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.
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.
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).
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.
This study compares the performance of object detection models through data augmentation with a severe weather dataset.
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.
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.
The aim of the study is to develop a suitable algorithm for interference minimizing in 5G and satellite communication networks coexistence employing Nakagami-m and Shadowed Rician models. Based on this aim, the following research contributes to:
1-Develop a suitable theoretical strategy that evaluates interference scenarios for co-existence between5G and satellite communication networks.
2-Develop an algorithm based on Nakagami-m and Shadowed Rician models for interference minimization in the co-existence between 5G and satellite communication networks.
We analyze more than 400,000 tweets posted between 6-21 February 2023, and explore different use cases of Twitter networking site aftermath of the quake series. We carry out descriptive analysis of the tweets distribution, and analysis on hashtag agenda setting property. Topic distribution both in hashtags and tweet content is investigated.