NPS Australia Submission System
Forecasting of maximum temperature using ETS, ARIMA and Random Forest models: A case study for Karachi, Pakistan
An AI-Enabled Centralized Monitoring System to Predict SME Inventory Level

I. To utilize the historical data, and predict market needs in a dynamic environment to maintain inventory level. (To develop a data-driven inventory management system)
II. To observe, track, and learn about product movement by implementing an AI-powered system. (To implement an AI-powered product movement tracking system).
III. To optimize the accuracy of prediction for market demand forecasting. (To improve and refine the prediction model).

Deployment of AI Models and a TDA Mapper Algorithm to Enhance Clinical Decision-Making

This research presents a novel Clinical Decision Support System (CDSS) that integrates advanced AI technologies, including a hybrid model for COVID-19 diagnosis and a custom GPT Assistant, into a scalable, real-world tool for resource-limited healthcare settings. The CDSS enhances the quality of care by providing accurate, timely diagnostics and decision support, while also improving response efficiency through real-time monitoring and predictive analytics. The system’s adaptability, supported by open-source platforms like R Shiny, and its potential for wide adoption, particularly after a planned pilot and impact evaluation, highlight its significance in advancing healthcare delivery where resources are scarce. The deployment of this AI-driven CDSS has the potential to transform clinical decision-making, improve healthcare delivery, and enhance the overall response to public health emergencies. The structured pilot and impact evaluation will provide critical insights into the system’s effectiveness in real-world settings, paving the way for broader adoption and sustained improvements in healthcare quality and efficiency.

Topological Machine Learning: Integrating Topological Data Analysis with Machine Learning to Enhance Breast Cancer Classification

Novel TML Approach: Introduces Topological Machine Learning (TML), integrating Topological Data Analysis (TDA) with Machine Learning (ML) to enhance breast cancer classification.

Improved Accuracy: Demonstrates that TML significantly improves classification accuracy on the Wisconsin Breast Cancer (WBCD) dataset compared to traditional methods and standalone ML techniques.

Feature Evaluation: Provides a detailed assessment of topological features such as cluster means, node features, and link features, highlighting their impact on model performance.

Practical Insights: Offers valuable insights into the integration of topological features for better diagnostic accuracy, with implications for improving breast cancer classification and patient outcomes.

Is Benford’s Law Based Detectors Effective for GAI Generated Images?

Benford’s Law is used in image forensics. We tested GAI generated images to see whether BL is able to detect artificially generated images. The experiments show that only about 60\% of the images can be detected using a simple similarity threshold.

ActJOLO: Action Recognition Guided by Actionlets Using Joint Lightweight Optical Flow Information

In this study, we propose a novel method, named ActJOLO, which builds upon the existing JOLO model by incorporating an advanced self-supervised learning technique as an upstream guide for posture recognition. Our approach emphasizes the analysis of high-intensity motion features within the human body, thereby enhancing the efficiency of action modeling.
Experimental results on the NTU RGB+D dataset demonstrate that our framework improves processing speed compared to the original model, while maintaining high ccuracy. This work offers a new perspective on skeleton-based human action recognition and highlights its potential for deployment on low-performance processors.

Retrofitting Legacy CNC Machines with a Focus on Energy Consumption

We are outlining the detailed process of retrofitting a legacy CNC machine to add connectivity and integrate it into the Industry 4.0 framework. This work offers significant benefits, especially for small and medium-sized companies. The outcome has the potential to enhance sustainability in industrial processes by focusing on the machine’s energy consumption.

A Digital Twin Framework in Manufacturing Systems with a Focus on Energy Consumption

We have developed a framework for integrating digital twins into older CNC machines, focusing on monitoring their electrical energy consumption. The digital twin’s virtual component utilizes discrete event simulation software in a digital manufacturing environment. Our goal with this proposal is to promote greater sustainability in manufacturing systems.

Spatial Characteristics of CA: A Narrative into San Francisco

This study delves into the spatial characteristics of housing prices within California, with a specific focus on San Francisco. We explore various models to predict housing prices based on different feature sets, including coordinate and non-coordinate attributes. Through extensive analysis, we find that incorporating geographic coordinates significantly enhances the predictive accuracy of housing prices. Utilizing a neural network model, we achieve nearly 100% accuracy in predicting the quartile of median housing prices, underscoring the complexity and influence of location-specific features. Finally, we utilized i-SLFN algorithm for developing a precise price prediction model and describing communities characteristics.

An Interpretable Transformer-Based Approach to Classify Malaria From Blood Cell Images

Malaria is a disease that can be fatal, and it is spread through the bite of the female Anopheles mosquito. The life of the sufferer is put in jeopardy as a result of the presence of numerous plasmodium parasites, which spread throughout their blood cells. Malaria can potentially be fatal if it is not treated within the first few stages of the disease. A well-known method for diagnosing malaria, microscopy involves taking blood samples from the patient, calculating the number of parasites, and counting the victim’s red blood cells. Nevertheless, the procedure of microscopy takes a lot of time, and, in certain circumstances, it can produce an incorrect result. When compared to the more conventional approach of microscopic examination, the recent successes of deep learning (DL) in the field of medical diagnosis make it quite conceivable to reduce the expenses associated with the diagnosis while simultaneously improving overall detection accuracy. This study proposes a transformer-based DL technique for diagnosing the malaria parasite using blood cell images. An explainable AI technique called Grad-CAM was applied in order to determine which aspects of an image the proposed model paid significantly more attention to in comparison to the other aspects of the image through saliency mapping. This was done in order to demonstrate the usefulness of the models. According to the findings of this research, the performance of the vision transformer and the VGG16 are identical. Both models have reached an accuracy score of approximately 96%, which is very impressive.