This research paper explores the imperative to digitize indigenous knowledge (IK) to counter the underrepresentation of indigenous communities in Artificial Intelligence (AI). With 5% of the global population being indigenous, holding diverse cultures and languages, there is a risk of losing their rich heritage amid technological advancements. The paper contends that digitizing IK can preserve cultural history and mitigate bias in AI, historically skewed due to the absence of global South and indigenous perspectives. The methods involve a Correlational Research study examining the relationship between digitization of culture and bias in AI, with a future pathway incorporating experimental research to gauge the impact of missing data on AI systems. The paper underscores challenges faced by initiatives preserving indigenous knowledge, such as funding and historical biases, and stresses the urgency for research to address these issues and foster equitable AI technologies.

In this research project, the focus is on improving the effectiveness of cancer detection and treatment using hybrid molecule Nitrogen-Doped Graphene Quantum Dots (N-GQDs). Motivated by the pressing need to enhance current cancer diagnosis and treatment methods, given cancer's status as the second leading cause of global death, the study aims to address the limitations of existing diagnostic techniques, which often fail to detect early-stage cancers promptly. N-GQDs show promise as nanoscale molecules with unique quantum properties for cancer imaging and drug delivery. The objective is to refine the drug delivery aspect of N-GQDs to enable more efficient detection and destruction of early-stage, multidrug-resistant lung, breast, and ovarian cancers. The research seeks to create less demanding yet more effective treatment methods, exploring properties of N-GQDs and comparing anticancer drugs, such as pyrimethamine, methotrexate, and doxorubicin, bonded to N-GQDs. The methodology involves collecting experimental data from existing scholarly articles, analyzing quantitative and qualitative factors, and proposing an ideal anticancer drug for delivery via N-GQDs, contributing to the development of a safer, more efficient cancer treatment option.

This research paper explores the integration of artificial intelligence (AI) into healthcare to improve accessibility and efficiency, particularly in the aftermath of the COVID-19 pandemic. It addresses issues like delays in healthcare due to stressed systems, time-consuming diagnostic processes, and equipment limitations. The focus is on examining how AI can enhance diagnostic speed and accuracy. Ethical concerns and financial barriers related to AI implementation are acknowledged, with an intent to propose solutions. The literature review underscores ethical challenges, biases in AI data, and financial constraints, while also highlighting potential benefits in terms of faster and more accurate diagnostics.

My research explores challenges faced by actors, especially minorities, in the film industry, emphasizing the 2023 SAG AFTRA Labor Union Strike's impact on working conditions. Despite progress, new threats, particularly from AI technology, pose risks to Asian American representation. The literature review underscores the need for improved Asian American visibility, addressing historical stereotypes. The study aims to offer valuable insights into actor challenges, stereotypes' impact, and the role of AI in shaping the film industry's future.

The project revolves around training supervised machine learning models to predict plant alpha diversity using environmental DNA (eDNA) data. With a dataset spanning 325 global locations, the models are trained on various environmental factors alongside eDNA sequences. One model incorporates all available eDNA data, while the other focuses solely on taxonomically identifiable sequences. By comparing the performance of these models, the project aims to elucidate the impact of unidentifiable eDNA on diversity scores and assess the bias in reference databases towards indicator species. This approach represents a novel endeavor to analyze global land-based ecosystems with machine learning, akin to existing methods in aquatic environments, thereby enhancing our understanding of biodiversity dynamics on a broader scale.

The aim is to investigate the potential of artificial intelligence (AI) in assisting MRI technologists to identify insufficient fat suppression. The problem arises when the MRI detects fat tissue due to inconsistent magnetic fields during imaging, leading to unclear images that must be re-imaged, causing inconvenience for both patients and radiologists. The research questions focus on understanding how AI can assist technologists, with the objective of building an AI model for identifying inadequate fat suppression. The information gained will be used to train an AI model for implementation in MRI scanners, benefitting both technologists and the community with clearer and more efficient diagnoses.

Acknowledging the outdated nature of current educational systems and the rising popularity of video games among students, the focus is on the future of serious games in K-12 education, and finding out what features make them effective. The research questions center on the viability of video games as an educational option and the characteristics that make them effective teaching tools. The goal is to contribute insights to the potential overhaul of the current educational curriculum and advocate for the design and testing of more serious games for K-12 students. As a student with game design experience, the aim is to use this information to test the effectiveness of existing serious games and review how serious games can be incorporated into K-12 curriculums.

Recognizing the limitations of existing apps designed for crosswalks, this research focuses on developing an innovative solution—a mobile app that utilizes computer vision to detect moving cars on the road and alerts users through vibrations about the safety of crossing. The rationale behind this project stems from the increasing population of legally blind individuals and the need for independent street navigation. Though a comprehensive application would be ideal, the ultimate goal of this project is to contribute valuable insights into the feasibility and benefits of such technology, paving the way for future advancements in assisting visually impaired individuals in crossing residential roads safely. This will be done by testing an object detection model in different scenarios, analyzing successful detections and detection failures. If successful, this research could inspire further development by companies that tackle the problems outlined in my study, offering a valuable tool for the visually impaired community.

Plastic pollution is found everywhere in the environment due to its incapability to decompose. Plastic also poses health risks with microplastics entering the food chain and potentially causing issues like fertility problems and inflammation. The motivation behind this study arises from the shortcomings of current plastic waste management methods, such as recycling and incineration, and their adverse environmental impacts. To address this, I will conduct a comprehensive literature review of studies over the past five years to assess the feasibility, success, and challenges of various methods and advances in biological approaches to plastic degradation. Utilizing qualitative and quantitative methods, I plan to analyze the efficiency, capability, and cost of different biological methods. The significance of this research lies in its potential to provide innovative solutions to plastic pollution, contributing valuable insights that can inform policymakers, researchers, and the public. Ultimately, the goal is to publish this research in scientific journals or news sources, sharing crucial information that could lead to a safer and more sustainable way of managing plastic waste.

The goal is to investigate the current ramifications of the rising demand for minerals like lithium, copper, cobalt, and nickel due to the growing mobile and electric vehicle infrastructure. As technology rapidly advances, the demand for eco-friendly energy sources, such as lithium-ion batteries, has surged. This research seeks to uncover the economic, environmental, medical, and social consequences of this heightened demand, with a focus on mining practices. Existing data is outdated, and projections don't capture the on-the-ground reality, making it crucial to gather current information. Through interviews with medical professionals and individuals in mining-affected communities, the aim is to understand the long-term impacts and inform future actions.