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The manual specifies system requirements including MacOS hardware and required software (Anaconda, Jupyter Notebook, Python) and provides code execution steps. It documents dataset sourcing from a Kaggle repository, followed by data exploration, image preprocessing, and image augmentation. 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It also explains how to run the code in Jupyter Notebook.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Where is the dataset obtained for the project?",{"text":117,"@type":113},"The dataset is taken from a Kaggle public repository for Alaska2 image steganalysis. It includes cover images and examples with hidden information using steganography algorithms such as JMiPOD, JUNIWARD, and UERD.",{"name":119,"@type":110,"acceptedAnswer":120},"What steps are covered after dataset collection?",{"text":121,"@type":113},"The manual describes data exploration (listing necessary Python libraries, creating image lists by category, setting global variables, and label dictionary creation). 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All information other than my own contribution will be fully referenced and listed in the relevant bibliography section at the rear of the project.  \nALL internet material must be referenced in the bibliography section . Students are required to use the Referencing Standard specified in the report template. To use other author's written or electronic work is illegal ( plagiarism) and may result in disciplinary action .  \nHardik Sudhir Sawant  \nSignature:    \n13-08-2023  \nDate:    \nPLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST  \n\n| Attach a completed copy of this sheet to each project (including multiple copies) | □ |\n| --- | --- |\n| Attach a Moodle submission receipt of the online project submission, to each project (including multiple copies) . | □ |\n| You must ensure that you retain a HARD COPY of the project, both for your own reference and in case a project is lost or mislaid. It is not sufficient to keep a copy on computer. | □ |\n\nAssignments that are submitted to the Programme Coordinator Office must be placed into the assignment box located outside the office.  \n\n| Office Use Only |  |\n| --- | --- |\n| Signature: | Hardik Sudhir Sawant |\n| Date: | 13-08-2023 |\n| Penalty Applied (if applicable) : |  |\n\nConfiguration Manual  \nHardik Sawant  \nStudent ID: x21232105  \n1 Introduction  \nThis Configuration Manual lists together all prerequisites needed to duplicate the studies and its effects on a specific setting. A glimpse of the source for Data Importing & Image Preprocessing and after that Image augmentation while taking into consideration about class balancing ṁṇ, all the created algorithms, and Evaluations is also supplied, together with the necessary hardware components as well as Software applications. The report is organized as follows, with details relating environment configuration provided in Section 2.  \nInformation about data gathering is detailed in Section 3. Image pre-processing included in Section 4's information extraction section. In section 5, the Image Augmentation is described. Details well about models that were created and tested are provided in Section 6. How the results are calculated and shown is described in Section 7.  \n2 System Requirements  \nThe specific needs for hardware as well as software to put the research into use are detailed in this section.  \n2. 1 Hardware Requirements  \nThe necessary hardware specs are shown in Figure 1 below. MacOs M1 Chip, macOS 10.15.x (Catalilna) operating system , 8GB RAM, 256GB Storage , 24’’ Display.  \nFigure 1: Hardware Requirements  \n2.2 Software Requirements  \n􀁸 Anaconda 3 (Version 4.8.0)  \n􀁸 Jupyter Notebook (Version 6.0.3)  \n􀁸 Python (Version 3.7.6)  \n2.3 Code Execution  \nThe code can be run in jupyter notebook. The jupyter notebook comes with Anaconda 3, run the jupyter notebook from startup. This will open jupyter notebook in web browser. The web browser will show the folder structure of the system, move to the folder where the code file is located. Open the code file from the folder and to run the code, go to Kernel menu and Run all cells.  \n3 Data Collection  \nThe dataset is taken from Kaggle public repository from the link [https://www.kaggle.com/c/alaska2-image-steganalysis. The dataset](https://www.kaggle.com/","cbCaitXM21cBQQRH","https://ap.wps.com/l/cbCaitXM21cBQQRH","pdf",1268300,18,"English","# 1 Introduction\n# 2 System Requirements\n## 2.1 Hardware Requirements\n## 2.2 Software Requirements\n## 2.3 Code Execution\n# 3 Data Collection\n# 4 Data Exploration\n# 5 Image Augmentation\n# 6 Models and Evaluation\n# 7 Results Calculation and Display","[{\"question\":\"What system requirements does the configuration manual specify?\",\"answer\":\"It lists hardware specs (MacOS M1 Chip, macOS 10.15.x, 8GB RAM, 256GB storage, 24-inch display) and required software including Anaconda 3, Jupyter Notebook, and Python versions. It also explains how to run the code in Jupyter Notebook.\"},{\"question\":\"Where is the dataset obtained for the project?\",\"answer\":\"The dataset is taken from a Kaggle public repository for Alaska2 image steganalysis. It includes cover images and examples with hidden information using steganography algorithms such as JMiPOD, JUNIWARD, and UERD.\"},{\"question\":\"What steps are covered after dataset collection?\",\"answer\":\"The manual describes data exploration (listing necessary Python libraries, creating image lists by category, setting global variables, and label dictionary creation). It then covers image preprocessing and image augmentation, including threshold contouring and organizing images into category folders.\"}]","Configuration Manual - Project Title: Enhancing Digital Image Forensics in Cybersecurity Using Machine Learning | PDF",1789641926,45]