In this presentation we will discuss about the ways of accessing nonstatic
members and calling non-static functions from the static main
method which is the entry point of any Java class
Learning schemes are machine learning algorithms that can automatically discover hypotheses from data to use for future predictions. They learn models from training data and apply these models to unlabeled data to predict labels. RapidMiner includes many common learning schemes directly as well as integration with Weka's learning operators. Examples of learning schemes in RapidMiner are AdaBoost, additive regression, agglomerative clustering, bagging, basic rule learner, Bayesian boosting, and CHAID.
Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
The document discusses methods in C# programming. Some key points:
1. Methods are blocks of code that perform specific tasks and can be reused by calling the method multiple times. Parameters can pass data into methods.
2. To define a method, use the name followed by parentheses and place the method code inside curly braces. The method type (void, int, etc.) indicates if it returns a value.
3. Methods are called by writing the name followed by parentheses and passing arguments for any parameters. Parameters act as variables inside the method. Default parameter values can be specified.
4. Methods can return values using the return keyword, take multiple parameters, use named arguments, and be overloaded
How to transform and select variables/features when creating a predictive model using machine learning. To see the source code visit https://github.com/Davisy/Feature-Engineering-and-Feature-Selection
RapidMiner offers many machine learning algorithms including support vector machines, decision trees, rule learners, lazy learners, Bayesian learners, and logistic regression. It also supports association rule mining and clustering. Specific algorithms include decision trees similar to C4.5, neural networks using backpropagation, and Bayesian Boosting which trains an ensemble of classifiers. RapidMiner also provides techniques for preprocessing data like feature selection, discretization, normalization, and sampling as well as validation and genetic algorithms for feature selection.
This document discusses various software testing techniques including coding standards, code reviews, code walkthroughs, code inspections, test case design, black box testing, and white box testing. It provides examples of statement coverage, branch coverage, and path coverage testing strategies to ensure all statements, branches, and paths are executed at least once. Testing approaches like equivalence partitioning and boundary value analysis are discussed for black box testing, while coverage criteria guide test case design for white box testing.
Supervised learning and Unsupervised learning Usama Fayyaz
This document discusses supervised and unsupervised machine learning. Supervised learning uses labeled training data to learn a function that maps inputs to outputs. Unsupervised learning is used when only input data is available, with the goal of modeling underlying structures or distributions in the data. Common supervised algorithms include decision trees and logistic regression, while common unsupervised algorithms include k-means clustering and dimensionality reduction.
Learning schemes are machine learning algorithms that can automatically discover hypotheses from data to use for future predictions. They learn models from training data and apply these models to unlabeled data to predict labels. RapidMiner includes many common learning schemes directly as well as integration with Weka's learning operators. Examples of learning schemes in RapidMiner are AdaBoost, additive regression, agglomerative clustering, bagging, basic rule learner, Bayesian boosting, and CHAID.
Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
The document discusses methods in C# programming. Some key points:
1. Methods are blocks of code that perform specific tasks and can be reused by calling the method multiple times. Parameters can pass data into methods.
2. To define a method, use the name followed by parentheses and place the method code inside curly braces. The method type (void, int, etc.) indicates if it returns a value.
3. Methods are called by writing the name followed by parentheses and passing arguments for any parameters. Parameters act as variables inside the method. Default parameter values can be specified.
4. Methods can return values using the return keyword, take multiple parameters, use named arguments, and be overloaded
How to transform and select variables/features when creating a predictive model using machine learning. To see the source code visit https://github.com/Davisy/Feature-Engineering-and-Feature-Selection
RapidMiner offers many machine learning algorithms including support vector machines, decision trees, rule learners, lazy learners, Bayesian learners, and logistic regression. It also supports association rule mining and clustering. Specific algorithms include decision trees similar to C4.5, neural networks using backpropagation, and Bayesian Boosting which trains an ensemble of classifiers. RapidMiner also provides techniques for preprocessing data like feature selection, discretization, normalization, and sampling as well as validation and genetic algorithms for feature selection.
This document discusses various software testing techniques including coding standards, code reviews, code walkthroughs, code inspections, test case design, black box testing, and white box testing. It provides examples of statement coverage, branch coverage, and path coverage testing strategies to ensure all statements, branches, and paths are executed at least once. Testing approaches like equivalence partitioning and boundary value analysis are discussed for black box testing, while coverage criteria guide test case design for white box testing.
Supervised learning and Unsupervised learning Usama Fayyaz
This document discusses supervised and unsupervised machine learning. Supervised learning uses labeled training data to learn a function that maps inputs to outputs. Unsupervised learning is used when only input data is available, with the goal of modeling underlying structures or distributions in the data. Common supervised algorithms include decision trees and logistic regression, while common unsupervised algorithms include k-means clustering and dimensionality reduction.
A method is a block of code that performs a specific task and can be called from other parts of a program. Methods allow programmers to break programs into smaller, reusable pieces of code. Declaring a method involves specifying its name, parameters, return type, and body. Methods make code more organized and reusable, and allow avoiding duplicated code. Parameters allow passing information to methods to change their behavior. Methods can return values using the return statement.
Model extraction attacks on the bert based NLP models leads to potential risk of data being stolen. This presentation provides explanation on how models being extracted by the adversaries and naive defense strategies to prevent the model from being stolen.
As it is suggested in the name, we use recommender systems to recommend items to users bases on their preferences, and the preferences of other users.
We will talk about two categories of recommoncder systems : Content based filtering and Collaborative filtering. In the later one, there are two approaches: neighborhood approach, and model based approach. In this section, we see the first one.
[Notebook](https://colab.research.google.com/drive/12gM8EEa6gxhgpMB-QvCbfmwwZm7MVrku)
An introduction to variable and feature selectionMarco Meoni
Presentation of a great paper from Isabelle Guyon (Clopinet) and André Elisseeff (Max Planck Institute) back in 2003, which outlines the main techniques for feature selection and model validation in machine learning systems
This lab report describes a program that checks if strings match regular expressions. It tests strings against 6 regular expressions: a(bc)*de, a(bc)+de, a(bc)?de, [a-m]*, [^aeiou], and [^aeiou]{6}. The program takes a number of strings as input and checks each one, printing "Accepted" if it matches a regular expression and "Not Accepted" otherwise. Key parts of the program include functions for each regular expression, an algorithm to check strings, and test cases showing the output. The report claims the program works correctly but no bugs were found.
The document covers key concepts in Java expressions and flow control including:
- Distinguishing between instance and local variables and how instance variables are initialized
- Recognizing and correcting reference before assignment compiler errors
- Using operators and ensuring legal assignments of primitive types
- Applying boolean expressions in control constructs like if, switch, for, while, and do statements
- Leveraging the instanceof operator to test an object's class at runtime
- Understanding the differences between the basic for loop and enhanced for loop syntax
This document provides an overview of a 5-day Java programming workshop covering operators and conditionals. It discusses arithmetic, assignment, relational and logical operators as well as operator precedence. It also covers conditional statements using if/else and switch/case and provides examples of evaluating grades based on percentages. Additional learning resources on Java programming concepts and documentation are recommended.
The Presentation answers various questions such as what is machine learning, how machine learning works, the difference between artificial intelligence, machine learning, deep learning, types of machine learning, and its applications.
This document discusses item-based collaborative filtering for recommender systems. It describes how item-based collaborative filtering works by predicting a target user's rating for an item based on the ratings of similar items. It highlights advantages over user-based filtering like lower computational cost and more stable similarity computations. Key aspects covered include using cosine similarity to calculate item similarities, adjusting for individual rating biases, selecting the top K similar items, and predicting ratings based on similar items' ratings.
The document discusses control structures in Java, including selection statements like if-else and switch statements, and iteration statements like for, while, do-while loops. It provides examples and explanations of how each statement works. Key points covered include how if-else statements evaluate conditions and execute the appropriate block, how switch statements can be used as a replacement for long if-else-if chains, and how the different loop constructs like for, while, do-while iterate until a condition is met. It also discusses concepts like break, continue and return which change the flow of control.
video link => http://youtu.be/D9PBX8FmtpQ
Tweets Classifier which categorises tweets into these 6 categories:
Business
Politics
Music
Health
Sports
Technology
This document discusses Item Response Theory (IRT), which is a psychometric theory used to analyze test items and scores. IRT aims to estimate examinee ability independently of the test items used and provides sample-independent item and test statistics. Some key benefits of IRT include obtaining ability estimates and measurement errors that are independent of the particular test used. IRT also allows test developers to select test items to achieve specific test properties like matching items to ability levels or targeting information.
This document discusses feature selection in machine learning and data mining. It begins by asking how to select the most important features from a set of features to reduce dimensionality while retaining discriminatory information. The document emphasizes the importance of preprocessing data before feature selection, including removing outliers, normalizing data to account for different feature scales, and handling missing data. It then discusses various statistical and mathematical techniques for feature selection such as hypothesis testing, scatter matrices, and sequential backward selection.
Svm and maximum entropy model for sentiment analysis of tweetsS M Raju
This document summarizes a student project on sentiment analysis of tweets about Apple using two classification algorithms: Support Vector Machine (SVM) and Maximum Entropy. The project collected tweet data, preprocessed it by removing duplicates and correcting errors, and manually labeled the tweets as positive, negative or neutral. The algorithms were tested on this labeled tweet data and evaluated based on accuracy, precision, recall and F-measure. SVM performed better for sentiment classification of tweets. Future work could explore using tweets in other languages and combining SVM kernel subclasses.
SE_Lec 06_Object Oriented Analysis and DesignAmr E. Mohamed
This document discusses object-oriented (OO) system development. It describes how OO development builds self-contained modules that can be more easily replaced, modified, and reused. The key aspects of OO development covered include objects, classes, inheritance, encapsulation, polymorphism, and relationships between objects. The document also compares structured and OO approaches to programming.
This presentation covers the intricacies of the Item Response Theory. I made this presentation to explain the concepts of IRT to my lab research group at the University of Minnesota. I have taken the contents from various sources so apologies for the poor design of the presentation.
The class diagram shows the key classes and relationships in a school information modeling system. The main classes are School, Department, Subject, Student, and Instructor. A school has departments and a department offers subjects. A student can enroll in up to 5 subjects and an instructor can teach up to 3 subjects. An instructor is assigned to one or more departments. The class diagram also shows the relationships between these classes such as a student attending a school and taking subjects, and an instructor teaching subjects.
CIS 1403 lab 3 functions and methods in JavaHamad Odhabi
This lab discusses and provides examples of both built-in and user-defined functions. In Java function are referred to as methods. Therefore, in the rest of this lab, the term methods will be used to refer to functions. The lab will cover the type of methods, naming of functions, the scope of variables and recursion.
The document discusses the final keyword in Java and provides examples of using final with variables, methods, and classes. It then summarizes abstract classes and interfaces in Java, including how to declare abstract classes and methods and how interfaces are used to achieve abstraction and multiple inheritance. The document also covers packages, access modifiers, encapsulation, and arrays in Java.
This document discusses methods in Java programming. It defines a method as a block of code that performs a specific task, similar to a function. There are standard library methods provided by Java and user-defined methods that programmers can create. The document provides examples of calling methods and how they can accept arguments and return values. It also discusses the advantages of using methods such as code reusability.
A method is a block of code that performs a specific task and can be called from other parts of a program. Methods allow programmers to break programs into smaller, reusable pieces of code. Declaring a method involves specifying its name, parameters, return type, and body. Methods make code more organized and reusable, and allow avoiding duplicated code. Parameters allow passing information to methods to change their behavior. Methods can return values using the return statement.
Model extraction attacks on the bert based NLP models leads to potential risk of data being stolen. This presentation provides explanation on how models being extracted by the adversaries and naive defense strategies to prevent the model from being stolen.
As it is suggested in the name, we use recommender systems to recommend items to users bases on their preferences, and the preferences of other users.
We will talk about two categories of recommoncder systems : Content based filtering and Collaborative filtering. In the later one, there are two approaches: neighborhood approach, and model based approach. In this section, we see the first one.
[Notebook](https://colab.research.google.com/drive/12gM8EEa6gxhgpMB-QvCbfmwwZm7MVrku)
An introduction to variable and feature selectionMarco Meoni
Presentation of a great paper from Isabelle Guyon (Clopinet) and André Elisseeff (Max Planck Institute) back in 2003, which outlines the main techniques for feature selection and model validation in machine learning systems
This lab report describes a program that checks if strings match regular expressions. It tests strings against 6 regular expressions: a(bc)*de, a(bc)+de, a(bc)?de, [a-m]*, [^aeiou], and [^aeiou]{6}. The program takes a number of strings as input and checks each one, printing "Accepted" if it matches a regular expression and "Not Accepted" otherwise. Key parts of the program include functions for each regular expression, an algorithm to check strings, and test cases showing the output. The report claims the program works correctly but no bugs were found.
The document covers key concepts in Java expressions and flow control including:
- Distinguishing between instance and local variables and how instance variables are initialized
- Recognizing and correcting reference before assignment compiler errors
- Using operators and ensuring legal assignments of primitive types
- Applying boolean expressions in control constructs like if, switch, for, while, and do statements
- Leveraging the instanceof operator to test an object's class at runtime
- Understanding the differences between the basic for loop and enhanced for loop syntax
This document provides an overview of a 5-day Java programming workshop covering operators and conditionals. It discusses arithmetic, assignment, relational and logical operators as well as operator precedence. It also covers conditional statements using if/else and switch/case and provides examples of evaluating grades based on percentages. Additional learning resources on Java programming concepts and documentation are recommended.
The Presentation answers various questions such as what is machine learning, how machine learning works, the difference between artificial intelligence, machine learning, deep learning, types of machine learning, and its applications.
This document discusses item-based collaborative filtering for recommender systems. It describes how item-based collaborative filtering works by predicting a target user's rating for an item based on the ratings of similar items. It highlights advantages over user-based filtering like lower computational cost and more stable similarity computations. Key aspects covered include using cosine similarity to calculate item similarities, adjusting for individual rating biases, selecting the top K similar items, and predicting ratings based on similar items' ratings.
The document discusses control structures in Java, including selection statements like if-else and switch statements, and iteration statements like for, while, do-while loops. It provides examples and explanations of how each statement works. Key points covered include how if-else statements evaluate conditions and execute the appropriate block, how switch statements can be used as a replacement for long if-else-if chains, and how the different loop constructs like for, while, do-while iterate until a condition is met. It also discusses concepts like break, continue and return which change the flow of control.
video link => http://youtu.be/D9PBX8FmtpQ
Tweets Classifier which categorises tweets into these 6 categories:
Business
Politics
Music
Health
Sports
Technology
This document discusses Item Response Theory (IRT), which is a psychometric theory used to analyze test items and scores. IRT aims to estimate examinee ability independently of the test items used and provides sample-independent item and test statistics. Some key benefits of IRT include obtaining ability estimates and measurement errors that are independent of the particular test used. IRT also allows test developers to select test items to achieve specific test properties like matching items to ability levels or targeting information.
This document discusses feature selection in machine learning and data mining. It begins by asking how to select the most important features from a set of features to reduce dimensionality while retaining discriminatory information. The document emphasizes the importance of preprocessing data before feature selection, including removing outliers, normalizing data to account for different feature scales, and handling missing data. It then discusses various statistical and mathematical techniques for feature selection such as hypothesis testing, scatter matrices, and sequential backward selection.
Svm and maximum entropy model for sentiment analysis of tweetsS M Raju
This document summarizes a student project on sentiment analysis of tweets about Apple using two classification algorithms: Support Vector Machine (SVM) and Maximum Entropy. The project collected tweet data, preprocessed it by removing duplicates and correcting errors, and manually labeled the tweets as positive, negative or neutral. The algorithms were tested on this labeled tweet data and evaluated based on accuracy, precision, recall and F-measure. SVM performed better for sentiment classification of tweets. Future work could explore using tweets in other languages and combining SVM kernel subclasses.
SE_Lec 06_Object Oriented Analysis and DesignAmr E. Mohamed
This document discusses object-oriented (OO) system development. It describes how OO development builds self-contained modules that can be more easily replaced, modified, and reused. The key aspects of OO development covered include objects, classes, inheritance, encapsulation, polymorphism, and relationships between objects. The document also compares structured and OO approaches to programming.
This presentation covers the intricacies of the Item Response Theory. I made this presentation to explain the concepts of IRT to my lab research group at the University of Minnesota. I have taken the contents from various sources so apologies for the poor design of the presentation.
The class diagram shows the key classes and relationships in a school information modeling system. The main classes are School, Department, Subject, Student, and Instructor. A school has departments and a department offers subjects. A student can enroll in up to 5 subjects and an instructor can teach up to 3 subjects. An instructor is assigned to one or more departments. The class diagram also shows the relationships between these classes such as a student attending a school and taking subjects, and an instructor teaching subjects.
CIS 1403 lab 3 functions and methods in JavaHamad Odhabi
This lab discusses and provides examples of both built-in and user-defined functions. In Java function are referred to as methods. Therefore, in the rest of this lab, the term methods will be used to refer to functions. The lab will cover the type of methods, naming of functions, the scope of variables and recursion.
The document discusses the final keyword in Java and provides examples of using final with variables, methods, and classes. It then summarizes abstract classes and interfaces in Java, including how to declare abstract classes and methods and how interfaces are used to achieve abstraction and multiple inheritance. The document also covers packages, access modifiers, encapsulation, and arrays in Java.
This document discusses methods in Java programming. It defines a method as a block of code that performs a specific task, similar to a function. There are standard library methods provided by Java and user-defined methods that programmers can create. The document provides examples of calling methods and how they can accept arguments and return values. It also discusses the advantages of using methods such as code reusability.
Exception handling in Java allows programmers to manage runtime errors programmatically. The try block contains code that may throw exceptions, while catch blocks define how to handle specific exceptions. Finally blocks contain cleanup code that always executes regardless of exceptions. Common exceptions include NullPointerException, ArrayIndexOutOfBoundsException, and ArithmeticException. The throw keyword explicitly throws exceptions, while throws in method declarations informs callers about checked exceptions they must handle or propagate.
RapidMiner offers many machine learning algorithms including support vector machines, decision trees, rule learners, lazy learners, Bayesian learners, and logistic regression. It also supports association rule mining and clustering. Specific algorithms include decision trees similar to C4.5, neural networks using backpropagation, and Bayesian Boosting which trains an ensemble of classifiers. RapidMiner also provides techniques for preprocessing data like feature selection, discretization, normalization, and sampling as well as validation and genetic algorithms for feature selection.
There are three main ways to create and start a new thread in Java: 1) by extending the Thread class, 2) by implementing the Runnable interface, and 3) by using an anonymous class that implements Runnable. The Thread scheduler determines which thread will execute first based on priority and other factors. The main thread lifecycle states are new, runnable, running, and dead. Methods like yield(), join(), setPriority(), etc. allow controlling thread behavior.
Feature extraction for classifying students based on theirac ademic performanceVenkat Projects
This document describes a project to classify student academic performance using machine learning algorithms. It extracts four features from a university dataset to label students as poor or good performers. These features identify failing, dropout, lower than expected grade, and lower grade with course difficulty students. It then applies SVM, Random Forest, Decision Tree, and Gradient Boosting algorithms. Decision Tree achieved the highest accuracy at 89% while Gradient Boosting had the best F1 score. The models are used to predict performance reasons for new student records.
The document discusses Java programming concepts such as classes, methods, strings, comments, and identifiers. It provides examples of Java code that declare classes with a main method and static methods that are called from main. It explains how to write comments to document code and describes syntax rules for identifiers, keywords, and strings. The document is intended to teach programmers how to write, compile, and run basic Java programs.
Question 1 1 pts Skip to question text.As part of a bank account.docxamrit47
Question 1 1 pts Skip to question text.
As part of a bank account implementation, there is an account class and a checking account class. These two classes should be related by:
polymorphism
abstract classes
both composition and inheritance
inheritance
composition
Flag this Question
Question 2 1 pts
When using OOP, which of the following terms refers to a mechanism for a behavior, basically how it’s implemented?
composition
inheritance
polymorphism
dynamic binding
Flag this Question
Question 3 1 pts
To access an element in an Array object,
Use the ArrayList's get() method.
Use the ArrayList's element() method.
Individual elements in an ArrayList can’t be accessed without doing a sequential query getSequential(), returning every element up to and including the element requested.
Use square brackets around an index value.
Flag this Question
Question 4 1 pts
To access an element in an ArrayList object,
Use square brackets around an index value.
Use the ArrayList element() method.
Use the ArrayList get() method.
Individual elements in an ArrayList can’t be accessed without doing a sequential query getSequential(), returning every element up to and including the element requested.
Flag this Question
Question 5 1 pts
What term below is defined as a message that tells the program that something has happened?
an interaction
a listener
an action
an event
Flag this Question
Question 6 1 pts
Which item below is defined as an object?
A String
An Array
All of the above
An ArrayList
Flag this Question
Question 7 1 pts
When a text-box-enter event occurs, which method and parameter are required to handle this type of action? (1 point)
actionEvent with an actionperformed parameter
actionListener with an interfaceID parameter
windowListener with an eventID parameter
actionPerformed with an actionEvent parameter
Flag this Question
Question 8 1 pts
Which class includes the setTitle and setSize methods?
JFrame
JWindow
JBox
JOptionpane
Flag this Question
Question 9 1 pts
Before utilizing the binary search method, __________ must be done to the array?
indexing
splitting
sorting
importing
Flag this Question
Question 10 1 pts
Which layout manager implements a one-compartment layout scheme?
GridlessLayout
GridBagLayout
GridLayout
FlowLayout
BorderLayout
Flag this Question
Question 11 1 pts
What is the default layout manager for a JFrame window?
GridBagLayout
GridLayout
FlowLayout
GridlessLayout
BorderLayout
Flag this Question
Question 12 1 pts
To call the superclass constructor, super() must be the first line in a constructor.
True
False
Flag this Question
Question 13 1 pts
Method overriding is when a method has the same name, same sequence of parameter types, and the same return type as a method in a superclass.
True
False
Flag this Question
Question 14 1 pts
Type casting, also known as promotion is ...
The document discusses various software testing techniques including black box testing, white box testing, and grey box testing. It provides details on specific techniques such as equivalence partitioning, boundary value analysis, statement coverage, condition coverage, function coverage, and cyclomatic complexity. The objective is to understand these techniques so they can be used effectively to test applications and find defects.
CIS 407 STUDY Inspiring Innovation--cis407study.comKeatonJennings91
This document contains information about various CIS 407 exams, labs, assignments, and case studies for an online course. It includes sample exam questions, lab exercise instructions, assignment descriptions, and a case study on creating bar charts in Java. The assignments involve building an interactive Java application for an insurance agent to generate quotes. Students are tasked with implementing classes, calculating premiums, getting user input, and modifying the application to use different input/output methods.
The document describes modifications made to an existing active learning algorithm for multi-class image classification. The original algorithm minimized expected risk but had high time complexity. The modifications 1) consider only misclassification risk instead of cost for query selection and 2) compute risk for unlabeled images instead of retraining models, reducing time complexity. Evaluation on easy, moderate, and difficult datasets shows the modified active learning algorithm outperforms a random learner in prediction accuracy and learning speed for most cases. Feature selection is also used to preprocess the difficult dataset.
This presentation is a part of the COP2272C college level course taught at the Florida Polytechnic University located in Lakeland Florida. The purpose of this course is to introduce students to the C++ language and the fundamentals of object orientated programming..
The course is one semester in length and meets for 2 hours twice a week. The Instructor is Dr. Jim Anderson.
The document discusses Java methods, explaining that a method is a block of code that performs a specific task and that complex problems can be divided into smaller reusable methods. It describes two types of methods - predefined methods that are defined in Java class libraries and can be directly called, and user-defined methods that are written by programmers to meet specific needs. Examples are provided of how to declare, define, and call both predefined and user-defined methods in Java programs.
Optimization is considered to be one of the pillars of statistical learning and also plays a major role in the design and development of intelligent systems such as search engines, recommender systems, and speech and image recognition software. Machine Learning is the study that gives the computers the ability to learn and also the ability to think without being explicitly programmed. A computer is said to learn from an experience with respect to a specified task and its performance related to that task. The machine learning algorithms are applied to the problems to reduce efforts. Machine learning algorithms are used for manipulating the data and predict the output for the new data with high precision and low uncertainty. The optimization algorithms are used to make rational decisions in an environment of uncertainty and imprecision. In this paper a methodology is presented to use the efficient optimization algorithm as an alternative for the gradient descent machine learning algorithm as an optimization algorithm.
Solutions manual for absolute java 5th edition by walter savitchAlbern9271
Solutions manual for absolute java 5th edition by walter savitch
Full clear download( no error formatting) at:
https://goo.gl/Aic8JR
absolute java 5th edition pdf free
absolute java 5th edition by walter savitch pdf
absolute java 5th edition solutions pdf
absolute java 6th edition solutions pdf
absolute java programming projects solutions
pearson absolute java 5th ed walter savitch 2012 pdf
walter savitch absolute java 4th edition or newer addison wesley 3rd edition is also fine
The document provides an overview of Lesson 3 which will address random and math classes, conditional statements including if, if-else, and switch statements. It discusses variables, data types, operators, and methods from classes like Random, Math, and Scanner. The document also covers formatting output, indentation, block statements, nested if statements, and the logic of if and if-else statements.
you will learn how to create your own methods with or without return values, invoke a method with or without parameters, and apply method abstraction in the program design.
Finding latent code errors via machine learning over program ...butest
The document proposes a technique that uses machine learning to identify program properties from dynamic analysis that are likely to indicate errors. It trains models on properties from erroneous and fixed programs, and applies the models to rank properties of new code based on their likelihood of revealing errors. An implementation demonstrates it can increase the concentration of useful error-indicating properties in its output by factors of 50x for C programs and 4.8x for Java programs.
Similar to Accessing non static members from the main (20)
Perpetual and periodic inventory method – inventories perpetual inventory methodTutors On Net
The perpetual inventory method involves continuously updating inventory records on a daily basis for additions and subtractions from inventory. This method is suited for businesses with high-value, frequently sold items. The periodic inventory method only involves physically counting inventory at set intervals, usually at the end of an accounting period. This method is more expensive and time-consuming. Under the periodic method, the cost of goods sold is determined by opening inventory, purchases, and closing inventory.
Methods of absorption ii - factory overheads distribution prime cost percentageTutors On Net
This document discusses different methods for distributing factory overhead costs, including prime cost percentage, unit of production basis, labor hour rate, and machine hour rate. The prime cost percentage method uses direct materials and labor costs as the base. The unit of production method divides total overhead by total planned units of production. The labor hour rate divides overhead by total direct labor hours. The machine hour rate divides overhead by total machine hours. Formulas and examples are provided for each method.
What are the different modes of fund transfer Tutors On Net
The document discusses different modes of fund transfer that were covered in a finance management class. It outlines several common methods: (1) digital money transfers between bank accounts which allows sending funds instantly, (2) cheque transactions which can be deposited via drop boxes for convenience, (3) direct deposit of salaries electronically for ease and saving paper, and (4) plastic money like credit/debit cards and services like PayPal and Text Pay Me which have revolutionized money transfer. The student felt informed by the material from their assignment help and prepared to present the best presentation on the topic.
Mistakes made with string object in javaTutors On Net
This document discusses three common mistakes made when using String objects in Java programs. The first mistake is using the '==' operator instead of the '.equals()' method to compare String objects. The second mistake is using the 'new' operator when creating String literals, which wastes memory. The third mistake is modifying String objects, which creates new String instances due to Strings being immutable. It is better to use StringBuffer or StringBuilder when frequent string modifications are needed.
Accessing non static members from the mainTutors On Net
The document discusses accessing non-static members and calling non-static methods from a static main method in Java. There are two ways to do this: make the member or method static, or create an object of the class and use it to access non-static members and call non-static methods. The document provides an example of accessing a non-static method from main without an object, which causes a compiler error, and two solutions using either making the method static or creating an object.
Major steps to make successful email marketing more mobile friendlyTutors On Net
This document discusses making email marketing more mobile friendly. It notes that 60% of people in the US now use smartphones to check email. To accommodate this change, email marketing needs to be compatible with both smartphones and computers. The document outlines seven key steps for planning mobile-friendly email marketing: 1) earn subscriber trust, 2) use pre-headers for context, 3) have clear calls-to-action, 4) use responsive design, 5) include informative images, 6) strategically place unsubscribe buttons, and 7) test on multiple devices. Taking these steps will help ensure subscribers can properly view marketing emails on their phones.
Accounting can be broken down into different fields based on the domain of work. Some common fields include accounts payable, bookkeeping, accounts receivable, tax accounting, and payroll accounting. Accounting can also be categorized into four main types: management accounting, government accounting, internal audit accounting, and public accounting, with each type dealing with different organizations. Accounting plays a fundamental role in running organizations and there are various courses that teach accounting principles and management.
There are multitudes of scope available in management accounting and moreover it also entails a wide range of aspects of business operations. The primary role of management accounting is to direct, analyze and control various facets of management issues in an organization from accounting point of view.
Product mix decision under capacity constraint cost analysis and decision m...Tutors On Net
When a firm manufactures more than one article, an issue frequently arises as to the
product mix or the sales mix which will capitulate the maximum profits. In ascertaining the
maximum or profitable sales mix, the articles which give the optimum contribution are to be
reserved and their production must be augmented. The production of articles which give
relatively lesser contribution must be decreased or plunged altogether.
Internal departmental services factory overheadsTutors On Net
The whole amount of a servicing department is to be allocated to only the manufacturing
departments. This does not integrate any practical complexity and offers the simplest and
swiftest method for allocating costs of the servicing department.
Wholesale and retail profit at branch branch accountingTutors On Net
At times, the head office predominantly the manufacturing firm sells articles
to actual consumers through its retail outlets. In this case, the head office sends articles to the
branches at wholesale rates that is, cost plus a percentage of profit
Guidelines and uses of financial statement analysisTutors On Net
Computing ratios help in questioning
correctly about the company’s financial position, even though accurate answers may
be given, ratios form a mode in understanding company’s affairs
Admission of a partner average profit method in valuation of non-purchased ...Tutors On Net
In this method, primarily average profit is computed on the basis of the
previous few years’ profits. At the time of computing average profit
preventative measure must be taken with regards to any abnormal items of
profit or loss which may affect profit in the mere prospect. It must be denoted
that average profit may be based on either weighted average or simple
average.
Coverage ratios analysis of financial statementsTutors On Net
There are three significant coverage ratios which students must learn and apply while
calculating financial ratios for companies. They are explained as under
Interest of capital of partners appropriation of profit and lossTutors On Net
Interest on Capital is entitled to partners only when it is specified
in the Partnership Deed. The proposal for offering the interest on capital
is to recompense the opportunity cost underwent by the partners by not
endowing the money elsewhere in securities with modest or no hazard.
Accounting is an ancient practice that has evolved from early record keeping of crop and herd growth by Assyrians to modern professional accounting adapted for the internet era. It remains a priority task for accountants to carefully track the smallest accounting units and ensure accurate valuation of assets. Studying accounting provides knowledge to assess an organization's financial health, but concepts can be complex and analyzing real-world problems is challenging, so students sometimes seek accounting homework help from trusted sources.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
Pride Month Slides 2024 David Douglas School District
Accessing non static members from the main
1.
2. Accessing Non-Static Members from the Main:
Summary:
In this presentation we will discuss about the ways of accessing nonstatic members and calling non-static functions from the static main
method which is the entry point of any Java class.
Theory:
• Margin of error is the statistic that represents the magnitude of
sampling error associated with the results of a research that used a
sample for representing the entire population.
•Sampling error occurs by chance we cannot avoid this error because
when we select a sample for representing a population the value of the
sample statistic depends on the elements included in the sample.
Therefore, each sample provides different value of sample statistic.
Therefore, the difference between the value of sample statistic and the
exact population parameter is called sampling error or margin of error.
Higher the margin of error lower is the confidence that the research
result is close to the true population parameter and vice versa.
http://www.tutorsonnet.com/java-programming-homework-help.php
Page 1
3. Formula:
In general formula for computing confidence interval for a population
parameter is,
C.I= Sample statistic ± Margin of error
Margin of error depends on the confidence level with which we want to
estimate the population parameter, standard deviation of the sample
(population if given) and the sample size.
Width of a confidence interval is given by,
Width = 2 * margin of error
Margin of error = half the width of the confidence interval =
Width/2
Accordingly formula for computing confidence interval is,
C.I= Sample statistic± (width/2)
Problem:
http://www.tutorsonnet.com/java-programming-homework-help.php
Page 2
4. • Initial entry point of any Java class is a public static method named as
'main'.
• As 'main' is a static method, we cannot call any non-static member
function of a class without creating class object from the 'main' method
body.
•Similarly we cannot access the non-static members of a class without
creating class object from inside the 'main' method.
Solution:
There are two ways to deal with this kind of a problem.
• Make the function or the class member static. Lithe function does not
callaccess the other non-static functionsmembers of the class, then this
is the way we can solve this problem.
• Create an object of a class and use it to call the non-static functions and
to access the non-static members from inside the 'main' method.
http://www.tutorsonnet.com/java-programming-homework-help.php
Page 3
5. Mistake Example:
public class EvenOdd {
{ boolean isEven(int num) {
return (num%2 == 0); }
public static void main(String args[]) {
int num = 23;
// compiler will give error for this line
if(isEven(num)) {
System.out.println(num + " is an even numbed");
}
}
}
http://www.tutorsonnet.com/java-programming-homework-help.php
Page 4
6. Solution 1:
public class EvenOdd {
static boolean isEven(int num) {
return (num%2 == 0);
}
public static void main(String args[]) {
int num = 24;
// compiler will not give error for this line as the function is declared as
static
if(isEven(num)) {
System.out.println(num + " is an even number!");
}
}
}
http://www.tutorsonnet.com/java-programming-homework-help.php
Page 5
7. Solution 2:
public class EvenOdd {
boolean isEven(int num) {
return (num%2 == 0);
}
public static void main(String args[]) {
int num = 24; EvenOdd evenOddObj = new EvenOdd();
// compiler will not give error for this line as we access the function by
creating an object of
// an EvenOdd class.
if(evenOddObj.isEven(num)) {
System.out.println(num + " is an even number!");
}
}
}
http://www.tutorsonnet.com/java-programming-homework-help.php
Page 6
8. For More Details:
Website: http://www.tutorsonnet.com
Mail Us: sendquestions@tutorsonnet.com
Phone: +91 98803 85500
List of Subjects which we Provide Homework Help:
1 Accounting
9 C
17 JavaScript
2 Finance
10 Visual Basic
18 Shell Scripting
3 Statistics
11 C# (C Sharp)
19 Math
4 Economics
12 Matlab Programming
20 Chemistry
5 Operations
13 Database
21 Physics
6 Marketing
14 ER Diagrams
22 Dissertations
7 Java
15 Oracle
23 Case Study
8 C++
16 Microsoft (MS) Access
24 Project Management