I-COSTE 2024 – Page 7 – NPS Australia Submission System

The AI Pentad, the CHARME2D Model, and an Assessment of Current-State AI Regulation

The contributions of this article are threefold: 1) we first introduce the AI Pentad to better understand and identify regulatory intervention points within AI’s core components, 2) we present the CHARME$^{2}$D model, a universal framework that can help frame, construct, and evaluate legislative efforts, 3) we conduct a broad assessment of the AI regulatory progress of selected countries and regions against the CHARME$^{2}$D model to highlight strengths, weaknesses, and gaps. This comparative evaluation offers insights for future legislative work in the AI domain.

Fly Ash as Sustainable Modifier, Using RSM as Modelling and Optimization Tool

This study uses Response Surface Methodology (RSM) to investigate the effect of using fly Ash type F as partial cement replacement on the compressive strength in concrete pavement. The response surface Methodology (RSM) is increasingly utilized in concrete mix design, as it offers a more effective approach to analyzing and optimizing experimental responses. RSM outperforms traditional experimental design methods in various ways, such as reducing the number of required tests, thus lowering test costs, and identifying optimal input variables based on test results. It can construct a scientific mathematical model and offer insights into the impact of individual factors and factor interactions on test results within the specified numerical boundary.
Additionally, a three-dimensional response surface is created to illustrate the connection between preparation parameters and the response index, allowing for a clearer understanding of the relationship between each factor and the response value. Accordingly, The researcher has utilized the RSM method to assess the influence of various factors on concrete performance
The study also aims to optimize the fly ash percentage in the concrete mix design. To evaluate the impact, the research methodology utilized mathematical modeling and methodical experimentation. The experiment considered various variables, including the amount of fly ash and the length of the curing process. The experiment concluded that the ideal fly ash concrete included a fly ash concentration of 15% and a requirement for an 90 day curing duration to produce a peak compressive strength of 52 MPa, and according to the RSM optimization the fly ash concentration is 14.28% , considering 90 day curing duration achive a peak compressive strength of 51.28 MPa. These results highlight fly ash’s ability to improve concrete performance. These kinds of developments are essential for infrastructure projects such as airports, roads, and infrastructure, where long-term viability and environmental effects are major priorities, It also advances environmentally friendly construction methods by optimizing fly ash concrete mixtures using RSM modelling tool. It offers insightful information on how to maximize concrete strength while reducing environmental impact and points the way for the next improvements in concrete pavement engineering.
Keywords—Optimization, Response Surface Methodology, Concrete pavement, Compressive strength, Airport, Curing time.

Functionalities of harvesting machines for industrial intercropping use cases

This paper contributes by first describing industrial types of intercropping harvests and second deriving necessary harvesting machine/robot functionalities from the types. These findings are important to design the needed machinery in order to realize industrial intercropping use cases.

Enhancing Lifecycle Sustainability through Optimized Supplier Quality Management in Heating Manufacturing

This research shows that improving supplier quality management for Company A’s wall-hung boilers leads to notable sustainability gains, including a 20% reduction in material defects, a 25% decrease in carbon emissions, and a 30% increase in product durability. These findings highlight the critical role of supplier quality in enhancing product lifecycle sustainability.

Application of Artificial Intelligence to Diagnose Neurological Disorders in a Wearable EEG Device

Our system integrates AI with EEG applications to diagnose neurological disorders. It is able to classify the specified mental health disorder and wirelessly transmit the data obtained from the EEG device to a remote server.

DESIGN, SIMULATION, AND INTEGRATION OF 5MWp FLOATING SOLAR PV WITH 760MW KAINJI HYDROELECTRIC POWER PLANT

This research work assists in analysing accurate data and modelling a suitable model for integrating floating solar PV with hydropower plants. As a result, greenhouse gas emissions are reduced, and the best or most effective/proper system configuration is achieved. It can also benefit stakeholders and investors in implementing a hybrid system design and development.
According to the National Renewable Energy Laboratory (NREL), the critical differences between ground-mounted photovoltaic solar plants and floating photovoltaic solar plants

An Automated Diagnosis of Diabetic Retinopathy Grading Using DenseNet169

The significant contribution of the rapid and accurate DR detection and evaluation method proposed in this article is: 1) a rapid grading method to solve DR detection problems, and 2) the proposed method can effectively and accurately assist in completing DR screening by helping to automate DR detection and evaluation. This verifies that the proposed method can be used for large-scale DR medical imaging screening, effectively assisting doctors in achieving efficient diagnosis.

MACHINE LEARNING-BASED LIVER DISEASE PREDICTION: ENHANCING DIAGNOSIS AND PROGNOSIS

Our research addresses the pressing global issue of liver diseases by developing a robust Liver Disease Prediction (LDP) system using comprehensive patient datasets. We evaluate and compare multiple machine learning algorithms such as K-Means, Logistic Regression, Decision Trees, and Support Vector Machines to accurately classify chronic liver conditions. Through extensive data analysis and confusion matrix evaluations, we demonstrate significant improvements in prediction accuracy, providing reliable tools for early diagnosis and intervention. This innovative application of machine learning not only aids healthcare professionals in managing liver disorders effectively but also reduces diagnostic workload, thereby enhancing overall patient care and medical outcomes.

Conference Site: I-COSTE 2024