used by machines, while non-primitive structures like arrays and lists are more sophisticated structures derived from primitive ones.
3) It provides details about common operations on data structures like creation, destruction, selection, updating, searching, sorting, splitting and merging.
The document discusses data structures and provides information on various types of data structures including linear and non-linear data structures. It defines data structures as specialized formats for organizing, processing, retrieving and storing data. Some key points discussed include:
- Data structures include arrays, linked lists, stacks, queues, trees and graphs. They provide efficient methods for storing and accessing data.
- Linear data structures like stacks and queues arrange data in a sequential order while non-linear structures like trees and graphs connect data in a non-sequential manner.
- Common operations on data structures include creation, destruction, selection, updating, searching, sorting, splitting and merging of data.
- Arrays are a basic data structure that
A data structure is a specialized format for organizing, processing, retrieving and storing data. There are several basic and advanced types of data structures, all designed to arrange data to suit a specific purpose.
What are Data Structures? - Definition from WhatIs.com
TechTarget
In this you will learn about
1. Definitions
2. Introduction to Data Structures
3. Classification of Data structures
a. Primitive Data structures
i. int
ii. Float
iii. char
iv. Double
b. Non- Primitive Data structures
i. Linear Data structures
1. Arrays
2. Linked Lists
3. Stack
4. Queue
ii. Non Linear Data structures
1. Trees
2. Graphs
Unit.1 Introduction to Data Structuresresamplopsurat
The document provides an introduction to data structures. It defines a data structure as a way of storing and organizing data efficiently to allow operations to be performed quickly. Data structures can be static or dynamic. An abstract data type (ADT) is a mathematical description of an object and its operations. Algorithms implement ADTs using data structures. There are many data structures because there are tradeoffs between speed, memory usage, elegance, and other factors. Common data structures include lists, trees, hash tables. Operations on data structures include traversing, searching, insertion, deletion and others. Static structures have fixed sizes while dynamic structures have variable sizes.
Data structures are used to organize data efficiently to perform operations on large amounts of data. They include primitive structures like integers and floats, as well as linear structures like arrays, stacks, and queues and non-linear structures like trees and graphs. Common operations on data structures include traversing, inserting, deleting, searching, and sorting data elements. Understanding which data structure to use for a given problem is important to write efficient programs as data volumes continue growing rapidly.
Basic Terminology, Elementary data structure organization, Classification of data structure,
Operations on data structures-Traversing, Inserting, deleting, Searching, sorting, merging
Different Approaches to designing an algorithm · Top-Down approach · Bottom-up approach
Complexity -Time complexity ,Space complexity , Big ‘O’ Notation
This document provides an overview of unit 1 of a data structures course. It discusses prerequisites, contents including definitions of data structures, algorithms, and abstract data types. It also covers different types of arrays like one-dimensional, two-dimensional, and multidimensional arrays. Examples are provided to demonstrate initializing and accessing elements of one-dimensional arrays in C. Key concepts covered include linear and non-linear data structures, abstract data types versus data structures, and common data operations.
The document discusses data structures and provides information on various types of data structures including linear and non-linear data structures. It defines data structures as specialized formats for organizing, processing, retrieving and storing data. Some key points discussed include:
- Data structures include arrays, linked lists, stacks, queues, trees and graphs. They provide efficient methods for storing and accessing data.
- Linear data structures like stacks and queues arrange data in a sequential order while non-linear structures like trees and graphs connect data in a non-sequential manner.
- Common operations on data structures include creation, destruction, selection, updating, searching, sorting, splitting and merging of data.
- Arrays are a basic data structure that
A data structure is a specialized format for organizing, processing, retrieving and storing data. There are several basic and advanced types of data structures, all designed to arrange data to suit a specific purpose.
What are Data Structures? - Definition from WhatIs.com
TechTarget
In this you will learn about
1. Definitions
2. Introduction to Data Structures
3. Classification of Data structures
a. Primitive Data structures
i. int
ii. Float
iii. char
iv. Double
b. Non- Primitive Data structures
i. Linear Data structures
1. Arrays
2. Linked Lists
3. Stack
4. Queue
ii. Non Linear Data structures
1. Trees
2. Graphs
Unit.1 Introduction to Data Structuresresamplopsurat
The document provides an introduction to data structures. It defines a data structure as a way of storing and organizing data efficiently to allow operations to be performed quickly. Data structures can be static or dynamic. An abstract data type (ADT) is a mathematical description of an object and its operations. Algorithms implement ADTs using data structures. There are many data structures because there are tradeoffs between speed, memory usage, elegance, and other factors. Common data structures include lists, trees, hash tables. Operations on data structures include traversing, searching, insertion, deletion and others. Static structures have fixed sizes while dynamic structures have variable sizes.
Data structures are used to organize data efficiently to perform operations on large amounts of data. They include primitive structures like integers and floats, as well as linear structures like arrays, stacks, and queues and non-linear structures like trees and graphs. Common operations on data structures include traversing, inserting, deleting, searching, and sorting data elements. Understanding which data structure to use for a given problem is important to write efficient programs as data volumes continue growing rapidly.
Basic Terminology, Elementary data structure organization, Classification of data structure,
Operations on data structures-Traversing, Inserting, deleting, Searching, sorting, merging
Different Approaches to designing an algorithm · Top-Down approach · Bottom-up approach
Complexity -Time complexity ,Space complexity , Big ‘O’ Notation
This document provides an overview of unit 1 of a data structures course. It discusses prerequisites, contents including definitions of data structures, algorithms, and abstract data types. It also covers different types of arrays like one-dimensional, two-dimensional, and multidimensional arrays. Examples are provided to demonstrate initializing and accessing elements of one-dimensional arrays in C. Key concepts covered include linear and non-linear data structures, abstract data types versus data structures, and common data operations.
This document provides an introduction to data structures. It discusses primitive and non-primitive data structures and their classifications. Linear data structures like arrays, stacks, queues and linked lists are covered, along with non-linear structures like trees and graphs. Common operations on data structures are also summarized such as traversing, searching, inserting and deleting. Finally, abstract data types and examples of common ADTs like lists, stacks and queues are introduced.
This document discusses data structures and provides an introduction and overview. It defines data structures as specialized formats for organizing and storing data to allow efficient access and manipulation. Key points include:
- Data structures include arrays, linked lists, stacks, queues, trees and graphs. They allow efficient handling of data through operations like traversal, insertion, deletion, searching and sorting.
- Linear data structures arrange elements in a sequential order while non-linear structures do not. Common examples are discussed.
- Characteristics of data structures include being static or dynamic, homogeneous or non-homogeneous. Efficiency and complexity are also addressed.
- Basic array operations like traversal, insertion, deletion and searching are demonstrated with pseudocode examples
The document provides an overview of data structures and algorithms. It defines data structures as collections of data organized in a way that allows efficient access and modification. Algorithms are sets of instructions to solve problems or accomplish tasks. Common categories of algorithms include sort, search, delete, insert, and update. Data structures can be classified as primitive, linear, or non-linear. Linear structures include arrays, linked lists, stacks, and queues while non-linear structures include trees and graphs. Common operations on data structures are searching, insertion, deletion, traversing, sorting, and merging.
This document provides an introduction to data structures. It discusses primitive and non-primitive data structures and their classifications. Linear data structures like arrays, stacks, queues and linked lists are covered, along with non-linear structures like trees and graphs. Common operations on data structures like traversing, searching, inserting and deleting are also summarized. Finally, the document introduces abstract data types and provides examples of common ADT specifications for lists, stacks and queues.
This document discusses data structures. It defines data as information stored in computers in various formats like numeric, non-numeric, and character. Data structures organize data in a way that allows for efficient operations. The simplest data structure is a variable, but arrays and structures allow storing multiple data. Linear data structures like stacks, queues, and linked lists as well as non-linear ones like trees and graphs support insertion, deletion and other operations better than variables and arrays. Data structures are used in nearly all programs and software to efficiently store and manipulate customer, contact, and other user data.
Data Structures and algoithms Unit - 1.pptxmexiuro901
it is about data structures and algorithms. this ppt has all data structures like linkedlist, trees, graph,it is about data structures and algorithms. this ppt has all data structures like linkedlist, trees, graph,it is about data structures and algorithms. this ppt has all data structures like linkedlist, trees, graph,
The document discusses linear data structures and lists. It introduces the list abstract data type (ADT) and describes common list operations like finding an element or inserting and deleting elements. It also describes different types of lists, including singly linked lists, circularly linked lists, and doubly linked lists. The document then discusses stack and queue ADTs and their applications.
introduction about data structure_i.pptxpoonamsngr
This document provides an introduction to data structures. It defines data and data structures, and describes different types of data structures including primitive and non-primitive, linear and non-linear structures. It also discusses operations on data structures like creation, destruction, selection and updating. Finally, it covers analyzing the time and space complexity of algorithms.
Data structures provide efficient ways to store and organize data in computers. They are widely used in computer science fields like operating systems, compilers, and artificial intelligence. Data structures enhance performance by allowing fast storage and retrieval of user data. There are two main types - primitive and non-primitive. Primitive types are basic data types predefined in languages like integers while non-primitive are custom types like linked lists. Common data structures include arrays, linked lists, stacks, queues, trees, and graphs, each suited to different tasks. Understanding data structures is essential for optimizing algorithms and improving computational efficiency.
Data Structures & Recursion-Introduction.pdfMaryJacob24
This document provides an introduction to data structures and recursion. It defines data structures as organized collections of data and discusses common data structures like arrays, linked lists, stacks, and queues. Data structures are classified as primitive (like integers and characters) or non-primitive (like arrays and linked lists). Non-primitive structures are further divided into linear (arrays, linked lists) and non-linear (trees, graphs). Memory allocation techniques like static and dynamic allocation are also covered. The document concludes with an overview of recursion, including direct and indirect recursion, and examples of recursive functions like factorial and Fibonacci.
BCA DATA STRUCTURES INTRODUCTION AND OVERVIEW SOWMYA JYOTHISowmya Jyothi
This document introduces basic data structure concepts and terminology. It defines data, data items, entities, records, and files. It classifies data structures as primitive and non-primitive, with arrays, linked lists, stacks, and queues as examples of linear data structures and trees and graphs as examples of non-linear data structures. It describes common operations on data structures like traversing, searching, inserting, deleting, sorting, and merging.
This document provides an overview of advanced data structures and analysis of algorithms. It discusses the need for data structures due to large amounts of data and multiple requests. Data structures provide efficiency, reusability, and abstraction. Linear data structures include arrays and linked lists, while non-linear structures include trees and graphs. Common linear data structures like stacks and queues are also described based on their insertion and deletion rules.
This document discusses topics related to data structures and algorithms. It covers structured programming and its advantages and disadvantages. It then introduces common data structures like stacks, queues, trees, and graphs. It discusses algorithm time and space complexity analysis and different types of algorithms. Sorting algorithms and their analysis are also introduced. Key concepts covered include linear and non-linear data structures, static and dynamic memory allocation, Big O notation for analyzing algorithms, and common sorting algorithms.
This document provides an overview of common data structures and algorithms. It discusses static and dynamic data structures, including arrays, linked lists, stacks, and queues. Arrays allow storing multiple elements of the same type and can be one-dimensional, two-dimensional, or multidimensional. Linked lists connect nodes using pointers and can be singly linked, doubly linked, or circular linked. Stacks follow LIFO principles using push and pop operations, while queues use enqueue and dequeue following FIFO order. These data structures find applications in areas like memory management, expression evaluation, job scheduling, and graph searches.
The document describes data structures and arrays. It defines a data structure as a particular way of organizing data in computer memory. Arrays are described as a basic linear data structure that stores elements at contiguous memory locations that can be accessed using an index. The disadvantages of arrays include a fixed size, slow insertion and deletion, and needing to shift elements to insert in the middle.
This document provides an introduction and overview of data structures and dynamic memory allocation in C programming. It defines key terminology related to data structures like data, records, files, attributes, and fields. It also describes different types of data structures like primitive, non-primitive, homogeneous, non-homogeneous, static, and dynamic data structures. The document explains the need for data structures and their advantages and disadvantages. It also discusses operations that can be performed on data structures and introduces dynamic memory allocation using functions like malloc(), calloc(), free(), and realloc(). Finally, it provides a brief introduction to recursion as a programming concept.
This document provides an introduction and overview of data structures and dynamic memory allocation in C programming. It defines key terminology related to data structures like data, records, files, attributes, and fields. It also describes different types of data structures like primitive, non-primitive, homogeneous, non-homogeneous, static, and dynamic data structures. The document explains the need for data structures and their advantages and disadvantages. It then covers dynamic memory allocation using functions like malloc(), calloc(), realloc(), and free() in C. Finally, it provides a brief introduction to recursion as a process of repeating items in a self-similar way by allowing a function to call itself.
The document discusses height balanced binary trees. A height balanced binary tree is one where, for each node, the heights of the left and right subtrees differ by no more than 1. An AVL tree is a type of height balanced binary tree. The document provides an example of a height balanced tree that is not completely balanced. It also gives a formula to calculate the maximum number of nodes in a balanced binary tree of height h as 2h-1 - 1 internal nodes plus up to 2h leaf nodes.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
This document provides an introduction to data structures. It discusses primitive and non-primitive data structures and their classifications. Linear data structures like arrays, stacks, queues and linked lists are covered, along with non-linear structures like trees and graphs. Common operations on data structures are also summarized such as traversing, searching, inserting and deleting. Finally, abstract data types and examples of common ADTs like lists, stacks and queues are introduced.
This document discusses data structures and provides an introduction and overview. It defines data structures as specialized formats for organizing and storing data to allow efficient access and manipulation. Key points include:
- Data structures include arrays, linked lists, stacks, queues, trees and graphs. They allow efficient handling of data through operations like traversal, insertion, deletion, searching and sorting.
- Linear data structures arrange elements in a sequential order while non-linear structures do not. Common examples are discussed.
- Characteristics of data structures include being static or dynamic, homogeneous or non-homogeneous. Efficiency and complexity are also addressed.
- Basic array operations like traversal, insertion, deletion and searching are demonstrated with pseudocode examples
The document provides an overview of data structures and algorithms. It defines data structures as collections of data organized in a way that allows efficient access and modification. Algorithms are sets of instructions to solve problems or accomplish tasks. Common categories of algorithms include sort, search, delete, insert, and update. Data structures can be classified as primitive, linear, or non-linear. Linear structures include arrays, linked lists, stacks, and queues while non-linear structures include trees and graphs. Common operations on data structures are searching, insertion, deletion, traversing, sorting, and merging.
This document provides an introduction to data structures. It discusses primitive and non-primitive data structures and their classifications. Linear data structures like arrays, stacks, queues and linked lists are covered, along with non-linear structures like trees and graphs. Common operations on data structures like traversing, searching, inserting and deleting are also summarized. Finally, the document introduces abstract data types and provides examples of common ADT specifications for lists, stacks and queues.
This document discusses data structures. It defines data as information stored in computers in various formats like numeric, non-numeric, and character. Data structures organize data in a way that allows for efficient operations. The simplest data structure is a variable, but arrays and structures allow storing multiple data. Linear data structures like stacks, queues, and linked lists as well as non-linear ones like trees and graphs support insertion, deletion and other operations better than variables and arrays. Data structures are used in nearly all programs and software to efficiently store and manipulate customer, contact, and other user data.
Data Structures and algoithms Unit - 1.pptxmexiuro901
it is about data structures and algorithms. this ppt has all data structures like linkedlist, trees, graph,it is about data structures and algorithms. this ppt has all data structures like linkedlist, trees, graph,it is about data structures and algorithms. this ppt has all data structures like linkedlist, trees, graph,
The document discusses linear data structures and lists. It introduces the list abstract data type (ADT) and describes common list operations like finding an element or inserting and deleting elements. It also describes different types of lists, including singly linked lists, circularly linked lists, and doubly linked lists. The document then discusses stack and queue ADTs and their applications.
introduction about data structure_i.pptxpoonamsngr
This document provides an introduction to data structures. It defines data and data structures, and describes different types of data structures including primitive and non-primitive, linear and non-linear structures. It also discusses operations on data structures like creation, destruction, selection and updating. Finally, it covers analyzing the time and space complexity of algorithms.
Data structures provide efficient ways to store and organize data in computers. They are widely used in computer science fields like operating systems, compilers, and artificial intelligence. Data structures enhance performance by allowing fast storage and retrieval of user data. There are two main types - primitive and non-primitive. Primitive types are basic data types predefined in languages like integers while non-primitive are custom types like linked lists. Common data structures include arrays, linked lists, stacks, queues, trees, and graphs, each suited to different tasks. Understanding data structures is essential for optimizing algorithms and improving computational efficiency.
Data Structures & Recursion-Introduction.pdfMaryJacob24
This document provides an introduction to data structures and recursion. It defines data structures as organized collections of data and discusses common data structures like arrays, linked lists, stacks, and queues. Data structures are classified as primitive (like integers and characters) or non-primitive (like arrays and linked lists). Non-primitive structures are further divided into linear (arrays, linked lists) and non-linear (trees, graphs). Memory allocation techniques like static and dynamic allocation are also covered. The document concludes with an overview of recursion, including direct and indirect recursion, and examples of recursive functions like factorial and Fibonacci.
BCA DATA STRUCTURES INTRODUCTION AND OVERVIEW SOWMYA JYOTHISowmya Jyothi
This document introduces basic data structure concepts and terminology. It defines data, data items, entities, records, and files. It classifies data structures as primitive and non-primitive, with arrays, linked lists, stacks, and queues as examples of linear data structures and trees and graphs as examples of non-linear data structures. It describes common operations on data structures like traversing, searching, inserting, deleting, sorting, and merging.
This document provides an overview of advanced data structures and analysis of algorithms. It discusses the need for data structures due to large amounts of data and multiple requests. Data structures provide efficiency, reusability, and abstraction. Linear data structures include arrays and linked lists, while non-linear structures include trees and graphs. Common linear data structures like stacks and queues are also described based on their insertion and deletion rules.
This document discusses topics related to data structures and algorithms. It covers structured programming and its advantages and disadvantages. It then introduces common data structures like stacks, queues, trees, and graphs. It discusses algorithm time and space complexity analysis and different types of algorithms. Sorting algorithms and their analysis are also introduced. Key concepts covered include linear and non-linear data structures, static and dynamic memory allocation, Big O notation for analyzing algorithms, and common sorting algorithms.
This document provides an overview of common data structures and algorithms. It discusses static and dynamic data structures, including arrays, linked lists, stacks, and queues. Arrays allow storing multiple elements of the same type and can be one-dimensional, two-dimensional, or multidimensional. Linked lists connect nodes using pointers and can be singly linked, doubly linked, or circular linked. Stacks follow LIFO principles using push and pop operations, while queues use enqueue and dequeue following FIFO order. These data structures find applications in areas like memory management, expression evaluation, job scheduling, and graph searches.
The document describes data structures and arrays. It defines a data structure as a particular way of organizing data in computer memory. Arrays are described as a basic linear data structure that stores elements at contiguous memory locations that can be accessed using an index. The disadvantages of arrays include a fixed size, slow insertion and deletion, and needing to shift elements to insert in the middle.
This document provides an introduction and overview of data structures and dynamic memory allocation in C programming. It defines key terminology related to data structures like data, records, files, attributes, and fields. It also describes different types of data structures like primitive, non-primitive, homogeneous, non-homogeneous, static, and dynamic data structures. The document explains the need for data structures and their advantages and disadvantages. It also discusses operations that can be performed on data structures and introduces dynamic memory allocation using functions like malloc(), calloc(), free(), and realloc(). Finally, it provides a brief introduction to recursion as a programming concept.
This document provides an introduction and overview of data structures and dynamic memory allocation in C programming. It defines key terminology related to data structures like data, records, files, attributes, and fields. It also describes different types of data structures like primitive, non-primitive, homogeneous, non-homogeneous, static, and dynamic data structures. The document explains the need for data structures and their advantages and disadvantages. It then covers dynamic memory allocation using functions like malloc(), calloc(), realloc(), and free() in C. Finally, it provides a brief introduction to recursion as a process of repeating items in a self-similar way by allowing a function to call itself.
The document discusses height balanced binary trees. A height balanced binary tree is one where, for each node, the heights of the left and right subtrees differ by no more than 1. An AVL tree is a type of height balanced binary tree. The document provides an example of a height balanced tree that is not completely balanced. It also gives a formula to calculate the maximum number of nodes in a balanced binary tree of height h as 2h-1 - 1 internal nodes plus up to 2h leaf nodes.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
3. Syllabus
Unit 1
Data Structure: introduction to data structure, types of data structure: primitive
and non primitive, linear and non linear DS, Data structure operations.
Linear Arrays: Definition and concepts, representation, operations on arrays:
traversing, inserting, operations.
Stacks: Definition and concepts, representations, operations on stack: Push and
Pop.
4. Computer is an electronic machine which is used for data processing and manipulation.
When programmer collects such type of data for processing, he would require to store all of
them in computers main memory.
In order to make how computer work we need to know
Representation of data in computer.
Accessing of data.
How to solve problem step by step.
For doing all of this task we used Data Structure
5. What is Data
Structure
A data structure is a specialized
format for organizing,
processing, retrieving and
storing data.
In computer programming, a
data structure may be selected
or designed to store data for the
purpose of working on it with
various algorithms.
6. Data Structure can be defined as the group of data elements which provides an efficient way of
storing and organizing data in the computer so that it can be used efficiently.
examples of Data Structures are arrays, Linked List, Stack, Queue, etc.
Data Structures are widely used in almost every aspect of Computer Science i.e. Operating System,
Compiler Design, Artificial intelligence, Graphics and many more.
Data Structures are the main part of many computer science algorithms as they enable the
programmers to handle the data in an efficient way.
It plays a vital role in enhancing the performance of a software or a program as the main function of
the software is to store and retrieve the user’s data as fast as possible
7. Data Structure
◦ A data structure is a particular way of organizing data in a computer so that it can be used effectively.
◦ For example, we can store a list of items having the same data-type using the array data structure.
8. The representation of particular data structure in the main memory of a computer is called as storage structure.
The storage structure representation in auxiliary memory is called as file structure.
It is define as the way of storing and manipulating data in organized form so that it can be used efficiently
Data Structure mainly specifies the following four things:
1)organization of data 2)accessing method 3)degree of associativity 4) processing alternative for information
Algorithm + Data Structure = Program
Data Structure study Covers the following points
1) Amount of memory require to store
2) Amount of time require to process
3) Representation of data in memory
4) Operations performs on data
9. Types Of DS
The DS are divided into two
types:
1) Primitive
2) Non primitive
Non primitive divided into two
type
1) Linear DS
2) Non linear DS
10. DATA TYPES
A particular kind of data item, as defined by the values it can take, the Programming language
used, or the operations that can be performed on it.
◦ Primitive Data Structure
◦ Primitive Data Structure are basic structure and directly operated upon by machine instructions.
◦ Primitive data structures have different representations on different computers.
◦ Integers, floats, character and pointers are example of primitive data structures.
◦ These data types are available in most programming languages as built in type.
Integer: It is a data type which allows all values without fraction part. We can used it for whole numbers.
Float: It is a data type which is use for storing fraction numbers.
Character: It is a data type which is used for character values.
Pointer: A variable that hold memory address of another variable are called pointer.
11. Non Primitive Data Type
◦ These are more sophisticated data structures.
◦ These are derived from primitive data structure.
◦ The non – primitive data structures emphasize structuring of a group of homogeneous or heterogeneous data items.
◦ Example of non – primitive data types are Array, List, and File etc.
◦ A non – primitive data type is further divided into Linear and non – Linear data structure.
Array: An array is a fixed size sequenced collection of elements of the same data type.
List: An ordered set containing variable number of elements is called as List.
File: A file is a collection of logically related information. It can be viewed as a large list of records consisting of
various fields.
12. Linear Data Structures
A linear data structure simply means that it is a storage format of the data in
the memory in which the data are arranged in contiguous blocks of memory.
Example is the array of characters it represented by one character after
another.
In the linear data structure, member elements form a sequence in the
storage.
There are two ways to represent a linear data structure in memory.
static memory allocation
dynamic memory allocation
The possible operations on the linear data structure are:
1) Traversing 2) Insertion 3) Deletion 4) searching 5) sorting
6) merging
13. ◦ Example of Linear data structure are Stack and Queue
Stack
◦ Stack is a data structure in which insertion and deletion
operations are performed at one end only.
◦ The insertion operation is referred to as ‘PUSH’ and deletion
is referred as ‘POP’ operation
◦ Stack is also called as Last In First Out (LIFO) data structure.
Queue
◦ The data structure which permits the insertion at one and
deletion at another end, known as Queue.
◦ End at which deletion is occurs is known as FRONT end and
another end at which insertion occurs is known as REAR end.
◦ Queue is also called as First In First Out (FIFO)
14. ◦ Non linear DS are those data structure in which data items are not arranged in a
sequence.
◦ Example on Non Linear DS are Tree and Graph.
TREE
◦ A Tree can be define as finite data items (nodes) in which data items are arranged
in branches and sub branches
◦ Tree represent the hierarchical relationship between various elements
◦ Tree consist of nodes connected by edge, the represented by circle and edge lives
connecting to circle.
Graph
◦ Graph is collection of nodes (information) and connecting edges (Logical relation)
between nodes.
◦ A tree can be viewed as restricted graph
◦ Graph have many types: 1) Simple graph 2) Mixed graph 3) Multi graph 4) Directed
graph 5) Un-directed graph
Components of Graph
15. Difference Between Linear and Non Linear Data Structure
Linear Data Structure
◦ Every item is related to its previous and next item.
◦ Data is arranged in linear sequence.
◦ Data items can be traversed in a single run
◦ E.g. Array, Stacks, Linked list, Queue
◦ Implementation is easy.
Non – Linear Data Structure
◦ Every item is attached with many other items.
◦ Data is not arranged in sequence.
◦ Data cannot be traversed in a single run.
◦ E.g. Tree, Graph
◦ Implementation is difficult.
16. Operation on Data Structures
Design of efficient data structure must take operations to be performed on the DS into account. The most commonly
used operations on DS are broadly categorized into following types
1. Create: This operation results in reserving memory for program elements. This can be done by declaration
statement Creation of DS may take place either during compile-time or run-time.
2. Destroy: This operation destroy memory space allocated for specified data structure .
3. Selection: This operation deals with accessing a particular data within a data structure.
4. Updation: It updates or modifies the data in the data structure.
5. Searching: It finds the presence of desired data item in the list of data items, it may also find locations of all
elements that satisfy certain conditions.
6. Sorting: This is a process of arranging all data items in a DS in particular order, for example either ascending order or
in descending order.
7. Splitting: It is a process of partitioning single list to multiple list.
8. Merging: It is a process of combining data items of two different sorted list into single sorted list.
9. Traversing: It is a process of visiting each and every node of a list in systematic manner.
17. What are Arrays?
Array is a container which can
hold a fix number of items and
these items should be of the
same type.
Most of the data structures make
use of arrays to implement their
algorithms.
•Following are the important
terms to understand the concept
of Array.
Element − Each item stored
in an array is called an element.
Index − Each location of an
element in an array has a
numerical index, which is used to
identify the element.
1. An array is a container of elements.
2. Elements have a specific value and data type, like "ABC", TRUE or
FALSE, etc.
3. Each element also has its own index, which is used to access the
element.
18. • Elements are stored at
contiguous memory
locations.
• An index is always less
than the total number of
array items.
• In terms of syntax, any
variable that is declared as
an array can store multiple
values.
• Almost all languages have
the same comprehension
of arrays but have different
ways of declaring and
initializing them.
• However, three parts will
always remain common in
all the initializations, i.e.,
array name, elements, and
the data type of elements.
•Array name: necessary for easy reference to the collection of
elements
•Data Type: necessary for type checking and data integrity
•Elements: these are the data values present in an array
19. How to access
a specific
array value?
You can access any array
item by using its index
Syntax
arrayName[indexNum]
Example
balance[1]
Here, we have accessed the second value of the array using its
index, which is 1. The output of this will be 200, which is basically
the second value of the balance array.
20. ◦ Array Representation
◦ Arrays can be declared in various ways in different languages. For illustration, let's take C array declaration.