IEEE i-COSTE 2025 – Page 6 – NPS Australia Submission System

Explainable Ensemble Machine Learning Framework for Accurate Detection of Thyroid Disorders

• We have introduced a highly developed Ensemble-Based Thyroid Disorder Detector (ETDD) that uses Random Forest, CatBoost, LightGBM, and XGBoost in sequence, and feature engineering has been used to enhance the detector’s effectiveness.

• We have improved model interpretability and clinician trust by integrating explainable AI techniques, including SHAP and LIME, to explain predictions and validate model decisions.

• We have conducted a comprehensive evaluation against state-of-the-art models, demonstrating that the ETDD system achieves a high accuracy of 96.24%, establishing its effectiveness as a clinical decision-support tool.

Dynamic Modeling of Solar Shading Effects in Photovoltaic Power Generation System

Solar photovoltaic (PV) systems are highly vulnerable to changes in irradiance, and shading is one of the most detrimental issues affecting their efficiency. Even minor shading can cause disproportionate power losses, current mismatch, and the emergence of multiple peaks in I–V and P–V characteristics, complicating maximum power point tracking (MPPT). To address these challenges, this study develops a modeling and simulation framework to systematically analyze shading effects on PV arrays. The model evaluates different irradiance distributions, partial shading scenarios, and the effectiveness of bypass diodes in minimizing mismatch losses. Simulation outcomes demonstrate the direct influence of shading patterns on the I–V response, the reduction in maximum power output, and the nonlinear behavior that hinders MPPT accuracy. Incorporating bypass diodes is shown to mitigate hotspot risks and recover part of the lost power under shaded conditions. Overall, this research highlights the value of simulation tools in predicting real-world PV behavior, offering a practical framework for researchers and system designers to quantify shading impacts and optimize PV performance for enhanced energy yield and reliability.

Autonomous Self-Powered Solar Panel Cleaner Design with Linear Resonant Actuators and Machine Learning

This research presents a novel, waterless, self-sustaining autonomous solar-panel cleaner that combines LDR sensing for dust monitoring, LRA vibration for dust removal, ML-based scheduling, and heat to energy harvesting using TEGs, achieving 95% dust clearance, consuming only 0.366 Wh/day, costing approximately $35 per unit, and delivering an estimated 70% reduction in maintenance costs.

Results of a research on the operation of CoAP, MQTT and 6LoWPAN algorithms in Internet of Things telecommunication networks

Abstract. The article presents the results of a research of the CoAP, MQTT and 6LoWPAN protocols used in Internet of Things (IoT) telecommunications networks. A comparative analysis of the protocols’ performance in transmitting data under resource-constrained conditions typical of IoT environments, such as low bandwidth, high latency, and limited power consumption, is conducted. The research examines the key characteristics and features of each protocol: CoAP implements a lightweight application-layer protocol for exchanging data between devices with limited computing resources; MQTT is focused on transmitting messages in a publish/subscribe mode, ensuring high reliability and scalability of systems, 6LoWPAN provides efficient adaptation of IPv6 to low-data-rate networks. Experimental analysis showed that CoAP exhibits the lowest packet transmission latency for small data volumes, while MQTT performs better with a large number of nodes and high loads. 6LoWPAN, in turn, optimizes the network layer and reduces node power consumption. The obtained results allow us to conclude that the combined use of these protocols is feasible depending on the requirements of specific IoT scenarios—from environmental monitoring to smart home systems and the Industrial Internet of Things.

Calculation of Reliability of Sensors and Detectors in Intelligent System of Telecommunication Network Using Fuzzy Set Methods

The paper presents the calculation of the reliability of sensors and sensors in the intelligent telecommunication network system using fuzzy set methods. The relevance of using fuzzy set methods and fuzzy neural networks in the analysis and study of the main reliability parameters of Internet of Things sensors is shown. It has been shown that neural networks can effectively predict sensor failure, time to failure, or classify the state of a sensor, which increases the reliability of the entire system and allows for timely measures to be taken for its maintenance. Genetic algorithms provide a powerful tool for optimizing mathematical models, especially in problems with many variables and complex dependencies, such as calculating the reliability of sensors. This method allows for efficient search for optimal solutions in multidimensional spaces and can be used to predict failures, estimate time to failure, or other characteristics that affect the reliability of a system. Modeling the reliability parameters of sensors using genetic algorithms allows for efficient optimization of complex models that include many variables and nonlinear dependencies.

Decision-Making Framework for Sustainable Management of the Aral Sea Desiccated Basin under Uncertainty

The desiccated basin of the Aral Sea, once a major inland water body, exemplifies the severe ecological consequences of long-term mismanagement and climate-induced stress. Addressing this environmental crisis requires decision-making frameworks capable of balancing environmental, social, and economic objectives amidst significant uncertainty and data incompleteness. This study proposes a decision-support model grounded in Intuitionistic Fuzzy Set (IFS) theory, integrated within a Multi-Criteria Decision-Making (MCDM) approach, to manage the complex trade-offs inherent in the sustainable restoration of the Aral Sea region. By incorporating hesitation degrees alongside traditional membership and non-membership values, the model captures expert uncertainty and conflicting information more effectively than classical fuzzy or deterministic methods. A real-world case study – focused on water allocation and land rehabilitation – demonstrates the practical utility of the framework. The results highlight the model’s ability to enhance decision robustness, transparency, and reliability in uncertainty-dominated ecological systems, offering a scalable tool for environmental policy and sustainability planning in similar high-risk regions.

Medipath: An Intelligent Emergency Medical Service System with NLP and Real-Time Coordination

This paper discusses the creation and assessment
of MediPath, a smart web-based emergency medical assistance
system. It connects patients, hospitals, and ambulance services
in one digital platform. The system uses technologies like
Natural Language Processing (NLP) to match hospital
specialties with patient symptoms. It also uses real-time location
services and smart route planning to address gaps in emergency
medical response. MediPath has a three-part structure that
supports logins for patients or attendants, hospital management
portals, and navigation systems for ambulance drivers. The
platform uses machine learning to link patient symptoms with
hospital specialties, Firebase Realtime Database for easy data
syncing, and mapping APIs for better traffic-aware routes.
Performance tests show average response times of under 30
seconds for hospital matches and 95% accuracy in matching
symptoms to specialties. There is also a significant drop in
ambulance dispatch delays. User studies at different healthcare
facilities indicate improved coordination, shorter emergency
response times, and better use of resources. Although there are
challenges with highly specialized medical conditions and
connectivity in rural areas, MediPath greatly enhances
emergency medical service delivery by creating a unified
ecosystem that connects all parties in real time. This research
presents a scalable, smart solution that addresses disconnected
emergency medical services. It uses modern web technologies
alongside healthcare knowledge to help save lives through
quicker and more coordinated emergency responses.

Youth Perception of Electric Vehicles: Harmonizing Employment, Environment and Sustainability

understanding urban citizens behavior

A Conceptual Framework for the Buying Intention of Energy Efficient LED Bulbs: Evidence from A Developing Country

Environmental challenges and their risks to human health have drawn the attention of academics, policymakers, and industry leaders. This study examines the factors influencing purchase intentions for energy-efficient LED bulbs in the context of sustainable development, which are relevant from business, marketing, and economic perspectives. Moreover, energy-efficient LED bulbs contribute to the attainment of the Sustainable Development Goals (SDGs) through their direct and indirect impacts. By promoting clean energy and sustainable urban growth, energy-efficient LED bulbs are consistent with SDGs 7, 11, and 13.
Theoretically, the research extends UTAUT2 to explain consumer behavior in the context of green technology adoption. Practically, it provides insights for segmentation, targeting, and brand positioning in emerging markets. Managers and stakeholders are encouraged to focus on product design, packaging, quality, energy efficiency, and cost to enhance adoption. Social factors, including family and peer influence, play a key role, suggesting strategies such as social proof, influencer marketing, and media campaigns to boost engagement and loyalty.

From a societal perspective, promoting energy-efficient LED bulbs supports sustainable consumption, energy savings, and community well-being. Economically, the findings guide producers and marketers in building brand value, competitive pricing, and using digital platforms to educate consumers. At the national level, the study emphasizes government support in regulating fair pricing, ensuring sustainable production, and offering financial incentives such as subsidies, tax breaks, and loans. Policymakers can align environmental objectives with national strategies, foster global collaborations, and promote adoption to reduce carbon emissions.Overall, the study contributes theoretically by enhancing UTAUT2, offers actionable insights for business and policy, and supports Bangladesh’s progress toward sustainable development through widespread adoption of energy-efficient technologies.

A Framework for a Green IoT Blockchain Environment: From the Smart Farming Perspective

We propose an energy-efficient blockchain-IoT framework for smart farming that integrates a lightweight Tangle consensus protocol with renewable energy sources to reduce energy consumption and carbon emissions while maintaining security and scalability.

Conference Site: IEEE i-COSTE 2025