In Tutorial 1, you will start building a Visual C# interface for database management system project with SQL Server. The database, named DBMS, is created. The designed interface in this tutorial will used as the main terminal in accessing other forms. This tutorial will also discuss how to create login form and login table. In Tutorial 2, you will build a project, as part of database management system, where you can store information about valuables in school. In Tutorial 3 up to Tutorial 4, you will perform the steps necessary to add 6 tables into DBMS database. You will build each table and add the associated fields as needed. In this tutorials, you will create a library database project, as part of database management system, where you can store all information about library including author, title, and publisher. In Tutorial 5 up to Tutorial 7, you will perform the steps necessary to add 6 more tables into DBMS database. You will build each table and add the associated fields as needed. In this tutorials, you will create a high school database project, as part of database management system, where you can store all information about school including parent, teacher, student, subject, and, title, and grade.
In chapter one, you will learn to know the properties and events of each control in a Windows Visual C# application. You need to learn and know in order to be more familiar when applying them to some applications in this book. In chapter two, you will go through step by step to build a SALES database using MySQL. You will build each table and add associated data fields (along with the necessary keys and indexes). The first field in the Client table is ClientID. Enter the clien ID in the Name Field and select AutoNumber in the Data Type. You define primary key and other indexes which are useful for quick searching. ClientID is a primary field. You will define FamilyName as an index. You then will create Ordering table with three fields: OrderID, ClientID, and OrderDate. You then will create Purchase table with three fields: OrderID, ProductID, and Quantity. And you will create Product table with four fields: ProductID, Description, Price, and QtySold. Before designing Visual C# interface, you will build the relationships between four tables. The interface will be used to enter new orders into the database. The order form will be used to enter the following information into the database: order ID, order date, client ID, client’s first name and family name, client’s address, product information ordered. The form will have the ability to add new orders, find clients, add new clients. The completed order invoice will be provided in a printed report. In chapter three, you will build a database management system where you can store information about valuables in your warehouse. The table will have seven fields: Item (description of the item), Location (where the item was placed), Shop (where the item was purchased), DatePurchased (when the item was purchased), Cost (how much the item cost), SerialNumber (serial number of the item), PhotoFile (path of the photo file of the item), and Fragile (indicates whether a particular item is fragile or not). The development of this Warehouse Inventory Project will be performed, as usual, in a step-by-step manner. You will first create the database. Furthermore, the interface will be built so that the user can view, edit, add, or add data records from the database. Finally, you add code to create a printable list of information from the database. In chapter four, you will build an application that can be used to track daily high and low pollutant PM2.5 and air quality level. The steps that need to be taken in building Siantar Air Quality Index (SAQI) database project are: Build and test a Visual C# interface; Create an empty database using code; and Report database. The designed interface will allow the user to enter max pollutant, min pollutant, and air quality for any date that the user chooses in a particular year. This information will be stored in a database. Graphical result of the data will be provided, along with summary information relating to the maximum value, minimum value, and mean value. You will use a tab control as the main component of the interface. The control has three tabs: one for viewing and editing data, one for viewing graph of pollutant data, and another for viewing graph of air quality data. Each tab on this control operates like a Visual C# control panel. In chapter five, you will perform the steps necessary to build a MySQL book inventory database that contains 4 tables. You will build each table and add the associated fields as needed. You will have four tables in the database and define the relationship between the primary key and foreign key. You will associate AuthorID (foreign key) field in the Title_Author table with AuthorID (primary key) in the Author table. Then, you want to associate the ISBN (foreign key) field in Title_Author table with ISBN (primary key) in the Title table.
In this book, you will learn how to use OpenCV, NumPy library and other libraries to perform signal processing, image processing, object detection, and feature extraction with Python GUI (PyQt). You will learn how to filter signals, detect edges and segments, and denoise images with PyQt. You will also learn how to detect objects (face, eye, and mouth) using Haar Cascades and how to detect features on images using Harris Corner Detection, Shi-Tomasi Corner Detector, Scale-Invariant Feature Transform (SIFT), and Features from Accelerated Segment Test (FAST). In Chapter 1, you will learn: Tutorial Steps To Create A Simple GUI Application, Tutorial Steps to Use Radio Button, Tutorial Steps to Group Radio Buttons, Tutorial Steps to Use CheckBox Widget, Tutorial Steps to Use Two CheckBox Groups, Tutorial Steps to Understand Signals and Slots, Tutorial Steps to Convert Data Types, Tutorial Steps to Use Spin Box Widget, Tutorial Steps to Use ScrollBar and Slider, Tutorial Steps to Use List Widget, Tutorial Steps to Select Multiple List Items in One List Widget and Display It in Another List Widget, Tutorial Steps to Insert Item into List Widget, Tutorial Steps to Use Operations on Widget List, Tutorial Steps to Use Combo Box, Tutorial Steps to Use Calendar Widget and Date Edit, and Tutorial Steps to Use Table Widget. In Chapter 2, you will learn: Tutorial Steps To Create A Simple Line Graph, Tutorial Steps To Create A Simple Line Graph in Python GUI, Tutorial Steps To Create A Simple Line Graph in Python GUI: Part 2, Tutorial Steps To Create Two or More Graphs in the Same Axis, Tutorial Steps To Create Two Axes in One Canvas, Tutorial Steps To Use Two Widgets, Tutorial Steps To Use Two Widgets, Each of Which Has Two Axes, Tutorial Steps To Use Axes With Certain Opacity Levels, Tutorial Steps To Choose Line Color From Combo Box, Tutorial Steps To Calculate Fast Fourier Transform, Tutorial Steps To Create GUI For FFT, Tutorial Steps To Create GUI For FFT With Some Other Input Signals, Tutorial Steps To Create GUI For Noisy Signal, Tutorial Steps To Create GUI For Noisy Signal Filtering, and Tutorial Steps To Create GUI For Wav Signal Filtering. In Chapter 3, you will learn: Tutorial Steps To Convert RGB Image Into Grayscale, Tutorial Steps To Convert RGB Image Into YUV Image, Tutorial Steps To Convert RGB Image Into HSV Image, Tutorial Steps To Filter Image, Tutorial Steps To Display Image Histogram, Tutorial Steps To Display Filtered Image Histogram, Tutorial Steps To Filter Image With CheckBoxes, Tutorial Steps To Implement Image Thresholding, and Tutorial Steps To Implement Adaptive Image Thresholding. In Chapter 4, you will learn: Tutorial Steps To Generate And Display Noisy Image, Tutorial Steps To Implement Edge Detection On Image, Tutorial Steps To Implement Image Segmentation Using Multiple Thresholding and K-Means Algorithm, and Tutorial Steps To Implement Image Denoising. In Chapter 5, you will learn: Tutorial Steps To Detect Face, Eye, and Mouth Using Haar Cascades, Tutorial Steps To Detect Face Using Haar Cascades with PyQt, Tutorial Steps To Detect Eye, and Mouth Using Haar Cascades with PyQt, and Tutorial Steps To Extract Detected Objects. In Chapter 6, you will learn: Tutorial Steps To Detect Image Features Using Harris Corner Detection, Tutorial Steps To Detect Image Features Using Shi-Tomasi Corner Detection, Tutorial Steps To Detect Features Using Scale-Invariant Feature Transform (SIFT), and Tutorial Steps To Detect Features Using Features from Accelerated Segment Test (FAST). You can download the XML files from https://viviansiahaan.blogspot.com/2023/06/learn-from-scratch-signal-and-image.html.
In chapter one, you will learn to know the properties and events of each control in a Windows Visual C# application. You need to learn and know in order to be more familiar when applying them to some applications in this book. In chapter two, you will build a project so that children can practice basic skills in addition, subtraction, multiplication, and division operations. This Math Game project can be used to choose the types of questions and what factors you want to use. This project has three timing options. Random math problems using values from 0 to 9 will be presented. Timing options are provided to measure accuracy and speed. There are many controls used. Two label controls are used for title information, two for displaying scores. There is a wide label in the middle of the form to display math questions. And, long skinny label is used as separator. Two button controls are used to start and stop question and one button to exit the project. There are three group control boxes. The first group box holds four check box controls that are used to select the type of questions. The second group box holds eleven radio buttons that are used to select values that are used as factors in calculations. The third group box contains three radio button controls for timing options. A scroll bar control rod is used to change the time. In chapter three, you will build Bank Code game. The storage box is locked and can only be opened if you enter the correct digit combination. Combinations can be 2 to 4 non-repetitive digits (range of digits from 1 to 9). After a guess is given, you will be notified of how many digits are right and how many digits are in the right position. Based on this information, you will give another guess. You continue to guess until you get the right combination or until you stop the game. On the left side of the form is a large picture box control. On the right side, two group box controls and two button controls are placed. In the picture box, a control panel is placed. In the panel, there are four label controls (set the AutoSize property to False) and nine button controls. In the first group box control, place three radio buttons. In the second group box control, a text box control is placed. The picture box contains an image of bank and a panel. The label controls in the panel are used to display the combinations entered (the BorderStyle property set to FixedSingle to display the label size). The nine buttons on the panel are used to enter combinations. Radio buttons are used to set options. The buttons (one to start and stop the game and another to exit the project) are used to control game operations. The text box displays the results of the combinations entered. In chapter four, you will build Horse Racing game. This is a simple game. Up to 10 horses will race to the finish line. You guessed two horses that you thought could win the race. By clicking on the Start button, the race will start. All horses will race speed to get to the finish line. Labels are used to display instructions and number of horses in a race. Four button controls are used: two buttons to change number of horses, one button to start the game, and one other button to stop the game. The picture box control is used to load the horse image. A timer control is used to update the horse's movement during the race. In chapter five, you will build Catching Ball game. The bird flew and dropped ball from the sky. Users are challenged to position man under the fallen ball to catch it. Labels are used for instructions and to display game information (remaining time, number of balls captured, and game difficulty level). Two buttons are used to change the game difficulty level, one button to start the game, and another button to stop the game. Picture box controls hold images for man, bird, and ball. In chapter six, you will build Smart Tic Tac Toe game. That said, this is the first game ever programmed on a computer and one that had been programmed by Bill Gates himself when he was a teenager while attending Lakeside School in Seattle. The aim of this game is to win the game on a 3 x 3 grid with the victory of three identical symbols (X or O) on horizontal, diagonal, or vertical lines. The players will play alternately. In this game given two game options: player 1 against player 2 or human player against computer. A smart but simple strategy will be developed for computer logic to be a formidable opponent for humans. In chapter seven, you will build Fighting Plane program. This program can be played by two human players or human player versus computer. The controls of the player are done via the keyboard. Player 1 presses A key to move up, Z key to move down, and S key to throw rudal. When you choose Two players from the Options button, this game can be played by two human players. Player 1 presses the same keys, while player 2 presses key K to move up, M to move down, and key J to throw rudal. All label controls are used for titles and provide scoring and game information. The large panel (Panel1) is the playing field. Three button controls are used to start / stop a program, set options, and exit the program. One timer control is used to control game animation and another is used to represent the computer's decision process. The second control panel (Panel2) is used to select game options. One group box contains radio buttons which are used to select number of players. A group box contains radio buttons to select the level of difficulty of the game, when playing against a computer. A small button is used to close the options panel. The default properties are set for one-player games with the easiest game difficulty.
In this book, you will learn how to use NumPy, Pandas, OpenCV, Scikit-Learn and other libraries to how to plot graph and to process digital image. Then, you will learn how to classify features using Perceptron, Adaline, Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN) models. You will also learn how to extract features using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Kernel Principal Component Analysis (KPCA) algorithms and use them in machine learning. In Chapter 1, you will learn: Tutorial Steps To Create A Simple GUI Application, Tutorial Steps to Use Radio Button, Tutorial Steps to Group Radio Buttons, Tutorial Steps to Use CheckBox Widget, Tutorial Steps to Use Two CheckBox Groups, Tutorial Steps to Understand Signals and Slots, Tutorial Steps to Convert Data Types, Tutorial Steps to Use Spin Box Widget, Tutorial Steps to Use ScrollBar and Slider, Tutorial Steps to Use List Widget, Tutorial Steps to Select Multiple List Items in One List Widget and Display It in Another List Widget, Tutorial Steps to Insert Item into List Widget, Tutorial Steps to Use Operations on Widget List, Tutorial Steps to Use Combo Box, Tutorial Steps to Use Calendar Widget and Date Edit, and Tutorial Steps to Use Table Widget. In Chapter 2, you will learn: Tutorial Steps To Create A Simple Line Graph, Tutorial Steps To Create A Simple Line Graph in Python GUI, Tutorial Steps To Create A Simple Line Graph in Python GUI: Part 2, Tutorial Steps To Create Two or More Graphs in the Same Axis, Tutorial Steps To Create Two Axes in One Canvas, Tutorial Steps To Use Two Widgets, Tutorial Steps To Use Two Widgets, Each of Which Has Two Axes, Tutorial Steps To Use Axes With Certain Opacity Levels, Tutorial Steps To Choose Line Color From Combo Box, Tutorial Steps To Calculate Fast Fourier Transform, Tutorial Steps To Create GUI For FFT, Tutorial Steps To Create GUI For FFT With Some Other Input Signals, Tutorial Steps To Create GUI For Noisy Signal, Tutorial Steps To Create GUI For Noisy Signal Filtering, and Tutorial Steps To Create GUI For Wav Signal Filtering. In Chapter 3, you will learn: Tutorial Steps To Convert RGB Image Into Grayscale, Tutorial Steps To Convert RGB Image Into YUV Image, Tutorial Steps To Convert RGB Image Into HSV Image, Tutorial Steps To Filter Image, Tutorial Steps To Display Image Histogram, Tutorial Steps To Display Filtered Image Histogram, Tutorial Steps To Filter Image With CheckBoxes, Tutorial Steps To Implement Image Thresholding, and Tutorial Steps To Implement Adaptive Image Thresholding. You will also learn: Tutorial Steps To Generate And Display Noisy Image, Tutorial Steps To Implement Edge Detection On Image, Tutorial Steps To Implement Image Segmentation Using Multiple Thresholding and K-Means Algorithm, Tutorial Steps To Implement Image Denoising, Tutorial Steps To Detect Face, Eye, and Mouth Using Haar Cascades, Tutorial Steps To Detect Face Using Haar Cascades with PyQt, Tutorial Steps To Detect Eye, and Mouth Using Haar Cascades with PyQt, Tutorial Steps To Extract Detected Objects, Tutorial Steps To Detect Image Features Using Harris Corner Detection, Tutorial Steps To Detect Image Features Using Shi-Tomasi Corner Detection, Tutorial Steps To Detect Features Using Scale-Invariant Feature Transform (SIFT), and Tutorial Steps To Detect Features Using Features from Accelerated Segment Test (FAST). In Chapter 4, In this tutorial, you will learn how to use Pandas, NumPy and other libraries to perform simple classification using perceptron and Adaline (adaptive linear neuron). The dataset used is Iris dataset directly from the UCI Machine Learning Repository. You will learn: Tutorial Steps To Implement Perceptron, Tutorial Steps To Implement Perceptron with PyQt, Tutorial Steps To Implement Adaline (ADAptive LInear NEuron), and Tutorial Steps To Implement Adaline with PyQt. In Chapter 5, you will learn how to use the scikit-learn machine learning library, which provides a wide variety of machine learning algorithms via a user-friendly Python API and to perform classification using perceptron, Adaline (adaptive linear neuron), and other models. The dataset used is Iris dataset directly from the UCI Machine Learning Repository. You will learn: Tutorial Steps To Implement Perceptron Using Scikit-Learn, Tutorial Steps To Implement Perceptron Using Scikit-Learn with PyQt, Tutorial Steps To Implement Logistic Regression Model, Tutorial Steps To Implement Logistic Regression Model with PyQt, Tutorial Steps To Implement Logistic Regression Model Using Scikit-Learn with PyQt, Tutorial Steps To Implement Support Vector Machine (SVM) Using Scikit-Learn, Tutorial Steps To Implement Decision Tree (DT) Using Scikit-Learn, Tutorial Steps To Implement Random Forest (RF) Using Scikit-Learn, and Tutorial Steps To Implement K-Nearest Neighbor (KNN) Using Scikit-Learn. In Chapter 6, you will learn how to use Pandas, NumPy, Scikit-Learn, and other libraries to implement different approaches for reducing the dimensionality of a dataset using different feature selection techniques. You will learn about three fundamental techniques that will help us to summarize the information content of a dataset by transforming it onto a new feature subspace of lower dimensionality than the original one. Data compression is an important topic in machine learning, and it helps us to store and analyze the increasing amounts of data that are produced and collected in the modern age of technology. You will learn the following topics: Principal Component Analysis (PCA) for unsupervised data compression, Linear Discriminant Analysis (LDA) as a supervised dimensionality reduction technique for maximizing class separability, Nonlinear dimensionality reduction via Kernel Principal Component Analysis (KPCA). You will learn: 6.1 Tutorial Steps To Implement Principal Component Analysis (PCA), Tutorial Steps To Implement Principal Component Analysis (PCA) Using Scikit-Learn, Tutorial Steps To Implement Principal Component Analysis (PCA) Using Scikit-Learn with PyQt, Tutorial Steps To Implement Linear Discriminant Analysis (LDA), Tutorial Steps To Implement Linear Discriminant Analysis (LDA) with Scikit-Learn, Tutorial Steps To Implement Linear Discriminant Analysis (LDA) Using Scikit-Learn with PyQt, Tutorial Steps To Implement Kernel Principal Component Analysis (KPCA) Using Scikit-Learn, and Tutorial Steps To Implement Kernel Principal Component Analysis (KPCA) Using Scikit-Learn with PyQt. In Chapter 7, you will learn how to use Keras, Scikit-Learn, Pandas, NumPy and other libraries to perform prediction on handwritten digits using MNIST dataset. You will learn: Tutorial Steps To Load MNIST Dataset, Tutorial Steps To Load MNIST Dataset with PyQt, Tutorial Steps To Implement Perceptron With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Perceptron With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Perceptron With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Logistic Regression (LR) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Logistic Regression (LR) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Logistic Regression (LR) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement , Tutorial Steps To Implement Support Vector Machine (SVM) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Support Vector Machine (SVM) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Decision Tree (DT) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Decision Tree (DT) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Decision Tree (DT) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Random Forest (RF) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Random Forest (RF) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Random Forest (RF) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement K-Nearest Neighbor (KNN) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement K-Nearest Neighbor (KNN) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, and Tutorial Steps To Implement K-Nearest Neighbor (KNN) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt.
This book aims to develop a database-driven desktop application that readers can develop for their own purposes to implement database-oriented digital image processing, machine learning, and image retrieval applications. In Tutorial 1, you will perform the steps necessary to add 6 tables using Visual C# into ImageProc database. You will build each table and add the associated fields as needed. In this tutorial, you will also build such a form for Officer table. This table has sixteen fields: OfficerID, FirstName, LastName, RegNumber, BirthDate, AppDate, Gender, Status, Rank, Address, Mobile, Phone, Email, Description, PhotoFile, and FingerFile). You need seventeen label controls, two picture boxes, ten text boxes, two comboxes, one check box, two date time pickers, one openfiledialog, and one printpreviewdialog. You also need four buttons for navigation, eight buttons for utilites, one button for searching officer’s name, one button to upload officer’s photo, and one button to upload officer’s fingerprint. In Tutorial 2, you will perform the steps necessary to create and implement police station form. In this tutorial, you will build such a form for PoliceStation table. This table has seven fields: PSID, OfficerID, PSName, City, Address, Phone, and Description. You need an input form so that user can edit existing records, delete records, or add new records. The form will also have the capability of navigating from one record to another. You need eight label controls, six text boxes, two comboxes, one check box, and one printpreviewdialog. You also need four buttons for navigation, eight buttons for utilites, and one button for searching officer’s name. Place these controls on the form. In Tutorial 3, you will build such a form for Accused table. This table has thirteen fields: AccusedID, FullName, MotherName, CrimeCase, BirthDate, Gender, Address, Mobile, Phone, Email, Description, PhotoFile, and FingerFile). You need an input form so that user can edit existing records, delete records, or add new records. The form will also have the capability of navigating from one record to another. You need fourteen label controls, two picture boxes, nine text boxes, two comboxes, one date time picker, one openfiledialog, and one printpreviewdialog. You also need four buttons for navigation, eight buttons for utilites, one button for searching accused’s name, one button to upload accused’s photo, and one button to upload accused’s fingerprint. In Tutorial 4, you will build such a form for Witness table. This table has thirteen fields: WitnessID, FullName, MotherName, CrimeCase, BirthDate, Gender, Address, Mobile, Phone, Email, Description, PhotoFile, and FingerFile). You need an input form so that user can edit existing records, delete records, or add new records. The form will also have the capability of navigating from one record to another. You need fourteen label controls, two picture boxes, nine text boxes, two comboxes, one date time picker, one openfiledialog, and one printpreviewdialog. You also need four buttons for navigation, eight buttons for utilites, one button for searching witness’s name, one button to upload witness’s photo, and one button to upload witness’s fingerprint. In Tutorial 5, you will build such a form for Victim table. This table has thirteen fields: VictimID, FullName, MotherName, CrimeCase, BirthDate, Gender, Address, Mobile, Phone, Email, Description, PhotoFile, and FingerFile). You need an input form so that user can edit existing records, delete records, or add new records. The form will also have the capability of navigating from one record to another. You need fourteen label controls, two picture boxes, nine text boxes, two comboxes, one date time picker, one openfiledialog, and one printpreviewdialog. You also need four buttons for navigation, eight buttons for utilites, one button for searching victim’s name, one button to upload victim’s photo, and one button to upload victim’s fingerprint. In Tutorial 6, you will build such a form for CrimeReg table. This table has fourteen fields: CRID, CRNumber, PSID, VictimID, AccusedID, DateReport, DateCrime, Arrested, CaseStatus, Description, Feature1, Feature2, Feature3, and Feature4. You need an input form so that user can edit existing records, delete records, or add new records. The form will also have the capability of navigating from one record to another. You need thirty two label controls, seven text boxes, ten comboxes, one check box, two date time pickers, six picture boxes, and one printpreviewdialog. You then need four buttons for navigation, eight buttons for utilites, and one button for searching crime register number. You also need button to save every feature.
In chapter one, you will learn to know the properties and events of each control in a Windows Visual C# application. You need to learn and know in order to be more familiar when applying them to some applications in this book. In chapter two, you will go through step by step to build a SALES database using Microsoft Access and SQL Server. You will build each table and add associated data fields (along with the necessary keys and indexes). The first field in the Client table is ClientID. Enter the clien ID in the Name Field and select AutoNumber in the Data Type. You define primary key and other indexes which are useful for quick searching. ClientID is a primary field. If the small lock symbol is not displayed next to the ClientID row, then you need to place it there. Right click on ClientID row and select Primary Key. A small key is now displayed next to the entry indicating it is the primary key. You will define FamilyName as an index. Select the FamilyName line. On the General tab, set the Indexed property to Yes (Duplicates OK). You then will create Ordering table with three fields: OrderID, ClientID, and OrderDate. You then will create Purchase table with three fields: OrderID, ProductID, and Quantity. And you will create Product table with four fields: ProductID, Description, Price, and QtySold. Before designing Visual C# interface, you will build the relationships between four tables. In chapter three, you will build a Visual C# interface for the database. The interface will be used to enter new orders into the database. The order form will be used to enter the following information into the database: order ID, order date, client ID, client’s first name and family name, client’s address, product information ordered. The form will have the ability to add new orders, find clients, add new clients. The completed order invoice will be provided in a printed report. In chapter four, you will build a database management system where you can store information about valuables in your warehouse. The table will have seven fields: Item (description of the item), Location (where the item was placed), Shop (where the item was purchased), DatePurchased (when the item was purchased), Cost (how much the item cost), SerialNumber (serial number of the item), PhotoFile (path of the photo file of the item), and Fragile (indicates whether a particular item is fragile or not). The development of this Warehouse Inventory Project will be performed, as usual, in a step-by-step manner. You will first create the database. Furthermore, the interface will be built so that the user can view, edit, add, or add data records from the database. Finally, you add code to create a printable list of information from the database. In chapter five, you will build an application that can be used to track daily high and low pollutant PM2.5 and air quality level. You will do this in stages, from database development to creation of distribution packages. These steps are the same as those used in developing a commercial database application. The steps that need to be taken in building Siantar Air Quality Index (SAQI) database project are: Build and test a Visual C# interface; Create an empty database using code; and Report database. The designed interface will allow the user to enter max pollutant, min pollutant, and air quality for any date that the user chooses in a particular year. This information will be stored in a database. Graphical result of the data will be provided, along with summary information relating to the maximum value, minimum value, and mean value. You will use a tab control as the main component of the interface. The control has three tabs: one for viewing and editing data, one for viewing graph of pollutant data, and another for viewing graph of air quality data. Each tab on this control operates like a Visual C# control panel. In chapter six, you will perform the steps necessary to build a SQL Server book inventory database that contains 4 tables using Microsoft Visual Studio 2019. You will build each table and add the associated fields as needed. You will have four tables in the database and define the relationship between the primary key and foreign key. You will associate AuthorID (foreign key) field in the Title_Author table with AuthorID (primary key) in the Author table. Then, you want to associate the ISBN (foreign key) field in Title_Author table with ISBN (primary key) in the Title table.
In chapter one, you will learn to know the properties and events of each control in a Windows Visual C# applications. You need to learn and know in order to be more familiar when applying them to some desktop applications in this book. In chapter two, you will build Throwing Fire program. This program can be played by two human players or human player versus computer. You will use 12 labels, a large control panel, and three control buttons on the form. In the control panel, a smaller panel with two group box controls and a button control are placed. In the first group box, you will use 2 radio buttons; in the second box group, place 4 radio buttons. Next, two timer controls are added to the project. All label controls are used for titles and provide scoring and game information. The large panel (Panel1) is the playing field. Three button controls are used to start / stop a program, set options, and exit the program. One timer control is used to control game animation and another is used to represent the computer's decision process. The second control panel (Panel2) is used to select game options. One group box contains radio buttons which are used to select number of players. A group box contains radio buttons to select the level of difficulty of the game, when playing against a computer. A small button is used to close the options panel. The default properties are set for one-player games with the easiest game difficulty. In chapter three, you will build Roasted Duck Delivery simulation. In this simulation, a number of decisions are needed. The basic idea is to read the order by incoming telephone and tell the delivery scooter to go to the location of the order. You also need to make sure that you always provide a roasted duck ready to be transported by the delivery scooter. The delivery area is a 20 by 20 square grid. The more roasted duck is sold, the more profit it gets. The panel control on the left side of the form contains the delivery grid. On the upper right are group boxes with two label controls to display the time or hour and sale results. The computer monitor (in a picture box) displays order and delivery status using a list box and label control. Another group box contains a roasting oven when the roasted ducks are displayed using eight picture box controls. Two button controls on the group box control the operation of the oven. Group boxes under the oven show how many ducks are ready to be delivered and how many are in the delivery scooter (a button control is to load the roasted duck into the scooter). The two button controls beneath are used to start/pause the game and to stop the game or exit the game. In the area under the form there are several timers for controlling a number of aspects in the program. The delivery grid consists of 400 label controls on 20 rows (marked with numbers) and 20 columns (marked with letters). Here, you will learn how to place controls on a form (or panel in this case) using code (when the program runs, not when designing the form). This mechanism can save time designing the form. In chapter four, you will build a Drone Simulation. In this simulation, you control both vertical and horizontal thrusters to maneuver the ride to the landing pad. You will adjust the landing speed so that it is slow enough so that no accident occurs. You build the form in two stages, the first stage creates two option group boxes, and then the second stage uses both those group boxes as landing controls. Two control panels are placed on the left side of the form: one panel for drawing and another panel for the edge. On the right side of the form, place the two group control boxes. In the first group box, five radio buttons and a check box are added. In the second group box, two radio buttons are placed. In the below section of the form, three buttons are added. Finally, one timer control is added. Then in the form, a group box is added overlap panel. Then, 11 label controls are added to the group box. After that, a progress bar is added. Under the bar, two control panels are added, one high panel and one short panel. In the second (short) panel control, two small label controls are added. Underneath, three button controls are placed. Under these three buttons, a label control is added. For each label control, set the AutoSize property to False to be resized and set (temporarily) the BorderStyle property to FixedSingle so that you can see the edges to facilitate the layout process. In this chapter, you will build Jumper game. In this game, you will move the jumper across the busy road, avoid the tiger, and cross the river with the changing current to get to house safely. You will place four label controls on the top part of the form (set the AutoSize property to False so that it can be resized and the BorderStyle property temporarily becomes FixedSingle so you can see the edges). Then, you use five panel controls below the labels. These panels will be a place for image graphics. Each panel has a width of 16 jumpers or 640 pixels, because one jumper will be given a width of 40 pixels. The first panel will be the jumper house, which will be given a height of 80 pixels. The next panel will become a river, with a height of 120 pixels. The next panel will be a place for tiger, 40 pixels high. Under the snake panel, there is a road panel. This panel will contain three boat lanes. Each boat has a height of 40 pixels, but you will give it a height of 140 pixels (not 120 pixels) to make room for lane markers. The fifth panel is the place where the jumper will begin its journey or leap. This panel will be given a height of 40 pixels. Add the last control panel below the form with three button controls. Then, finally, add four timer controls. Adjust the size of the form so that the panel controls can occupy according to the width of the form.
PROJECT 1: DEFAULT LOAN PREDICTION BASED ON CUSTOMER BEHAVIOR Using Machine Learning and Deep Learning with Python In finance, default is failure to meet the legal obligations (or conditions) of a loan, for example when a home buyer fails to make a mortgage payment, or when a corporation or government fails to pay a bond which has reached maturity. A national or sovereign default is the failure or refusal of a government to repay its national debt. The dataset used in this project belongs to a Hackathon organized by "Univ.AI". All values were provided at the time of the loan application. Following are the features in the dataset: Income, Age, Experience, Married/Single, House_Ownership, Car_Ownership, Profession, CITY, STATE, CURRENT_JOB_YRS, CURRENT_HOUSE_YRS, and Risk_Flag. The Risk_Flag indicates whether there has been a default in the past or not. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 2: AIRLINE PASSENGER SATISFACTION Analysis and Prediction Using Machine Learning and Deep Learning with Python The dataset used in this project contains an airline passenger satisfaction survey. In this case, you will determine what factors are highly correlated to a satisfied (or dissatisfied) passenger and predict passenger satisfaction. Below are the features in the dataset: Gender: Gender of the passengers (Female, Male); Customer Type: The customer type (Loyal customer, disloyal customer); Age: The actual age of the passengers; Type of Travel: Purpose of the flight of the passengers (Personal Travel, Business Travel); Class: Travel class in the plane of the passengers (Business, Eco, Eco Plus); Flight distance: The flight distance of this journey; Inflight wifi service: Satisfaction level of the inflight wifi service (0:Not Applicable;1-5); Departure/Arrival time convenient: Satisfaction level of Departure/Arrival time convenient; Ease of Online booking: Satisfaction level of online booking; Gate location: Satisfaction level of Gate location; Food and drink: Satisfaction level of Food and drink; Online boarding: Satisfaction level of online boarding; Seat comfort: Satisfaction level of Seat comfort; Inflight entertainment: Satisfaction level of inflight entertainment; On-board service: Satisfaction level of On-board service; Leg room service: Satisfaction level of Leg room service; Baggage handling: Satisfaction level of baggage handling; Check-in service: Satisfaction level of Check-in service; Inflight service: Satisfaction level of inflight service; Cleanliness: Satisfaction level of Cleanliness; Departure Delay in Minutes: Minutes delayed when departure; Arrival Delay in Minutes: Minutes delayed when Arrival; and Satisfaction: Airline satisfaction level (Satisfaction, neutral or dissatisfaction) The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 3: CREDIT CARD CHURNING CUSTOMER ANALYSIS AND PREDICTION USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON The dataset used in this project consists of more than 10,000 customers mentioning their age, salary, marital_status, credit card limit, credit card category, etc. There are 20 features in the dataset. In the dataset, there are only 16.07% of customers who have churned. Thus, it's a bit difficult to train our model to predict churning customers. Following are the features in the dataset: 'Attrition_Flag', 'Customer_Age', 'Gender', 'Dependent_count', 'Education_Level', 'Marital_Status', 'Income_Category', 'Card_Category', 'Months_on_book', 'Total_Relationship_Count', 'Months_Inactive_12_mon', 'Contacts_Count_12_mon', 'Credit_Limit', 'Total_Revolving_Bal', 'Avg_Open_To_Buy', 'Total_Amt_Chng_Q4_Q1', 'Total_Trans_Amt', 'Total_Trans_Ct', 'Total_Ct_Chng_Q4_Q1', and 'Avg_Utilization_Ratio',. The target variable is 'Attrition_Flag'. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 4: MARKETING ANALYSIS AND PREDICTION USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON This data set was provided to students for their final project in order to test their statistical analysis skills as part of a MSc. in Business Analytics. It can be utilized for EDA, Statistical Analysis, and Visualizations. Following are the features in the dataset: ID = Customer's unique identifier; Year_Birth = Customer's birth year; Education = Customer's education level; Marital_Status = Customer's marital status; Income = Customer's yearly household income; Kidhome = Number of children in customer's household; Teenhome = Number of teenagers in customer's household; Dt_Customer = Date of customer's enrollment with the company; Recency = Number of days since customer's last purchase; MntWines = Amount spent on wine in the last 2 years; MntFruits = Amount spent on fruits in the last 2 years; MntMeatProducts = Amount spent on meat in the last 2 years; MntFishProducts = Amount spent on fish in the last 2 years; MntSweetProducts = Amount spent on sweets in the last 2 years; MntGoldProds = Amount spent on gold in the last 2 years; NumDealsPurchases = Number of purchases made with a discount; NumWebPurchases = Number of purchases made through the company's web site; NumCatalogPurchases = Number of purchases made using a catalogue; NumStorePurchases = Number of purchases made directly in stores; NumWebVisitsMonth = Number of visits to company's web site in the last month; AcceptedCmp3 = 1 if customer accepted the offer in the 3rd campaign, 0 otherwise; AcceptedCmp4 = 1 if customer accepted the offer in the 4th campaign, 0 otherwise; AcceptedCmp5 = 1 if customer accepted the offer in the 5th campaign, 0 otherwise; AcceptedCmp1 = 1 if customer accepted the offer in the 1st campaign, 0 otherwise; AcceptedCmp2 = 1 if customer accepted the offer in the 2nd campaign, 0 otherwise; Response = 1 if customer accepted the offer in the last campaign, 0 otherwise; Complain = 1 if customer complained in the last 2 years, 0 otherwise; and Country = Customer's location. The machine and deep learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 5: METEOROLOGICAL DATA ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON Meteorological phenomena are described and quantified by the variables of Earth's atmosphere: temperature, air pressure, water vapour, mass flow, and the variations and interactions of these variables, and how they change over time. Different spatial scales are used to describe and predict weather on local, regional, and global levels. The dataset used in this project consists of meteorological data with 96453 total number of data points and with 11 attributes/columns. Following are the columns in the dataset: Formatted Date; Summary; Precip Type; Temperature (C); Apparent Temperature (C); Humidity; Wind Speed (km/h); Wind Bearing (degrees); Visibility (km); Pressure (millibars); and Daily Summary. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM classifier, Gradient Boosting, XGB classifier, and MLP classifier. Finally, you will plot boundary decision, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy.
Skin cancer develops primarily on areas of sun-exposed skin, including the scalp, face, lips, ears, neck, chest, arms and hands, and on the legs in women. But it can also form on areas that rarely see the light of day — your palms, beneath your fingernails or toenails, and your genital area. Skin cancer affects people of all skin tones, including those with darker complexions. When melanoma occurs in people with dark skin tones, it's more likely to occur in areas not normally exposed to the sun, such as the palms of the hands and soles of the feet. Dataset used in this project contains a balanced dataset of images of benign skin moles and malignant skin moles. The data consists of two folders with each 1800 pictures (224x244) of the two types of moles. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. The deep learning models used are CNN and MobileNet.
