17 research outputs found
PRISMA-DFLLM: An Extension of PRISMA for Systematic Literature Reviews using Domain-specific Finetuned Large Language Models
With the proliferation of open-sourced Large Language Models (LLMs) and
efficient finetuning techniques, we are on the cusp of the emergence of
numerous domain-specific LLMs that have been finetuned for expertise across
specialized fields and applications for which the current general-purpose LLMs
are unsuitable. In academia, this technology has the potential to revolutionize
the way we conduct systematic literature reviews (SLRs), access knowledge and
generate new insights. This paper proposes an AI-enabled methodological
framework that combines the power of LLMs with the rigorous reporting
guidelines of the Preferred Reporting Items for Systematic Reviews and
Meta-Analyses (PRISMA). 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. Additionally, the proposed approach for leveraging LLMs
for SLRs enables the dissemination of finetuned models, empowering researchers
to accelerate advancements and democratize cutting-edge research. This paper
presents the case for the feasibility of finetuned LLMs to support rigorous
SLRs and the technical requirements for realizing this. This work then proposes
the extended PRISMA-DFLLM checklist of reporting guidelines as well as the
advantages, challenges, and potential implications of implementing
PRISMA-DFLLM. Finally, a future research roadmap to develop this line of
AI-enabled SLRs is presented, paving the way for a new era of evidence
synthesis and knowledge discovery
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. The overarching feature of the majority of
these research studies has been on the science of prediction only. The
component of predictive analytics concerned with interpreting the internals of
the models and explaining their predictions for individual cases to
stakeholders has largely been neglected. Additionally, works that attempt to
employ data-driven prescriptive analytics to automatically generate
evidence-based remedial advice for at-risk learners are in their infancy.
eXplainable AI is a field that has recently emerged providing cutting-edge
tools which support transparent predictive analytics and techniques for
generating tailored advice for at-risk students. This study proposes a novel
framework that unifies both transparent machine learning as well as techniques
for enabling prescriptive analytics, while integrating the latest advances in
large language models. This work practically demonstrates the proposed
framework using predictive models for identifying at-risk learners of programme
non-completion. The study then further demonstrates how predictive modelling
can be augmented with prescriptive analytics on two case studies in order to
generate human-readable prescriptive feedback for those who are at risk using
ChatGPT.Comment: revision of the original paper to include ChatGPT integratio
Forecasting Patient Flows with Pandemic Induced Concept Drift using Explainable Machine Learning
Accurately forecasting patient arrivals at Urgent Care Clinics (UCCs) and
Emergency Departments (EDs) is important for effective resourcing and patient
care. However, correctly estimating patient flows is not straightforward since
it depends on many drivers. The predictability of patient arrivals has recently
been further complicated by the COVID-19 pandemic conditions and the resulting
lockdowns. 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. This research also uniquely contributes to the body of work in this
domain by employing tools from the eXplainable AI field to investigate more
deeply the internal mechanics of the models than has previously been done. The
Voting ensemble-based method combining machine learning and statistical
techniques was the most reliable in our experiments. Our study showed that the
prevailing COVID-19 Alert Level feature together with Google search terms and
pedestrian traffic were effective at producing generalisable forecasts. The
implications of this study are that proxy variables can effectively augment
standard autoregressive features to ensure accurate forecasting of patient
flows. The experiments showed that the proposed features are potentially
effective model inputs for preserving forecast accuracies in the event of
future pandemic outbreaks
Assessment of the Local Tchebichef Moments Method for Texture Classification by Fine Tuning Extraction Parameters
In this paper we use machine learning to study the application of Local
Tchebichef Moments (LTM) to the problem of texture classification. The original
LTM method was proposed by Mukundan (2014).
The LTM method can be used for texture analysis in many different ways,
either using the moment values directly, or more simply creating a relationship
between the moment values of different orders, producing a histogram similar to
those of Local Binary Pattern (LBP) based methods. The original method was not
fully tested with large datasets, and there are several parameters that should
be characterised for performance. Among these parameters are the kernel size,
the moment orders and the weights for each moment.
We implemented the LTM method in a flexible way in order to allow for the
modification of the parameters that can affect its performance. Using four
subsets from the Outex dataset (a popular benchmark for texture analysis), we
used Random Forests to create models and to classify texture images, recording
the standard metrics for each classifier. We repeated the process using several
variations of the LBP method for comparison. This allowed us to find the best
combination of orders and weights for the LTM method for texture
classification
Gendered objectification of weight stigma in social media: a mixed method analysis
Rising popularity of social media platforms has led to many online exchanges on emergent topics by citizens globally. The growth in obesity rates worldwide has fuelled ongoing obesity-related discussions over social media. This study investigates the existence of weight stigma targeted towards different genders in online discussions. Using a mixed method analysis approach, we examined sentiments and word co-occurrences associated with weight stigma from the data corpus captured from Twitter and YouTube. Using the objectification theory as the underlying theory to examine the experiential consequences, our study reveals many sentiments over online discourses and reports significant gender-based differences in the stigmatising content, with more intensity in negative emotions targeting female objectification than males. This study bridges data mining and social construction studies with embedded analytics to share new insights on human behaviours that can help extend our understanding of sentiments that lead to male and female objectification
On predicting academic performance with process mining in learning analytics
Purpose - The purpose of this paper is to propose a process mining approach to help in making early predictions to improve studentsâ learning experience in massive open online courses (MOOCs). It investigates the impact of various machine learning techniques in combination with process mining features to measure effectiveness of these techniques. Design/methodology/approach - Studentâs data (e.g. assessment grades, demographic information) and weekly interaction data based on event logs (e.g. video lecture interaction, solution submission time, time spent weekly) have guided this design. This study evaluates four machine learning classification techniques used in the literature (logistic regression (LR), NaĂŻve Bayes (NB), random forest (RF) and K-nearest neighbor) to monitor weekly progression of studentsâ performance and to predict their overall performance outcome. Two data sets â one, with traditional features and second, with features obtained from process conformance testing â have been used. Findings - The results show that techniques used in the study are able to make predictions on the performance of students. Overall accuracy (F1-score, area under curve) of machine learning techniques can be improved by integrating process mining features with standard features. Specifically, the use of LR and NB classifiers outperforms other techniques in a statistical significant way. Practical implications - Although MOOCs provide a platform for learning in highly scalable and flexible manner, they are prone to early dropout and low completion rate. This study outlines a data-driven approach to improve studentsâ learning experience and decrease the dropout rate. Social implications - Early predictions based on individualâs participation can help educators provide support to students who are struggling in the course. Originality/value - This study outlines the innovative use of process mining techniques in education data mining to help educators gather data-driven insight on student performances in the enrolled courses
RGB-D And Thermal Sensor Fusion: A Systematic Literature Review
In the last decade, the computer vision field has seen significant progress
in multimodal data fusion and learning, where multiple sensors, including
depth, infrared, and visual, are used to capture the environment across diverse
spectral ranges. Despite these advancements, there has been no systematic and
comprehensive evaluation of fusing RGB-D and thermal modalities to date. While
autonomous driving using LiDAR, radar, RGB, and other sensors has garnered
substantial research interest, along with the fusion of RGB and depth
modalities, the integration of thermal cameras and, specifically, the fusion of
RGB-D and thermal data, has received comparatively less attention. This might
be partly due to the limited number of publicly available datasets for such
applications. This paper provides a comprehensive review of both,
state-of-the-art and traditional methods used in fusing RGB-D and thermal
camera data for various applications, such as site inspection, human tracking,
fault detection, and others. The reviewed literature has been categorised into
technical areas, such as 3D reconstruction, segmentation, object detection,
available datasets, and other related topics. Following a brief introduction
and an overview of the methodology, the study delves into calibration and
registration techniques, then examines thermal visualisation and 3D
reconstruction, before discussing the application of classic feature-based
techniques as well as modern deep learning approaches. The paper concludes with
a discourse on current limitations and potential future research directions. It
is hoped that this survey will serve as a valuable reference for researchers
looking to familiarise themselves with the latest advancements and contribute
to the RGB-DT research field.Comment: 33 pages, 20 figure
Accelerating classifier training using AdaBoost within cascades of boosted ensembles : a thesis presented in partial fulfillment of the requirements for the degree of Master of Science in Computer Sciences at Massey University, Auckland, New Zealand
This thesis seeks to address current problems encountered when training classifiers
within the framework of cascades of boosted ensembles (CoBE). At present, a signifi-
cant challenge facing this framework are inordinate classifier training runtimes. In some
cases, it can take days or weeks (Viola and Jones, 2004; Verschae et al., 2008) to train
a classifier. The protracted training runtimes are an obstacle to the wider use of this
framework (Brubaker et al., 2006). They also hinder the process of producing effective
object detection applications and make the testing of new theories and algorithms, as well
as verifications of others research, a considerable challenge (McCane and Novins, 2003).
An additional shortcoming of the CoBE framework is its limited ability to train clas-
sifiers incrementally. Presently, the most reliable method of integrating new dataset in-
formation into an existing classifier, is to re-train a classifier from beginning using the
combined new and old datasets. This process is inefficient. It lacks scalability and dis-
cards valuable information learned in previous training.
To deal with these challenges, this thesis extends on the research by Barczak et al.
(2008), and presents alternative CoBE frameworks for training classifiers. The alterna-
tive frameworks reduce training runtimes by an order of magnitude over common CoBE
frameworks and introduce additional tractability to the process. They achieve this, while
preserving the generalization ability of their classifiers.
This research also introduces a new framework for incrementally training CoBE clas-
sifiers and shows how this can be done without re-training classifiers from beginning.
However, the incremental framework for CoBEs has some limitations. Although it is able
to improve the positive detection rates of existing classifiers, currently it is unable to lower
their false detection rates
Forecasting patient demand at urgent care clinics using explainable machine learning
Abstract Urgent care clinics and emergency departments around the world periodically suffer from extended wait times beyond patient expectations due to surges in patient flows. The delays arising from inadequate staffing levels during these periods have been linked with adverse clinical outcomes. Previous research into forecasting patient flows has mostly used statistical techniques. These studies have also predominately focussed on shortâterm forecasts, which have limited practicality for the resourcing of medical personnel. This study joins an emerging body of work which seeks to explore the potential of machine learning algorithms to generate accurate forecasts of patient presentations. Our research uses datasets covering 10Â years from two large urgent care clinics to develop longâterm patient flow forecasts up to one quarter ahead using a range of stateâofâtheâart algorithms. A distinctive feature of this study is the use of eXplainable Artificial Intelligence (XAI) tools like Shapely and LIME that enable an inâdepth analysis of the behaviour of the models, which would otherwise be uninterpretable. These analysis tools enabled us to explore the ability of the models to adapt to the volatility in patient demand during the COVIDâ19 pandemic lockdowns and to identify the most impactful variables, resulting in valuable insights into their performance. The results showed that a novel combination of advanced univariate models like Prophet as well as gradient boosting, into an ensemble, delivered the most accurate and consistent solutions on average. This approach generated improvements in the range of 16%â30% over the existing inâhouse methods for estimating the daily patient flows 90Â days ahead