Digital Commons Network^™

The Impact Of Social Media On Improving English Learning Skills: A Case Study Of Uae, Sameer O. A Baniyassen

Dissertations

Social media are one of the current forms of media that have diverse qualities and characteristics. Today, the use of social media is growing day by day at a significant rate. It is in this domain that students can virtually meet with their classmates and their teachers. They can communicate issues that are of significance and are related to their learning process. This study aimed to explore how social media platforms could affect English learning skills and to find whether social media could improve English language skills. The current study used mixed methods involving quantitative and qualitative approaches, such as …

A Delphi Study Of Effective Strategies And Supporting Activities For Reducing Chronic Absenteeism With Tk-5th Grade Students, Phillip Shelley

Dissertations

Purpose: The purpose of this Delphi study was to identify strategies expert Student Services Administrators/Attendance Supervisors recommend as most effective for reducing chronic absenteeism with TK-5th Grade public school students. In addition, it was the purpose to rate the effectiveness of the identified strategies, and to identify activities that would support the implementation of the most effective strategies.

Methodology: This study used a Delphi method, consisting of three rounds, to collect data from expert Student Service Administrators/Attendance Supervisors. In Round 1, expert panel members were asked to identify strategies to reduce chronic absenteeism with TK-5th grade students. In Round 2, …

Leading For Excellence: Behaviors And Strategies Of Assistant Superintendents Of Curriculum And Instruction That Support A High-Achieving School Environment, Robert Sherlock

Dissertations

Purpose: The purpose of this explanatory mixed-methods study was to determine the degree of importance for the 12-step principles proposed by Harvey et al. (2014) to create a K-12 school environment that fosters high student achievement as perceived by public school assistant superintendents of curriculum and instruction (ASCIs). A secondary purpose was to explore and describe the leadership strategies for implementing the 12-step principles proposed by Harvey et al. (2014) to create a K-12 school environment that fosters high student achievement as perceived by public school ASCIs.

Methodology: This explanatory mixed-methods research design used qualitative and quantitative data to analyze …

Psychoimmunological Outcomes Of A Dementia Caregiver Intervention: The Progressively Lowered Stress Threshold, Linda J. Garand

Dissertations

The goal of this study was to evaluate mood and immune outcomes of a community-based psychoeducational intervention (the Progressively Lowered Stress Threshold, PLST Model) designed to teach family caregivers to manage behavioral problems of persons with dementia. A randomized, repeated measures, experimental design was used to evaluate immune and mood outcomes of teaching the PLST model to family caregivers.

Life After Prematurity: Special Health Care Needs, Working Memory, And Health-Related Quality Of Life Among 9- To 11-Year-Old Children Born Prematurely, Michelle L. Kelly

Dissertations

Premature birth, defined as birth before the completion of 37 weeks of gestation, places children at risk for medical, physical, psychosocial and neurodevelopmental impairments. These impairments may vary in severity. Beyond infancy, children born prematurely are typically not studied as a group, but rather fall within many other diagnostic labels and categories. Non-categorical research, using consequence-based measures, is a means of capturing the effect of prematurity. In this study three major areas of functioning were assessed in 9- to 11-year-old children born prematurely and a group of full-term controls: special health care needs, working memory capacity and health-related quality of …

The Relationship Between Perceived Clinical Decision Making Ability And Medication Dosage Calculation Ability Of Registered Nurses, Lynn Marie Stover Nichols

Dissertations

To be an effective registered nurse, one must be able to integrate clinical decision-making skills into the medication calculation process in order to provide safe and effective care. Medication errors have been associated with negative outcomes for both patients and registered nurses. The execution of clinical decision-making skills throughout the medication administration process is crucial; however, the literature has not revealed a linking of these 2 concepts. The purpose of this study was to describe the relationship between registered nurses' clinical decision-making skills and the accuracy of medication dosage calculation. Simon's (1977) theory of decision making served as the conceptual …

An Evaluation Of Text Representation Techniques For Fake News Detection Using: Tf-Idf, Word Embeddings, Sentence Embeddings With Linear Support Vector Machine., Sangita Sriram

Dissertations

In a world where anybody can share their views, opinions and make it sound like these are facts about the current situation of the world, Fake News poses a huge threat especially to the reputation of people with high stature and to organizations. In the political world, this could lead to opposition parties making use of this opportunity to gain popularity in their elections. In the medical world, a fake scandalous message about a medicine giving side effects, hospital treatment gone wrong or even a false message against a practicing doctor could become a big menace to everyone involved in …

Drug Reviews: Cross-Condition And Cross-Source Analysis By Review Quantification Using Regional Cnn-Lstm Models, Ajith Mathew Thoomkuzhy

Dissertations

Pharmaceutical drugs are usually rated by customers or patients (i.e. in a scale from 1 to 10). Often, they also give reviews or comments on the drug and its side effects. It is desirable to quantify the reviews to help analyze drug favorability in the market, in the absence of ratings. Since these reviews are in the form of text, we should use lexical methods for the analysis. The intent of this study was two-fold: First, to understand how better the efficiency will be if CNN-LSTM models are used to predict ratings or sentiment from reviews. These models are known …

Lightgwas: A Novel Genome-Wide Association Study Procedure, Bruno Ambrozio

Dissertations

This dissertation proposes LightGWAS, a novel machine learning procedure for genome-wide association study (GWAS) based on LightGBM and k-fold cross-validation. The conducted literature review identified that the currently available GWAS implementations rely on massive manual quality control steps to address statistical issues, such as controlling for false-positive inflation and power reduction. It also showed they demand a specific GWAS method for each type of genomic dataset morphology, which consequently increases the human dependency and open margins for misleadings. LightGWAS is a potential single, resilient, autonomous and scalable solution to address such concerns. Through this research, LightGWAS was contrasted against the …

Think Like A Teacher: How Reflective Educators Use Multiple Data Sources To Inform Their Practice, Jana Grabarek

Dissertations

Teacher data use has become an increasingly central feature of American education. Messages from the public policy, research, philanthropy, and professional development arenas assert that collecting, analyzing, and interpreting data€”particularly number-based achievement data€”will increase student achievement. in this dissertation, a review of literature exploring the link between data use and achievement yielded mixed results. While increased achievement was slightly more likely when data use involved multiple types of data and/or data use strategies, its effectiveness was not consistently supported, and least apparent when tied to marginalized students. to better understand what effective data use might look like at the micro …

Effect Of Sulfur And A Perfluoropolyether On The Ignition And Decomposition Of Solid Fuels For Ramjets, Daniel A. Jones

Dissertations

Widening the flammability limits of solid fuels used in ramjet engines broadens the operational range of altitudes and speeds and enhances the possibility of autoigni- tion from incoming air alone. This experimental research studies how to decrease the solid fuel lower flammability limit while maintaining or improving other key thermal and mechanical properties. Candidate fuels are formulated using epoxy cured liquid polysulfide doped with sulfur and a perfluoropolyether. Sulfur doped formulations are hypothesized to produce greater mass fractions of low ignition temperature species to widen flammability limits, but degrade low-temperature mechanical properties. Perfluoropolyether is added to the mixtures to preserve …

Mission Specific Solar Radiation Environment Model (Mssrem) : Peak Flux Model, Zachary Donald Robinson

Dissertations

A new probabilistic model has been presented in the dissertation. The Mission-Specific Solar Radiation Environment Model (MSSREM) peak flux model provides a design reference environment that can be tailored to a specific mission with the mission length, mission duration, and confidence level specified. These reference environments will provide the bounding-case flux for solar energetic particles that will be encountered during the mission at the specified confidence level. The MSSREM peak flux model probabilistically models the environment for six elements (hydrogen, helium, carbon, oxygen, silicon, and iron) and scales these environments to produce the remaining 86 environments for elements Z=1-92. The …

Ablation Heat Transfer Characteristics Of A Polymer Coolant Medium For Warm Gas Generator Applications, Dakota J. Haring

Dissertations

Transporting high temperature solid propellant combustion gas through an ablating coolant tube is one method of lowering gas temperatures and providing clean, soot-free, pressurant for use in roll or divert attitude control systems. By utilizing the emerging technology of ablative warm gas generators, many of the challenges facing future space and kinetic kill vehicle designs may be mitigated. The data provided herein could assist in decreasing the mass, volume, and cost of warm gas generators capable of meeting the higher performance demands of the future. The objective of this experimental research is to characterize the convective heat transfer correlations for …

Voices Of African American Women In Computer Science: Implications For K-12 Stem Education And Beyond, Yolanda B. Sanders

Dissertations

Voices of African American Women in Computer Science is a qualitative dissertation about African American women who have successfully completed a computer science degree. This research explored how computer science education departments in predominantly White institutions (PWIs) contribute to the oppression of African American females. Critical race feminism (CRF) provides the platform that places the voices of women of color who have successfully obtained a computer science degree despite difficulties that have been imposed by the reality of racial biases that are present throughout the American education system and American culture at large. This research was guided by the following …

Teacher And Principal Perceptions Of Principal Leadership Practices Having The Strongest Relationship To Student Achievement, Michael C. Conran

Dissertations

This qualitative case study examined the perceptions of high performing elementary, middle and high school teachers and principals, as evidenced by their students' high achievement levels, relative to the principal leadership practices having the strongest relationship to student achievement. The Marzano Focused School Leader Evaluation Model, which contains six domains and 21 elements identifying comprehensively researched and evidence-based principal leadership practices, was used as a conceptual framework. The identification of the specific principal leadership practices having the strongest relationship to student achievement hinged on the delineation of the perceptions of the teacher and principal study participants relative to the principal …

Image Instance Segmentation: Using The Cirsy System To Identify Small Objects In Low Resolution Images, Orghomisan William Omatsone

Dissertations

The CIRSY system (or Chick Instance Recognition System) is am image processing system developed as part of this research to detect images of chicks in highly-populated images that uses the leading algorithm in instance segmentation tasks, called the Mask R-CNN. It extends on the Faster R-CNN framework used in object detection tasks, and this extension adds a branch to predict the mask of an object along with the bounding box prediction. Mask R-CNN has proven to be effective ininstance segmentation and object de-tection tasks after outperforming all existing models on evaluation of the Microsoft Common Objects in Context (MS COCO) …

Machine Learning Assisted Gait Analysis For The Determination Of Handedness In Able-Bodied People, Hugh Gallagher

Dissertations

This study has investigated the potential application of machine learning for video analysis, with a view to creating a system which can determine a person’s hand laterality (handedness) from the way that they walk (their gait). To this end, the convolutional neural network model VGG16 underwent transfer learning in order to classify videos under two ‘activities’: “walking left-handed” and “walking right-handed”. This saw varying degrees of success across five transfer learning trained models: Everything – the entire dataset; FiftyFifty – the dataset with enough right-handed samples removed to produce a set with parity between activities; Female – only the female …

An Examination Of The Smote And Other Smote-Based Techniques That Use Synthetic Data To Oversample The Minority Class In The Context Of Credit-Card Fraud Classification, Eduardo Parkinson De Castro

Dissertations

This research project seeks to investigate some of the different sampling techniques that generate and use synthetic data to oversample the minority class as a means of handling the imbalanced distribution between non-fraudulent (majority class) and fraudulent (minority class) classes in a credit-card fraud dataset. The purpose of the research project is to assess the effectiveness of these techniques in the context of fraud detection which is a highly imbalanced and cost-sensitive dataset. Machine learning tasks that require learning from datasets that are highly unbalanced have difficulty learning since many of the traditional learning algorithms are not designed to cope …

Transformer Neural Networks For Automated Story Generation, Kemal Araz

Dissertations

Towards the last two-decade Artificial Intelligence (AI) proved its use on tasks such as image recognition, natural language processing, automated driving. As discussed in the Moore’s law the computational power increased rapidly over the few decades (Moore, 1965) and made it possible to use the techniques which were computationally expensive. These techniques include Deep Learning (DL) changed the field of AI and outperformed other models in a lot of fields some of which mentioned above. However, in natural language generation especially for creative tasks that needs the artificial intelligent models to have not only a precise understanding of the given …

Identifying Online Sexual Predators Using Support Vector Machine, Yifan Li

Dissertations

A two-stage classification model is built in the research for online sexual predator identification. The first stage identifies the suspicious conversations that have predator participants. The second stage identifies the predators in suspicious conversations. Support vector machines are used with word and character n-grams, combined with behavioural features of the authors to train the final classifier. The unbalanced dataset is downsampled to test the performance of re-balancing an unbalanced dataset. An age group classification model is also constructed to test the feasibility of extracting the age profile of the authors, which can be used as features for classifier training. The …

Classification Of Animal Sound Using Convolutional Neural Network, Neha Singh

Dissertations

Recently, labeling of acoustic events has emerged as an active topic covering a wide range of applications. High-level semantic inference can be conducted based on main audioeffects to facilitate various content-based applications for analysis, efficient recovery and content management. This paper proposes a flexible Convolutional neural network-based framework for animal audio classification. The work takes inspiration from various deep neural network developed for multimedia classification recently. The model is driven by the ideology of identifying the animal sound in the audio file by forcing the network to pay attention to core audio effect present in the audio to generate Mel-spectrogram. …

Finetuning Pre-Trained Language Models For Sentiment Classification Of Covid19 Tweets, Arjun Dussa

Dissertations

It is a common practice in today’s world for the public to use different micro-blogging and social networking platforms, predominantly Twitter, to share opinions, ideas, news, and information about many things in life. Twitter is also becoming a popular channel for information sharing during pandemic outbreaks and disaster events. The world has been suffering from economic crises ever since COVID-19 cases started to increase rapidly since January 2020. The virus has killed more than 800 thousand people ever since the discovery as per the statistics from Worldometer [1] which is the authorized tracking website. So many researchers around the globe …

Investigating The Predictability Of A Chaotic Time-Series Data Using Reservoir Computing, Deep-Learning And Machine- Learning On The Short-, Medium- And Long-Term Pricing Of Bitcoin And Ethereum., Molly Kenny

Dissertations

This study will investigate the predictability of a Chaotic time-series data using Reservoir computing (Echo State Network), Deep-Learning(LSTM) and Machine- Learning(Linear, Bayesian, ElasticNetCV , Random Forest, XGBoost Regression and a machine learning Neural Network) on the short (1-day out prediction), medium (5-day out prediction) and long-term (30-day out prediction) pricing of Bitcoin and Ethereum Using a range of machine learning tools, to perform feature selection by permutation importance to select technical indicators on the individual cryptocurrencies, to ensure the datasets are the best for predictions per cryptocurrency while reducing noise within the models. The predictability of these two chaotic time-series …

Motion Tracking Through Moiré Images Of Fork Gratings, Grant Bergstue

Dissertations

A highly sensitive passive motion sensing technique is presented. This strategy is reliant on two fundamental components: the generation of moiré images and the use of fork gratings as the imaged pattern. The moiré phenomenon is well known and has been used in the past for measuring small changes in position or topography due to the large scale changes in the moiré pattern relative to the change in the system. This research effort is unique in the pairing of the moiré effect with a particular pattern known as a fork grating. Fork gratings differ from Ronchi gratings, the typical pattern …

Efficient Predictive Lossless Hyperspectral Image Compression Using Machine Learning, Zhuocheng Jiang

Dissertations

Hyperspectral imaging technology has found many useful applications in various domains such as remote sensing. Data compression allows for efficient storage and transmission of massive hyperspectral image datasets. In this dissertation, we study efficient predictive coding schemes for lossless compression of hyperspectral images. We use machine learning techniques to improve the following two key components of the predictive coding process: (i) accurate pixel value prediction, and (ii) more efficient entropy coding of the prediction errors (residues). To this end, we propose an adaptive filtering framework based on concatenated neural networks, which are capable of extracting both spatial and spectral correlations …

Pulsar Timing As A Direct Probe Of Galactic Dark Matter, Kiley Heflin

Dissertations

In the currently accepted paradigm, dark matter is hypothesized as an explanation for the flat rotation curves of galaxies under the assumption of virialized orbits. The use of millisecond pulsar (MSP) timing as a probe of Galactic dark matter content is explored as a means of challenging this assumption. A method of inference of the Galactic potential using frequency derivative $\dot{\nu}$ is produced, and an upper limit for a virialized Galactic rotation curve is given through direct observation of acceleration. The data set used includes 210 pulsars with known $\dot{\nu}$ and astrometric properties, a subset of which also have measured …

A Comparative Study Of Text Summarization On E-Mail Data Using Unsupervised Learning Approaches, Tijo Thomas

Dissertations

Over the last few years, email has met with enormous popularity. People send and receive a lot of messages every day, connect with colleagues and friends, share files and information. Unfortunately, the email overload outbreak has developed into a personal trouble for users as well as a financial concerns for businesses. Accessing an ever-increasing number of lengthy emails in the present generation has become a major concern for many users. Email text summarization is a promising approach to resolve this challenge. Email messages are general domain text, unstructured and not always well developed syntactically. Such elements introduce challenges for study …

Customer Churn Prediction, Deepshikha Wadikar

Dissertations

Churned customers identification plays an essential role for the functioning and growth of any business. Identification of churned customers can help the business to know the reasons for the churn and they can plan their market strategies accordingly to enhance the growth of a business. This research is aimed at developing a machine learning model that can precisely predict the churned customers from the total customers of a Credit Union financial institution. A quantitative and deductive research strategies are employed to build a supervised machine learning model that addresses the class imbalance problem handled feature selection and efficiently predict the …

A Single Camera 360-Degree Real Time Vision-Based Localization Method With Application To Mobile Robot Trajectory Tracking, Khomsun Singhirunnusorn

Dissertations

This dissertation proposes a new method for real time vision-based localization in the full 360-degree area around a 3D reference object. In the past, Mirage pose estimation was developed for multi-camera systems. To achieve 360-degree localization with a single camera, we introduce the Mirage-S pose estimation method that uses a single camera given a 3D reference object as input. Mirage-S finds the solution by using an iterative numerical method with non-linear equations. This dissertation splits the localization problem into three parts: 360-degree 3D object recognition, 360-degree pose estimation, and 360-degree localization application. First, 360-degree 3D object recognition is required to …

A Measurable Cybersecurity Quality Framework And Its Application To The Department Of Transportation, Mohammad Shojaeshafiei

Dissertations

Numerous organizations are running businesses around the clock and have interaction with users through computer networks. The substantial issue here is that the organizations may not be able to assure the users that all operations can be performed completely securely while the users are engaged with the system’s operations. On a broader scale, the necessity of security assurance goes beyond business affairs such that lack of system security in organizations and agencies may endanger national security and public safety. All the records, research, and scientific studies show that it is almost impossible to guarantee all services and transactions on a …