THE MOST BRUTAL AND BARBARIC CASE OF DIGITAL TAMPERING EVER, Unveiling the Truth Behind Jessica Wongso CCTV Footage Manipulation by Indonesian Police Officers: Tito Karnavian, Krishna Murti, Muhammad Nuh Al-Azhar, and Christopher Hariman Rianto - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook THE MOST BRUTAL AND BARBARIC CASE OF DIGITAL TAMPERING EVER, Unveiling the Truth Behind Jessica Wongso CCTV Footage Manipulation by Indonesian Police Officers: Tito Karnavian, Krishna Murti, Muhammad Nuh Al-Azhar, and Christopher Hariman Rianto write by RISMON HASIHOLAN SIANIPAR. This book was released on 2024-09-14. THE MOST BRUTAL AND BARBARIC CASE OF DIGITAL TAMPERING EVER, Unveiling the Truth Behind Jessica Wongso CCTV Footage Manipulation by Indonesian Police Officers: Tito Karnavian, Krishna Murti, Muhammad Nuh Al-Azhar, and Christopher Hariman Rianto available in PDF, EPUB and Kindle. The case of Jessica Kumala Wongso garnered significant attention due to its complex and contentious nature. It began with the mysterious death of Mirna Salihin, who collapsed and died shortly after consuming a coffee at a café in Jakarta, Indonesia, in January 2016. Jessica Kumala Wongso, a close friend of Mirna, was accused of poisoning her with cyanide-laced coffee, leading to her arrest and subsequent trial. The trial unfolded with intense scrutiny from the media and the public, as well as heated debates surrounding the evidence presented. The prosecution argued that Jessica had a motive to harm Mirna due to personal conflicts, while the defense contended that there was insufficient evidence to prove Jessica's guilt beyond a reasonable doubt. The case delved into various aspects, including forensic analysis of CCTV footage, witness testimonies, and expert opinions, further complicating the legal proceedings. After a lengthy trial spanning several months, Jessica Kumala Wongso was ultimately convicted of premeditated murder and sentenced to 20 years in prison in October 2016. The case sparked widespread discussions about the reliability of forensic evidence, the integrity of legal proceedings, and the role of the media in shaping public perception. Despite the verdict, the case continues to be a subject of controversy and debate, with many questioning the fairness and transparency of the trial process. The manipulation of Café Olivier's CCTV footage through downscaling played a pivotal role in the legal proceedings of Jessica Wongso's case. Downscaling refers to the deliberate reduction of the video's resolution, from a higher resolution 1920x1080 pixels (1080P) to a lower one 960x576 pixels (960H). Throughout the trial, forensic experts, including those appointed by Wongso's defense team, revealed how this manipulation technique obscured crucial details and compromised the integrity of the evidence presented. As the trial unfolded, it became evident that the downscaling of CCTV footage significantly impacted the perception of events. The reduced resolution made it challenging for the court to discern specific actions or movements, leading to debates over the accuracy and reliability of the footage. Moreover, the discrepancies introduced by downscaling raised doubts about the prosecution's narrative and highlighted the need for comprehensive forensic analysis to uncover the truth. Ultimately, the revelation of digital tampering through downscaling became a focal point of Wongso's defense strategy. By exposing the manipulation of evidence, Wongso's legal team aimed to cast doubt on the prosecution's case and challenge the credibility of the allegations against her. The downscaling of CCTV footage emerged as a critical factor in the trial's proceedings, underscoring the importance of technological expertise and forensic scrutiny in the pursuit of justice. From my perspective, addressing an international audience, it is paramount to shed light on one of the most egregious instances of digital manipulation and tampering witnessed in recent history. This pertains to the CCTV footage from Café Olivier, which has been subjected to a series of calculated alterations by two individuals acting as digital forensic experts within the Indonesian police force: Muhammad Nuh Al-Azhar and Christopher Hariman Rianto. Their actions constitute a flagrant disregard for truth and justice, warranting a comprehensive examination to uncover the extent of their deceit and the repercussions thereof. The manipulation of CCTV footage from Café Olivier represents a brazen attempt to distort reality and obfuscate the truth. Through their purported expertise in digital forensics, Muhammad Nuh Al-Azhar and Christopher Hariman Rianto orchestrated a meticulous campaign of alteration, wherein crucial details and events within the footage were systematically tampered with or omitted altogether. This manipulation extends beyond mere technical adjustments; it undermines the integrity of the entire legal process and threatens the very foundation of justice.
The 37 Scientific Evidence of Digital Evidence Tampering on CCTV Footage at Olivier Café: The Jessica Kumala Wongso Case (2016)
The 37 Scientific Evidence of Digital Evidence Tampering on CCTV Footage at Olivier Café: The Jessica Kumala Wongso Case (2016) - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook The 37 Scientific Evidence of Digital Evidence Tampering on CCTV Footage at Olivier Café: The Jessica Kumala Wongso Case (2016) write by Rismon Hasiholan Sianipar. This book was released on 2024-03-13. The 37 Scientific Evidence of Digital Evidence Tampering on CCTV Footage at Olivier Café: The Jessica Kumala Wongso Case (2016) available in PDF, EPUB and Kindle. This is digital documentation of how two expert witnesses, Muhammad Nuh Al-Azhar and Christopher Hariman Rianto, manipulated the CCTV video at Olivier cafe. For more details, it can be watched on the Balige Academy YouTube channel. Author: Dr.Eng Rismon Hasiholan Sianipar, S.T, M.T, M.Eng
Step By Step Database Programming using Python GUI & MySQL
Step By Step Database Programming using Python GUI & MySQL - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Step By Step Database Programming using Python GUI & MySQL write by Hamzan Wadi. This book was released on . Step By Step Database Programming using Python GUI & MySQL available in PDF, EPUB and Kindle. This book provides a practical explanation of database programming using Python GUI & MySQL. The discussion in this book is presented in step by step so that it will help readers understand each material and also will make it easier for the readers to follow all of the instructions. This book is very suitable for students, programmers, and anyone who want to learn database programming using Python GUI & MySQL from scratch. This book is divided into two parts: The first part of this book will discuss about the fundamentals of database programming using Python GUI & MySQL. This part will discuss in detail about how to setup your working environment and how to understand GUI programming using Python. This part will also discuss in detail about how to start your database programming using Python GUI & MySQL. This part will discuss in detail about the basic of database programming using Python GUI & MySQL. The second part of this book will discuss about how to build database application using Python GUI & MySQL. This part will discuss in detail about how to build Multiple Document Interface (MDI) database application through real project-based example. This part will discuss in detail about how to design and create database for Library Management System application, and how to create all forms for the application. The final objective of this book is that the readers are able to create real database application using Python GUI & MySQL. Here are the materials that you will learn in this book. PART I: THE FUNDAMENTAL OF DATABASE PROGRAMMING USING PYTHON GUI & MySQL CHAPTER 1: The discussion in this chapter will guide you in preparing what software are needed to start your database programming using Python GUI. This chapter will guide you to install all software including Python, MySQL, and Qt Designer. In addition, this chapter also will discuss about how to understand and use Qt Designer for user interface design, and how to create a GUI application using Python and Qt Designer. CHAPTER 2: The discussion in this chapter will guide you to start your database programming using Python GUI & MySQL. This chapter will discuss in detail about the basic of database programming using Python GUI & MySQL. The discussion in this chapter will talk about how to create and drop database, how to create and drop table, how to insert data into table, how to display data from table, how to update data in table, and how to delete data in table. All discussions in this chapter will give you deep understanding of database programming using Python GUI & MySQL. PART II: BUILDING DATABASE APPLICATION USING PYTHON GUI & MySQL, CASE STUDY: LIBRARY MANAGEMENT SYSTEM APPLICATION CHAPTER 3: The discussion in this chapter will guide you to design and create database for library management system application. This is the first step that must be taken to create database application using Python GUI & MySQL. This chapter will discuss in detail about how to design the Entity Relationship Diagram (ERD) for library management system application. The discussion in this chapter will also talk about how to create database and its tables based on the ERD design using MySQL server. CHAPTER 4: The discussion in this chapter will guide you to create main form and login form for the application. This chapter will discuss in detail about how to create these two forms. These forms are the first two forms that we will create in building library management system application. This chapter will also discuss about how to run the application. CHAPTER 5: The discussion in this chapter will guide you to create user accounts form and members form for Library Management System application. This chapter will discuss in detail about how to create these two forms. This chapter will also discuss about how to add these two forms as MDI sub windows of the main form. And the final discussion of this chapter will guide you to use the forms to manage user accounts and members data of Library Management System application. CHAPTER 6: The discussion in this chapter will guide you to create authors form, genres form, and books form for Library Management System application. This chapter will discuss in detail about how to create these three forms. This chapter will also discuss about how to add books form as MDI sub window of the main form. And the final discussion of this chapter will guide you to use the forms to manage authors, genres, and books data in Library Management System application. CHAPTER 7: The discussion in this chapter will guide you to create member search form, book search form, and loan transaction form for Library Management System application. This chapter will discuss in detail about how to create these three forms. This chapter will also discuss about how to add loan transaction form as MDI sub window of the main form. And the final discussion of this chapter will guide you to use the forms to manage loan transactions in Library Management System application. CHAPTER 8: The discussion in this chapter will guide you to create members statistic form, books statistic form, and loan statistic form for Library Management System application. This chapter will discuss in detail about how to create these three forms. This chapter will also discuss about how to add all of the forms as MDI sub windows of the main form. And the final discussion of this chapter will guide you to use all of the forms to display the statistics in the library.
THE APPLIED DATA SCIENCE WORKSHOP: Prostate Cancer Classification and Recognition Using Machine Learning and Deep Learning with Python GUI
THE APPLIED DATA SCIENCE WORKSHOP: Prostate Cancer Classification and Recognition Using Machine Learning and Deep Learning with Python GUI - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook THE APPLIED DATA SCIENCE WORKSHOP: Prostate Cancer Classification and Recognition Using Machine Learning and Deep Learning with Python GUI write by Vivian Siahaan. This book was released on 2023-07-19. THE APPLIED DATA SCIENCE WORKSHOP: Prostate Cancer Classification and Recognition Using Machine Learning and Deep Learning with Python GUI available in PDF, EPUB and Kindle. The Applied Data Science Workshop on Prostate Cancer Classification and Recognition using Machine Learning and Deep Learning with Python GUI involved several steps and components. The project aimed to analyze prostate cancer data, explore the features, develop machine learning models, and create a graphical user interface (GUI) using PyQt5. The project began with data exploration, where the prostate cancer dataset was examined to understand its structure and content. Various statistical techniques were employed to gain insights into the data, such as checking the dimensions, identifying missing values, and examining the distribution of the target variable. The next step involved exploring the distribution of features in the dataset. Visualizations were created to analyze the characteristics and relationships between different features. Histograms, scatter plots, and correlation matrices were used to uncover patterns and identify potential variables that may contribute to the classification of prostate cancer. Machine learning models were then developed to classify prostate cancer based on the available features. Several algorithms, including Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Naive Bayes, Adaboost, Extreme Gradient Boosting, Light Gradient Boosting, and Multi-Layer Perceptron (MLP), were implemented. Each model was trained and evaluated using appropriate techniques such as cross-validation and grid search for hyperparameter tuning. The performance of each machine learning model was assessed using evaluation metrics such as accuracy, precision, recall, and F1-score. These metrics provided insights into the effectiveness of the models in accurately classifying prostate cancer cases. Model comparison and selection were based on their performance and the specific requirements of the project. In addition to the machine learning models, a deep learning model based on an Artificial Neural Network (ANN) was implemented. The ANN architecture consisted of multiple layers, including input, hidden, and output layers. The ANN model was trained using the dataset, and its performance was evaluated using accuracy and loss metrics. To provide a user-friendly interface for the project, a GUI was designed using PyQt, a Python library for creating desktop applications. The GUI allowed users to interact with the machine learning models and perform tasks such as selecting the prediction method, loading data, training models, and displaying results. The GUI included various graphical components such as buttons, combo boxes, input fields, and plot windows. These components were designed to facilitate data loading, model training, and result visualization. Users could choose the prediction method, view accuracy scores, classification reports, and confusion matrices, and explore the predicted values compared to the actual values. The GUI also incorporated interactive features such as real-time updates of prediction results based on user selections and dynamic plot generation for visualizing model performance. Users could switch between different prediction methods, observe changes in accuracy, and examine the history of training loss and accuracy through plotted graphs. Data preprocessing techniques, such as standardization and normalization, were applied to ensure the consistency and reliability of the machine learning and deep learning models. The dataset was divided into training and testing sets to assess model performance on unseen data and detect overfitting or underfitting. Model persistence was implemented to save the trained machine learning and deep learning models to disk, allowing for easy retrieval and future use. The saved models could be loaded and utilized within the GUI for prediction tasks without the need for retraining. Overall, the Applied Data Science Workshop on Prostate Cancer Classification and Recognition provided a comprehensive framework for analyzing prostate cancer data, developing machine learning and deep learning models, and creating an interactive GUI. The project aimed to assist in the accurate classification and recognition of prostate cancer cases, facilitating informed decision-making and potentially contributing to improved patient outcomes.
STROKE: Analysis and Prediction Using Scikit-Learn, Keras, and TensorFlow with Python GUI
STROKE: Analysis and Prediction Using Scikit-Learn, Keras, and TensorFlow with Python GUI - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook STROKE: Analysis and Prediction Using Scikit-Learn, Keras, and TensorFlow with Python GUI write by Vivian Siahaan. This book was released on 2023-07-15. STROKE: Analysis and Prediction Using Scikit-Learn, Keras, and TensorFlow with Python GUI available in PDF, EPUB and Kindle. In this project, we will perform an analysis and prediction task on stroke data using machine learning and deep learning techniques. The entire process will be implemented with Python GUI for a user-friendly experience. We start by exploring the stroke dataset, which contains information about various factors related to individuals and their likelihood of experiencing a stroke. We load the dataset and examine its structure, features, and statistical summary. Next, we preprocess the data to ensure its suitability for training machine learning models. This involves handling missing values, encoding categorical variables, and scaling numerical features. We utilize techniques such as data imputation and label encoding. To gain insights from the data, we visualize its distribution and relationships between variables. We create plots such as histograms, scatter plots, and correlation matrices to understand the patterns and correlations in the data. To improve model performance and reduce dimensionality, we select the most relevant features for prediction. We employ techniques such as correlation analysis, feature importance ranking, and domain knowledge to identify the key predictors of stroke. Before training our models, we split the dataset into training and testing subsets. The training set will be used to train the models, while the testing set will evaluate their performance on unseen data. We construct several machine learning models to predict stroke. These models include Support Vector, Logistic Regression, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Gradient Boosting, Light Gradient Boosting, Naive Bayes, Adaboost, and XGBoost. Each model is built and trained using the training dataset. We train each model on the training dataset and evaluate its performance using appropriate metrics such as accuracy, precision, recall, and F1-score. This helps us assess how well the models can predict stroke based on the given features. To optimize the models' performance, we perform hyperparameter tuning using techniques like grid search or randomized search. This involves systematically exploring different combinations of hyperparameters to find the best configuration for each model. After training and tuning the models, we save them to disk using joblib. This allows us to reuse the trained models for future predictions without having to train them again. With the models trained and saved, we move on to implementing the Python GUI. We utilize PyQt libraries to create an interactive graphical user interface that provides a seamless user experience. The GUI consists of various components such as buttons, checkboxes, input fields, and plots. These components allow users to interact with the application, select prediction models, and visualize the results. In addition to the machine learning models, we also implement an ANN using TensorFlow. The ANN is trained on the preprocessed dataset, and its architecture consists of a dense layer with a sigmoid activation function. We train the ANN on the training dataset, monitoring its performance using metrics like loss and accuracy. We visualize the training progress by plotting the loss and accuracy curves over epochs. Once the ANN is trained, we save the model to disk using the h5 format. This allows us to load the trained ANN for future predictions. In the GUI, users have the option to choose the ANN as the prediction model. When selected, the ANN model is loaded from disk, and predictions are made on the testing dataset. The predicted labels are compared with the true labels for evaluation. To assess the accuracy of the ANN predictions, we calculate various evaluation metrics such as accuracy score, precision, recall, and classification report. These metrics provide insights into the ANN's performance in predicting stroke. We create plots to visualize the results of the ANN predictions. These plots include a comparison of the true values and predicted values, as well as a confusion matrix to analyze the classification accuracy. The training history of the ANN, including the loss and accuracy curves over epochs, is plotted and displayed in the GUI. This allows users to understand how the model's performance improved during training. In summary, this project covers the analysis and prediction of stroke using machine learning and deep learning models. It encompasses data exploration, preprocessing, model training, hyperparameter tuning, GUI implementation, ANN training, and prediction visualization. The Python GUI enhances the user experience by providing an interactive and intuitive platform for exploring and predicting stroke based on various features.
