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Found 15 papers, 1 papers with code
Automating Research Synthesis with Domain-Specific Large Language Model Fine-Tuning
This study broadens the appeal of AI-enhanced tools across various academic and research fields, setting a new standard for conducting comprehensive and accurate literature reviews with more efficiency in the face of ever-increasing volumes of academic studies.
From COBIT to ISO 42001: Evaluating Cybersecurity Frameworks for Opportunities, Risks, and Regulatory Compliance in Commercializing Large Language Models
This study investigated the integration readiness of four predominant cybersecurity Governance, Risk and Compliance (GRC) frameworks - NIST CSF 2. 0, COBIT 2019, ISO 27001:2022, and the latest ISO 42001:2023 - for the opportunities, risks, and regulatory compliance when adopting Large Language Models (LLMs), using qualitative content analysis and expert validation.
Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence
The rapid rise in popularity of Large Language Models (LLMs) with emerging capabilities has spurred public curiosity to evaluate and compare different LLMs, leading many researchers to propose their LLM benchmarks.
From Google Gemini to OpenAI Q* (Q-Star): A Survey of Reshaping the Generative Artificial Intelligence (AI) Research Landscape
This comprehensive survey explored the evolving landscape of generative Artificial Intelligence (AI), with a specific focus on the transformative impacts of Mixture of Experts (MoE), multimodal learning, and the speculated advancements towards Artificial General Intelligence (AGI).
Towards Clinical Prediction with Transparency: An Explainable AI Approach to Survival Modelling in Residential Aged Care
Objectives: Develop an interpretable survival model for elderly residential aged care residents using advanced machine learning.
PRISMA-DFLLM: An Extension of PRISMA for Systematic Literature Reviews using Domain-specific Finetuned Large Language Models
By finetuning LLMs on domain-specific academic papers that have been selected as a result of a rigorous SLR process, the proposed PRISMA-DFLLM (for Domain-specific Finetuned LLMs) reporting guidelines offer the potential to achieve greater efficiency, reusability and scalability, while also opening the potential for conducting incremental living systematic reviews with the aid of LLMs.
RGB-D And Thermal Sensor Fusion: A Systematic Literature Review
This might be partly due to the limited number of publicly available datasets for such applications.
Applying BERT and ChatGPT for Sentiment Analysis of Lyme Disease in Scientific Literature
This chapter presents a practical guide for conducting Sentiment Analysis using Natural Language Processing (NLP) techniques in the domain of tick-borne disease text.
ChatGPT: The End of Online Exam Integrity?
This study evaluated the ability of ChatGPT, a recently developed artificial intelligence (AI) agent, to perform high-level cognitive tasks and produce text that is indistinguishable from human-generated text.
Predicting Football Match Outcomes with eXplainable Machine Learning and the Kelly Index
An approach was developed that minimises risk by combining the Kelly Index with the predefined confidence thresholds of the predictive models.
Forecasting Patient Flows with Pandemic Induced Concept Drift using Explainable Machine Learning
This study investigates how a suite of novel quasi-real-time variables like Google search terms, pedestrian traffic, the prevailing incidence levels of influenza, as well as the COVID-19 Alert Level indicators can both generally improve the forecasting models of patient flows and effectively adapt the models to the unfolding disruptions of pandemic conditions.
A Prescriptive Learning Analytics Framework: Beyond Predictive Modelling and onto Explainable AI with Prescriptive Analytics and ChatGPT
A significant body of recent research in the field of Learning Analytics has focused on leveraging machine learning approaches for predicting at-risk students in order to initiate timely interventions and thereby elevate retention and completion rates.
Forecasting Patient Demand at Urgent Care Clinics using Machine Learning
This study explores the ability of machine learning methods to generate accurate patient presentations at two large urgent care clinics located in Auckland, New Zealand.
The Application of Machine Learning Techniques for Predicting Results in Team Sport: A Review
In this paper, we provide a review of studies that have used ML for predicting results in team sport, covering studies from 1996 to 2019.
Assessment of the Local Tchebichef Moments Method for Texture Classification by Fine Tuning Extraction Parameters
The original method was not fully tested with large datasets, and there are several parameters that should be characterised for performance.
