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Paper Type: Work-in-Progress Paper

Bone Fracture Detection And Localisation In X-Ray Using Real Time Object Detection Model

The proposed
model achieved a mean Average Precision at IoU 0.50 (mAP50) of 0.93, which represents a significant improvement over established benchmarks —a 9.4% relative increase over YOLOv5 (0.85 mAP50) and a 13.4% relative increase over Faster R-CNN (0.82 mAP50). It also demonstrated high precision (0.91) and recall (0.92), indicating robust performance in accurately identifying and localising fractures with a low rate of false positives and negatives.

Bone Fracture Detection And Localisation In X-Ray Using Real Time Object Detection Model

The proposed
model achieved a mean Average Precision at IoU 0.50 (mAP50) of 0.93, which represents a significant improvement over established benchmarks —a 9.4% relative increase over YOLOv5 (0.85 mAP50) and a 13.4% relative increase over Faster R-CNN (0.82 mAP50). It also demonstrated high precision (0.91) and recall (0.92), indicating robust performance in accurately identifying and localising fractures with a low rate of false positives and negatives.

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.

Solar-Powered IoT-Based Smart Farming Model (SPISFM)
Title: Leveraging Web Applications for Enhanced Transportation Mobility: Integrating Taxi Booking and Volunteer Ride Services in Fiji

The significant research contribution of this project is the development of a web-based platform that integrates real-time taxi booking, ride-sharing, and volunteer ride services tailored for Fiji. This innovative solution addresses key challenges in Fiji’s urban transportation, such as traffic congestion, vehicle overuse, and lack of affordable transport for low-income individuals. By promoting environmental sustainability, fostering community engagement through volunteer rides, and leveraging secure online payment systems, this platform contributes to enhancing mobility and reducing greenhouse gas emissions in a unique socio-economic context.

Leveraging ChatGPT for Sponsored Ad Detection and Keyword Extraction in YouTube Videos

This study is significant for several reasons. First, it provides a scalable and automated solution for detecting and analyzing advertisements within video content, which is typically labor-intensive when done manually. Second, it offers insights into the relationship between advertisements and video content, which can have profound implications for advertisers seeking to improve targeting strategies and for content creators aiming to optimize sponsored ad placements within their videos. Third, the research lays the groundwork for future advancements in content-based advertising, where the alignment between ad messaging and content themes can be refined using advanced natural language processing (NLP).

Leveraging Web Applications for Enhanced Transportation Mobility: Integrating Taxi Booking and Volunteer Ride Services in Fiji’s

The significant research contribution of this project is the development of a web-based platform that integrates real-time taxi booking, ride-sharing, and volunteer ride services tailored for Fiji. This innovative solution addresses key challenges in Fiji’s urban transportation, such as traffic congestion, vehicle overuse, and lack of affordable transport for low-income individuals. By promoting environmental sustainability, fostering community engagement through volunteer rides, and leveraging secure online payment systems, this platform contributes to enhancing mobility and reducing greenhouse gas emissions in a unique socio-economic context.

A Study on Object Detection Performance through Data Augmentation under Adverse Weather Conditions

This study compares the performance of object detection models through data augmentation with a severe weather dataset.

PREDICTING THE CUSTOMER BEHAVIOR UTILIZING TREE BASED MACHINE LEARNING ALGORITHMS

The goal of this project is to predict customer behavior from a large real-world e-commerce dataset using tree-based machine learning modeling techniques that will employ decision tree, random forest, and gradient boosting. Each of the models will be evaluated and compared to determine which of the three is the best model for predicting customer behavior.

PREDICTING THE CUSTOMER BEHAVIOR UTILIZING TREE BASED MACHINE LEARNING ALGORITHMS

The goal of this project is to predict customer behavior from a large real-world e-commerce dataset using tree-based machine learning modeling techniques that will employ decision tree, random forest, and gradient boosting. Each of the models will be evaluated and compared to determine which of the three is the best model for predicting customer behavior.