Digital Commons Network^™

Navigating Home Language Practice For Children With Disabilities: Insights From Korean-American Mothers' Online Communications, Jemma Kim, Young Suk Hwang, Yeon Kim, Sang Seok Nam

The Journal of Special Education Apprenticeship

This study explores the home language practice (HLP) of Korean-American mothers with children who have developmental disabilities, including autism spectrum disorder. Data was collected from an online forum where these mothers discussed their experiences and decision-making processes following their child's diagnosis. Thematic analysis was conducted on the collected data to identify and develop themes related to their experiences. The study's framework is based on family-centered practices, emphasizing the importance of equal partnerships, cultural responsiveness, and information sharing between families and early intervention and early childhood special education (EI/ECSE) professionals. Factors that influence HLP decisions include mothers' perceptions of disabilities, professional …

Navigating Iep Meetings: Effective Approaches For Supporting Asian Families Of Children With Idd In Special Education, Kristina Rios, Wei-Mo Tu

The Journal of Special Education Apprenticeship

Family involvement is an essential component of the special education process for youth with intellectual and developmental disabilities (IDD). In addition to the legal requirement that parents should be equal partners in the decision-making of the student’s IEP program (IDEA, 2004), a bulk of empirical research demonstrates the positive impact of parent involvement on student outcomes. However, many families face barriers to participation in the special education process. Culturally and linguistically diverse (CLD), including Asian families, especially face systemic barriers when accessing services for their children with disabilities. In order to better understand parents’ perceptions of stress in relation to …

A Group Reading Intervention With Individualized Error Correction For Middle School Students With Reading Difficulties, Shengtian Wu, Kasee K. Stratton, Daniel L. Gadke

The Journal of Special Education Apprenticeship

Reading difficulties are common among middle school students in the US, especially among those with disabilities. Unfortunately, there is a significant shortage of professionals (e.g., special educators) who can provide high-quality reading interventions. Small group (SG) intervention is a group instruction that helps more students in need per intervention session and may mitigate the aforementioned shortage. SG intervention packages often include various intervention components that address skill and performance difficulties. However, SG reading intervention research has mostly focused on helping elementary school students without disabilities. Also, many SG reading interventions used one-size-fits all approach which restricted individualization of error correction …

Editor's Note

The Journal of Special Education Apprenticeship

No abstract provided.

Education As A Solution To Combat Rising Cybercrime Rates Against Children And Teenagers, Christian Javier Solis-Diaz

Electronic Theses, Projects, and Dissertations

Ninety seven percent (97%) of people between the ages of 3 and 18 are found to be users of technology and internet services daily. This number also correlates with rising cyber crime rates against people in this age bracket. It is found that people between 3 and 18 years old are found to be technologically savvy but often lack the knowledge of how to protect themselves in online environments. Researchers have suggested that cybersecurity awareness training is an effective method at combating common forms of cyberattack such as social engineering. Social engineering attacks are found to make up 98% of …

Building An Application Model For Efficient Ride Booking In Ride-Hailing Industry, Nikunjkumar Butani

Electronic Theses, Projects, and Dissertations

The purpose of this study is to develop an efficient ride booking application in the ride-hailing sector. The objective of the research is to provide users with an easy way to book rides at reasonable prices and convenient times. This project helps to promote the ride-hailing industry by providing guidance for the creation of real-time applications. This project answered three questions. 1. What are the technology and infrastructure requirements for developing a consolidated online application for centralized ride sharing? 2. What are other examples of application aggregators in other industries than transportation and how do they work? 3. What are …

Using Behavior Skills Training And A Group Contingency To Promote Mask-Wearing In An Early Childhood Special Education Classroom, Kaitlyn Smith, Hannah Macnaul, Marie Kirkpatrick

The Journal of Special Education Apprenticeship

Coronavirus (COVID-19) and the resulting pandemic had widespread implications on the safety of the job tasks teachers are charged with each day. The Center for Disease Control (CDC, 2020) recommends people age 2 years and older should wear masks in public settings; however, for children with disabilities, wearing a mask may be difficult and as such, is not required. Special education teachers and students in particular are at high risk for exposure and contracting COVID-19. Therefore, behavior-analytic strategies that can teach and reinforce appropriate mask-wearing should be evaluated. Given the environment of schools at the time of the study, mask-wearing …

Improving Credit Card Fraud Detection Using Transfer Learning And Data Resampling Techniques, Charmaine Eunice Mena Vinarta

Electronic Theses, Projects, and Dissertations

This Culminating Experience Project explores the use of machine learning algorithms to detect credit card fraud. The research questions are: Q1. What cross-domain techniques developed in other domains can be effectively adapted and applied to mitigate or eliminate credit card fraud, and how do these techniques compare in terms of fraud detection accuracy and efficiency? Q2. To what extent do synthetic data generation methods effectively mitigate the challenges posed by imbalanced datasets in credit card fraud detection, and how do these methods impact classification performance? Q3. To what extent can the combination of transfer learning and innovative data resampling techniques …

Biomarkers Of Objective Criteria For Subtle Cognitive Decline In Parkinson’S Disease, Mary Ellen Garcia

Electronic Theses, Projects, and Dissertations

Mild cognitive impairment in Parkinson’s disease (PD-MCI) is the continuum from normal cognitive function to dementia. Recent studies suggest that objectively defined subtle cognitive decline (Obj-SCD), which uses non-traditional “process” neuropsychological scores, may be a better pathway to earlier detection of cognitive impairment. Obj-SCD has been defined as the stage where cognition is not impaired, but biomarkers are present or cognitive impairment is minimal but not sufficient to meet MCI or dementia criteria. We examined the longitudinal trajectories of neurodegenerative markers among individuals who are classified as cognitive normal (CN), Obj-SCD, and PD-MCI. Past literature has been inconsistent about the …

Predictive Model For Cfpb Consumer Complaints, Vyshnavi Nalluri

Electronic Theses, Projects, and Dissertations

Within the dynamic and highly competitive financial industry, the timely and efficient resolution of customer complaints stands as a central challenge, particularly in the intricate domain of mortgage services. The traditional processes for handling these complaints have long been recognized as laborious and resource-intensive, a situation that financial institutions, including the esteemed Wells Fargo, are keen to improve.

Currently, the industry largely relies on basic data analytics for identifying trends in customer complaints. However, this approach has its limitations, especially when dealing with complaints within the mortgage services domain. In response to this challenge, this research advocates the adoption of …

Automated Medical Notes Labelling And Classification Using Machine Learning, Akhil Prabhakar Thota

Electronic Theses, Projects, and Dissertations

The amount of data generated in medical records, especially in a modern context, is growing significantly. As the amount of data grows, it is very useful to classify the data into relevant classes for further interventions. Different methods that are not automated are very time-consuming and require manual effort have been tried for this before.

Recently deep learning has been used for this task but due to the complexity of the dataset, specifically due to inter-class similarities in the dataset and specific terminology having different meanings in medical contexts has caused significant problems in having a definitive approach to medical …

Lung Lesion Segmentation Using Deep Learning Approaches, Sree Snigdha Tummala

Electronic Theses, Projects, and Dissertations

The amount of data generated in the medical imaging field, especially in a modern context, is growing significantly. As the amount of data grows, it's prudent to make use of automated techniques that can leverage datasets to solve problems that are error-prone or have inconsistent solutions.

Deep learning approaches have gained traction in medical imaging tasks due to their superior performance with larger datasets and ability to discern the intricate features of 3D volumes, a task inefficient if done manually. Specifically for the task of lung nodule segmentation, several different methods have been tried before such as region growing etc. …

Twitter Policing, Hemanth Kumar Medisetty

Electronic Theses, Projects, and Dissertations

Police departments are frequently utilizing social media platforms to actively interact with the public. Social media offers an opportunity to share information, facilitate communication, and foster stronger connections between police departments and the communities they serve. In this context sentiment analysis of social media data has become a tool, for identifying sentiments and tracking emerging trends.

This project utilizes sentiment analysis to examine the social media interactions with particular data obtained from the Twitter (X). Initially, the project gathers social media data, from twitter mentioned accounts on Twitter utilizing web scraping techniques. Afterwards, we perform a thorough sentiment analysis using …

The Impact Of Covid-19 Pandemic On Fan Attendance In Major League Soccer (Mls) 2018-2022, Benjamin Appiah

Electronic Theses, Projects, and Dissertations

ABSTRACT

This culminating experience project investigates the impact of the COVID-19- on Major League Soccer (MLS). The research questions are: (Q1) How did the pandemic impact the trend of fan attendance in the MLS during the COVID-19 pandemic? (Q2) What factors impacted fan attendance in MLS games before the COVID-19 pandemic? (Q3) What factors impact fan attendance in MLS games after COVID-19 pandemic? The findings were: (Q1) Average annual fan attendance declined by 93.61% in 2019 and 2020, and an annua l increase of 49.47% between the year of 2020 and 2021. (Q2) The study revealed that 72.9% of the …

Hypothyroid Disease Analysis By Using Machine Learning, Sanjana Seelam

Electronic Theses, Projects, and Dissertations

Thyroid illness frequently manifests as hypothyroidism. It is evident that people with hypothyroidism are primarily female. Because the majority of people are unaware of the illness, it is quickly becoming more serious. It is crucial to catch it early on so that medical professionals can treat it more effectively and prevent it from getting worse. Machine learning illness prediction is a challenging task. Disease prediction is aided greatly by machine learning. Once more, unique feature selection strategies have made the process of disease assumption and prediction easier. To properly monitor and cure this illness, accurate detection is essential. In order …

Enhancing Accident Investigation Using Traffic Cctv Footage, Aksharapriya Peddi

Electronic Theses, Projects, and Dissertations

This Culminating Experience Project investigated how the densenet-161 model will perform on accident severity prediction compared to proposed methods. The research questions are: (Q1) What is the impact of usage of augmentation techniques on imbalanced datasets? (Q2) How will the hyper parameter tuning affect the model performance? (Q3) How effective is the proposed model compared to existing work? The findings are: Q1. The effectiveness of our model depends on the implementation of augmentation techniques that pay attention to handling imbalanced datasets. Our dataset poses a challenge due to distribution of classes in terms of accident severity. To address this challenge …

Classification Of Large Scale Fish Dataset By Deep Neural Networks, Priyanka Adapa

Electronic Theses, Projects, and Dissertations

The development of robust and efficient fish classification systems has become essential to preventing the rapid depletion of aquatic resources and building conservation strategies. A deep learning approach is proposed here for the automated classification of fish species from underwater images. The proposed methodology leverages state-of-the-art deep neural networks by applying the compact convolutional transformer (CCT) architecture, which is famous for faster training and lower computational cost. In CCT, data augmentation techniques are employed to enhance the variability of the training data, reducing overfitting and improving generalization. The preliminary outcomes of our proposed method demonstrate a promising accuracy level of …

Disease Of Lung Infection Detection Using Cnn Model -Bayesian Optimization, Poojitha Gutha

Electronic Theses, Projects, and Dissertations

Auscultation plays a role, in diagnosing and identifying diseases during examinations. However, it requires training and expertise, for application. This study aims to tackle this challenge by introducing a model that categorizes respiratory sounds into eight groups: URTI, Healthy, Asthma, COPD, LRTI, Bronchiectasis, Pneumonia, and Bronchiolitis. To achieve this categorization the study utilizes a Convolutional Neural Network (CNN) model that has been optimized using techniques. The dataset used in the study consists of 920 audio samples obtained from 126 patients with durations ranging from 10 to 90 seconds. Impressively, the model demonstrates a noteworthy 83% validation accuracy and an impressive …

2023-11-28, Csusb

Coyote Chronicle (1984-)

No abstract provided.

2023-11-10, Csusb

Coyote Chronicle (1984-)

No abstract provided.

2023-10-26, Csusb

Coyote Chronicle (1984-)

No abstract provided.

The Influence Of Hybrid Working In The Context Of Agile Software Development Within The Dutch Financial Sector, Joris Zomerdijk, Benny De Waal

Communications of the IIMA

The COVID-19 pandemic has accelerated remote working and working at the office. This hybrid working is an indispensable part of today's life even within Agile Software Development (ASD) teams. Before COVID-19 ASD teams were working closely together in an Agile way at the office. The Agile Manifesto describes 12 principles to make agile working successful. These principles are about working closely together, face-to-face contact and continuously responding to changes. To what extent does hybrid working influence these agile principles that have been indispensable in today's software development since its creation in 2001? Based on a quantitative study within 22 Dutch …

A Framework For Building Cognitive Knowledge Management Systems, Samir Jarjoui, Renita Murimi

Communications of the IIMA

Knowledge Management Systems (KMSs) are a critical component of economic development and growth. The accumulation and effective utilization of knowledge capabilities allow firms to create value and improve competitiveness. However, recent technological advances in KMSs have outpaced research in this area, which continues to be siloed and characterized by a lack of cohesive frameworks and a limited focus on cognitive learning. This paper provides a conceptual framework for the development of cognitive KMSs. The proposed framework comprises of strategy, people, processes, learning, and technology that are designed to improve knowledge management and organizational memory.

Enterprise Architecture In Healthcare Networks: A Systematic Literature Review, Arjen Maris, Stijn Hoppenbrouwers, Jos Van Hillegersberg

Communications of the IIMA

Healthcare organizations collaborate, share knowledge, and need to be accountable to each other. Therefore, healthcare organizations manage a dynamic information system landscape. Enterprise Architecture (EA) is a management tool for aligning these landscapes to the primary information needs that healthcare organizations have. EA is of value in some environments, but it seems to be not well suited to the dynamics of healthcare. Despite the publication of several systematic literature reviews on EA in healthcare, a systematic literature study comparing EA applicability at various levels of cooperation (intra, inter, and network collaboration) is lacking. Therefore, we posed the following research question: …

Electronic Clinical Quality Measures Of Health Information Technology And Hospital Performance: Evidence Of U.S. Hospitals, C. Christopher Lee, Hyoun Sook Lim, Decorti Rodgers-Tonge, Lisa Frank

Communications of the IIMA

Electronic clinical quality measures (eCQMs) are an integral part of health information technology (HIT). This study explores the effect of eCQM implementation on hospital performance. The study proposes hospital profitability, efficiency, and quality to measure hospital performance. Based on the literature, this research hypothesizes that implementing eCQMs would positively impact hospital profitability, efficiency, and quality. The sample data are drawn from the 2017 American Hospital Association (AHA) U.S. Hospital Survey datasets (N = 6282), the 2017 AHA U.S. Hospital I.T. Survey dataset (N = 3451), and the 2017 Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) datasets (N = …

Towards A Domain – Specific Comparative Analysis Of Data Mining Tools, May Oo, Daniel Lozovikas, Ramesh Subramanian

Communications of the IIMA

Advancement in technology has brought in widespread adoption and utilization of data mining tools. Successful implementation of data mining requires a careful assessment of the various data mining tools. Although several works have compared data mining tools based on usability, opensource, integrated data mining tools for statistical analysis, big/small scale, and data visualization, none of them has suggested the tools for various industry-sectors. This paper attempts to provide a comparative study of various data mining tools based on popularity and usage among various industry-sectors such as business, education, and healthcare. The factors used in the comparison are performance and scalability, …

Examining The Relevance Of Indian Logical Traditions And Present-Day Ai Developments, Ramesh Subramanian

Communications of the IIMA

This paper is an essay on the differences between “formal” Western logic and Indian logical traditions and how the latter impacts present-day AI developments. Upon the colonization of India, Western philosophers often dismissed Indian logical constructs as being underdeveloped or clumsy. Others, however, saw such denigration as emanating from Western racial prejudice rather than objectivity. This debate has persisted. I discuss the salient aspects of this debate, and then focus on the inductive aspects of Indian logic. This is especially relevant to the present, when there is an explosion of artificial intelligence based applications. I discuss the salient features of …

Forecasting Gasoline Price With Time Series Models, Xin James He

Communications of the IIMA

This research forecasts the gasoline price in U.S. and analyzes its managerial implications by means of both univariate and multivariate time series forecasting models. Gasoline price forecast is among the most difficulty time series variables during its importance to the economy and extremely volatile nature. The average regular gasoline price in U.S. reached $5.06 per gallon in the month of June 2022, as opposed to $3.41 at the beginning of 2022, a 48% increase. While gasoline prices had been rising over the first half of 2022 due to supply chain disruptions as a result of global Covid 19 lockdowns and …

Measuring Circularity: The Gordian Knot Of The 21st Century, Arjen Wierikx, Pascal Ravesteijn, Néomie Raassens, Alex Alblas

Communications of the IIMA

Organizations are currently facing substantial challenges regarding becoming circular by 2050 – also referred to as Circular Economy (CE). Subsequently, increasing complexity on all organizational levels creates uncertainty about respective organizational and technological capabilities and adequate strategies to develop these capabilities. Organizations are struggling in picking up the CE ambitions and answering the what’s in it for me question. Scholars are developing models and frameworks to enable organizations to measure CE performance. Over 125 assessment methods are available for micro level assessment – measuring up to 365 different metrics. Moreover, extant literature is available presenting barriers and opportunities for CE …

2023-10-12, Csusb

Coyote Chronicle (1984-)

No abstract provided.