NPS Australia Submission System
  • 0

    No products in the cart.

Archives: Paper Submission

Application of Artificial intelligence for prediction of Brucellosis in Bangladesh

This research pioneers the application of artificial intelligence for predicting brucellosis in dairy cattle in Bangladesh by developing a highly accurate deep learning model (up to 93.94%). A significant contribution is the use of the SMOTE technique to effectively manage imbalanced veterinary data, a common challenge in disease diagnostics. Furthermore, the study identifies and ranks critical clinical risk factors, establishing that a retained placenta is the most significant predictor. By creating association rules to clarify the interplay between these factors, this work provides veterinarians and farmers with a powerful and practical tool for early diagnosis, paving the way to mitigate substantial economic losses in the dairy industry.

Pedestrian Flow Analysis Method in Public Spaces by Integrating Visual Information and Pedestrian Model

This paper proposed a method to improve the accuracy of pedestrian flow analysis by applying machine learning-based object detection to urban video data. To handle occlusions and maintain robust tracking, a pedestrian modeling approach was introduced, which allows detection even when objects overlap and enables class identification using past detection results. The effectiveness of the proposed method was verified in a real-world setting. It was confirmed that the method can achieve stable detection and reduce class assignment errors, achieving a mean absolute percentage error of 1.0% for pedestrians and 2.2% for bikes.

Clegora – Legal ChatBot for Indian Consumer and Civil Rights

The current paper brings a design, development and evaluation of an AI-based legal assistant named Clegora to suit the requirements of the Indian legal system. However, based on a state-of- the-art large language model, Groq Llama 3.2, deployed in a Retrieval-Augmented Generation (RAG) framework, Clegora delivers contextual and high-quality responses through the dynamic retrieval of relevant legal documents in a specialized vector database. The system uses a new token management approach that scales input lengths based on query complexity with the aim of being efficient in resource utilization without compromising on the quality of responses. Clegora has privacy and ethical standards, as it is built with secure authentication of the user by Firebase and strong content safety measures. Performance tests show short response times of less than five seconds to simultaneous users and citation efficiency of more than 95%. The responses of the users point out how the assistant could be used to simplify complex legal cases, multi-turn conversations, and provide users with confidence when handling legal cases. Though the existing constraints are the processing of language diversity in the region and very narrow domain queries, the modular design and continual data refreshing makes Clegora a scalable solution that will democratize the access to legal information, enabling informed decision-making in diverse Indian populations.

Design and Modelling of a Small-Scale Automated Biogas Digester with Monitoring and Control Capabilities

This research presents the integration of IoT and Mechatronic Systems in a compact, automated biogas digester designed for Small-Scale household use. The system enhances biogas yield efficiency through control of key parameters and real-time monitoring. The system offers a scalable solution for decentralized renewable energy and sustainable waste management in Small Island Developing States.

EfficientNetV2S with End Ensemble for Robust Bangla Handwritten Character Recognition

Recognition of handwritten characters is an important task in the field of image processing. Bangla handwritten characters exhibit greater complexity and variation in shape, along with high inter-class similarity, making their recognition particularly challenging. To address this, we propose the “EfficientNetV2S” model, designed to recognize complex and visually similar characters in the Bangla language. Although Bangla is one of the most spoken languages in the world, research on Bangla character recognition is still comparatively limited. In this study, we apply a deep learning model trained and tested on a custom dataset of Bangla handwritten characters. The model is utilized both for feature extraction and for its proven efficiency in image classification tasks, enabling it to learn image features effectively and recognize characters with remarkable accuracy. Our approach achieves an impressive 96.63% accuracy, highlighting the strength and reliability of the proposed method. To broaden the range of classes, we combined the “BanglaLekha-Isolated” and “Matrivasa-raw (Ekush)” datasets. The results show that the End Ensemble technique effectively tackles recognition challenges and offers strong potential for real-world applications like automation and education. This research advances Bangla character recognition and encourages further exploration in the field.

IGNN: An Attentional Graph-Based Approach for Vision Transformer-Empowered Plant Disease Detection

This study’s primary contribution is the development of a novel ViT-GNN hybrid deep learning model callled IGNN for plant disease classification. By combining the feature-learning prowess of a Vision Transformer with the relational-modeling capability of a Graph Neural Network, the model achieves superior performance and interpretability over traditional methods. This work demonstrates a new paradigm for leveraging both rich visual features and structural dataset relationships, advancing the field of automated plant disease diagnosis.

Comprehensive Analysis of Machine Learning Models on Physical Layer Anomaly in Smart Grids

It contributes a lot to provide information about selection of best machine learning models for anomaly detection.

Techno-Economic Evaluation of Solid-State and Sodium-Ion Batteries in E-Mobility Using a MATLAB Tool

The selection of suitable battery technologies is a key lever in shaping the global transition to sustainable mobility. This study investigates the techno-economic potential of two emerging battery types – solid-state batteries and sodium-ion batteries. While solid-state batteries promise higher energy densities, sodium-ion batteries offer advantages in material availability and potential cost stability due to the abundance of sodium. A MATLAB-based evaluation is used to conduct a holistic analysis focusing on their application within the Tesla Model Y and the VW ID.3. The assessment considers key indicators such as energy density, specific costs, CO2 emissions, and achievable driving range. The results highlight technological trade-offs and demonstrate how different battery choices impact the ecological and economic performance of electric vehicles. Moreover, the flexibility of the modeling approach enables its application across a range of current and future vehicle configurations, supporting strategic decision making in battery selection.

Advanced Machine Learning Models for Prediction, and Performance Optimization in Renewable Energy

The research by Mohammed Alghassab, conducted at Shaqra University, significantly advances renewable energy systems through the application of advanced machine learning (ML) models—Random Forest, Support Vector Regressor, Gradient Boosting, and CatBoost. Key contributions include achieving high prediction accuracy (Gradient Boosting: 94.2% classification accuracy, 1.86 MW RMSE) and perfect scalability prediction (CatBoost: 1.0 accuracy) for solar, wind, hydro, and geothermal systems. These models enhance energy output forecasting, resource classification, and performance optimization by leveraging feature engineering and hyperparameter tuning. The study demonstrates a 12% accuracy improvement and 10% error reduction over baselines, supporting grid stability, cost-efficiency, and CO2 reduction. By addressing intermittency, scalability, and dependability challenges, the research aligns with global sustainability goals, fostering innovation in smart grids and policy-driven energy planning for a low-carbon future.

Data Resilience in Cyber-Physical Systems: Overcoming MQTT Data Loss for Reliable AI Subsystems

The paper introduces and experimentally validates an end-to-end data-resilience layer for MQTT-based CPS that maintains AI subsystem reliability under adverse network conditions. Concretely, it contributes practical mechanisms (loss-aware buffering/retransmission, QoS/retention strategies, deduplication/back-pressure, and gap-tolerant ingestion for AI) and shows, through controlled tests with packet loss and instability, that operational continuity and model performance can be sustained despite communication faults.