Learn From Scratch Signal And Image Processing With Python Gui

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Learn from Scratch Signal and Image Processing with Python GUI

Rismon Hasiholan Sianipar,Vivian Siahaan
Learn from Scratch Signal and Image Processing with Python GUI Book PDF

Author : Rismon Hasiholan Sianipar,Vivian Siahaan
Publisher : Independently Published
Page : 302 pages
File Size : 45,7 Mb
Release : 2021-01-08
Category : Electronic
ISBN : 9798592058851

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Learn from Scratch Signal and Image Processing with Python GUI by Rismon Hasiholan Sianipar,Vivian Siahaan Pdf

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).

LEARN FROM SCRATCH SIGNAL AND IMAGE PROCESSING WITH PYTHON GUI

Vivian Siahaan,Rismon Hasiholan Sianipar
LEARN FROM SCRATCH SIGNAL AND IMAGE PROCESSING WITH PYTHON GUI Book PDF

Author : Vivian Siahaan,Rismon Hasiholan Sianipar
Publisher : BALIGE PUBLISHING
Page : 372 pages
File Size : 43,6 Mb
Release : 2023-06-14
Category : Technology & Engineering
ISBN : 8210379456XXX

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LEARN FROM SCRATCH SIGNAL AND IMAGE PROCESSING WITH PYTHON GUI by Vivian Siahaan,Rismon Hasiholan Sianipar Pdf

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 MACHINE LEARNING WITH PYTHON GUI

Vivian Siahaan,Rismon Hasiholan Sianipar
LEARN FROM SCRATCH MACHINE LEARNING WITH PYTHON GUI Book PDF

Author : Vivian Siahaan,Rismon Hasiholan Sianipar
Publisher : BALIGE PUBLISHING
Page : 624 pages
File Size : 54,8 Mb
Release : 2021-03-03
Category : Computers
ISBN : 8210379456XXX

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LEARN FROM SCRATCH MACHINE LEARNING WITH PYTHON GUI by Vivian Siahaan,Rismon Hasiholan Sianipar Pdf

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.

START FROM SCRATCH DIGITAL IMAGE PROCESSING WITH TKINTER

Vivian Siahaan,Rismon Hasiholan Sianipar
START FROM SCRATCH DIGITAL IMAGE PROCESSING WITH TKINTER Book PDF

Author : Vivian Siahaan,Rismon Hasiholan Sianipar
Publisher : BALIGE PUBLISHING
Page : 490 pages
File Size : 45,5 Mb
Release : 2023-10-21
Category : Computers
ISBN : 8210379456XXX

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START FROM SCRATCH DIGITAL IMAGE PROCESSING WITH TKINTER by Vivian Siahaan,Rismon Hasiholan Sianipar Pdf

"Start from Scratch: Digital Image Processing with Tkinter" is a beginner-friendly guide that delves into the basics of digital image processing using Python and Tkinter, a popular GUI library. The project is divided into distinct modules, each focusing on a specific aspect of image manipulation. The journey begins with an exploration of Image Color Space. Here, readers encounter the Main Form, which serves as the entry point to the application. It provides a user-friendly interface for loading images, selecting color spaces, and visualizing various color channels. The Fundamental Utilities play a crucial role by providing core functionalities like loading images, converting color spaces, and manipulating pixel data. The project also includes forms dedicated to displaying individual color channels and offering insights into the current color space through histograms. The Plotting Utilities module facilitates the creation of visual representations such as plots and graphs, enhancing the user's understanding of color spaces. Moving on, the Image Transformation section introduces readers to techniques like the Fast Fourier Transform (FFT). The Fast Fourier Transform Utilities module enables the implementation of FFT algorithms for converting images from spatial to frequency domains. A corresponding form allows users to view images in the frequency domain, with additional adjustments made to the plotting utilities for effective visualization. In the context of Discrete Cosine Transform (DCT), readers gain insights into algorithms and functions for transforming images. The Form for Discrete Cosine Transform aids in visualizing images in the DCT domain, while the plotting utilities are modified to accommodate these transformed images. The Discrete Sine Transform (DST) section introduces readers to DST algorithms and their role in image transformation. A dedicated form for visualizing images in the DST domain is provided, and the plotting utilities are further extended to handle these transformations effectively. Moving Average Smoothing is another critical aspect covered in the project. The Filter2D Utilities facilitate the application of moving average smoothing techniques. Additionally, metrics utilities enable the assessment of the smoothing process, with forms available for displaying both metrics and the smoothed images. Next, the project addresses Exponential Moving Average techniques, modifying the existing utilities to accommodate this specific approach. Similarly, forms for visualizing results and metrics are provided. Readers are then introduced to techniques like Median Filtering, Savitzky-Golay Filtering, and Wiener Filtering. The Filter2D Utilities are adapted to facilitate these filtering methods, and metrics utilities are employed to assess the effectiveness of each technique. Forms dedicated to each filtering method provide a platform for visualizing the results. The final section of the project explores techniques such as Total Variation Denoising, Non-Local Means Denoising, and PCA Denoising. The Filter2D Utilities are once again modified to support these denoising techniques. Metrics utilities are employed to evaluate the denoising process, and dedicated forms offer visualization capabilities. By breaking down the project into these modules, readers can systematically grasp the fundamentals of digital image processing, gradually building their skills from one concept to the next. Each section provides hands-on experience and practical knowledge, making it an ideal starting point for beginners in image processing.

Step By Step Neural Networks for Image Classification using Python GUI

Hamzan Wadi
Step By Step Neural Networks for Image Classification using Python GUI Book PDF

Author : Hamzan Wadi
Publisher : Turida Publisher
Page : 207 pages
File Size : 49,8 Mb
Release : 2024-06-29
Category : Computers
ISBN : 8210379456XXX

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Step By Step Neural Networks for Image Classification using Python GUI by Hamzan Wadi Pdf

This book provides a practical explanation of the backpropagation neural networks algorithm and how it can be implemented for image classification. The discussion in this book is presented in step by step so that it will help readers understand the fundamental of the backpropagation neural networks and its steps. This book is very suitable for students, researchers, and anyone who want to learn and implement the backpropagation neural networks for image classification using PYTHON GUI. The discussion in this book will provide readers deep understanding about the backpropagation neural networks architecture and its parameters. The readers will be guided to understand the steps of the backpropagation neural networks for image classification through case example. The readers will be guided to create their own neural networks class and build their complete applications for data image classification. The final objective of this book is that the readers are able to realize each step of the multilayer perceptron neural networks for image classification. In Addition, the readers also are able to create the neural networks applications which consists of two types of applications which are command window based application and GUI based application. Here are the material that you will learn in this book. CHAPTER 1: This chapter will guide you in preparing what software are needed to realize the backpropagation neural networks using Python GUI. The discussion in this chapter will start from installing Python and the libraries that will be used, installing Qt Designer, understanding and using Qt Designer to design the application UI, and the last is about how to create a GUI program using Python and Qt Designer. CHAPTER 2: This chapter discusses the important parts in the backpropagation neural networks algorithm which includes the architecture of the backpropagation neural networks, the parameters contained in the backpropagation neural networks, the steps of the backpropagation neural networks algorithm, and the mathematical calculations of the backpropagation neural networks. CHAPTER 3: This chapter discusses in detail the mathematical calculations of fruit quality classification using the backpropagation neural networks which includes the feature extraction process of fruit images, data normalization, the training process, and the classification process. The feature extraction method used in this case is GLCM (Gray Level Co-occurrence Matrix). The image features that will be used in this case are energy, contrast, entropy, and homogeneity. CHAPTER 4: This chapter discusses how to implement the backpropagation neural networks algorithm for fruit quality classification using Python. This chapter will present the steps to create your backpropagation neural networks class and to define the functions that represent each process of the backpropagation neural networks. This chapter will also present the steps to create a class for image processing. And in final discussion you will be guided to create your backpropagation neural networks application from scratch to classify the quality of fruit. CHAPTER 5: This chapter will discuss how to create a GUI based application for fruit quality classification using the backpropagation neural networks algorithm. This chapter will discuss in detail the steps for designing the application UI by using Qt Designer, the steps for creating a class for the backpropagation neural networks GUI based application, and how to run the GUI based application to classify the fruit data.

Learn From Scratch Backpropagation Neural Networks Using Python GUI & MariaDB

Hamzan Wadi
Learn From Scratch Backpropagation Neural Networks Using Python GUI MariaDB Book PDF

Author : Hamzan Wadi
Publisher : Turida Publisher
Page : 583 pages
File Size : 52,8 Mb
Release : 2024-06-29
Category : Computers
ISBN : 8210379456XXX

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Learn From Scratch Backpropagation Neural Networks Using Python GUI & MariaDB by Hamzan Wadi Pdf

This book provides a practical explanation of the backpropagation neural networks and how it can be implemented for data prediction and data classification. The discussion in this book is presented in step by step so that it will help readers understand the fundamental of the backpropagation neural networks and its steps. This book is very suitable for students, researchers, and anyone who want to learn and implement the backpropagation neural networks for data prediction and data classification using PYTHON GUI and MariaDB. The discussion in this book will provide readers deep understanding about the backpropagation neural networks architecture and its parameters. The readers will be guided to understand the steps of the backpropagation neural networks for data prediction and data classification through case examples. In addition, readers are also guided step by step to implement the backpropagation neural networks for data prediction and data classification using PYTHON GUI and MariaDB. The readers will be guided to create their own backpropagation neural networks class and build their complete applications for data prediction and data classification. This book consists of three cases which are realized into complete projects using the Python GUI and MariaDB. The three cases that will be learned in this book are as follow. 1. Sales prediction using the backpropagation neural networks. 2. Earthquake data prediction using the backpropagation neural networks. 3. Fruit quality classification using the backpropagation neural networks. Each case in this book is equipped with a mathematical calculation that will help the reader understand each step that must be taken. The cases in this book are realized into three types of applications which are command window based application, GUI based application, and database application using Python GUI and MariaDB. The final result of this book is that the readers are able to realize each step of the backpropagation neural networks for data prediction and data classification. In Addition, the readers also are able to create the backpropagation neural networks applications which consists of three types of applications which are command window based application, GUI based application, and database application using Python GUI and MariaDB.

Python GUI For Signal and Image Processing

Vivian Siahaan,Rismon Hasiholan Sianipar
Python GUI For Signal and Image Processing Book PDF

Author : Vivian Siahaan,Rismon Hasiholan Sianipar
Publisher : SPARTA PUBLISHING
Page : 220 pages
File Size : 47,7 Mb
Release : 2019-10-05
Category : Computers
ISBN : 8210379456XXX

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Python GUI For Signal and Image Processing by Vivian Siahaan,Rismon Hasiholan Sianipar Pdf

You will learn to create GUI applications using the Qt toolkit. The Qt toolkit, also popularly known as Qt, is a cross-platform application and UI framework developed by Trolltech, which is used to develop GUI applications. You will develop an existing GUI by adding several Line Edit widgets to read input, which are used to set the range and step of the graph (signal). Next, Now, you can use a widget for each graph. Add another Widget from Containers in gui_graphics.ui using Qt Designer. Then, Now, you can use two Widgets, each of which has two canvases. The two canvases has QVBoxLayout in each Widget. Finally, you will apply those Widgets to display the results of signal and image processing techniques.

START FROM SCRATCH DIGITAL SIGNAL PROCESSING WITH TKINTER

Vivian Siahaan,Rismon Hasiholan Sianipar
START FROM SCRATCH DIGITAL SIGNAL PROCESSING WITH TKINTER Book PDF

Author : Vivian Siahaan,Rismon Hasiholan Sianipar
Publisher : BALIGE PUBLISHING
Page : 506 pages
File Size : 48,5 Mb
Release : 2023-10-13
Category : Computers
ISBN : 8210379456XXX

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START FROM SCRATCH DIGITAL SIGNAL PROCESSING WITH TKINTER by Vivian Siahaan,Rismon Hasiholan Sianipar Pdf

In this project, you will create a multi-form GUI to implement digital signal processing. Creating a GUI involves designing an interface where users can input parameters and visualize the results of various signal processing techniques. Each form corresponds to a specific technique and is implemented using the tkinter library. The "Simple Sinusoidal Form" allows users to generate and visualize a basic sinusoidal signal. It includes input fields for parameters like frequency, amplitude, and time period. The utilities associated with this form provide functions to generate and plot the simple sinusoidal signal. The "Two Sinusoidals Form" extends the previous form, enabling users to generate and visualize two combined sinusoidal signals. It provides input fields for frequencies, amplitudes, and time periods of both signals. The utilities handle the generation and plotting of the combined sinusoidal signals. The "More Two Sinusoidals Form" further extends the previous form to generate and visualize additional combined sinusoidal signals. It includes input fields for frequencies, amplitudes, and time periods of three sinusoidal signals. The utilities handle the generation and plotting of these combined signals. Forms for various modulation techniques (AM, FM, PM, ASK, FSK, PSK) are available. These allow users to generate and visualize modulated signals by providing input fields for modulation indices, carrier frequencies, and time periods. The utilities in each form handle the signal generation and modulation process, as well as the plotting of the modulated signals. Forms for different filter designs (FIR, Butterworth, Chebyshev Type 1) cover lowpass, highpass, bandpass, and bandstop filters. They include input fields for filter order, cutoff frequencies, and other relevant parameters. The utilities in each form implement the filter design and frequency response plotting. Wavelet transformation forms focus on wavelet-based techniques, including scaling, decomposition, and denoising. They provide input fields for wavelet type, thresholding methods, and other wavelet-specific parameters. The utilities handle the wavelet transformations, denoising, and visualizing the results. Forms for various denoising techniques (MA, EMA, Median, SGF, Wiener, TV, NLM, PCA) cover different smoothing and denoising methods. They offer input fields for relevant denoising parameters. The utilities for each form implement the denoising process and display the denoised signals. Each form's utility methods interact with the GUI elements, taking user inputs and performing the corresponding signal processing tasks. These utilities encapsulate the underlying algorithms and ensure a seamless interaction between the user interface and the backend computations. In summary, this session involves creating a comprehensive GUI for a wide range of signal processing techniques, including signal generation, modulation, filtering, wavelet transformations, and various denoising methods. Each form and its associated utilities handle specific tasks, ensuring an intuitive and effective user experience.

LEARN FROM SCRATCH VISUAL BASIC .NET WITH MYSQL

Vivian Siahaan,Rismon Hasiholan Sianipar
LEARN FROM SCRATCH VISUAL BASIC NET WITH MYSQL Book PDF

Author : Vivian Siahaan,Rismon Hasiholan Sianipar
Publisher : BALIGE PUBLISHING
Page : 304 pages
File Size : 43,8 Mb
Release : 2020-11-04
Category : Computers
ISBN : 8210379456XXX

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LEARN FROM SCRATCH VISUAL BASIC .NET WITH MYSQL by Vivian Siahaan,Rismon Hasiholan Sianipar Pdf

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.

LEARN FROM SCRATCH VISUAL C# .NET WITH MYSQL

Vivian Siahaan,Rismon Hasiholan Sianipar
LEARN FROM SCRATCH VISUAL C NET WITH MYSQL Book PDF

Author : Vivian Siahaan,Rismon Hasiholan Sianipar
Publisher : BALIGE PUBLISHING
Page : 684 pages
File Size : 43,9 Mb
Release : 2020-10-05
Category : Computers
ISBN : 8210379456XXX

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LEARN FROM SCRATCH VISUAL C# .NET WITH MYSQL by Vivian Siahaan,Rismon Hasiholan Sianipar Pdf

In Tutorial 1, you will start building a Visual C# interface for database management system project using MySQL. 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. 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). In Tutorial 3 up to Tutorial 4, you will perform the steps necessary to add 6 tables using phpMyAdmin 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 8 more tables using phpMyAdmin 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.

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

Vivian Siahaan,Rismon Hasiholan Sianipar
Two Books In One LEARN FROM SCRATCH VISUAL BASIC NET WITH MYSQL Book PDF

Author : Vivian Siahaan,Rismon Hasiholan Sianipar
Publisher : BALIGE PUBLISHING
Page : 603 pages
File Size : 52,8 Mb
Release : 2020-11-05
Category : Computers
ISBN : 8210379456XXX

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Two Books In One: LEARN FROM SCRATCH VISUAL BASIC .NET WITH MYSQL by Vivian Siahaan,Rismon Hasiholan Sianipar Pdf

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.

SUPERMARKET SALES ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI

Vivian Siahaan,Rismon Hasiholan Sianipar
SUPERMARKET SALES ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI Book PDF

Author : Vivian Siahaan,Rismon Hasiholan Sianipar
Publisher : BALIGE PUBLISHING
Page : 187 pages
File Size : 55,6 Mb
Release : 2022-04-15
Category : Computers
ISBN : 8210379456XXX

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SUPERMARKET SALES ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI by Vivian Siahaan,Rismon Hasiholan Sianipar Pdf

The dataset used in this project consists of the growth of supermarkets with high market competitions in most populated cities. The dataset is one of the historical sales of supermarket company which has recorded in 3 different branches for 3 months data. Predictive data analytics methods are easy to apply with this dataset. Attribute information in the dataset are as follows: Invoice id: Computer generated sales slip invoice identification number; Branch: Branch of supercenter (3 branches are available identified by A, B and C); City: Location of supercenters; Customer type: Type of customers, recorded by Members for customers using member card and Normal for without member card; Gender: Gender type of customer; Product line: General item categorization groups - Electronic accessories, Fashion accessories, Food and beverages, Health and beauty, Home and lifestyle, Sports and travel; Unit price: Price of each product in $; Quantity: Number of products purchased by customer; Tax: 5% tax fee for customer buying; Total: Total price including tax; Date: Date of purchase (Record available from January 2019 to March 2019); Time: Purchase time (10am to 9pm); Payment: Payment used by customer for purchase (3 methods are available – Cash, Credit card and Ewallet); COGS: Cost of goods sold; Gross margin percentage: Gross margin percentage; Gross income: Gross income; and Rating: Customer stratification rating on their overall shopping experience (On a scale of 1 to 10). In this project, you will perform predicting rating using machine learning. The machine learning models used in this project to predict clusters as target variable are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM, Gradient Boosting, XGB, and MLP. 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.

The Beginner’s Guide to Learn Python GUI with MySQL and SQLite

Vivian Siahaan,Rismon Hasiholan Sianipar
The Beginner s Guide to Learn Python GUI with MySQL and SQLite Book PDF

Author : Vivian Siahaan,Rismon Hasiholan Sianipar
Publisher : SPARTA PUBLISHING
Page : 551 pages
File Size : 40,8 Mb
Release : 2020-01-14
Category : Computers
ISBN : 8210379456XXX

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The Beginner’s Guide to Learn Python GUI with MySQL and SQLite by Vivian Siahaan,Rismon Hasiholan Sianipar Pdf

This book explains relational theory in practice, and demonstrates through two projects how you can apply it to your use of MySQL and SQLite databases. This book covers the important requirements of teaching databases with a practical and progressive perspective. This book offers the straightforward, practical answers you need to help you do your job. This hands-on tutorial/reference/guide to MySQL and SQLite is not only perfect for students and beginners, but it also works for experienced developers who aren't getting the most from both databases. In designing a GUI and as an IDE, you will make use Qt Designer. In the first chapter, you will learn to use several widgets in PyQt5: Display a welcome message; Use the Radio Button widget; Grouping radio buttons; Displays options in the form of a check box; and Display two groups of check boxes. In chapter two, you will learn to use the following topics: Using Signal / Slot Editor; Copy and place text from one Line Edit widget to another; Convert data types and make a simple calculator; Use the Spin Box widget; Use scrollbars and sliders; Using the Widget List; Select a number of list items from one Widget List and display them on another Widget List widget; Add items to the Widget List; Perform operations on the Widget List; Use the Combo Box widget; Displays data selected by the user from the Calendar Widget; Creating a hotel reservation application; and Display tabular data using Table Widgets. In chapter three, you will learn: How to create the initial three tables project in the School database: Teacher, Class, and Subject tables; How to create database configuration files; How to create a Python GUI for inserting and editing tables; How to create a Python GUI to join and query the three tables. In chapter four, you will learn how to: Create a main form to connect all forms; Create a project will add three more tables to the school database: Student, Parent, and Tuition tables; Create a Python GUI for inserting and editing tables; Create a Python GUI to join and query over the three tables. In chapter five, you will join the six classes, Teacher, TClass, Subject, Student, Parent, and Tuition and make queries over those tables. In chapter six, you will create dan configure database. In this chapter, you will create Suspect table in crime database. This table has eleven columns: suspect_id (primary key), suspect_name, birth_date, case_date, report_date, suspect_ status, arrest_date, mother_name, address, telephone, and photo. You will also create GUI to display, edit, insert, and delete for this table. In chapter seven, you will create a table with the name Feature_Extraction, which has eight columns: feature_id (primary key), suspect_id (foreign key), feature1, feature2, feature3, feature4, feature5, and feature6. The six fields (except keys) will have VARBINARY(MAX) data type. You will also create GUI to display, edit, insert, and delete for this table. In chapter eight, you will create two tables, Police and Investigator. The Police table has six columns: police_id (primary key), province, city, address, telephone, and photo. The Investigator table has eight columns: investigator_id (primary key), investigator_name, rank, birth_date, gender, address, telephone, and photo. You will also create GUI to display, edit, insert, and delete for both tables. In the last chapter, you will create two tables, Victim and Case_File. The Victim table has nine columns: victim_id (primary key), victim_name, crime_type, birth_date, crime_date, gender, address, telephone, and photo. The Case_File table has seven columns: case_file_id (primary key), suspect_id (foreign key), police_id (foreign key), investigator_id (foreign key), victim_id (foreign key), status, and description. You will create GUI to display, edit, insert, and delete for both tables.

The Fast Way to Learn Python GUI with MariaDB and SQLite

Vivian Siahaan,Rismon Hasiholan Sianipar
The Fast Way to Learn Python GUI with MariaDB and SQLite Book PDF

Author : Vivian Siahaan,Rismon Hasiholan Sianipar
Publisher : SPARTA PUBLISHING
Page : 547 pages
File Size : 53,9 Mb
Release : 2020-01-15
Category : Computers
ISBN : 8210379456XXX

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The Fast Way to Learn Python GUI with MariaDB and SQLite by Vivian Siahaan,Rismon Hasiholan Sianipar Pdf

This book explains relational theory in practice, and demonstrates through two projects how you can apply it to your use of MariaDB and SQLite databases. This book covers the important requirements of teaching databases with a practical and progressive perspective. This book offers the straightforward, practical answers you need to help you do your job. This hands-on tutorial/reference/guide to MariaDB and SQLite is not only perfect for students and beginners, but it also works for experienced developers who aren't getting the most from both databases. In designing a GUI and as an IDE, you will make use Qt Designer. In the first chapter, you will learn to use several widgets in PyQt5: Display a welcome message; Use the Radio Button widget; Grouping radio buttons; Displays options in the form of a check box; and Display two groups of check boxes. In chapter two, you will learn to use the following topics: Using Signal / Slot Editor; Copy and place text from one Line Edit widget to another; Convert data types and make a simple calculator; Use the Spin Box widget; Use scrollbars and sliders; Using the Widget List; Select a number of list items from one Widget List and display them on another Widget List widget; Add items to the Widget List; Perform operations on the Widget List; Use the Combo Box widget; Displays data selected by the user from the Calendar Widget; Creating a hotel reservation application; and Display tabular data using Table Widgets. In chapter three, you will learn: How to create the initial three tables project in the School database: Teacher, Class, and Subject tables; How to create database configuration files; How to create a Python GUI for inserting and editing tables; How to create a Python GUI to join and query the three tables. In chapter four, you will learn how to: Create a main form to connect all forms; Create a project will add three more tables to the school database: Student, Parent, and Tuition tables; Create a Python GUI for inserting and editing tables; Create a Python GUI to join and query over the three tables. In chapter five, you will join the six classes, Teacher, TClass, Subject, Student, Parent, and Tuition and make queries over those tables. In chapter six, you will create dan configure database. In this chapter, you will create Suspect table in crime database. This table has eleven columns: suspect_id (primary key), suspect_name, birth_date, case_date, report_date, suspect_ status, arrest_date, mother_name, address, telephone, and photo. You will also create GUI to display, edit, insert, and delete for this table. In chapter seven, you will create a table with the name Feature_Extraction, which has eight columns: feature_id (primary key), suspect_id (foreign key), feature1, feature2, feature3, feature4, feature5, and feature6. The six fields (except keys) will have a VARCHAR data type (200). You will also create GUI to display, edit, insert, and delete for this table. In chapter eight, you will create two tables, Police and Investigator. The Police table has six columns: police_id (primary key), province, city, address, telephone, and photo. The Investigator table has eight columns: investigator_id (primary key), investigator_name, rank, birth_date, gender, address, telephone, and photo. You will also create GUI to display, edit, insert, and delete for both tables. In chapter nine, you will create two tables, Victim and Case_File. The Victim table has nine columns: victim_id (primary key), victim_name, crime_type, birth_date, crime_date, gender, address, telephone, and photo. The Case_File table has seven columns: case_file_id (primary key), suspect_id (foreign key), police_id (foreign key), investigator_id (foreign key), victim_id (foreign key), status, and description. You will create GUI to display, edit, insert, and delete for both tables as well.

POSTGRESQL FOR JAVA GUI: Database, Cryptography, and Image Processing

Vivian Siahaan,Rismon Hasiholan Sianipar
POSTGRESQL FOR JAVA GUI Database Cryptography and Image Processing Book PDF

Author : Vivian Siahaan,Rismon Hasiholan Sianipar
Publisher : SPARTA PUBLISHING
Page : 490 pages
File Size : 48,5 Mb
Release : 2019-09-01
Category : Computers
ISBN : 8210379456XXX

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POSTGRESQL FOR JAVA GUI: Database, Cryptography, and Image Processing by Vivian Siahaan,Rismon Hasiholan Sianipar Pdf

In this book, you will learn how to build from scratch a criminal records management database system using Java/PostgreSQL. All Java code for cryptography and digital image processing in this book is Native Java. Intentionally not to rely on external libraries, so that readers know in detail the process of extracting digital images from scratch in Java. There are only three external libraries used in this book: Connector / J to facilitate Java to PostgreSQL connections, JCalendar to display calendar controls, and JFreeChart to display graphics. Digital image techniques to extract image features used in this book are grascaling, sharpening, invertering, blurring, dilation, erosion, closing, opening, vertical prewitt, horizontal prewitt, Laplacian, horizontal sobel, and vertical sobel. For readers, you can develop it to store other advanced image features based on descriptors such as SIFT and others for developing descriptor based matching. In the first chapter, you will learn: How to install NetBeans, JDK 11, and the PostgreSQL connector; How to integrate external libraries into projects; How the basic PostgreSQL commands are used; How to query statements to create databases, create tables, fill tables, and manipulate table contents is done. In the second chapter, you will learn querying data from the postgresql using jdbc including establishing a database connection, creating a statement object, executing the query, processing the resultset object, querying data using a statement that returns multiple rows, querying data using a statement that has parameters, inserting data into a table using jdbc, updating data in postgresql database using jdbc, calling postgresql stored function using jdbc, deleting data from a postgresql table using jdbc, and postgresql jdbc transaction. In the second chapter, you will learn the basics of cryptography using Java. Here, you will learn how to write a Java program to count Hash, MAC (Message Authentication Code), store keys in a KeyStore, generate PrivateKey and PublicKey, encrypt / decrypt data, and generate and verify digital prints. In the third chapter, you will learn how to create and store salt passwords and verify them. You will create a Login table. In this case, you will see how to create a Java GUI using NetBeans to implement it. In addition to the Login table, in this chapter you will also create a Client table. In the case of the Client table, you will learn how to generate and save public and private keys into a database. You will also learn how to encrypt / decrypt data and save the results into a database. In the fourth chapter, you will create an Account table. This account table has the following ten fields: account_id (primary key), client_id (primarykey), account_number, account_date, account_type, plain_balance, cipher_balance, decipher_balance, digital_signature, and signature_verification. In this case, you will learn how to implement generating and verifying digital prints and storing the results into a database. In the fifth chapter, you create a table with the name of the Account, which has ten columns: account_id (primary key), client_id (primarykey), account_number, account_date, account_type, plain_balance, cipher_balance, decipher_balance, digital_signature, and signature_verification. In the sixth chapter, you will create a Client_Data table, which has the following seven fields: client_data_id (primary key), account_id (primary_key), birth_date, address, mother_name, telephone, and photo_path. In the seventh chapter, you will be taught how to create Crime database and its tables. In eighth chapter, you will be taught how to extract image features, utilizing BufferedImage class, in Java GUI. In the nineth chapter, you will be taught to create Java GUI to view, edit, insert, and delete Suspect table data. This table has eleven columns: suspect_id (primary key), suspect_name, birth_date, case_date, report_date, suspect_ status, arrest_date, mother_name, address, telephone, and photo. In the tenth chapter, you will be taught to create Java GUI to view, edit, insert, and delete Feature_Extraction table data. This table has eight columns: feature_id (primary key), suspect_id (foreign key), feature1, feature2, feature3, feature4, feature5, and feature6. In the eleventh chapter, you will add two tables: Police_Station and Investigator. These two tables will later be joined to Suspect table through another table, File_Case, which will be built in the seventh chapter. The Police_Station has six columns: police_station_id (primary key), location, city, province, telephone, and photo. The Investigator has eight columns: investigator_id (primary key), investigator_name, rank, birth_date, gender, address, telephone, and photo. Here, you will design a Java GUI to display, edit, fill, and delete data in both tables. In the twelfth chapter, you will add two tables: Victim and File_Case. The File_Case table will connect four other tables: Suspect, Police_Station, Investigator and Victim. The Victim table has nine columns: victim_id (primary key), victim_name, crime_type, birth_date, crime_date, gender, address, telephone, and photo. The File_Case has seven columns: file_case_id (primary key), suspect_id (foreign key), police_station_id (foreign key), investigator_id (foreign key), victim_id (foreign key), status, and description. Here, you will also design a Java GUI to display, edit, fill, and delete data in both tables. Finally, this book is hopefully useful for you.