The document discusses object oriented programming concepts in C++ including classes, objects, data members, member functions, data abstraction, encapsulation, inheritance, polymorphism, access specifiers, and constructors. It provides examples of defining a class with private, public, and protected data members and member functions. Constructors such as the default, parameterized, and copy constructor are demonstrated. Inheritance concepts such as the base class, derived class, types of inheritance and visibility modes are explained.
- A class is the most important feature of C++ that supports object-oriented programming (OOP). It allows a program to be designed using classes which are a collection of data and functions.
- When an object of a class is declared, memory is allocated for that object's data members. However, defining a class alone does not allocate memory - it only specifies the data members and member functions.
- Member functions can access and manipulate the class's data members. They are called through an object using the dot operator. Constructors are special member functions that initialize an object's data members when it is created.
Object Oriented Programming involves modeling real-world entities as objects that encapsulate both data and behavior. Classes define these objects by grouping related data members and functions together, and objects are instantiated from classes. Some key aspects of OOP include:
1. Encapsulation which involves hiding implementation details within classes and exposing a public interface.
2. Inheritance which allows a derived class to extend a base class while retaining shared properties.
3. Dynamic binding which enables polymorphic behavior where derived classes can exhibit different behavior than base classes in the same context.
Object Oriented Programming involves modeling real-world entities as objects that encapsulate both data and behavior. Classes define these objects by grouping the data (attributes) and functions (methods) that operate on that data. In C++, classes use access specifiers like public and private to control whether data and methods can be accessed from outside the class or only within the class. Methods are defined either inside or outside the class using the scope resolution operator. Objects are instantiated from classes and their methods and data can be accessed using dot or arrow operators.
C++ppt. Classs and object, class and objectsecondakay
1. Classes are blueprints that define objects with attributes (data members) and behaviors (member functions). Objects are instantiated from classes.
2. The Time class implements a time abstract data type with data members for hours, minutes, seconds and member functions to set time and print time in different formats.
3. Classes allow for encapsulation of data and functions, information hiding of implementation details, and software reusability through class libraries.
Object Oriented Programming Concepts using JavaGlenn Guden
This document discusses object-oriented programming and compares old procedural programming techniques using structures to the newer object-oriented approach using classes. Specifically:
- Old programming used structures to define data types and separate functions to operate on those structures.
- The new object-oriented approach defines classes that encapsulate both the data structure and functions together as objects.
- Some key benefits of the object-oriented approach are that new types of objects can be added without changing existing code, and that new objects can inherit features from existing objects, making the code easier to modify.
The document discusses classes and objects in C++. It defines a class as a collection of objects that have identical properties and behaviors. A class binds data and functions together. It then explains class declarations and definitions, access specifiers (private, public, protected), member functions defined inside and outside the class, arrays as class members, objects as function arguments, difference between structures and classes, and provides an example program to calculate simple interest using classes and objects.
The document outlines the course content for a C++ introductory course, including introductions to OOP concepts like classes and objects, pointers, functions, inheritance, and polymorphism. It also covers basic C++ programming concepts like I/O, data types, operators, and data structures. The course aims to provide students with fundamental C++ programming skills through explanations and examples of key C++ features.
Here is a C++ program that implements a Polynomial class with overloaded operators as specified in the question:
#include <iostream>
using namespace std;
class Term {
public:
int coefficient;
int exponent;
Term(int coeff, int exp) {
coefficient = coeff;
exponent = exp;
}
};
class Polynomial {
public:
Term* terms;
int numTerms;
Polynomial() {
terms = NULL;
numTerms = 0;
}
Polynomial(Term t[]) {
terms = t;
numTerms = sizeof(t)/sizeof(t[0]);
}
~Polynomial() {
delete[] terms;
}
Polynomial
Evaluating Your Learning to Rank Model: Dos and Don’ts in Offline/Online Eval...Sease
1. The document discusses evaluating learning to rank models, including offline and online evaluation methods. Offline evaluation involves building a test set from labeled data and evaluating metrics like NDCG, while online evaluation uses methods like A/B testing and interleaving to directly measure user behavior and business metrics.
2. Common mistakes in offline evaluation include having only one sample per query, single relevance labels per query, and unrepresentative test samples. While offline evaluation provides efficiency, online evaluation allows observing real user interactions and model impact on key metrics.
3. Recommendations are given to test models both offline and online, with online testing providing advantages like measuring actual business outcomes and interpreting model effects.
This document provides an overview of C++ programming concepts including:
- Procedure-oriented programming focuses on tasks like reading, calculating and printing using functions, while object-oriented programming emphasizes data through objects and classes.
- C++ was developed to include object-oriented features while retaining C's simplicity, with classes, inheritance, and other features enabling abstract data types.
- Key concepts covered include classes, objects, member functions, inline functions, passing objects as parameters, returning objects, arrays of objects, and function overloading. Examples are provided to illustrate each concept.
This C++ PowerPoint presentation stands out for its clear and concise explanation of complex concepts, making it accessible to both beginners and advanced programmers. The structured flow from basic syntax to more advanced topics like object-oriented programming and memory management showcases a deep understanding of the language. The visuals, including diagrams and code snippets, enhance comprehension and keep the audience engaged. Overall, the presentation strikes a perfect balance between technical depth and visual clarity, making it an excellent resource for anyone learning or teaching C++.
This document provides an overview of C++ programming concepts including:
- Procedure-oriented programming focuses on tasks like reading, calculating and printing using functions, while object-oriented programming emphasizes data through objects and classes.
- Some problems with C include lack of consideration for data elements and lack of security for networks.
- C++ classes contain variables and functions to characterize objects. Data and functions are tied together and data is hidden.
- Key concepts explained include objects, member functions, constructors, destructors, inheritance and polymorphism.
- Examples demonstrate basic C++ programs, classes, objects, arrays of objects, function overloading and the this pointer.
The document discusses object-oriented programming concepts like classes, objects, encapsulation, abstraction, and information hiding. It provides examples of procedural programming versus object-oriented programming. Key topics from the document include defining a C++ class, creating objects from classes, accessing data members of objects, defining member functions, and encapsulating data and functions into classes.
The document discusses object-oriented programming concepts like classes, objects, encapsulation, abstraction, and information hiding. It provides examples of procedural programming versus object-oriented programming. Key topics from the document include defining a C++ class, creating objects from classes, accessing data members of objects, defining member functions within and outside of classes, and the principles of data encapsulation, abstraction, and information hiding in OOP. The document also contrasts procedural, object-oriented, and generic programming techniques.
Chapter 2 OOP using C++ (Introduction).pptxFiraolGadissa
Introduction to Object-Oriented Programming
Object-Oriented Programming (OOP) is a programming paradigm that organizes software design around data, or objects, rather than functions and logic. It is widely used for developing complex, scalable, and maintainable software systems. The core principles of OOP include encapsulation, abstraction, inheritance, and polymorphism.
Key Concepts of OOP
Encapsulation: This involves bundling data and methods that operate on that data within a single unit, called an object. It helps protect the internal state of an object from external interference23.
Abstraction: This principle focuses on exposing only necessary information while hiding complex details. It allows users to interact with objects without knowing their internal workings23.
Inheritance: This feature enables a new class (subclass) to inherit properties and behaviors from an existing class (superclass), promoting code reuse and hierarchical organization23.
Polymorphism: This allows objects of different classes to be treated as objects of a common superclass. It enables multiple behaviors to be implemented through a common interface23.
Object Technology and Programming Environment
Object Technology: This refers to the use of objects to model real-world entities in software development. It includes classes, objects, inheritance, polymorphism, and encapsulation7.
Programming Environment: OOP is typically supported in class-based languages like Java, Python, and C++. These environments provide tools for designing, developing, and testing object-oriented software
The document discusses object-oriented programming concepts like objects, classes, and encapsulation. It provides examples of a Circle class to illustrate these concepts. The Circle class defines data fields like radius and methods like getArea(). Objects of the Circle class can be created, each with their own radius value. The document emphasizes that classes should encapsulate data to prevent direct modification and make the code easier to maintain. It also covers other class concepts like constructors, the dot operator, constant object names, and inline functions.
The document discusses various concepts related to C++ classes including:
1. Structure definitions, accessing structure members using dot operator and pointers. Class definitions include data members and member functions.
2. Class objects are created using the class name followed by the object name. Class members are accessed using dot operator. Member functions can be defined inside or outside the class.
3. Access specifiers like private, public, and protected control access to class members. Constructors and destructors are special member functions for object initialization and destruction.
The document discusses object-oriented programming (OOP) concepts in C++. It defines key OOP concepts like classes, objects, encapsulation, inheritance and polymorphism. It explains that in OOP, classes encapsulate both data and functions that operate on that data. Classes define public and private sections to control access to members. The document also provides examples to demonstrate class definition, object creation, member functions and destructors.
This document provides an introduction to classes and objects in C++. It defines key concepts like class, object, member functions, access specifiers, and arrays of objects. It also discusses defining objects of a class, accessing class members, passing objects as function arguments, and the differences between classes and structures in C++.
This document discusses object-oriented programming and C++ classes. It defines key concepts like encapsulation, classes, objects, and methods. A class defines the data and functions that operate on that data. Classes encapsulate attributes and behaviors into user-defined types. The document provides examples of classes like Circle and Time that model real-world entities, demonstrating how to define classes, create objects, declare methods, access members, and handle constructors and destructors. Object-oriented programming simplifies code through modularity and reuse via classes.
This document discusses object-oriented programming concepts in C++ including classes, objects, constructors, destructors, and friend functions. It begins by explaining that classes are abstract data types that contain data members and member functions. It then provides examples of declaring a class, creating objects, and accessing class members. It also covers topics like static class members, arrays of objects, constructor and destructor definitions and uses, and declaring friend functions to allow non-member functions access to private class members.
Python is one of the most widely used high-level programming languages in the world. Known for its simplicity, readability, and versatility, Python has become a go-to language for beginners and experienced developers alike. From web development and data science to machine learning and automation, Python offers a range of applications across different fields. Learning Python can be an exciting journey, especially for those just starting with coding. It’s designed to be user-friendly, with a syntax that is easy to understand and intuitive, making it a popular choice for beginners who are interested in mastering programming concepts. In this essay, we will explore the basic coding skills that anyone new to Python should focus on, including variables, data types, control structures, functions, object-oriented programming, and more.
1. Variables and Data Types
One of the first concepts to grasp when learning Python is the idea of variables and data types. Variables are used to store information that can be used and manipulated later in a program. In Python, you don’t need to declare the type of variable explicitly; it is inferred based on the value assigned to it. The basic data types in Python include integers (whole numbers), floating-point numbers (decimal numbers), strings (text), and booleans (True/False). For example, a variable can store an integer like x = 10 or a string like name = "Alice". Understanding how to use these data types effectively is crucial for performing mathematical calculations, manipulating text, and making logical comparisons in your programs. Additionally, Python provides more complex data types like lists, tuples, dictionaries, and sets, which are essential for working with collections of data.
2. Control Structures: Conditionals and Loops
Control structures are fundamental to any programming language, and Python is no exception. They allow programmers to control the flow of execution in a program. The two most common types of control structures are conditionals (if-else statements) and loops (for and while loops). Conditionals allow the program to make decisions based on specific conditions. For instance, you can use an if statement to check whether a number is positive or negative and then print an appropriate message. Loops, on the other hand, enable you to repeat a block of code multiple times, which is useful for tasks like iterating over items in a list or performing an action until a condition is met. Python’s for loop is typically used when you know how many times to iterate, while the while loop is useful when you want to repeat an action until a certain condition is met.
3. Functions: Modular Code for Reusability
Functions in Python are blocks of reusable code that allow you to group related operations together. Functions help break down large programs into smaller, more manageable pieces. They can be called multiple times with different inputs, which makes your code more efficient and easier to read.A fun
In the rapidly evolving field of machine learning (ML), the focus is often placed on developing sophisticated algorithms and models that can learn patterns, make predictions, and generate insights from data. However, one of the most critical challenges in building effective machine learning systems lies in ensuring the quality of the data used for training, testing, and validating these models. Data quality directly influences the model's performance, accuracy, and ability to generalize to unseen examples. Unfortunately, in real-world applications, data is rarely perfect, and it is often riddled with various types of errors that can lead to misleading conclusions, flawed predictions, and potentially harmful outcomes. These errors in experimental observations, also referred to as data errors or measurement errors, can significantly compromise the effectiveness of machine learning systems. The sources of these errors are diverse, ranging from technical failures, such as malfunctioning sensors or corrupted datasets, to human errors in data collection, labeling, or interpretation. Furthermore, errors may emerge during the data preprocessing stages, such as incorrect normalization, improper handling of missing data, or the introduction of noise through faulty sampling techniques. These errors can manifest in several ways, including outliers, missing values, mislabeled instances, noisy data, or data imbalances, each of which can influence how well a machine learning model performs. Understanding the nature of these errors and developing strategies to mitigate their impact is crucial for building robust and reliable machine learning models that can operate in real-world environments. Moreover, the impact of errors is not only a technical issue; it also raises significant ethical concerns, particularly when the models are used to inform high-stakes decisions, such as in healthcare, criminal justice, or finance. If errors are not properly addressed, models may inadvertently perpetuate biases, amplify inequalities, or produce inaccurate predictions that negatively affect individuals and communities. Therefore, a thorough understanding of errors in experimental observations is essential for improving the reliability, fairness, and ethical standards of machine learning applications. This introductory discussion provides the foundation for exploring the various types of errors that arise in machine learning datasets, examining their origins, their effects on model performance, and the various methods and techniques available for detecting, correcting, and mitigating these errors. By delving into the challenges posed by errors in experimental observations, we aim to provide a comprehensive framework for addressing data quality issues in machine learning and to highlight the importance of maintaining data integrity in the development and deployment of machine learning systems. This exploration of errors will also touch upon the broader implications for research
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The document discusses classes and objects in C++. It defines a class as a collection of objects that have identical properties and behaviors. A class binds data and functions together. It then explains class declarations and definitions, access specifiers (private, public, protected), member functions defined inside and outside the class, arrays as class members, objects as function arguments, difference between structures and classes, and provides an example program to calculate simple interest using classes and objects.
The document outlines the course content for a C++ introductory course, including introductions to OOP concepts like classes and objects, pointers, functions, inheritance, and polymorphism. It also covers basic C++ programming concepts like I/O, data types, operators, and data structures. The course aims to provide students with fundamental C++ programming skills through explanations and examples of key C++ features.
Here is a C++ program that implements a Polynomial class with overloaded operators as specified in the question:
#include <iostream>
using namespace std;
class Term {
public:
int coefficient;
int exponent;
Term(int coeff, int exp) {
coefficient = coeff;
exponent = exp;
}
};
class Polynomial {
public:
Term* terms;
int numTerms;
Polynomial() {
terms = NULL;
numTerms = 0;
}
Polynomial(Term t[]) {
terms = t;
numTerms = sizeof(t)/sizeof(t[0]);
}
~Polynomial() {
delete[] terms;
}
Polynomial
Evaluating Your Learning to Rank Model: Dos and Don’ts in Offline/Online Eval...Sease
1. The document discusses evaluating learning to rank models, including offline and online evaluation methods. Offline evaluation involves building a test set from labeled data and evaluating metrics like NDCG, while online evaluation uses methods like A/B testing and interleaving to directly measure user behavior and business metrics.
2. Common mistakes in offline evaluation include having only one sample per query, single relevance labels per query, and unrepresentative test samples. While offline evaluation provides efficiency, online evaluation allows observing real user interactions and model impact on key metrics.
3. Recommendations are given to test models both offline and online, with online testing providing advantages like measuring actual business outcomes and interpreting model effects.
This document provides an overview of C++ programming concepts including:
- Procedure-oriented programming focuses on tasks like reading, calculating and printing using functions, while object-oriented programming emphasizes data through objects and classes.
- C++ was developed to include object-oriented features while retaining C's simplicity, with classes, inheritance, and other features enabling abstract data types.
- Key concepts covered include classes, objects, member functions, inline functions, passing objects as parameters, returning objects, arrays of objects, and function overloading. Examples are provided to illustrate each concept.
This C++ PowerPoint presentation stands out for its clear and concise explanation of complex concepts, making it accessible to both beginners and advanced programmers. The structured flow from basic syntax to more advanced topics like object-oriented programming and memory management showcases a deep understanding of the language. The visuals, including diagrams and code snippets, enhance comprehension and keep the audience engaged. Overall, the presentation strikes a perfect balance between technical depth and visual clarity, making it an excellent resource for anyone learning or teaching C++.
This document provides an overview of C++ programming concepts including:
- Procedure-oriented programming focuses on tasks like reading, calculating and printing using functions, while object-oriented programming emphasizes data through objects and classes.
- Some problems with C include lack of consideration for data elements and lack of security for networks.
- C++ classes contain variables and functions to characterize objects. Data and functions are tied together and data is hidden.
- Key concepts explained include objects, member functions, constructors, destructors, inheritance and polymorphism.
- Examples demonstrate basic C++ programs, classes, objects, arrays of objects, function overloading and the this pointer.
The document discusses object-oriented programming concepts like classes, objects, encapsulation, abstraction, and information hiding. It provides examples of procedural programming versus object-oriented programming. Key topics from the document include defining a C++ class, creating objects from classes, accessing data members of objects, defining member functions, and encapsulating data and functions into classes.
The document discusses object-oriented programming concepts like classes, objects, encapsulation, abstraction, and information hiding. It provides examples of procedural programming versus object-oriented programming. Key topics from the document include defining a C++ class, creating objects from classes, accessing data members of objects, defining member functions within and outside of classes, and the principles of data encapsulation, abstraction, and information hiding in OOP. The document also contrasts procedural, object-oriented, and generic programming techniques.
Chapter 2 OOP using C++ (Introduction).pptxFiraolGadissa
Introduction to Object-Oriented Programming
Object-Oriented Programming (OOP) is a programming paradigm that organizes software design around data, or objects, rather than functions and logic. It is widely used for developing complex, scalable, and maintainable software systems. The core principles of OOP include encapsulation, abstraction, inheritance, and polymorphism.
Key Concepts of OOP
Encapsulation: This involves bundling data and methods that operate on that data within a single unit, called an object. It helps protect the internal state of an object from external interference23.
Abstraction: This principle focuses on exposing only necessary information while hiding complex details. It allows users to interact with objects without knowing their internal workings23.
Inheritance: This feature enables a new class (subclass) to inherit properties and behaviors from an existing class (superclass), promoting code reuse and hierarchical organization23.
Polymorphism: This allows objects of different classes to be treated as objects of a common superclass. It enables multiple behaviors to be implemented through a common interface23.
Object Technology and Programming Environment
Object Technology: This refers to the use of objects to model real-world entities in software development. It includes classes, objects, inheritance, polymorphism, and encapsulation7.
Programming Environment: OOP is typically supported in class-based languages like Java, Python, and C++. These environments provide tools for designing, developing, and testing object-oriented software
The document discusses object-oriented programming concepts like objects, classes, and encapsulation. It provides examples of a Circle class to illustrate these concepts. The Circle class defines data fields like radius and methods like getArea(). Objects of the Circle class can be created, each with their own radius value. The document emphasizes that classes should encapsulate data to prevent direct modification and make the code easier to maintain. It also covers other class concepts like constructors, the dot operator, constant object names, and inline functions.
The document discusses various concepts related to C++ classes including:
1. Structure definitions, accessing structure members using dot operator and pointers. Class definitions include data members and member functions.
2. Class objects are created using the class name followed by the object name. Class members are accessed using dot operator. Member functions can be defined inside or outside the class.
3. Access specifiers like private, public, and protected control access to class members. Constructors and destructors are special member functions for object initialization and destruction.
The document discusses object-oriented programming (OOP) concepts in C++. It defines key OOP concepts like classes, objects, encapsulation, inheritance and polymorphism. It explains that in OOP, classes encapsulate both data and functions that operate on that data. Classes define public and private sections to control access to members. The document also provides examples to demonstrate class definition, object creation, member functions and destructors.
This document provides an introduction to classes and objects in C++. It defines key concepts like class, object, member functions, access specifiers, and arrays of objects. It also discusses defining objects of a class, accessing class members, passing objects as function arguments, and the differences between classes and structures in C++.
This document discusses object-oriented programming and C++ classes. It defines key concepts like encapsulation, classes, objects, and methods. A class defines the data and functions that operate on that data. Classes encapsulate attributes and behaviors into user-defined types. The document provides examples of classes like Circle and Time that model real-world entities, demonstrating how to define classes, create objects, declare methods, access members, and handle constructors and destructors. Object-oriented programming simplifies code through modularity and reuse via classes.
This document discusses object-oriented programming concepts in C++ including classes, objects, constructors, destructors, and friend functions. It begins by explaining that classes are abstract data types that contain data members and member functions. It then provides examples of declaring a class, creating objects, and accessing class members. It also covers topics like static class members, arrays of objects, constructor and destructor definitions and uses, and declaring friend functions to allow non-member functions access to private class members.
Python is one of the most widely used high-level programming languages in the world. Known for its simplicity, readability, and versatility, Python has become a go-to language for beginners and experienced developers alike. From web development and data science to machine learning and automation, Python offers a range of applications across different fields. Learning Python can be an exciting journey, especially for those just starting with coding. It’s designed to be user-friendly, with a syntax that is easy to understand and intuitive, making it a popular choice for beginners who are interested in mastering programming concepts. In this essay, we will explore the basic coding skills that anyone new to Python should focus on, including variables, data types, control structures, functions, object-oriented programming, and more.
1. Variables and Data Types
One of the first concepts to grasp when learning Python is the idea of variables and data types. Variables are used to store information that can be used and manipulated later in a program. In Python, you don’t need to declare the type of variable explicitly; it is inferred based on the value assigned to it. The basic data types in Python include integers (whole numbers), floating-point numbers (decimal numbers), strings (text), and booleans (True/False). For example, a variable can store an integer like x = 10 or a string like name = "Alice". Understanding how to use these data types effectively is crucial for performing mathematical calculations, manipulating text, and making logical comparisons in your programs. Additionally, Python provides more complex data types like lists, tuples, dictionaries, and sets, which are essential for working with collections of data.
2. Control Structures: Conditionals and Loops
Control structures are fundamental to any programming language, and Python is no exception. They allow programmers to control the flow of execution in a program. The two most common types of control structures are conditionals (if-else statements) and loops (for and while loops). Conditionals allow the program to make decisions based on specific conditions. For instance, you can use an if statement to check whether a number is positive or negative and then print an appropriate message. Loops, on the other hand, enable you to repeat a block of code multiple times, which is useful for tasks like iterating over items in a list or performing an action until a condition is met. Python’s for loop is typically used when you know how many times to iterate, while the while loop is useful when you want to repeat an action until a certain condition is met.
3. Functions: Modular Code for Reusability
Functions in Python are blocks of reusable code that allow you to group related operations together. Functions help break down large programs into smaller, more manageable pieces. They can be called multiple times with different inputs, which makes your code more efficient and easier to read.A fun
In the rapidly evolving field of machine learning (ML), the focus is often placed on developing sophisticated algorithms and models that can learn patterns, make predictions, and generate insights from data. However, one of the most critical challenges in building effective machine learning systems lies in ensuring the quality of the data used for training, testing, and validating these models. Data quality directly influences the model's performance, accuracy, and ability to generalize to unseen examples. Unfortunately, in real-world applications, data is rarely perfect, and it is often riddled with various types of errors that can lead to misleading conclusions, flawed predictions, and potentially harmful outcomes. These errors in experimental observations, also referred to as data errors or measurement errors, can significantly compromise the effectiveness of machine learning systems. The sources of these errors are diverse, ranging from technical failures, such as malfunctioning sensors or corrupted datasets, to human errors in data collection, labeling, or interpretation. Furthermore, errors may emerge during the data preprocessing stages, such as incorrect normalization, improper handling of missing data, or the introduction of noise through faulty sampling techniques. These errors can manifest in several ways, including outliers, missing values, mislabeled instances, noisy data, or data imbalances, each of which can influence how well a machine learning model performs. Understanding the nature of these errors and developing strategies to mitigate their impact is crucial for building robust and reliable machine learning models that can operate in real-world environments. Moreover, the impact of errors is not only a technical issue; it also raises significant ethical concerns, particularly when the models are used to inform high-stakes decisions, such as in healthcare, criminal justice, or finance. If errors are not properly addressed, models may inadvertently perpetuate biases, amplify inequalities, or produce inaccurate predictions that negatively affect individuals and communities. Therefore, a thorough understanding of errors in experimental observations is essential for improving the reliability, fairness, and ethical standards of machine learning applications. This introductory discussion provides the foundation for exploring the various types of errors that arise in machine learning datasets, examining their origins, their effects on model performance, and the various methods and techniques available for detecting, correcting, and mitigating these errors. By delving into the challenges posed by errors in experimental observations, we aim to provide a comprehensive framework for addressing data quality issues in machine learning and to highlight the importance of maintaining data integrity in the development and deployment of machine learning systems. This exploration of errors will also touch upon the broader implications for research
In the rapidly evolving field of machine learning (ML), the focus is often placed on developing sophisticated algorithms and models that can learn patterns, make predictions, and generate insights from data. However, one of the most critical challenges in building effective machine learning systems lies in ensuring the quality of the data used for training, testing, and validating these models. Data quality directly influences the model's performance, accuracy, and ability to generalize to unseen examples. Unfortunately, in real-world applications, data is rarely perfect, and it is often riddled with various types of errors that can lead to misleading conclusions, flawed predictions, and potentially harmful outcomes. These errors in experimental observations, also referred to as data errors or measurement errors, can significantly compromise the effectiveness of machine learning systems. The sources of these errors are diverse, ranging from technical failures, such as malfunctioning sensors or corrupted datasets, to human errors in data collection, labeling, or interpretation. Furthermore, errors may emerge during the data preprocessing stages, such as incorrect normalization, improper handling of missing data, or the introduction of noise through faulty sampling techniques. These errors can manifest in several ways, including outliers, missing values, mislabeled instances, noisy data, or data imbalances, each of which can influence how well a machine learning model performs. Understanding the nature of these errors and developing strategies to mitigate their impact is crucial for building robust and reliable machine learning models that can operate in real-world environments. Moreover, the impact of errors is not only a technical issue; it also raises significant ethical concerns, particularly when the models are used to inform high-stakes decisions, such as in healthcare, criminal justice, or finance. If errors are not properly addressed, models may inadvertently perpetuate biases, amplify inequalities, or produce inaccurate predictions that negatively affect individuals and communities. Therefore, a thorough understanding of errors in experimental observations is essential for improving the reliability, fairness, and ethical standards of machine learning applications. This introductory discussion provides the foundation for exploring the various types of errors that arise in machine learning datasets, examining their origins, their effects on model performance, and the various methods and techniques available for detecting, correcting, and mitigating these errors. By delving into the challenges posed by errors in experimental observations, we aim to provide a comprehensive framework for addressing data quality issues in machine learning and to highlight the importance of maintaining data integrity in the development and deployment of machine learning systems. This exploration of errors will also touch upon the broader implications for research
Java is a widely-used, object-oriented programming language known for its simplicity, portability, and reliability. At the core of Java are classes and objects, with classes serving as blueprints for creating objects that encapsulate data and behaviors. The entry point of any Java program is the main method, where execution begins. Java has a variety of data types, including primitive types like int, float, char, and boolean, as well as objects like String. Control flow is managed using conditional statements such as if-else and switch, as well as loops like for, while, and do-while to iterate over data. Arrays allow the storage of multiple values in a single variable, and methods are used to perform specific tasks or operations. Java is built around the principles of object-oriented programming (OOP), which includes encapsulation (hiding internal data), inheritance (deriving new classes from existing ones), polymorphism (using methods in different ways), and abstraction (simplifying complex systems). Exception handling in Java is done using try, catch, and finally blocks, providing a robust mechanism for managing errors. Java also includes packages for organizing code and provides tools for input and output operations, such as the Scanner class for reading user input. Overall, Java's combination of simplicity, powerful features, and cross-platform capabilities makes it a popular choice for building a wide range of applications, from web and mobile apps to enterprise-level systems.
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Java is a powerful, high-level, object-oriented programming language widely used for developing various types of applications, ranging from web applications and mobile apps to enterprise systems and large-scale server applications. One of the key features of Java is its platform independence, made possible by the Java Virtual Machine (JVM), which allows Java programs to run on any device that has the JVM installed. The foundational concept in Java programming is the use of classes and objects. A class serves as a blueprint or template for creating objects, which are instances of that class. Each object can have attributes (fields) and behaviors (methods). The main method is the entry point of any Java program, where the program starts its execution. For example, a simple Java program starts by defining a class with a main method that contains the instructions to be executed. Java supports a rich set of data types including primitive types such as int, float, char, boolean, and double, along with more complex types like String, arrays, and user-defined objects. These data types are essential for storing and manipulating data throughout the program.
Control flow in Java is managed using conditional statements and loops. The if-else statement is commonly used for making decisions based on conditions, while the switch statement is helpful when dealing with multiple potential conditions base
Java is a widely-used, object-oriented programming language known for its simplicity, portability, and reliability. At the core of Java are classes and objects, with classes serving as blueprints for creating objects that encapsulate data and behaviors. The entry point of any Java program is the main method, where execution begins. Java has a variety of data types, including primitive types like int, float, char, and boolean, as well as objects like String. Control flow is managed using conditional statements such as if-else and switch, as well as loops like for, while, and do-while to iterate over data. Arrays allow the storage of multiple values in a single variable, and methods are used to perform specific tasks or operations. Java is built around the principles of object-oriented programming (OOP), which includes encapsulation (hiding internal data), inheritance (deriving new classes from existing ones), polymorphism (using methods in different ways), and abstraction (simplifying complex systems). Exception handling in Java is done using try, catch, and finally blocks, providing a robust mechanism for managing errors. Java also includes packages for organizing code and provides tools for input and output operations, such as the Scanner class for reading user input. Overall, Java's combination of simplicity, powerful features, and cross-platform capabilities makes it a popular choice for building a wide range of applications, from web and mobile apps to enterprise-level systems.
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java basics long paragraph with 3000 characters
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Java is a powerful, high-level, object-oriented programming language widely used for developing various types of applications, ranging from web applications and mobile apps to enterprise systems and large-scale server applications. One of the key features of Java is its platform independence, made possible by the Java Virtual Machine (JVM), which allows Java programs to run on any device that has the JVM installed. The foundational concept in Java programming is the use of classes and objects. A class serves as a blueprint or template for creating objects, which are instances of that class. Each object can have attributes (fields) and behaviors (methods). The main method is the entry point of any Java program, where the program starts its execution. For example, a simple Java program starts by defining a class with a main method that contains the instructions to be executed. Java supports a rich set of data types including primitive types such as int, float, char, boolean, and double, along with more complex types like String, arrays, and user-defined objects. These data types are essential for storing and manipulating data throughout the program.
Control flow in Java is managed using conditional statements and loops. The if-else statement is commonly used for making decisions based on conditions, while the switch statement is helpful when dealing with multiple potential conditions base
Java is a widely-used, object-oriented programming language known for its simplicity, portability, and reliability. At the core of Java are classes and objects, with classes serving as blueprints for creating objects that encapsulate data and behaviors. The entry point of any Java program is the main method, where execution begins. Java has a variety of data types, including primitive types like int, float, char, and boolean, as well as objects like String. Control flow is managed using conditional statements such as if-else and switch, as well as loops like for, while, and do-while to iterate over data. Arrays allow the storage of multiple values in a single variable, and methods are used to perform specific tasks or operations. Java is built around the principles of object-oriented programming (OOP), which includes encapsulation (hiding internal data), inheritance (deriving new classes from existing ones), polymorphism (using methods in different ways), and abstraction (simplifying complex systems). Exception handling in Java is done using try, catch, and finally blocks, providing a robust mechanism for managing errors. Java also includes packages for organizing code and provides tools for input and output operations, such as the Scanner class for reading user input. Overall, Java's combination of simplicity, powerful features, and cross-platform capabilities makes it a popular choice for building a wide range of applications, from web and mobile apps to enterprise-level systems.
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java basics long paragraph with 3000 characters
ChatGPT said:
ChatGPT
Java is a powerful, high-level, object-oriented programming language widely used for developing various types of applications, ranging from web applications and mobile apps to enterprise systems and large-scale server applications. One of the key features of Java is its platform independence, made possible by the Java Virtual Machine (JVM), which allows Java programs to run on any device that has the JVM installed. The foundational concept in Java programming is the use of classes and objects. A class serves as a blueprint or template for creating objects, which are instances of that class. Each object can have attributes (fields) and behaviors (methods). The main method is the entry point of any Java program, where the program starts its execution. For example, a simple Java program starts by defining a class with a main method that contains the instructions to be executed. Java supports a rich set of data types including primitive types such as int, float, char, boolean, and double, along with more complex types like String, arrays, and user-defined objects. These data types are essential for storing and manipulating data throughout the program.
Control flow in Java is managed using conditional statements and loops. The if-else statement is commonly used for making decisions based on conditions, while the switch statement is helpful when dealing with multiple potential conditions base
Java is a widely-used, object-oriented programming language known for its simplicity, portability, and reliability. At the core of Java are classes and objects, with classes serving as blueprints for creating objects that encapsulate data and behaviors. The entry point of any Java program is the main method, where execution begins. Java has a variety of data types, including primitive types like int, float, char, and boolean, as well as objects like String. Control flow is managed using conditional statements such as if-else and switch, as well as loops like for, while, and do-while to iterate over data. Arrays allow the storage of multiple values in a single variable, and methods are used to perform specific tasks or operations. Java is built around the principles of object-oriented programming (OOP), which includes encapsulation (hiding internal data), inheritance (deriving new classes from existing ones), polymorphism (using methods in different ways), and abstraction (simplifying complex systems). Exception handling in Java is done using try, catch, and finally blocks, providing a robust mechanism for managing errors. Java also includes packages for organizing code and provides tools for input and output operations, such as the Scanner class for reading user input. Overall, Java's combination of simplicity, powerful features, and cross-platform capabilities makes it a popular choice for building a wide range of applications, from web and mobile apps to enterprise-level systems.
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java basics long paragraph with 3000 characters
ChatGPT said:
ChatGPT
Java is a powerful, high-level, object-oriented programming language widely used for developing various types of applications, ranging from web applications and mobile apps to enterprise systems and large-scale server applications. One of the key features of Java is its platform independence, made possible by the Java Virtual Machine (JVM), which allows Java programs to run on any device that has the JVM installed. The foundational concept in Java programming is the use of classes and objects. A class serves as a blueprint or template for creating objects, which are instances of that class. Each object can have attributes (fields) and behaviors (methods). The main method is the entry point of any Java program, where the program starts its execution. For example, a simple Java program starts by defining a class with a main method that contains the instructions to be executed. Java supports a rich set of data types including primitive types such as int, float, char, boolean, and double, along with more complex types like String, arrays, and user-defined objects. These data types are essential for storing and manipulating data throughout the program.
Control flow in Java is managed using conditional statements and loops. The if-else statement is commonly used for making decisions based on conditions, while the switch statement is helpful when dealing with multiple potential conditions base
Java is a widely-used, object-oriented programming language known for its simplicity, portability, and reliability. At the core of Java are classes and objects, with classes serving as blueprints for creating objects that encapsulate data and behaviors. The entry point of any Java program is the main method, where execution begins. Java has a variety of data types, including primitive types like int, float, char, and boolean, as well as objects like String. Control flow is managed using conditional statements such as if-else and switch, as well as loops like for, while, and do-while to iterate over data. Arrays allow the storage of multiple values in a single variable, and methods are used to perform specific tasks or operations. Java is built around the principles of object-oriented programming (OOP), which includes encapsulation (hiding internal data), inheritance (deriving new classes from existing ones), polymorphism (using methods in different ways), and abstraction (simplifying complex systems). Exception handling in Java is done using try, catch, and finally blocks, providing a robust mechanism for managing errors. Java also includes packages for organizing code and provides tools for input and output operations, such as the Scanner class for reading user input. Overall, Java's combination of simplicity, powerful features, and cross-platform capabilities makes it a popular choice for building a wide range of applications, from web and mobile apps to enterprise-level systems.
You said:
java basics long paragraph with 3000 characters
ChatGPT said:
ChatGPT
Java is a powerful, high-level, object-oriented programming language widely used for developing various types of applications, ranging from web applications and mobile apps to enterprise systems and large-scale server applications. One of the key features of Java is its platform independence, made possible by the Java Virtual Machine (JVM), which allows Java programs to run on any device that has the JVM installed. The foundational concept in Java programming is the use of classes and objects. A class serves as a blueprint or template for creating objects, which are instances of that class. Each object can have attributes (fields) and behaviors (methods). The main method is the entry point of any Java program, where the program starts its execution. For example, a simple Java program starts by defining a class with a main method that contains the instructions to be executed. Java supports a rich set of data types including primitive types such as int, float, char, boolean, and double, along with more complex types like String, arrays, and user-defined objects. These data types are essential for storing and manipulating data throughout the program.
Control flow in Java is managed using conditional statements and loops. The if-else statement is commonly used for making decisions based on conditions, while the switch statement is helpful when dealing with multiple potential conditions base
Random walks are a process to generate random step sequences that can model natural phenomena and be used in algorithms. They involve starting at a location and randomly selecting successive locations, similar to the path of a molecule in a liquid. Random walks can be modeled as Markov chains on graphs and simulated in higher dimensions to create fractal patterns. They are used in applications like recommender systems, stock market modeling, and generating random fractal images. A random walk corresponds to an irreducible, aperiodic Markov chain and will have a unique stationary probability distribution if the graph is well-behaved.
A stochastic process is a collection of random variables representing the evolution of a random system over time. It allows for indeterminacy, where even if the initial condition is known, there are several possible ways the process may evolve. A stochastic process can be used to model phenomena that change randomly over time, like traffic flow in a network or financial markets. Specific examples discussed include using continuous-time Markov chains to model packet networks and Poisson processes to model message generation in telecommunications systems. Aggregate stochastic models are also used to model and analyze large systems with many components, like air traffic control networks.
Tuples are immutable ordered sequences used to store multiple elements. They are more performant than lists for fixed data that does not need to be changed. Tuples use parentheses and cannot be modified once created. They provide count and index methods to access elements and can be used with operators like + for concatenation and * for replication. Tuples can be nested to group related data and the for loop used to access nested elements.
This paper proposes a shoulder inverse kinematics (IK) technique. Shoulder complex is comprised of the sternum, clavicle, ribs, scapula, humerus, and four joints.
Concept of Problem Solving, Introduction to Algorithms, Characteristics of Algorithms, Introduction to Data Structure, Data Structure Classification (Linear and Non-linear, Static and Dynamic, Persistent and Ephemeral data structures), Time complexity and Space complexity, Asymptotic Notation - The Big-O, Omega and Theta notation, Algorithmic upper bounds, lower bounds, Best, Worst and Average case analysis of an Algorithm, Abstract Data Types (ADT)
The role of the lexical analyzer
Specification of tokens
Finite state machines
From a regular expressions to an NFA
Convert NFA to DFA
Transforming grammars and regular expressions
Transforming automata to grammars
Language for specifying lexical analyzers
We introduce the Gaussian process (GP) modeling module developed within the UQLab software framework. The novel design of the GP-module aims at providing seamless integration of GP modeling into any uncertainty quantification workflow, as well as a standalone surrogate modeling tool. We first briefly present the key mathematical tools on the basis of GP modeling (a.k.a. Kriging), as well as the associated theoretical and computational framework. We then provide an extensive overview of the available features of the software and demonstrate its flexibility and user-friendliness. Finally, we showcase the usage and the performance of the software on several applications borrowed from different fields of engineering. These include a basic surrogate of a well-known analytical benchmark function; a hierarchical Kriging example applied to wind turbine aero-servo-elastic simulations and a more complex geotechnical example that requires a non-stationary, user-defined correlation function. The GP-module, like the rest of the scientific code that is shipped with UQLab, is open source (BSD license).
RICS Membership-(The Royal Institution of Chartered Surveyors).pdfMohamedAbdelkader115
Glad to be one of only 14 members inside Kuwait to hold this credential.
Please check the members inside kuwait from this link:
https://www.rics.org/networking/find-a-member.html?firstname=&lastname=&town=&country=Kuwait&member_grade=(AssocRICS)&expert_witness=&accrediation=&page=1
☁️ GDG Cloud Munich: Build With AI Workshop - Introduction to Vertex AI! ☁️
Join us for an exciting #BuildWithAi workshop on the 28th of April, 2025 at the Google Office in Munich!
Dive into the world of AI with our "Introduction to Vertex AI" session, presented by Google Cloud expert Randy Gupta.
International Journal of Distributed and Parallel systems (IJDPS)samueljackson3773
The growth of Internet and other web technologies requires the development of new
algorithms and architectures for parallel and distributed computing. International journal of
Distributed and parallel systems is a bimonthly open access peer-reviewed journal aims to
publish high quality scientific papers arising from original research and development from
the international community in the areas of parallel and distributed systems. IJDPS serves
as a platform for engineers and researchers to present new ideas and system technology,
with an interactive and friendly, but strongly professional atmosphere.
Analysis of reinforced concrete deep beam is based on simplified approximate method due to the complexity of the exact analysis. The complexity is due to a number of parameters affecting its response. To evaluate some of this parameters, finite element study of the structural behavior of the reinforced self-compacting concrete deep beam was carried out using Abaqus finite element modeling tool. The model was validated against experimental data from the literature. The parametric effects of varied concrete compressive strength, vertical web reinforcement ratio and horizontal web reinforcement ratio on the beam were tested on eight (8) different specimens under four points loads. The results of the validation work showed good agreement with the experimental studies. The parametric study revealed that the concrete compressive strength most significantly influenced the specimens’ response with the average of 41.1% and 49 % increment in the diagonal cracking and ultimate load respectively due to doubling of concrete compressive strength. Although the increase in horizontal web reinforcement ratio from 0.31 % to 0.63 % lead to average of 6.24 % increment on the diagonal cracking load, it does not influence the ultimate strength and the load-deflection response of the beams. Similar variation in vertical web reinforcement ratio leads to an average of 2.4 % and 15 % increment in cracking and ultimate load respectively with no appreciable effect on the load-deflection response.
ADVXAI IN MALWARE ANALYSIS FRAMEWORK: BALANCING EXPLAINABILITY WITH SECURITYijscai
With the increased use of Artificial Intelligence (AI) in malware analysis there is also an increased need to
understand the decisions models make when identifying malicious artifacts. Explainable AI (XAI) becomes
the answer to interpreting the decision-making process that AI malware analysis models use to determine
malicious benign samples to gain trust that in a production environment, the system is able to catch
malware. With any cyber innovation brings a new set of challenges and literature soon came out about XAI
as a new attack vector. Adversarial XAI (AdvXAI) is a relatively new concept but with AI applications in
many sectors, it is crucial to quickly respond to the attack surface that it creates. This paper seeks to
conceptualize a theoretical framework focused on addressing AdvXAI in malware analysis in an effort to
balance explainability with security. Following this framework, designing a machine with an AI malware
detection and analysis model will ensure that it can effectively analyze malware, explain how it came to its
decision, and be built securely to avoid adversarial attacks and manipulations. The framework focuses on
choosing malware datasets to train the model, choosing the AI model, choosing an XAI technique,
implementing AdvXAI defensive measures, and continually evaluating the model. This framework will
significantly contribute to automated malware detection and XAI efforts allowing for secure systems that
are resilient to adversarial attacks.
4. Object-Oriented Approach
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Ref. "Object-Oriented Programming in C++" by Robert Lafore
Data
Functions
that operate
on data
Object
An object’s functions, called member
functions in C++, typically provide the
only way to access its data
5. Object-Oriented Approach
25-03-2023 5
Ref. "Object-Oriented Programming in C++" by Robert Lafore
Data
Member
Functions
Object - 1
member functions or methods
Attributes or instance variables
6. Classes
• In OOP, Objects are instances of classes
25-03-2023 6
Ref. "Object-Oriented Programming in C++" by Robert Lafore
Class
Attributes
Member functions
Object-1
Attributes
Member functions
Object-2
Attributes
Member functions
Object-1 & 2 are of type
class
7. Implementation of classes
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Ref. "Object-Oriented Programming in C++" by Robert Lafore
class firstClass
{
int a,b;
int add(int x, int y)
{
a=x; b=y;
return (a+b);
}
};
Class
Attributes
Member functions
8. Object-Oriented Approach
25-03-2023 8
Ref. "Object-Oriented Programming in C++" by Robert Lafore
Data
Member
Functions
Object - 1
Data is
hidden So, its safe from accidental
alteration – DATA HIDING
9. Implementation of classes
25-03-2023 9
Ref. "Object-Oriented Programming in C++" by Robert Lafore
class firstClass
{
int a,b; // should be accessed by member function only
int add(int x, int y) // can be accessed by main ()
{
a=x; b=y;
return (a+b);
}
}; Set rights or give the access
specifications using “Access Specifier”
10. Implementation of classes
25-03-2023 10
Ref. "Object-Oriented Programming in C++" by Robert Lafore
class firstClass
{
private: // access specifier
int a,b;
public: // access specifier
int add(int x, int y)
{
a=x; b=y;
return (a+b);
}
};
Class is ready!!
11. Implementation of classes
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Ref. "Object-Oriented Programming in C++" by Robert Lafore
class firstClass
{
private: // access specifier
int a,b;
public: // access specifier
int add(int x, int y)
{
a=x; b=y;
return (a+b);
}
};
int main()
{
firstClass obj; //declaring an obj
int sum;
sum=obj.add(5,6); //msg passing
cout<<“Sum =”<<sum;
}
12. Implementation of classes
25-03-2023 12
Ref. "Object-Oriented Programming in C++" by Robert Lafore
class firstClass
{
private: // access specifier
int a,b;
public: // access specifier
int add(int x, int y)
{
a=x; b=y;
return (a+b);
}
};
int main()
{
firstClass obj; //declaring an obj
int x, y, sum;
cout<<“Enter values for x and y:”;
cin>>x>>y;
sum=obj.add(x,y); //msg passing
cout<<“Sum =”<<sum;
}
13. Classes
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Ref. "Object-Oriented Programming in C++" by Robert Lafore
• Class Data: The data items within a class are called data members (or
sometimes member data).
• Member Functions: Member functions are functions that are included
within a class.
• int add(int x, int y);
• Defining class: Defining objects in this way means creating them. This is
also called instantiating them.
• firstClass obj;
• The term instantiating arises because an instance of the class is created. An
object is an instance of a class.
• Objects are sometimes called instance variables.
14. Classes
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Ref. "Object-Oriented Programming in C++" by Robert Lafore
• Calling member function: The data items within a class are called
data members (or sometimes member data).
• obj.add(x, y); - The dot operator is also called the class member access
operator.)
32. Difference between Classes and Structures
• In C++, a structure works the same way as a class, except for just two
small differences. The most important of them is hiding
implementation details.
• A structure will by default not hide its implementation details from
whoever uses it in code, while a class by default hides all its
implementation details and will therefore by default prevent the
programmer from accessing them.
• The following table summarizes all of the fundamental differences.
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Ref. "Object-Oriented Programming in C++" by Robert Lafore