68 research outputs found
Growing Self Organising Map based Exploratory Analysis of Text Data
Textual data plays an important role in the modern world. The possibilities of applying data mining techniques to uncover hidden information present in large volumes of text collections is immense. The Growing Self Organizing Map (GSOM) is a highly successful member of the Self Organising Map family and has been used as a clustering and visualisation tool across wide range of disciplines to discover hidden patterns present in the data. A comprehensive analysis of the GSOM's capabilities as a text clustering and visualisation tool has so far not been published. These functionalities, namely map visualisation capabilities, automatic cluster identification and hierarchical clustering capabilities are presented in this paper and are further demonstrated with experiments on a benchmark text corpus
Growing Self Organising Map based Exploratory Analysis of Text Data
Textual data plays an important role in the modern world. The possibilities of applying data mining techniques to uncover hidden information present in large volumes of text collections is immense. The Growing Self Organizing Map (GSOM) is a highly successful member of the Self Organising Map family and has been used as a clustering and visualisation tool across wide range of disciplines to discover hidden patterns present in the data. A comprehensive analysis of the GSOM's capabilities as a text clustering and visualisation tool has so far not been published. These functionalities, namely map visualisation capabilities, automatic cluster identification and hierarchical clustering capabilities are presented in this paper and are further demonstrated with experiments on a benchmark text corpus
Growing Self Organizing Map with an Imposed Binary Search Tree for Discovering Temporal Input Patterns
In this paper the Binary Search Tree Imposed Growing Self Organizing Map (BSTGSOM) is presented as an extended version of the Growing Self Organizing Map (GSOM), which has proven advantages in knowledge discovery applications. A Binary Search Tree imposed on the GSOM is mainly used to investigate the dynamic perspectives of the GSOM based on the inputs and these generated temporal patterns are stored to further analyze the behavior of the GSOM based on the input sequence. Also, the performance advantages are discussed and compared with that of the original GSOM
AI-Copilot for Business Optimisation: A Framework and A Case Study in Production Scheduling
Business optimisation refers to the process of finding and implementing
efficient and cost-effective means of operation to bring a competitive
advantage for businesses. Synthesizing problem formulations is an integral part
of business optimisation, which relies on human expertise to construct problem
formulations using optimisation languages. Interestingly, with advancements in
Large Language Models (LLMs), the human expertise needed in problem formulation
can be minimized. However, developing an LLM for problem formulation is
challenging, due to training data, token limitations, and lack of appropriate
performance metrics. For the requirement of training data, recent attention has
been directed towards fine-tuning pre-trained LLMs for downstream tasks rather
than training an LLM from scratch for a specific task. In this paper, we adopt
an LLM fine-tuning approach and propose an AI-Copilot for business optimisation
problem formulation. For token limitations, we introduce modularization and
prompt engineering techniques to synthesize complex problem formulations as
modules that fit into the token limits of LLMs. Additionally, we design
performance evaluation metrics that are better suited for assessing the
accuracy and quality of problem formulations. The experiment results
demonstrate that with this approach we can synthesize complex and large problem
formulations for a typical business optimisation problem in production
scheduling
An Artificial Intelligence Framework for Bidding Optimization with Uncertainty inMultiple Frequency Reserve Markets
The global ambitions of a carbon-neutral society necessitate a stable and
robust smart grid that capitalises on frequency reserves of renewable energy.
Frequency reserves are resources that adjust power production or consumption in
real time to react to a power grid frequency deviation. Revenue generation
motivates the availability of these resources for managing such deviations.
However, limited research has been conducted on data-driven decisions and
optimal bidding strategies for trading such capacities in multiple frequency
reserves markets. We address this limitation by making the following research
contributions. Firstly, a generalised model is designed based on an extensive
study of critical characteristics of global frequency reserves markets.
Secondly, three bidding strategies are proposed, based on this market model, to
capitalise on price peaks in multi-stage markets. Two strategies are proposed
for non-reschedulable loads, in which case the bidding strategy aims to select
the market with the highest anticipated price, and the third bidding strategy
focuses on rescheduling loads to hours on which highest reserve market prices
are anticipated. The third research contribution is an Artificial Intelligence
(AI) based bidding optimization framework that implements these three
strategies, with novel uncertainty metrics that supplement data-driven price
prediction. Finally, the framework is evaluated empirically using a case study
of multiple frequency reserves markets in Finland. The results from this
evaluation confirm the effectiveness of the proposed bidding strategies and the
AI-based bidding optimization framework in terms of cumulative revenue
generation, leading to an increased availability of frequency reserves
Application of Deep Learning Long Short-Term Memory in Energy Demand Forecasting
The smart metering infrastructure has changed how electricity is measured in
both residential and industrial application. The large amount of data collected
by smart meter per day provides a huge potential for analytics to support the
operation of a smart grid, an example of which is energy demand forecasting.
Short term energy forecasting can be used by utilities to assess if any
forecasted peak energy demand would have an adverse effect on the power system
transmission and distribution infrastructure. It can also help in load
scheduling and demand side management. Many techniques have been proposed to
forecast time series including Support Vector Machine, Artificial Neural
Network and Deep Learning. In this work we use Long Short Term Memory
architecture to forecast 3-day ahead energy demand across each month in the
year. The results show that 3-day ahead demand can be accurately forecasted
with a Mean Absolute Percentage Error of 3.15%. In addition to that, the paper
proposes way to quantify the time as a feature to be used in the training phase
which is shown to affect the network performance
Kidney Tumor Detection using Attention based U-Net
The advancement of deep learning techniques has provoked the potential of using Medical Image Analysis (MIA) for disease detection and prediction in numerous ways. This has been mostly useful in identifying tumours and abnormalities in many organs of the human body. Particularly in kidney diseases, the treatment options such as surgery have largely benefitted by the ability to detect tumours in early stages, thereby shifting towards more efficient methods including conservative nephron procedures. Therefore, to enable the early detection of kidney tumours, we propose a convolutional neural network based U-Net architecture which is able to detect tumours using an attention mechanism. The proposed architecture was evaluated using KiTS19 Challenge dataset that includes a collection of multi-phase CT imaging, segmentation masks, and comprehensive clinical outcomes for 300 patients who underwent nephrectomy for kidney tumours. The outcomes demonstrate the ability of the proposed architecture to distinguish images with tumours in the kidney and support early tumour detection
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