Paper Submission - NPS Australia Submission System

StegoVision: Enhanced Video Steganography via Prewitt Edge Mapping and 3-XOR Secured LSB

The significant research contribution of this study is the development of an automated two-tier data concealment technique for video steganography that integrates Advanced Encryption Standard (AES) encryption with a robust steganographic method, enhancing data security and imperceptibility. This approach utilizes a 128-bit AES key for encrypting sensitive information, which is then embedded into video frames using a combination of the Least Significant Bit (LSB) method and a Prewitt pixel selection technique, achieving superior data invisibility while maintaining frame quality. The proposed model also incorporates a Fisher-Yates randomization method for frame selection, which increases the resilience and payload capacity of the steganographic process, and demonstrates improved performance metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) compared to existing techniques.

Exploring the Research Landscape of DCT-Based Video Steganography: A Systematic Review

The systematic literature review highlights significant advancements in DCT-based video steganography, particularly through hybrid techniques that combine DCT with other methods like DWT and error correction codes (ECC) to enhance imperceptibility and robustness. Notable contributions include the use of deep learning models, such as CNNs, to maintain visual quality while embedding data, and the development of coverless techniques that do not alter the original video content. The review also emphasizes the importance of performance metrics like PSNR and MSE for evaluating the effectiveness of these techniques.

Hyperparameter-Tuned Logistic Regression for Early-Stage Breast Cancer Prediction using Explainable AI

• Design of HTLR model using optimized logistic regression applied to early stage breast cancer prediction.

• Integration of SHAP for model interpretability, supporting clinicians in extracting key features affecting malignant and benign predictions.

• Comparative analysis with baseline state-of-the-art models and existing systems, demonstrating that the proposed HTLR achieved 99.12% accuracy while providing transparent and clinically meaningful explanations.

Deep Learning-Based Forecasting and Analysis of Urban Air Quality Using LSTM and Statistical Methods

Structured Prompting and Multi-Agent Reasoning for Open-Source Financial Sentiment Analysis

• A novel structured prompting methodology that provides context-aware, declarative instructions to coordinate multi-agent collaboration, and an output aggregation mechanism that assigns greater weight to more confident
agent responses, improving reliability and interpretability.
• A manually annotated, domain-specific dataset designed to support uncontaminated, realistic performance evaluation.
• A resource-efficient system combining LLaMA 3.1 8B for specialized subtasks and LLaMA 3 70B for synthesis, balancing scalability and performance.

Analytical Framework for a Green Room: An Integrated Passive Cooling Approach for Sustainable and Climate Resilient Buildings

This research introduces the Green Room framework, the first closed-form model integrating four passive cooling strategies. By quantifying synergistic effects and embedding environmental drivers, it delivers a retrofit-ready, simulation-free solution capable of 41–44 °C cooling, establishing a scalable pathway toward sustainable, climate-resilient, and energy-efficient building design.

Accelerated Skin Prick Test (aSPT): A Low-Cost, AI-Guided Allergy Diagnostic System Using Microneedles and Image-Based Severity Scoring

Introduces a low-cost allergy diagnostic integrating a dual-layer microneedle patch with offline AI image analysis, enabling painless, rapid, and accessible testing under $4 per test with inference in <1s per allergen site

AI Regulation in the U.S.: Lessons from Commodity Futures Legislative Journey and a Perspective CFTC-NFA Model for AI

The contributions of this article are threefold: first, it presented commodity futures, an equally disruptive and controversial invention as AI, that has culminated chaotic but successful legislative journey. Futures’ legislative path can serve as a guide for regulating AI. Second, it extracted key success factors and lessons learned from futures regulation, including principle-based laws, a designated coordinating agency, and a supporting self-regulatory organization (SRO) that represents the industry. Third, it recommended a potential CFTC-NFA-type legislative framework for AI regulation.

Evaluating YOLOv8 and YOLOv11 photovoltaic panels detection performance in large scale solar power plants

The principal contributions of this work can be summarized as follows:
-An analysis of frequently used YOLO architectures (v8 and v11) for the detection of photovoltaic (PV) panels, comparing precision and computational performance. Considering large and small parameters implementation for each of the YOLO versions.
-Creating a mixed dataset combining single-frame and orthomosaic images. Two different UAV systems and two geographic locations were chosen for providing variability to the image dataset.
-Adding transformations to the images captured by the UAV system for extending the variability of the dataset.

Amanot-Net: A Resilient Framework for Proactive Threat Neutralization in Unstructured Public Spaces

This research presents Amanot-Net, a proactive security framework featuring a novel dual-channel communication system (SIP and LoRaWAN) to guarantee alert delivery in unreliable networks. Key contributions include the public release of ‘BD-Threats-v1’, a new dataset with over 73,000 images contextualized for Bangladesh , and strong empirical results: 0.89 mAP@0.5 , end-to-end latency under 450ms , and 97% alert delivery resilience via the LoRaWAN fallback channel.

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