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Full-Text Articles in Engineering
Enhanced Neurologic Concept Recognition Using A Named Entity Recognition Model Based On Transformers, Sima Azizi, Daniel B. Hier, Donald C. Wunsch
Enhanced Neurologic Concept Recognition Using A Named Entity Recognition Model Based On Transformers, Sima Azizi, Daniel B. Hier, Donald C. Wunsch
Chemistry Faculty Research & Creative Works
Although Deep Learning Has Been Applied to the Recognition of Diseases and Drugs in Electronic Health Records and the Biomedical Literature, Relatively Little Study Has Been Devoted to the Utility of Deep Learning for the Recognition of Signs and Symptoms. the Recognition of Signs and Symptoms is Critical to the Success of Deep Phenotyping and Precision Medicine. We Have Developed a Named Entity Recognition Model that Uses Deep Learning to Identify Text Spans Containing Neurological Signs and Symptoms and Then Maps These Text Spans to the Clinical Concepts of a Neuro-Ontology. We Compared a Model based on Convolutional Neural Networks …
Use Of Machine Learning And Natural Language Processing To Enhance Traffic Safety Analysis, Md Abu Sayed
Use Of Machine Learning And Natural Language Processing To Enhance Traffic Safety Analysis, Md Abu Sayed
Theses and Dissertations
Despite significant advances in vehicle technologies, safety data collection and analysis, and engineering advancements, tens of thousands of Americans die every year in motor vehicle crashes. Alarmingly, the trend of fatal and serious injury crashes appears to be heading in the wrong direction. In 2021, the actual rate of fatalities exceeded the predicted rate. This worrisome trend prompts and necessitates the development of advanced and holistic approaches to determining the causes of a crash (particularly fatal and major injuries). These approaches range from analyzing problems from multiple perspectives, utilizing available data sources, and employing the most suitable tools and technologies …
Identification Of Factors Contributing To Traffic Crashes By Analysis Of Text Narratives, Cristian D. Arteaga-Sanchez
Identification Of Factors Contributing To Traffic Crashes By Analysis Of Text Narratives, Cristian D. Arteaga-Sanchez
UNLV Theses, Dissertations, Professional Papers, and Capstones
The fatalities, injuries, and property damage that result from traffic crashes impose a significant burden on society. Current research and practice in traffic safety rely on analysis of quantitative data from crash reports to understand crash severity contributors and develop countermeasures. Despite advances from this effort, quantitative crash data suffers from drawbacks, such as the limited ability to capture all the information relevant to the crashes and the potential errors introduced during data collection. Crash narratives can help address these limitations, as they contain detailed descriptions of the context and sequence of events of the crash. However, the unstructured nature …
Diacritics Correction In Turkish With Context-Aware Sequence To Sequence Modeling, Asi̇ye Tuba Özge, Özge Bozal, Umut Özge
Diacritics Correction In Turkish With Context-Aware Sequence To Sequence Modeling, Asi̇ye Tuba Özge, Özge Bozal, Umut Özge
Turkish Journal of Electrical Engineering and Computer Sciences
Digital texts in many languages have examples of missing or misused diacritics which makes it hard for natural language processing applications to disambiguate the meaning of words. Therefore, diacritics restoration is a crucial step in natural language processing applications for many languages. In this study we approach this problem as bidirectional transformation of diacritical letters and their ASCII counterparts, rather than unidirectional diacritic restoration. We propose a context-aware character-level sequence to sequence model for this transformation. The model is language independent in the sense that no language-specific feature extraction is necessary other than the utilization of word embeddings and is …
Applied Deep Learning: Case Studies In Computer Vision And Natural Language Processing, Md Reshad Ul Hoque
Applied Deep Learning: Case Studies In Computer Vision And Natural Language Processing, Md Reshad Ul Hoque
Electrical & Computer Engineering Theses & Dissertations
Deep learning has proved to be successful for many computer vision and natural language processing applications. In this dissertation, three studies have been conducted to show the efficacy of deep learning models for computer vision and natural language processing. In the first study, an efficient deep learning model was proposed for seagrass scar detection in multispectral images which produced robust, accurate scars mappings. In the second study, an arithmetic deep learning model was developed to fuse multi-spectral images collected at different times with different resolutions to generate high-resolution images for downstream tasks including change detection, object detection, and land cover …
Toward Suicidal Ideation Detection With Lexical Network Features And Machine Learning, Ulya Bayram, William Lee, Daniel Santel, Ali Minai, Peggy Clark, Tracy Glauser, John Pestian
Toward Suicidal Ideation Detection With Lexical Network Features And Machine Learning, Ulya Bayram, William Lee, Daniel Santel, Ali Minai, Peggy Clark, Tracy Glauser, John Pestian
Northeast Journal of Complex Systems (NEJCS)
In this study, we introduce a new network feature for detecting suicidal ideation from clinical texts and conduct various additional experiments to enrich the state of knowledge. We evaluate statistical features with and without stopwords, use lexical networks for feature extraction and classification, and compare the results with standard machine learning methods using a logistic classifier, a neural network, and a deep learning method. We utilize three text collections. The first two contain transcriptions of interviews conducted by experts with suicidal (n=161 patients that experienced severe ideation) and control subjects (n=153). The third collection consists of interviews conducted by experts …
Event-Related Microblog Retrieval In Turkish, Çağri Toraman
Event-Related Microblog Retrieval In Turkish, Çağri Toraman
Turkish Journal of Electrical Engineering and Computer Sciences
Microblogs, such as tweets, are short messages in which users are able to share any opinion and information. Microblogs are mostly related to real-life events reported in news articles. Finding event-related microblogs is important to analyze online social networks and understand public opinion on events. However, finding such microblogs is a challenging task due to the dynamic nature of microblogs and their limited length. In this study, assuming that news articles are given as queries and microblogs as documents, we find event-related microblogs in Turkish. In order to represent news articles and microblogs, we examine encoding methods, namely traditional bag-of-words …
Nlp@Vcu: Crop Characteristic Extraction Framework, Cora Lewis, Bridget Mcinnes, Getiria Onsongo
Nlp@Vcu: Crop Characteristic Extraction Framework, Cora Lewis, Bridget Mcinnes, Getiria Onsongo
Summer REU Program
We developed a crop characteristic extraction framework. Starting from a custom SpaCy named entity recognition model, we added pre-trained word embeddings and a part-of-speech based entity expansion post-processing step. Then, we implemented an evaluation framework that functioned as a 5-fold cross validation wrapper for SpaCy custom training. Preliminary results showed improvement in the extraction framework after these additions.
Machine Learning In Requirements Elicitation: A Literature Review, Cheligeer Cheligeer, Jingwei Huang, Guosong Wu, Nadia Bhuiyan, Yuan Xu, Yong Zeng
Machine Learning In Requirements Elicitation: A Literature Review, Cheligeer Cheligeer, Jingwei Huang, Guosong Wu, Nadia Bhuiyan, Yuan Xu, Yong Zeng
Engineering Management & Systems Engineering Faculty Publications
A growing trend in requirements elicitation is the use of machine learning (ML) techniques to automate the cumbersome requirement handling process. This literature review summarizes and analyzes studies that incorporate ML and natural language processing (NLP) into demand elicitation. We answer the following research questions: (1) What requirement elicitation activities are supported by ML? (2) What data sources are used to build ML-based requirement solutions? (3) What technologies, algorithms, and tools are used to build ML-based requirement elicitation? (4) How to construct an ML-based requirements elicitation method? (5) What are the available tools to support ML-based requirements elicitation methodology? Keywords …
Evaluating Similarity Of Cross-Architecture Basic Blocks, Elijah L. Meyer
Evaluating Similarity Of Cross-Architecture Basic Blocks, Elijah L. Meyer
Browse all Theses and Dissertations
Vulnerabilities in source code can be compiled for multiple processor architectures and make their way into several different devices. Security researchers frequently have no way to obtain this source code to analyze for vulnerabilities. Therefore, the ability to effectively analyze binary code is essential. Similarity detection is one facet of binary code analysis. Because source code can be compiled for different architectures, the need can arise for detecting code similarity across architectures. This need is especially apparent when analyzing firmware from embedded computing environments such as Internet of Things devices, where the processor architecture is dependent on the product and …
Computer Enabled Interventions To Communication And Behavioral Problems In Collaborative Work Environments, Ashutosh Shivakumar
Computer Enabled Interventions To Communication And Behavioral Problems In Collaborative Work Environments, Ashutosh Shivakumar
Browse all Theses and Dissertations
Task success in co-located and distributed collaborative work settings is characterized by clear and efficient communication between participating members. Communication issues like 1) Unwanted interruptions and 2) Delayed feedback in collaborative work based distributed scenarios have the potential to impede task coordination and significantly decrease the probability of accomplishing task objective. Research shows that 1) Interrupting tasks at random moments can cause users to take up to 30% longer to resume tasks, commit up to twice the errors, and experience up to twice the negative effect than when interrupted at boundaries 2) Skill retention in collaborative learning tasks improves with …