IEEE i-COSTE 2025 – Page 4 – NPS Australia Submission System

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.

Energy management in small and medium sized hotels on Mauritius Island

This paper sumarises several case studies on an energy audit in small and medium hotels in the island of Mauritius

A Transformer-Based Multimodal Framework for Enhanced Autism Spectrum Disorder Diagnosis

1) To improve identification performance, we proposed a multimodal framework that integrates medical imaging and clinical textual data.
2) Introduction of a classification token mechanism to enhance feature representation and determination with Vision Transformer.
3) Finally, we implement hyperparameter optimization techniques to improve model efficiency, generalization, and overall performance.

Improving Interpretability in Lung Cancer Prediction through Explainable Ensemble Voting Approach

• Presented an Explainable Ensemble Voting Approach (EEVA) which combines Random Forest (RF), Decision Tree (DT) and Multi-Layer Perceptron (MLP) using soft voting, to forecast lung cancer.

• Utilized LIME and SHAP to generate local and global explanations, enhancing model transparency and interpretability.

• Achieved an accuracy of 92.86%, outperforming baseline methods and demonstrating strong potential for clinical application.

Security Dilemma in Software Development: a Case Study on Understanding Developer Priorities and Practices in Stack Overflow

Applications have advanced rapidly in recent years, taking software development to a whole new level. These advancements have led to a growth in the complexity of applications, cloud computing, and the Internet of Things (IoT). As a result of this improvement, software security has become a paramount concern for developers, but it has traditionally been overlooked. But now, security issues are evolving day by day. In this paper, we explore developer priorities and practices in term of security in Stack Overflow website, whether they implement their software with security in mind or not. We utilize the Stack Exchange Data Dump to collect and compile a dataset of questions and their corresponding answers related to security vulnerabilities, specifically those that include user-submitted programming code snippets, our analysis concentrates on security-related topics. The experimental results indicate that Python emerged as the most commonly used language for security code snippets across these topics. From the expanded sample of code snippets, several vulnerabilities are flagged for security issues.

Deep Learning Based Bioimpedance Spectroscopy in Organic Communications: A Feasibility Analysis

The research contributions of this paper are:
-An SFD-BIS framework for vegetation-based OANs.
-A link established between channel response and sugarcane sucrose content using finite element analysis (FEA).
-A deep learning-based sucrose content estimator developed as a use case for the SFD-BIS framework.

AI -Based Tutor for Visually Impaired Students

Visually impaired students typically face intense
difficulties when handling electronic learning materials such as PDF
textbooks, academic papers, and study guides. Traditional assistive
technologies like screen readers have the basic text-to-speech
functionality without enabling smart interaction and understanding
document structure. This paper outlines the design and
implementation of an AI-Based Tutor system to enhance the
learning experience for visually impaired students. The system
integrates Optical Character Recognition (OCR) to extract
structured text from diverse PDF structures, including accurate line
and section detection. The system has high-quality Text-to-Speech
(TTS) capabilities for natural-sounding speech, and a Speech-toText (STT) functionality for real-time voice queries and
instructions. Essentially, the system employs leading-edge Natural
Language Processing (NLP) models such as GPT or Gemini to
provide intelligent question answering, context-based
conversations, and content summarization. A voice-enabled
interface affords hands-free and intuitive control of the learning
process. Preliminary evaluation with blind students shows increased
accessibility, comprehension, and interaction efficacy compared to
existing solutions. The current paper contributes a new, integrated
solution that provides blind students greater independence and
control over their studies

AI -Based Tutor for Visually Impaired Students

Visually impaired students typically face intense
difficulties when handling electronic learning materials such as PDF
textbooks, academic papers, and study guides. Traditional assistive
technologies like screen readers have the basic text-to-speech
functionality without enabling smart interaction and understanding
document structure. This paper outlines the design and
implementation of an AI-Based Tutor system to enhance the
learning experience for visually impaired students. The system
integrates Optical Character Recognition (OCR) to extract
structured text from diverse PDF structures, including accurate line
and section detection. The system has high-quality Text-to-Speech
(TTS) capabilities for natural-sounding speech, and a Speech-toText (STT) functionality for real-time voice queries and
instructions. Essentially, the system employs leading-edge Natural
Language Processing (NLP) models such as GPT or Gemini to
provide intelligent question answering, context-based
conversations, and content summarization. A voice-enabled
interface affords hands-free and intuitive control of the learning
process. Preliminary evaluation with blind students shows increased
accessibility, comprehension, and interaction efficacy compared to
existing solutions. The current paper contributes a new, integrated
solution that provides blind students greater independence and
control over their studies

Conference Site: IEEE i-COSTE 2025