Full Research Paper – Page 14 – NPS Australia Submission System

Identification of the Most Frequently Asked Questions in Financial Analyst Reports to Automate Equity Research Using Llama 3 and GPT-4

This is the first systematic analysis of the structure of equity research reports (ERRs). The paper uncovers the automation potential of ERRs, and validates their automation potential with the state-of-the-art language models GPT-4 and Llama-3-70B.

The study examines 72 ERRs, categorizing 4,964 sentences into 169 unique question archetypes across five main categories: Financials, Company, Product, Stock, and Market. The researchers classify each question based on its potential for automation, distinguishing between text-extractable, database-extractable, and non-extractable information.

Key findings include:

– 78.7% of the questions in ERRs are potentially automatable.
– 48.2% of questions are text-extractable (suited for processing by large language models).
– 30.5% of questions are database-extractable.
– Only 21.3% of questions require human judgment to answer.

The study validates these findings using two large language models: Llama-3-70B and GPT-4-turbo-2024-04-09. The results show that these models can extract relevant information from annual reports for a significant portion of the questions, with potential for even higher accuracy when used in combination.

ResiPlant-5: A CNN model for disease detection in citrus fruits and leaves

This paper introduces ResiPlant-5, a 5-layer con-
volutional neural network (CNN) designed for precise plant
disease diagnosis. Sequential CNN model suffer form vanishing
gradient problem. To overcome vanishing gradients and im-
prove deep model learning, the design uses skip connections,
inspired by Residual Networks (ResNets). Skip connections
provide connections between non-adjacent layers, improving
gradient propagation and feature retention. This approach lets
the model maintain important properties from previous layers
while training deeper networks without sacrificing speed. Using
deep learning and residual connections, ResiPlant-5 successfully
addresses difficult image classification challenges, hence making
it feasible to identify the disease in the plants. The model has been
trained and tested using two publicly available datasets. The first
dataset is the citrus dataset, which contains images of citrus leaves
and citrus fruit. The second dataset is the sweet orange dataset.
The results indicate that the proposed model demonstrates an
approximate increase in accuracy of 2%, 6%, and 8% on the
Sweet Orange, citrus leaves, and citrus fruit datasets, respectively,
compared to the VGG16, VGG19, and ResNet50 models

Power System Faults Analysis, Detection, and Localization in Underground Distribution and Transmission Networks by Deploying AI-based Matlab Model/Simulink

The study integrates advanced AI algorithms into MATLAB/Simulink to enhance the accuracy and efficiency of fault detection and localization. It specifically addresses the unique challenges of underground distribution and transmission networks, which are often harder to monitor and diagnose than overhead systems. The research utilizes MATLAB/Simulink for detailed simulations, enabling robust testing and validation of fault detection and localization methods under various conditions.

PMU-Based Short Circuit Capacity Estimation using System and Load Impedance Variation Ratio

This paper presents a new method for the estimation of short-circuit capacity using PMU (Phasor Measurement Units) measurements of voltage and current phasors. The proposed method has two functions: the improvement of the estimation accuracy by selecting only PMU measurements which are suitable for the estimation, and the determination of whether the estimation values are erroneous or not. The proposed method focuses on variations in system side Thevenin equivalent (TE) impedance and load impedance, which affect the estimation accuracy. First, it analyses how the changes in each component of TE or load impedance affect the short-circuit estimation. Based on these results, the proposed method is developed. Then, the proposed method is validated by numerical simulations, and it is confirmed that the proposed method can properly distinguish between the correct and incorrect estimates and obtain more accurate estimation values of short-circuit capacity than other existing methods.

Detection of wood cross-section regions with GrowCut for measurement of wood diameter grade

Proposal of a Method for Detecting the Cross-sectional Region and Measuring the Minimum Diameter of a Single Timber Cross-section Image and Verification of the Detection Accuracy

Improvement in detection accuracy was confirmed by modifying the confidence level in GrowCut.

The effectiveness of forward-seeded pixels in reducing the influence of false positives in seeded pixels was confirmed.

The effectiveness of the RANSAC method for removing false detection contour points was confirmed.

Elevating 5G Applications Performance: Harnessing Beamforming with MIMO Antennas

This paper centers on the significance of using beamforming over Multiple Input Multiple Output (MIMO) at the physical layer level within a Radio Access Network (RAN) for 5G network performance.

Semantic Segmentation of Food through Deep Learning: A Case Study

In this paper, we explore food image semantic segmentation at different levels. We identify two datasets that can be used for training and evaluating models. We also assess the performance of four semantic segmentation models for food segmentation tasks. Our results show that FCN and SegFormer achieve the best overall accuracy at 85.9% and 84.1% when applied to the UECFOODPIXCOMPLETE dataset. The study aims to offer valuable insights and guide future developments in dietary assessment tools that can underpin health management applications.

Optimising Efficiency: Leveraging Multi‑Criteria Decision-Making for Field Instrument Selection in Process Plants

This study contributes significantly to the field of process plant instrumentation by:
Addressing Selection Challenges: Provides a structured approach to overcome the difficulties in selecting field instruments and sensors, ensuring adherence to specified conditions and maximizing operational efficiency.
Developing MCDM Method: Introduces a comprehensive Multi-Criteria Decision Making (MCDM) method tailored specifically for instrument selection in process plants, serving as a valuable resource for practitioners.
Automated Tool Creation: Develop an automated tool using Visual Basic for Applications (VBA) and built-in Excel formulas to facilitate the implementation of various MCDM techniques, streamlining the selection process.
Exploring Alternative Methodologies: Investigates the use of Decision Trees and Machine Learning for instrument selection, providing insights into their efficacy and potential for optimization, thereby broadening the scope of available methodologies.

A Phased Training Method for Stabilizing the Training Process of Power Grid Voltage Control Agents with Deep Reinforcement Learning

In recent years, renewable energy sources such as photovoltaic power generation system (PV) have been rapidly integrated into many power grids around the world. The higher penetration of renewable energy resources has made more difficult to maintain proper voltage using conventional method of Load Ratio Transformer (LRT) tap-changing in view of rapid generation fluctuation caused by weather condition change. To solve this problem, the reactive power control with power conditioning system (PCS) of PV can be used as a voltage regulation resource. Recent works have developed multi-timescale voltage control with short-term control by PCS and long-term control by LRT using deep reinforcement learning (DRL).
Most of these methods achieve coordination between agents in different control cycles by reward calculation. However, the training becomes unstable due to the improper management of change of each agent’s strategy, and it may not be possible to control voltage of power grid.
In this paper, the authors have proposed a phased training method to improve the stability of the training process for each agent that performs either LRT control or PCS control in power grid voltage control with DRL. The effectiveness of the proposed method is verified by numerical simulations using a power grid model with large PVs.

Creating Competitive Game Situation Using Cognitive Load

This study identifies effective interventions that can create competitive situations in a memory-based card game. The competitive condition entertains people of all ages in the game. A competitive game situation would create a flow state in players. This study focuses on Pelmanism as a memory-based game. The study identifies gimmicks to interfere with a player superior to the others with the player unnoticed to create a competitive situation in a memory game. Experimental results show that interference with higher cognitive load has a greater effect on the outcome of the game. In the experiment, the interference requiring high cognitive load degrades the correct answer rate from 33.2% to 24.0%. The results of the random forest analysis indicate the importance of influencing the working memory in the memory game. It means the cognitive load on the working memory can lead to game situations that all people enjoy.

Paper Type: Full Research Paper