NPS Australia Submission System
Fuzzy Logic-Enhanced Oral Health Assessment in Substance Abuse Rehabilitation: A Novel Approach

This study presents a new method for evaluating oral health in substance misuse rehabilitation by using fuzzy logic into the diagnostic procedure. Our methodology improves the assessment of complicated oral health issues commonly linked to substance misuse by utilizing fuzzy logic’s capability to manage uncertainty.

Leveraging Deep Learning and Machine Learning for Enhanced Dental Diagnosis: A Review of Artificial Intelligence in Identifying Substance Abuse Related Oral Health

We conduct a thorough analysis of the present state of research on AI-driven systems used to identify substance addiction by utilizing dental imaging and patient data. Through the process of synthesizing many studies, we are able to identify the strengths, limitations, and areas of knowledge that are lacking.

Patterns In Twitter Use During a Disaster: Content Analysis of 2023 Türkiye-Syria Earthquake Tweets

We analyze more than 400,000 tweets posted between 6-21 February 2023, and explore different use cases of Twitter networking site aftermath of the quake series. We carry out descriptive analysis of the tweets distribution, and analysis on hashtag agenda setting property. Topic distribution both in hashtags and tweet content is investigated.

Design and Implementation of a High Performance Network Function Virtualization Platform

This study applied parallel processing of incoming packets to reduce processing time. Experimental results show that proposed mechanisms enhanced network performance, allowing efficient resource use, reducing network latency, and ensuring stable packet transmission to fit service requirements.

DataPoll: A Tool Facilitating Cross-Domain Big Data Research

We present DataPoll, an “end-to-end” Big Data analysis tool designed to simplify the process and enhance accessibility for scientists across disciplines. DataPoll introduces innovative features and techniques for analyzing and interpreting digital data. Its capabilities and effectiveness are demonstrated through a case study on multi-source data from the Ukrainian-Russian conflict.

A Hybrid Approach: Machine Learning and Blockchain in Health Insurance Fraud Detection

This research introduces a system that integrates machine learning with blockchain technology, ensuring data transparency, security, and immutability while enhancing predictive accuracy. Demonstrated with real-world health insurance data, this hybrid approach significantly improves fraud detection accuracy and efficiency. Advanced machine learning algorithms provide insights into patterns and anomalies, enabling proactive fraud prevention. The solution is scalable and adaptable to other sectors prone to fraud. The use of Hyperledger blockchain ensures robust data integrity and security, addressing challenges related to data tampering and unauthorized access. These contributions collectively advance fraud detection and prevention in the health insurance industry.

An Effective Method for Classifying Japanese Honorific

Japanese Keigo known as honorific, is a way to reflect social status, intimacy, and the relationships among speakers, listeners, and other participants in a conversation. It is a very special and important language phenomenon that conveys respect and politeness based on the social status of the speaker and listener and their relationships. Unlike many other languages, Japanese has various forms of honorific expressions, and these honorific forms change depending on social group relations and occasion fields.

The hyperparameter tuning of a Multilayer Perceptron for agricultural decision classification in Gabon

This study randomly experiments different combinations of the multilayer perceptron’s hyperparameters, to find those that best improve our model’s performance.

Evaluating Lightweight Asymmetric Cryptography for Secure Communication in Internet of Drones

Unmanned aerial vehicles (UAVs) are being successfully used in a variety of applications, including agriculture, search and rescue operations, surveillance systems, and mission-critical services, thanks to some technological and practical advantages, such as high mobility, the ability to extend wireless coverage areas, or the capacity to reach locations inaccessible to humans. In contrast, attacks against drones, as opposed to traditional cyberattacks, typically happen as a result of serious design flaws and a lack of wireless security protection methods. The study examines lightweight asymmetric cryptographic algorithms for secure Internet of Drones (IoD) communication, addressing cybersecurity
challenges within this emerging technology. It evaluates RSA, ElGamal, DiffieHellman, and Elliptic Curve Cryptography (ECC), focusing on their suitability for IoD through comparative analysis on calculation time, memory usage, key size, and security. The goal is to contribute to developing robust, efficient, and secure communication protocols for IoD, promoting growth while mitigating risks. This research is pivotal for the advancement of IoD security, exploring the application of these cryptographic techniques to ensure secure, efficient operations within the IoD framework.

Efficiently Using Deep Learning to Distinguish Early-Stage Hepatocellular Carcinoma (HCC) from non-HCC Based on Multi-Phase CT and Image Enhancement

This study uses image enhancement methods to analyze liver nodule progression and radiological features in liver cancer development. A detection strategy has been developed from CT image characteristics for early identification of liver cancer. The study also explores how the size of nodules influences detection accuracy and classification between benign and malignant types, which is vital for refining detection algorithms and improving diagnostic precision.