Feasibility of dashboard GUI design and optimization from machine learning : a thesis presented in partial fulfilment of the requirements for the degree of Masters in Mechatronics Engineering, School of Food and Advanced Technologies, Massey University, Palmerston North, New Zealand. EMBARGOED until 28 September 2024.

Embargoed until 28 September 2024Web dashboard Graphical User Interfaces (GUI’s) are becoming ever more important for today’s big data requirements. A number of industries from Internet of Things, to finance, to project management and everything in-between are producing enormous amounts of data and the need to display this data for users and internal employees in a useful way is incredibly necessary. This thesis attempts to answer the question; Is it feasible to design and optimize dashboard graphic user interfaces with machine learning? To explore this question, we devised two experiments. One using full dashboard images, and the other using dashboard cropped components. Two datasets were built using images pulled off the internet and processed using four software scripts built for the purpose of this project. These included an image downloader, image duplicate detector, and a cropping tool designed to allow the user to crop sub-images from a single image rapidly while assigning class labels to the main, and cropped images. This resulted in two data-sets of 1024 dashboard images of size 512x512 pixels, and 5133 dashboard component images of size 256x256 pixels. We labelled these data-set images as one of eight class labels of different dashboard types. These types were IoT, agriculture, finance, analytics, miscellaneous, fitness, project management, and social. Following this, a machine learning architecture was chosen based on an evaluation using a set of weighted criteria. The results of this evaluation settling on StyleGAN2-ADA developed by NVIDIA corporation. Chosen hardware was a purpose-built machine learning computer with two NVIDIA a-40 Graphics Processing Units (GPU’s) which was able to perform at speed with the high memory and processing requirements of this architecture.—Shortened abstrac