Paper Submission - NPS Australia Submission System

Preventing Local Minima in Lyapunov-Based Control Scheme through Iterative Repulsive Potential Augmentation

This study introduces the Iterative Repulsive Potential Augmentation (IRPA), a motionplanning framework designed to overcome the local minima problem in Artificial Potential Fields. IRPA builds upon the Lyapunov-based Control Scheme (LbCS) by iteratively increasing the repulsive potential in regions where the robot becomes trapped.
This augmentation is repeated across successive iterations until the robot can successfully escape the local minimum and reach its goal. The approach provides a computationally efficient solution to the local minima problem while preserving the inherent efficiency and scalability advantages of the LbCS.

Comparative Analysis of Supervised Machine Learning Anomaly Classification on Active Power in Smart Grids.

The primary contributions of this study are as follows. First, it presents a focused investigation on active power–based anomaly classification in smart grid physical infrastructure by utilizing the physical attack subset of a cyber–physical dataset. Second, it conducts a systematic comparative evaluation of multiple supervised classifiers under physical attack scenarios, employing rigorous multi-class performance metrics such as precision, recall, F1-score, and confusion matrices. Third, it applies Random Forest–based feature-importance methods, including Gini impurity and permutation importance, to identify influential features, reduce dimensionality, and enhance interpretability. Finally, the study advances toward an explainability-driven and practically deployable anomaly classification and detection framework for smart grid monitoring, addressing limitations of prior works constrained by dataset scope, conceptual focus, or narrow evaluation metrics.

Signet: A Low-Cost Prosthetic Glove For Deaf And Mute Patients

Speech is the key to human existence. These are
not easily communicable in the speech-and-hearing-impaired
people. If you consider traditional alternatives like sign
language or even writing in a notepad, the other person also
has to be familiar with those forms of communication rather
than there being a real-time disconnect. In this paper, a low
cost touch-sensor-based assistive communication glove is
proposed, which differentiates between seven specific touches
across fingers and converts them into text messages displayed
on the LCD screen. The system incorporates capacitive touch
sensors, Arduino Nano microcontroller for computation, and
an LCD with an I2C module for display. The proposed design
is intended to provide a portable, low-cost, and user-friendly
solution improving the independence of the deaf and mute
society. Experimental results indicate that the proposed
system is efficient, easy, and ideal for routine daily
communication.

An Efficient Attention-based Deep Learning Model for Masked and Unmasked Face Recognition

• Proposed the Custom Aug-CNN-5, a CNN-based architecture optimized for recognizing masked and partially occluded faces.
• Integrated attention mechanisms (CBAM) and cosine annealing to improve learning efficiency and enhance discriminative power.
• Conducted a comprehensive evaluation on the larger customized VGGFace2 dataset, demonstrating high accuracy, generalizability, and fairness across varied conditions.

Modeling Stop–and–Go Wave Dissipation under Partial CAV Penetration using a Multi-Class CTM

This paper investigates the dissipation of stop-and
go traffic waves under partial penetration of connected and
autonomous vehicles (CAVs) using a multi-class cell transmission
model. A controlled numerical study was conducted along a
corridor with a localized bottleneck, with experiments varying
CAV penetration levels and smoothing intensities. The validated
results show that even moderate penetration of CAVs substan
tially improves traffic performance: unstable density patterns are
suppressed, shockwave propagation is attenuated, and total delay
decreases monotonically with increasing adoption. Fundamental
diagrams with triangular envelopes confirmed capacity gains
consistent with reduced effective headways, while regression of
the jam front demonstrated qualitative agreement between mea
sured and theoretical shockwave speeds. These findings indicate
that CAVs can deliver network-level benefits well before full
market penetration, supporting their role as a viable strategy
for congestion mitigation. The study contributes both a method
ological framework for evaluating CAV impacts in macroscopic
models and empirical insights to guide deployment and traffic
management policy.

Leveraging Large Language Models to Investigate the Decoy Effect in Route Choice Behavior

Route choice behavior is a cornerstone of transport
research, traditionally modeled under the assumption that trav
elers act as rational agents who maximize utility by trading off
time, cost, and reliability. However, behavioral economics shows
that real-world decision making often departs from rationality
due to systematic biases. One such bias is the decoy effect, which
occurs when the introduction of a dominated alternative increases
the attractiveness of another option. While widely studied in
consumer contexts, its role in transport decision making remains
underexplored. This paper presents a novel methodology that
leverages large language models (LLMs) to investigate the decoy
effect in route choice. Using ChatGPT-4o mini, we generated
textual framings of a dominated route alternative and employed
the model as a synthetic respondent to simulate route selections.
Four experimental conditions were tested: baseline (two routes),
decoy with neutral framing, decoy with positive framing, and
decoy with negative framing. Results demonstrate that the decoy
increased the relative attractiveness of the premium route, partic
ularly under positive framing, while negative framing attenuated
the effect. Synthetic responses closely aligned with predictions
from a multinomial logit model, confirming consistency with
behavioral theory. The findings illustrate that LLMs can serve
as both framing generators and behavioral simulators, offering
a rapid testbed for prototyping hypotheses in transport research.
This work establishes proof-of-concept evidence that LLMs can
complement traditional behavioral models, opening pathways
for future integration of artificial intelligence into the study of
systematic biases in mobility decisions.

Explainable Flight State Recognition: Toward Intelligent Pilot Training Systems

This study presents a novel framework that integrates explainable artificial intelligence (XAI) to enhance classification of flight states of an aircraft in the context of pilot training. By combining probabilistic outputs from Artificial Intelligence (AI) models, the approach identifies high-confidence and low-confidence predictions. Further, Shapley Additive Explanations (SHAP) technique is employed to uncover feature-level insights.
The main contribution of the study is to know how model identifies the ambiguous flight states and to know the most influential features in classifying these states. Moreover, to demonstrate how interpretable AI can support the development of intelligent, data-driven pilot training systems that are grounded in real flight data and model transparency.

Effectiveness of Forensic Tools in Extracting Web Browser Artifacts

Comprehensive Multi-Browser Analysis
Scenario-Based Experimental Design
Multi-Tool Evaluation
Evidence of Residual Artifacts in Privacy Modes

Predicting Online Delivery Adoption During COVID-19: A Machine Learning Approach

The significant research contribution of this study lies in its comprehensive analysis of the sociodemographic and behavioral factors influencing online delivery adoption during the COVID-19 pandemic, utilizing advanced machine learning techniques. By employing a structured preprocessing pipeline and comparing multiple models, the study identified LightGBM as the most effective classifier, achieving an accuracy of 97% and an F1-score of 0.96 for both classes, which underscores its capability to capture complex patterns in consumer behavior. The findings revealed that younger age, higher household income, and education level were the most influential predictors of increased delivery use, providing valuable insights for transportation planners and policymakers. This research not only enhances understanding of consumer behavior during a critical period but also offers a framework for future studies on urban logistics and service adaptation in response to evolving consumer needs.

LiteFakeNet: Efficient Deepfake Image Detection with Depthwise Separable Convolutions

The significant research contribution of this study is the development of LiteFakeNet, a novel lightweight convolutional neural network (CNN) designed for efficient deepfake image detection, achieving an accuracy of 95%, precision of 96%, and recall of 94% on the CIFAKE dataset, which consists of 120,000 images. LiteFakeNet utilizes depthwise separable convolutions to balance high performance with low computational and energy costs, featuring less than 83,000 parameters and only 0.16 million FLOPs, making it more efficient than existing models like MobileNet and ResNet50. This model not only addresses the urgent need for effective deepfake detection but also aligns with the principles of Industry 5.0 by promoting human-AI collaboration and sustainable technology.

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