GC-345 BrainNet: Using Deep Learning to Classify Brain Tumors

Brain tumors are a common type of cancer, and they do not discriminate based on gender, age, or ethnicity. That said, the severity and type of tumor vary among the diagnosed individual, cancerous or benign. The three most common types for diagnosed individuals are glioma, meningioma, and pituitary. Even so, identifying the type of tumor can be an arduous process for both the doctor and the patient, but one technique known as Convolutional Neural Network (CNN) has been particularly effective in expediently and reliably determining the type of brain tumor. A CNN is a type of neural network that can independently extract imaging features (e.g., face shape, eye color of images of faces), one of these models is known as ResNet101, which contains several residual layers. Through these layers, data can be spread forward, and retain information about the findings of the patient\u27s images. The ResNet model layers were frozen in this case for the first 10 epochs, then when the model had 50 of the layers of the base unfrozen which resulted in the training accuracy being 98% after 10 more epochs and the test accuracy being 96% after it finished. The conclusion drawn is that the results mean that they have shown that they have better than most existing models for this application, due to that a majority of models can only achieve the low 90%

GR-485 Email Summarizer and Action Item Extractor

Countless emails are sent and received daily, and a lot of time is spent reading through and understanding the content of these emails. This project aims to increase the efficiency of reading and gathering relevant email information. Our solution includes two parts: abstractive text summarization and action item extraction. Currently, these two items are common in different domains, however, they have not been combined and used with email understanding. Abstractive text summarization is the process of outputting the ideas of the emails using different words without giving quotes from the document. In this way, a person would be able to tell if the email is something they will need to look at and if not, then the Action Item Extractor tells what actions need to be performed, it involves finding all instances in a piece of text that are instructions, dates, or require something from the recipient. These two solutions hope to improve the efficiency of parsing through emails

UR-46 BreastNet;

In the United states, 13% of women are diagnosed with breast cancer in their lifetime, and it is the second leading cause of death by cancer in women. Early detection and screening can result in an increase of life expectancy by 10 years on average. Unfortunately, breast cancer can be challenging to detect, since it can appear anywhere in the breast. Cancer that is detected in its early stages can give patients more options and save thousands of dollars in medical costs. Some of the most recent developments in computer science and machine learning are in the biomedical field, especially individualized healthcare. There is also an increase in the demand for telehealth options, reducing healthcare costs. With the help of computational technology, medical practitioners will be able to process data more quickly, which will allow more patients to have access to reliable treatment. Besides, systematic processes for interpreting various data types (such as clinical features, genetic information, and medical images) can identify trends that a human eye would not detect. This project aims to design and implement an artificial intelligence-based model called BreastNet to classify breast cancer into high and low-risk categories based on a combination of MRI images and clinical data. BreastNet uses a convolutional neural network (CNN), a type of machine learning methodology that imitates how the human brain learns information. Neurons fire in a connected pathway, reinforcing the relationship between a stimulus and the correct outcome. In this case, the CNN identifies characteristic features within the MRI that correspond to different life expectancy outcomes, which are notated in the clinical data. The clinical data serves as a loss function, which allows the network to identify how well the current model performs on images. We will evaluate the model by dividing the dataset into three partitions: training, validation, and testing, and then uses the evaluation metrics of Accuracy, Loss, F1 Score, Precision, Recall, Specificity, and Sensitivity.Advisors(s): Dr. Mohammed AledhariTopic(s): Artificial IntelligenceCS 426

The experience of international nursing students studying for a PhD in the U.K: A qualitative study

<p>Abstract</p> <p>Background</p> <p>Educating nurses to doctoral level is an important means of developing nursing capacity globally. There is an international shortage of doctoral nursing programmes, hence many nurses seek their doctorates overseas. The UK is a key provider of doctoral education for international nursing students, however, very little is known about international doctoral nursing students' learning experiences during their doctoral study. This paper reports on a national study that sought to investigate the learning expectations and experiences of overseas doctoral nursing students in the UK.</p> <p>Methods</p> <p>Semi-structured qualitative interviews were conducted in 2008/09 with 17 international doctoral nursing students representing 9 different countries from 6 different UK universities. Data were analysed thematically. All 17 interviewees were enrolled on 'traditional' 3 year PhD programmes and the majority (15/17) planned to work in higher education institutions back in their home country upon graduation.</p> <p>Results</p> <p>Studying for a UK PhD involved a number of significant transitions, including adjusting to a new country/culture, to new pedagogical approaches and, in some cases, to learning in a second language. Many students had expected a more structured programme of study, with a stronger emphasis on professional nursing issues as well as research - akin to the professional doctorate. Students did not always feel well integrated into their department's wider research environment, and wanted more opportunities to network with their UK peers. A good supervision relationship was perceived as the most critical element of support in a doctoral programme, but good relationships were sometimes difficult to attain due to differences in student/supervisor expectations and in approaches to supervision. The PhD was perceived as a difficult and stressful journey, but those nearing the end reflected positively on it as a life changing experience in which they had developed key professional and personal skills.</p> <p>Conclusions</p> <p>Doctoral programmes need to ensure that structures are in place to support international students at different stages of their doctoral journey, and to support greater local-international student networking. Further research is needed to investigate good supervision practice and the suitability of the PhD vis a vis other doctoral models (e.g. the professional doctorate) for international nursing students.</p

AN ANALYSIS OF U.S. ARMY ACQUISITION PROGRAM MANAGER RESPONSIBILITIES, AUTHORITIES, AND PROCESSES

This project defines specific program manager (PM) responsibilities, PM authorities, and acquisition stakeholder authorities based on Department of Defense (DoD) acquisition policies and Army regulations. It maps PM processes, revealing the conflicts and issues with existing definitions, and identifies opportunities to improve the Army acquisition process. Selected primary documentation of DoD and Army regulations, DoD and Army directives, specific program acquisition documentation, supplemental data from other federal organizations, and published research are used to identify the organizational structure, responsibilities, authorities, and acquisition processes of the U.S. Army. The analysis determines how the mission aligns or conflicts with the specific authorities prescribed throughout Army policies and regulations. Furthermore, the analysis uses IDEF0 function modeling to outline the process mechanisms that serve to enforce alignment or, conversely, to promote conflict between the PM’s mission and authority, and acquisition stakeholder authority.http://archive.org/details/ananalysisofusar1094563446Civilian, Department of the ArmyCivilian, Department of the ArmyCivilian, Department of the ArmyCivilian, Department of the ArmyApproved for public release; distribution is unlimited

Email Summarizer and Action Item Extractor

Countless emails are sent and received every day, and a lot of time is spent reading through and understanding the content of these emails. This project aims to increase the efficiency of reading and gathering relevant information from emails. Our solution includes two parts: abstractive text summarization and action item extraction. Currently, these two items are common in different domains, however, they have not been combined and used with email understanding. Abstractive text summarization is the process of outputting the ideas of the emails using different words without giving direct sentences from the document. We do this by taking an input of at most 6000 words from the chain of emails, and then we output 700 words for the summarization. In this way, a person would be able to tell if the email is something they will need to look at, and if not, then the Action Item Extractor tells what actions need to be performed Action item extraction involves finding all instances in a piece of text that are instructions, dates, or require something from the recipient. For example, “Please give me a list of names who are attending the party”, “Send the signed document to me by Friday \u27\u27, and “Don’t forget to sign-up for the costume contest”. Dates and deadlines are also extracted from the text. These two solutions hope to improve the efficiency of parsing through emails