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Homework answers / question archive / I have a total of 977 images(split into 2 subfolders car inside the lane & car outside the lane ) generated that I am using as the data set for my project
I have a total of 977 images(split into 2 subfolders car inside the lane & car outside the lane ) generated that I am using as the data set for my project. Because it was captured with a smartphone the image size is too big and need to be resized (reduced) within OpenCv down to 224 by 224
Section 1: Project Overview
Section 1.1: Credits and Project Size The project is a 15-credit module.
Section 1.2: Overview
Plan, research, design, implement and refine a data science model as part of a data analytics project. The project must use state of the art technologies for extraction, filtering and processing of data. This process must be documented as part of the final report.
The project must develop an appropriate data science model for predictive analytics or a model to solve/address a specific data science problem; this may include but is not limited to:
The model will be developed and refined as part of the implementation.
Students will be encouraged to work independently and show initiative. Students will be required to plan and manage their project in conjunction with a supervisor. Students will be required to present their projects and findings to staff.
Section 1.3 Learning outcomes for the project:
1.2) from data
Students are expected to work independently, with weekly updates with their supervisor.
Section 2.1 Panel Meetings
At multiple points during the project, a panel meeting will be held. This is a requirement for the student to attend in person or if hosted online attend the virtual session. This meeting will entail a panel review the students’ progress to date and will offer feedback on the work done to date and future work.
Section 3.1
Section 3.2 Grading Structure
Note: Depending on the project selected, the weights highlighted in grey, are subject to change to reflect the focus of the project.
The final documents should follow the below guidelines, each section will be a separate upload, requiring the relevant files and data, along with a descriptive document. The descriptive document must have your name and student number as the heading, along with the deliverable heading. The document must be single-spaced, font type Times New Roman, and font size 12pt.
4.1 Research
The Literature review must be a comprehensive review of models/ tools in the space of your project. The literature review should be a maximum of 1000 words. You must identify here:
Most students will rely heavily on the WWW as their main source of information for the research document. However, just because something comes packaged in a high-tech format, does not mean it's well researched or accurate. One approach to researching the Web is to start your search using a site that is more likely to focus on scholarly resources and critically evaluate your WWW search results. Other guidelines for ensuring that the information you get is more likely to be accurate are: Looking for articles published in journals or sources that require certain standards be met before publication.
While it acceptable (and you are encouraged to) assimilate and summarise the information you may find on the WWW or in references, you should NOT quote large sections of text verbatim, whether acknowledged or not. Unacknowledged quotation constitutes plagiarism and will result in an automatic fail grade being awarded for this section of the project.
4.2 Data set selection and preparation
This section should be a maximum of 1000 words. This should describe in detail some or all the following (project dependent), as well as the criteria for selecting specific dataset and the techniques involved in acquiring the dataset:
4.3 Data pre-pre-processing
This section should discuss the steps completed on the dataset to pre-process the data prior to use in a model. The word count is a maximum of 1000 words. This could follow many forms for example:
4.4 Model Development
This section should discuss model development. This must include an introduction on the tools and infrastructure selected (cloud, Tensorflow etc.). This section should be a maximum of 1000 words plus visualizations and tables. This also must discuss the development of the model, from initial investigations to the final refined version, for example:
4.5 Performance and Model Outcome
The results should present the results of the experiment/tool. The results should also include techniques used to validate the model/show that it would generalize. The selection of these techniques should also be detailed (with numerical values included). Usually, the findings should also be visualized and/or presented in a tabular manner. The performance should be briefly discussed here. This section should be a maximum of 1000 words plus visualizations and tables.
The final document should combine all of the deliverables into a single final submission detailed document forming the thesis for the project module. This can be supplemented with dataset upload, code and documents that were used in the development of the project. There will be a separate upload available for the additional items. These should be referenced in the final document. The document can be written in LaTeX
For the presentation, the student is required to submit a poster, no slides are required for the presentation. The student will present the poster with the findings on the presentation evening, dates will be posted on moodle. The poster can be written in LaTeX, where there is a TU Dublin template available at:
The poster should be submitted before the presentation.
The poster is a summary of all of the work to date, following the same headings, with the addition of an introduction. It is encouraged to include figures and tables in the poster taken from the final document.
