Breast cancer is one of the most common types of cancer worldwide. Early detection and diagnosis are crucial for improving the chances of successful treatment and survival. In this thesis, many different machine learning algorithms were evaluated and compared to predict breast cancer malignancy from diagnostic features extracted from digitized images of breast tissue samples, called fine-needle aspirates. Breast cancer diagnosis typically involves a combination of mammography, ultrasound, and biopsy. However, machine learning algorithms can assist in the detection and diagnosis of breast cancer by analyzing large amounts of data and identifying patterns that may not be discernible to the human eye. By using these algorithms, healthcare professionals can potentially detect breast cancer at an earlier stage, leading to more effective treatment and better patient outcomes. The results showed that the gradient boosting classifier performed the best, achieving an accuracy of 96% on the test set. This indicates that this algorithm can be a useful tool for healthcare professionals in the early detection and diagnosis of breast cancer, potentially leading to improved patient outcomes.

The aim of this project is to understand the basic algorithmic components of the transformer deep learning architecture. At a high level, a transformer is a machine learning model based off of a recurrent neural network that adopts a self-attention mechanism, which can weigh significant parts of sequential input data which is very useful for solving problems in natural language processing and computer vision. There are other approaches to solving these problems which have been implemented in the past (i.e., convolutional neural networks and recurrent neural networks), but these architectures introduce the issue of the vanishing gradient problem when an input becomes too long (which essentially means the network loses its memory and halts learning) and have a slow training time in general. The transformer architecture’s features enable a much better “memory” and a faster training time, which makes it a more optimal architecture in solving problems. Most of this project will be spent producing a survey that captures the current state of research on the transformer, and any background material to understand it. First, I will do a keyword search of the most well cited and up-to-date peer reviewed publications on transformers to understand them conceptually. Next, I will investigate any necessary programming frameworks that will be required to implement the architecture. I will use this to implement a simplified version of the architecture or follow an easy to use guide or tutorial in implementing the architecture. Once the programming aspect of the architecture is understood, I will then Implement a transformer based on the academic paper “Attention is All You Need”. I will then slightly tweak this model using my understanding of the architecture to improve performance. Once finished, the details (i.e., successes, failures, process and inner workings) of the implementation will be evaluated and reported, as well as the fundamental concepts surveyed. The motivation behind this project is to explore the rapidly growing area of AI algorithms, and the transformer algorithm in particular was chosen because it is a major milestone for engineering with AI and software. Since their introduction, transformers have provided a very effective way of solving natural language processing, which has allowed any related applications to succeed with high speed while maintaining accuracy. Since then, this type of model can be applied to more cutting edge natural language processing applications, such as extracting semantic information from a text description and generating an image to satisfy it.

Wardriving is when prospective malicious hackers drive with a portable computer to sniff out and map potentially vulnerable networks. With the advent of smart homes and other Internet of Things devices, this poses the possibility of more unsecure targets. The hardware available to the public has also miniaturized and gotten more powerful. One no longer needs to carry a complete laptop to carry out network mapping. With this miniaturization and greater popularity of quadcopter technology, the two can be combined to create a more efficient wardriving setup in a potentially more target-rich environment. Thus, we set out to create a prototype as a proof of concept of this combination. By creating a bracket for a Raspberry Pi to be mounted to a drone with other wireless sniffing equipment, we demonstrate that one can use various off the shelf components to create a powerful network detection device. In this write up, we also outline some of the challenges encountered by combining these two technologies, as well as the solutions to those challenges. Adding payload weight to drones that are not initially designed for it causes detrimental effects to various characteristics such as flight behavior and power consumption. Less computing power is available due to the miniaturization that must take place for a drone-mounted solution. Communication between the miniature computer and a ground control computer is also essential in overall system operation. Below, we highlight solutions to these various problems as well as improvements that can be implemented for maximum system effectiveness.