LEARN FROM SCRATCH VISUAL BASIC .NET WITH MYSQL
LEARN FROM SCRATCH VISUAL BASIC .NET WITH MYSQL

Author: Vivian Siahaan

Publisher: BALIGE PUBLISHING

Published: 2020-11-04

Total Pages: 304

ISBN-13:

DOWNLOAD EBOOK

This book will teach you with step-by-step approach to develop from scratch a MySQL-driven desktop application that readers can develop for their own purposes to implement school database project using Visual Basic .NET. In Tutorial 1, you will perform the steps necessary to add 8 tables using phpMyAdmin into School database that you will create. You will build each table and add the associated fields as needed. In this tutorial, you will also build login form and main form. In Tutorial 2, you will build such a form for Parent table. This table has thirteen fields: ParentID, FirstName, LastName, BirthDate, Status, Ethnicity, Nationality, Mobile, Phone, Religion, Gender, PhotoFile, and FingerFile). You need fourteen label controls, two picture boxes, six text boxes, four comboxes, one check box, one date time picker, one openfiledialog, and one printpreviewdialog. You also need four buttons for navigation, six buttons for other utilities, one button for searching member’s name, one button to upload parent’s photo, and button to upload parent’s finger. Place these controls on the form. In Tutorial 3, you will build such a form for Student table. This table has fifteen fields: StudentID, ParentID, FirstName, LastName, BirthDate, YearEntry, Status, Ethnicity, Nationality, Mobile, Phone, Religion, Gender, PhotoFile, and FingerFile). You need sixteen label controls, two picture boxes, six text boxes, five comboxes, one check box, two date time pickers, one openfiledialog, and one printpreviewdialog. You also need four buttons for navigation, seven buttons for controlling editing features, one button for searching parent’s name, one button to open parent form, one button to upload student’s photo, and one button to upload student’s finger. In Tutorial 4, you will build a form for Teacher table. This table has fifteen fields: TeacherID, RegNumber, FirstName, LastName, BirthDate, Rank, Status, Ethnicity, Nationality, Mobile, Phone, Religion, Gender, 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 sixteen label controls, one picture box, seven text boxes, five comboxes, one check box, one date time picker, one openfiledialog, and one printpreviewdialog. You also need four buttons for navigation, six buttons for controlling editing features, one button for searching teacher’s name, and one button to upload teacher’s photo. In Tutorial 5, you will build a form for Subject table. This table has only three fields: SubjectID, Name, and Description. You need four label controls, four text boxes, one openfiledialog, and one printpreviewdialog. You also need four buttons for navigation, secen buttons for utilities, and one button for searching subject name. Place these controls on the form. You will also build a form for Grade table. This table has seven fields: GradeID, Name, SubjectID, TeacherID, SchoolYear, TimaStart, and TimeFinish. You need to add seven label controls, one text box, four comboxes, and two date time pickers. You also need four buttons for navigation, seven buttons for controlling editing features, one button to open subject form, and one button to open teacher form. In Tutorial 6, you will build a form for Grade_Student table. This table has only three fields: Grade_StudentID, GradeID, and StudentID. 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 two label controls and two comboxes. You also need four buttons for navigation, seven buttons for controlling editing features, one button to open grade form, and one button to open student form.

Two Books In One: LEARN FROM SCRATCH VISUAL BASIC .NET WITH MYSQL
Two Books In One: LEARN FROM SCRATCH VISUAL BASIC .NET WITH MYSQL

Author: Vivian Siahaan

Publisher: BALIGE PUBLISHING

Published: 2020-11-05

Total Pages: 603

ISBN-13:

DOWNLOAD EBOOK

BOOK 1: VISUAL BASIC .NET AND DATABASE: PRACTICAL TUTORIALS This book aims to develop a MySQL-driven desktop application that readers can develop for their own purposes to implement library project using Visual Basic .NET. In Tutorial 1, you will build a Visual Basic interface for the database. This interface will used as the main terminal in accessing other forms. This tutorial will also discuss how to create login form and login table. You will create login form. Place on the form one picture box, two labels, one combo box, one text box, and two buttons. In Tutorial 2, you will build a school inventory project where you can store information about valuables in school. The table will have nine fields: Item (description of the item), Quantity, 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). In Tutorial 3, you will perform the steps necessary to add 5 new tables using phpMyAdmin into Academy database. You will build each table and add the associated fields as needed. Every table in the database will need input form. In this tutorial, you will build such a form for Author table. Although this table is quite simple (only four fields: AuthorID, Name, BirthDate, and PhotoFile), it provides a basis for illustrating the many steps in interface design. SQL statement is required by the Command object to read fields (sorted by Name). Then, you will build an interface so that the user can maintain the Publisher table in the database (Academy). The Publisher table interface is more or less the same as Author table interface. This Publisher table interface only requires more input fields. So you will use the interface for the Author table and modify it for the Publisher table. In Tutorial 4, you will perform the steps necessary to design and implement title form, library member form, and book borrowal form. You start by designing and testing the basic entry form for book titles. The Title table has nine fields: BookTitle, PublishYear, ISBN, PublisherID, AuthorID, Description, Note, Subject, and Comment. Then, you will build such a form for Member table. This table has twelve fields: MemberID, FirstName, LastName, BirthDate, Status, Ethnicity, Nationality, Mobile, Phone, Religion, Gender, and PhotoFile). You need thirteen label controls, one picture box, six text boxes, four comboxes, one check box, one date time picker, one openfiledialog, and one printpreviewdialog. You also need four buttons for navigation, six buttons for controlling editing features, one button for searching member’s name, and one button to upload member’s photo. Finally, you will build such a form for Borrow table. This table has seven fields: BorrowID, MemberID, BorrowCode, ISBN, BorrowDate, ReturnDate, and Penalty. In this form, you need fourteen label controls, seven text boxes, two comboxes, two date time pickers, and one printpreviewdialog. You also need four buttons for navigation, seven buttons for other utilities, one button to generate borrowal code, and one button to return book. BOOK 2: LEARN FROM SCRATCH VISUAL BASIC .NET WITH MYSQL This book will teach you with step-by-step approach to develop from scratch a MySQL-driven desktop application that readers can develop for their own purposes to implement school database project using Visual Basic .NET. In Tutorial 1, you will perform the steps necessary to add 8 tables using phpMyAdmin into School database that you will create. You will build each table and add the associated fields as needed. In this tutorial, you will also build login form and main form. In Tutorial 2, you will build such a form for Parent table. This table has thirteen fields: ParentID, FirstName, LastName, BirthDate, Status, Ethnicity, Nationality, Mobile, Phone, Religion, Gender, PhotoFile, and FingerFile). You need fourteen label controls, two picture boxes, six text boxes, four comboxes, one check box, one date time picker, one openfiledialog, and one printpreviewdialog. You also need four buttons for navigation, six buttons for other utilities, one button for searching member’s name, one button to upload parent’s photo, and button to upload parent’s finger. Place these controls on the form. In Tutorial 3, you will build such a form for Student table. This table has fifteen fields: StudentID, ParentID, FirstName, LastName, BirthDate, YearEntry, Status, Ethnicity, Nationality, Mobile, Phone, Religion, Gender, PhotoFile, and FingerFile). You need sixteen label controls, two picture boxes, six text boxes, five comboxes, one check box, two date time pickers, one openfiledialog, and one printpreviewdialog. You also need four buttons for navigation, seven buttons for controlling editing features, one button for searching parent’s name, one button to open parent form, one button to upload student’s photo, and one button to upload student’s finger. In Tutorial 4, you will build a form for Teacher table. This table has fifteen fields: TeacherID, RegNumber, FirstName, LastName, BirthDate, Rank, Status, Ethnicity, Nationality, Mobile, Phone, Religion, Gender, 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 sixteen label controls, one picture box, seven text boxes, five comboxes, one check box, one date time picker, one openfiledialog, and one printpreviewdialog. You also need four buttons for navigation, six buttons for controlling editing features, one button for searching teacher’s name, and one button to upload teacher’s photo. In Tutorial 5, you will build a form for Subject table. This table has only three fields: SubjectID, Name, and Description. You need four label controls, four text boxes, one openfiledialog, and one printpreviewdialog. You also need four buttons for navigation, secen buttons for utilities, and one button for searching subject name. Place these controls on the form. You will also build a form for Grade table. This table has seven fields: GradeID, Name, SubjectID, TeacherID, SchoolYear, TimaStart, and TimeFinish. You need to add seven label controls, one text box, four comboxes, and two date time pickers. You also need four buttons for navigation, seven buttons for controlling editing features, one button to open subject form, and one button to open teacher form. In Tutorial 6, you will build a form for Grade_Student table. This table has only three fields: Grade_StudentID, GradeID, and StudentID. 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 two label controls and two comboxes. You also need four buttons for navigation, seven buttons for controlling editing features, one button to open grade form, and one button to open student form.

VISUAL C# .NET WITH MYSQL
VISUAL C# .NET WITH MYSQL

Author: Vivian Siahaan

Publisher: BALIGE PUBLISHING

Published: 2020-09-13

Total Pages: 348

ISBN-13:

DOWNLOAD EBOOK

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.

LEARN FROM SCRATCH SIGNAL AND IMAGE PROCESSING WITH PYTHON GUI
LEARN FROM SCRATCH SIGNAL AND IMAGE PROCESSING WITH PYTHON GUI

Author: Vivian Siahaan

Publisher: BALIGE PUBLISHING

Published: 2023-06-14

Total Pages: 372

ISBN-13:

DOWNLOAD EBOOK

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.

Learn from Scratch Visual Basic .Net with MySQL
Learn from Scratch Visual Basic .Net with MySQL

Author: Rismon Hasiholan Sianipar

Publisher: Independently Published

Published: 2020-11-04

Total Pages: 304

ISBN-13:

DOWNLOAD EBOOK

This book will teach you with step-by-step approach to develop from scratch a MySQL-driven desktop application that readers can develop for their own purposes to implement school database project using Visual Basic .NET.In Tutorial 1, you will perform the steps necessary to add 8 tables using phpMyAdmin into School database that you will create. You will build each table and add the associated fields as needed. In this tutorial, you will also build login form and main form.In Tutorial 2, you will build such a form for Parent table. This table has thirteen fields: ParentID, FirstName, LastName, BirthDate, Status, Ethnicity, Nationality, Mobile, Phone, Religion, Gender, PhotoFile, and FingerFile). You need fourteen label controls, two picture boxes, six text boxes, four comboxes, one check box, one date time picker, one openfiledialog, and one printpreviewdialog. You also need four buttons for navigation, six buttons for other utilities, one button for searching member's name, one button to upload parent's photo, and button to upload parent's finger. Place these controls on the form.In Tutorial 3, you will build such a form for Student table. This table has fifteen fields: StudentID, ParentID, FirstName, LastName, BirthDate, YearEntry, Status, Ethnicity, Nationality, Mobile, Phone, Religion, Gender, PhotoFile, and FingerFile). You need sixteen label controls, two picture boxes, six text boxes, five comboxes, one check box, two date time pickers, one openfiledialog, and one printpreviewdialog. You also need four buttons for navigation, seven buttons for controlling editing features, one button for searching parent's name, one button to open parent form, one button to upload student's photo, and one button to upload student's finger.In Tutorial 4, you will build a form for Teacher table. This table has fifteen fields: TeacherID, RegNumber, FirstName, LastName, BirthDate, Rank, Status, Ethnicity, Nationality, Mobile, Phone, Religion, Gender, 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 sixteen label controls, one picture box, seven text boxes, five comboxes, one check box, one date time picker, one openfiledialog, and one printpreviewdialog. You also need four buttons for navigation, six buttons for controlling editing features, one button for searching teacher's name, and one button to upload teacher's photo.In Tutorial 5, you will build a form for Subject table. This table has only three fields: SubjectID, Name, and Description. You need four label controls, four text boxes, one openfiledialog, and one printpreviewdialog. You also need four buttons for navigation, secen buttons for utilities, and one button for searching subject name. Place these controls on the form. You will also build a form for Grade table. This table has seven fields: GradeID, Name, SubjectID, TeacherID, SchoolYear, TimaStart, and TimeFinish. You need to add seven label controls, one text box, four comboxes, and two date time pickers. You also need four buttons for navigation, seven buttons for controlling editing features, one button to open subject form, and one button to open teacher form.In Tutorial 6, you will build a form for Grade_Student table. This table has only three fields: Grade_StudentID, GradeID, and StudentID. 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 two label controls and two comboxes. You also need four buttons for navigation, seven buttons for controlling editing features, one button to open grade form, and one button to open student form.

VISUAL BASIC .NET AND DATABASE: PRACTICAL TUTORIALS
VISUAL BASIC .NET AND DATABASE: PRACTICAL TUTORIALS

Author: Vivian Siahaan

Publisher: BALIGE PUBLISHING

Published: 2020-10-31

Total Pages: 298

ISBN-13:

DOWNLOAD EBOOK

This book aims to develop a MySQL-driven desktop application that readers can develop for their own purposes to implement library project using Visual Basic .NET. In Tutorial 1, you will build a Visual Basic interface for the database. This interface will used as the main terminal in accessing other forms. This tutorial will also discuss how to create login form and login table. You will create login form. Place on the form one picture box, two labels, one combo box, one text box, and two buttons. In Tutorial 2, you will build a school inventory project where you can store information about valuables in school. The table will have nine fields: Item (description of the item), Quantity, 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). In Tutorial 3, you will perform the steps necessary to add 5 new tables using phpMyAdmin into Academy database. You will build each table and add the associated fields as needed. Every table in the database will need input form. In this tutorial, you will build such a form for Author table. Although this table is quite simple (only four fields: AuthorID, Name, BirthDate, and PhotoFile), it provides a basis for illustrating the many steps in interface design. SQL statement is required by the Command object to read fields (sorted by Name). Then, you will build an interface so that the user can maintain the Publisher table in the database (Academy). The Publisher table interface is more or less the same as Author table interface. This Publisher table interface only requires more input fields. So you will use the interface for the Author table and modify it for the Publisher table. In Tutorial 4, you will perform the steps necessary to design and implement title form, library member form, and book borrowal form. You start by designing and testing the basic entry form for book titles. The Title table has nine fields: BookTitle, PublishYear, ISBN, PublisherID, AuthorID, Description, Note, Subject, and Comment. Then, you will build such a form for Member table. This table has twelve fields: MemberID, FirstName, LastName, BirthDate, Status, Ethnicity, Nationality, Mobile, Phone, Religion, Gender, and PhotoFile). You need thirteen label controls, one picture box, six text boxes, four comboxes, one check box, one date time picker, one openfiledialog, and one printpreviewdialog. You also need four buttons for navigation, six buttons for controlling editing features, one button for searching member’s name, and one button to upload member’s photo. Finally, you will build such a form for Borrow table. This table has seven fields: BorrowID, MemberID, BorrowCode, ISBN, BorrowDate, ReturnDate, and Penalty. In this form, you need fourteen label controls, seven text boxes, two comboxes, two date time pickers, and one printpreviewdialog. You also need four buttons for navigation, seven buttons for other utilities, one button to generate borrowal code, and one button to return book.

LEARN FROM SCRATCH MACHINE LEARNING WITH PYTHON GUI
LEARN FROM SCRATCH MACHINE LEARNING WITH PYTHON GUI

Author: Vivian Siahaan

Publisher: BALIGE PUBLISHING

Published: 2021-03-03

Total Pages: 624

ISBN-13:

DOWNLOAD EBOOK

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.

Practical Database Programming with Visual Basic.NET
Practical Database Programming with Visual Basic.NET

Author: Ying Bai

Publisher: John Wiley & Sons

Published: 2012-05-09

Total Pages: 898

ISBN-13: 1118249828

DOWNLOAD EBOOK

Practical Database Programming with Visual Basic.NET The most up-to-date Visual Basic.NET programming textbook—covering both fundamentals and advanced-level programming techniques—complete with examples and solutions Visual Basic.NET (VB.NET) is an object-oriented computer programming language that can be viewed as an evolution of the classic Visual Basic (VB), which is implemented on the .NET Framework. Microsoft currently supplies two major implementations of Visual Basic: Microsoft Visual Studio (which is commercial software) and Microsoft Visual Studio Express (which is free of charge). Forgoing the large amounts of programming codes found in most database programming books, Practical Database Programming with Visual Basic.NET shows students and professionals both how to develop professional and practical database programs in a Visual Basic.NET environment by using Visual Studio.NET Data Tools and Wizards related to ADO.NET 4.0, and how to apply codes that are auto-generated by solely using Wizards. The fully updated Second Edition: Covers both fundamentals and advanced database programming techniques Introduces three popular database systems with practical examples including MS Access, SQL Server 2008, and Oracle Features more than fifty sample projects with detailed illustrations and explanations to help students understand key techniques and programming technologies Includes downloadable programming codes and exercise questions This book provides undergraduate and graduate students as well as database programmers and software engineers with the necessary tools to handle the database programming issues in the Visual Studio.NET environment.

DATA SCIENCE WORKSHOP: Cervical Cancer Classification and Prediction Using Machine Learning and Deep Learning with Python GUI
DATA SCIENCE WORKSHOP: Cervical Cancer Classification and Prediction Using Machine Learning and Deep Learning with Python GUI

Author: Vivian Siahaan

Publisher: BALIGE PUBLISHING

Published: 2023-08-13

Total Pages: 348

ISBN-13:

DOWNLOAD EBOOK

This book titled " Data Science Workshop: Cervical Cancer Classification and Prediction using Machine Learning and Deep Learning with Python GUI" embarks on an insightful journey starting with an in-depth exploration of the dataset. This dataset encompasses various features that shed light on patients' medical histories and attributes. Utilizing the capabilities of pandas, the dataset is loaded, and essential details like data dimensions, column names, and data types are scrutinized. The presence of missing data is addressed by employing suitable strategies such as mean-based imputation for numerical features and categorical encoding for non-numeric ones. Subsequently, the project delves into an illuminating visualization of categorized feature distributions. Through the ingenious use of pie charts, bar plots, and heatmaps, the project unveils the distribution patterns of key attributes such as 'Hormonal Contraceptives,' 'Smokes,' 'IUD,' and others. These visualizations illuminate potential relationships between these features and the target variable 'Biopsy,' which signifies the presence or absence of cervical cancer. Such exploratory analyses serve as a vital foundation for identifying influential trends within the dataset. Transitioning into the core phase of predictive modeling, the workshop orchestrates a meticulous ensemble of machine learning models to forecast cervical cancer outcomes. The repertoire includes Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Gradient Boosting, Naïve Bayes, and the power of ensemble methods like AdaBoost and XGBoost. The models undergo rigorous hyperparameter tuning facilitated by Grid Search and Random Search to optimize predictive accuracy and precision. As the workshop progresses, the spotlight shifts to the realm of deep learning, introducing advanced neural network architectures. An Artificial Neural Network (ANN) featuring multiple hidden layers is trained using the backpropagation algorithm. Long Short-Term Memory (LSTM) networks are harnessed to capture intricate temporal relationships within the data. The arsenal extends to include Self Organizing Maps (SOMs), Restricted Boltzmann Machines (RBMs), and Autoencoders, showcasing the efficacy of unsupervised feature learning and dimensionality reduction techniques. The evaluation phase emerges as a pivotal aspect, accentuated by an array of comprehensive metrics. Performance assessment encompasses metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. Cross-validation and learning curves are strategically employed to mitigate overfitting and ensure model generalization. Furthermore, visual aids such as ROC curves and confusion matrices provide a lucid depiction of the models' interplay between sensitivity and specificity. Culminating on a high note, the workshop concludes with the creation of a Python GUI utilizing PyQt. This intuitive graphical user interface empowers users to input pertinent medical data and receive instant predictions regarding their cervical cancer risk. Seamlessly integrating the most proficient classification model, this user-friendly interface bridges the gap between sophisticated data science techniques and practical healthcare applications. In this comprehensive workshop, participants navigate through the intricate landscape of data exploration, preprocessing, feature visualization, predictive modeling encompassing both traditional and deep learning paradigms, robust performance evaluation, and culminating in the development of an accessible and informative GUI. The project aspires to provide healthcare professionals and individuals with a potent tool for early cervical cancer detection and prognosis.

DATA SCIENCE WORKSHOP: Chronic Kidney Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI
DATA SCIENCE WORKSHOP: Chronic Kidney Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI

Author: Vivian Siahaan

Publisher: BALIGE PUBLISHING

Published: 2023-08-15

Total Pages: 361

ISBN-13:

DOWNLOAD EBOOK

In the captivating journey of our data science workshop, we embarked on the exploration of Chronic Kidney Disease classification and prediction. Our quest began with a thorough dive into data exploration, where we meticulously delved into the dataset's intricacies to unearth hidden patterns and insights. We analyzed the distribution of categorized features, unraveling the nuances that underlie chronic kidney disease. Guided by the principles of machine learning, we embarked on the quest to build predictive models. With the aid of grid search, we fine-tuned our machine learning algorithms, optimizing their hyperparameters for peak performance. Each model, whether K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Naive Bayes, Extreme Gradient Boosting, Light Gradient Boosting, or Multi-Layer Perceptron, was meticulously trained and tested, paving the way for robust predictions. The voyage into the realm of deep learning took us further, as we harnessed the power of Artificial Neural Networks (ANNs). By constructing intricate architectures, we designed ANNs to discern intricate patterns from the data. Leveraging the prowess of TensorFlow, we artfully crafted layers, each contributing to the ANN's comprehension of the underlying dynamics. This marked our initial foray into the world of deep learning. Our expedition, however, did not conclude with ANNs. We ventured deeper into the abyss of deep learning, uncovering the potential of Long Short-Term Memory (LSTM) networks. These networks, attuned to sequential data, unraveled temporal dependencies within the dataset, fortifying our predictive capabilities. Diving even further, we encountered Self-Organizing Maps (SOMs) and Restricted Boltzmann Machines (RBMs). These innovative models, rooted in unsupervised learning, unmasked underlying structures in the dataset. As our understanding of the data deepened, so did our repertoire of tools for prediction. Autoencoders, our final frontier in deep learning, emerged as our champions in dimensionality reduction and feature learning. These unsupervised neural networks transformed complex data into compact, meaningful representations, guiding our predictive models with newfound efficiency. To furnish a granular understanding of model behavior, we employed the classification report, which delineated precision, recall, and F1-Score for each class, providing a comprehensive snapshot of the model's predictive capacity across diverse categories. The confusion matrix emerged as a tangible visualization, detailing the interplay between true positives, true negatives, false positives, and false negatives. We also harnessed ROC and precision-recall curves to illuminate the dynamic interplay between true positive rate and false positive rate, vital when tackling imbalanced datasets. For regression tasks, MSE and its counterpart RMSE quantified the average squared differences between predictions and actual values, facilitating an insightful assessment of model fit. Further enhancing our toolkit, the R-squared (R2) score unveiled the extent to which the model explained variance in the dependent variable, offering a valuable gauge of overall performance. Collectively, this ensemble of metrics enabled us to make astute model decisions, optimize hyperparameters, and gauge the models' fitness for accurate disease prognosis in a clinical context. Amidst this whirlwind of data exploration and model construction, our GUI using PyQt emerged as a beacon of user-friendly interaction. Through its intuitive interface, users navigated seamlessly between model selection, training, and prediction. Our GUI encapsulated the intricacies of our journey, bridging the gap between data science and user experience. In the end, our odyssey illuminated the intricate landscape of Chronic Kidney Disease classification and prediction. We harnessed the power of both machine learning and deep learning, uncovering hidden insights and propelling our predictive capabilities to new heights. Our journey transcended the realms of data, algorithms, and interfaces, leaving an indelible mark on the crossroads of science and innovation.