Paper Submission - NPS Australia Submission System

Steganalysis For Still Images With LSB Steganography Using Machine Learning Algorithms

This paper investigated the application of machine learning for steganalysis using a feature-based dataset extracted from still images with the Least Significant Bit (LSB) steganography. We evaluated several models and found that Artificial Neural Networks (ANNs) achieve the highest classification accuracy within practical training times. The accuracy, however, is limited to 93% due to constraints within the dataset. To overcome this barrier, more comprehensive datasets and/or models should be examined in future.

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.

Designing and Evaluating an Innovative Text Analytics Solution for Online Retailers’ Operational Decision Support

This research contributes to the field of text analytics by providing a structured methodology for developing and evaluating solutions that can transform customer feedback into actionable insights. The findings highlight the potential of such solutions to significantly improve decision-making processes and strategic planning in online retail, ultimately leading to enhanced customer satisfaction and business performance.

Designing and Evaluating an Innovative Text Analytics Solution for Online Retailers’ Operational Decision Support

This research contributes to the field of text analytics by providing a structured methodology for developing and evaluating solutions that can transform customer feedback into actionable insights. The findings highlight the potential of such solutions to significantly improve decision-making processes and strategic planning in online retail, ultimately leading to enhanced customer satisfaction and business performance.

Feature enhancement and matching algorithms for material ablation measurement in high temperature wind tunnels

Aimed at the special requirements for dynamic measurement of material ablation inside high-temperature wind tunnels, a binocular stereo vision system based on straight slider rail laser projection and high-speed camera capture is designed. A feature enhancement method for ablation measurement objects in high-temperature and high-enthalpy environments is proposed, and a mathematical expression formula based on multi-line laser feature enhancement description and extraction of the light strip centerline is derived for adaptive rapid feature matching. This formula takes into account the grayscale centroid, camera frame rate, and the correlation between line laser scanning ranges, effectively reducing search complexity and dependence on high-frame-rate cameras. Experimental results show that the system can complete a 200mm scan within 1 second at a distance of 1350mm. Experiments on planar objects and spherical convex surface platform under various conditions demonstrate that the system can control the total error within 0.5mm at the normal distribution confidence levels of 1σ, 2σ, and 3σ which proving the efficiency, accuracy, and high dynamic characteristics of this method for non-contact measurement of material erosion in high-temperature wind tunnel environments.

Predicting Electricity Market Price Using Machine Learning and Quantifying Dependency Beyond Renewable Energy

We offer (i) a detailed analysis on the impact of variables beyond renewable energy sources on electricity price, and (ii) a unified machine learning-based platform that integrates other diverse factors beyond renewable energy to improve electricity price forecasting.
Our machine learning models predict electricity price by quantifying the dependency on renewable energy and other important diverse factors under unified settings.

Numerical Methods for the Minimum Energy Among Three Dynamic Systems Governed by a Class of Weakly Singular Integro-Differential Equations

In this study, we presented numerical methods for determining the minimum energy state among three dynamic systems governed by a class of integro-differential equation with weakly singular kernels (Abel-type). These equations were developed from a class of integro-differential equations originating from an aeroelasticity problem. By weighting energy criteria for the three systems, we intend to numerically reveal the most stable energy state for the systems with various initial conditions and tracking targets. A part of the numerical scheme is constructed by interchanging the differentiation and integration operations in the integro-differential equation. Promising numerical results are provided.

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.

Fine-Tuning Pre-trained model GPT for Educational Domain-Specific Corpus

Providing students with effective academic advising using low-energy consumption by fine-tuning pretrained model.

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.

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