Tag: reu

Investigating the Classification of Breast Cancer Subtypes using KMeans

This project provides an insight into an investigation of the classification of breast cancer sub-types using proetomic dataset through a machine learning approach.

Project: Detection of Autism Spectrum Disorder with a Facial Image using Artificial Intelligence

This project uses artificial intelligence to explore the possibility of using a facial image analysis to detect Autism in children. Early detection and diagnosis of Autism, along with treatment, is needed to minimize some of the difficulties that people with Autism encounter. Autism is usually diagnosed by a specialist through various Autism screening methods. This can be an expensive and complex process. Many children that display signs of Autism go undiagnosed because there families lack the expenses needed to pay for Autism screening and diagnosing. The development of a potential inexpensive, but accurate way to detect Autism in children is necessary for low-income families. In this project, a Convolutional Neural Network (CNN) is utilized, along with a dataset obtained from Kaggle. This dataset consists of collected images of male and female, autistic and non-autistic children between the ages of two to fourteen years old. These images are used to train and test the CNN model. When one of the images are received by the model and importance is assigned to various features in the image, an output variable (autistic or non-autistic) is received.

Project: Analyzing the Advantages and Disadvantages of Artificial Intelligence for Breast Cancer Detection in Women

Breast Cancer is one of the most dangerous type of disease that affects many women. For detecting Breast Cancer, machine learning techniques are applied to improve the accuracy of diagnosis.

Increasing Cervical Cancer Risk Analysis

Cervical Cancer is an increasing matter that is affecting various women across the nation, in this project we will be analyzing risk factors that are producing higher chances of this cancer. In order to analyize these risk factors a machine learning technique is implemented to help us understand the leading factors of cervical cancer.

Cyber Attacks Detection Using AI Algorithms

This research is analysing multiple artificial intelligence algorithms to detect cyber attacks

Report: Dentronics: Classifying Dental Implant Systems by using Automated Deep Learning

Artificial intelligence is a branch of computer science that focuses on building and programming machines to think like humans and mimic their actions. The proper concept definition of this term cannot be achieved simply by applying a mathematical, engineering, or logical approach but requires an approach that is linked to a deep cognitive scientific inquiry. The use of machine-based learning is constantly evolving the dental and medical field to assist with medical decision making process. In addition to diagnosis of visually confirmed dental caries and impacted teeth, studies applying machine learning based on artificial neural networks to dental treatment through analysis of dental magnetic resonance imaging, computed tomography, and cephalometric radiography are actively underway, and some visible results are emerging at a rapid pace for commercialization. Researchers have found deep convolutional neural networks to have a future place in the dental field when it comes to classification of dental implants using radiographic images.

Here comes the abstract

Report: Aquatic Animals Classification Using AI

Marine animals play an important role in the ecosystem. ‘Aquatic animals play an important role in nutrient cycles because they store a large proportion of ecosystem nutrients in their tissues, transport nutrients farther than other aquatic animals and excrete nutrients in dissolved forms that are readily available to primary producers’ (Vanni MJ 1) Fish images are captured by scuba divers, tourist, or underwater submarines. different angles of fishes image can be very difficult to get because of the constant movement of the fish. In addition to getting the right angles, the images of marine animals are usually low-quality because of the water. Underwater cameras that is required for a good quality image can be expensive. Using AI could potentially increase the marine population by the help of classification by testing the usage of machine learning using the images obtained from the aquarium combined with advanced technology. We collect 164 fish images data from Georgia acquarium to look at the different movements.

Project: Hand Tracking with AI

In this project we study the ability of an AI to recognize letters from the American Sign Language (ASL) alphabet. We use a Convolutional Neural Network and apply it to a dataset of hands in different positionings showing the letters ‘a’, ‘b’, and ‘c’ in ASL. With this we build a model to recognize the letter and output the letter it predicts.

Review: Handwriting Recognition Using AI

This study reviews two approaches and/or machine learning tools used by researchers/developers to convert handwritten information into digital forms using Artificial Intelligence.

Project: Analyzing Hashimoto disease causes, symptoms and cases improvements using Topic Modeling

Analyzing factors as immune systems, genetics and diets than can lead to Hashimoto disease

Project: Classification of Hyperspectral Images

Here comes the abstract

Project: Detecting Multiple Sclerosis Symptoms using AI

This work implements machine learning algorithim apply in Multiple Sclerosis symptoms and provides treatment options available

Report: AI in Orthodontics

In this effort we are analyzing X-ray images in AI and identifying cavitites

Time Series Analysis of Blockchain-Based Cryptocurrency Price Changes

This project applies neural networks and Artificial Intelligence (AI) to historical records of high-risk cryptocurrency coins to train a prediction model that guesses their price. The code in this project contains Jupyter notebooks, one of which outputs a timeseries graph of any cryptocurrency price once a csv file of the historical data is inputted into the program. Another Jupyter notebook trains an LSTM, or a long short-term memory model, to predict a cryptocurrency’s closing price. The LSTM is fed the close price, which is the price that the currency has at the end of the day, so it can learn from those values. The notebook creates two sets: a training set and a test set to assess the accuracy of the results. The data is then normalized using manual min-max scaling so that the model does not experience any bias; this also enhances the performance of the model. Then, the model is trained using three layers— an LSTM, dropout, and dense layer—minimizing the loss through 50 epochs of training; from this training, a recurrent neural network (RNN) is produced and fitted to the training set. Additionally, a graph of the loss over each epoch is produced, with the loss minimizing over time. Finally, the notebook plots a line graph of the actual currency price in red and the predicted price in blue. The process is then repeated for several more cryptocurrencies to compare prediction models. The parameters for the LSTM, such as number of epochs and batch size, are tweaked to try and minimize the root mean square error.

Analysis of Covid-19 Vaccination Rates in Different Races

With the ready availability of COVID-19 vaccinations, it is concerning that a suprising large portion of the U.S. population still refuses to recieve one. In order to control the spread of the pandemic and possibly even erradicate it completely, it is integral that the United States vaccinate as much of the population as possible. Not only does this require ensuring that everyone who wishes to be vaccinated recieves a vaccine, it also requires that those who are unwilling to recieve the vaccine are persuaded to take it. The goal of this report is to analyze the demographics of those who are hesitant to recieve the vaccine and find the reasoning behind their decision. This will make it easier to properly persuade them to recieve the vaccine and aid in raising the United States' vaccination rates.