使用移动技术众包深度学习数据集的增强

Chantelle Saliba, D. Seychell
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摘要

在过去十年中,移动通信发生了巨大的变化和改进,全球移动电话用户估计超过35亿。此外,普通的移动电话已经从一个简单的通信设备变成了一个能够浏览网页、视频会议、游戏、摄影、摄像和智能应用的智能手机设备。因此,公司和行业一直在探索这项技术,以创造机会,加强与客户的沟通,并创造更多的商业机会。在这项研究中,我们分析了使用移动技术作为一种技术来众包数据的方法,这些数据将通过创建数字资源来增强研究。在当今的现代科技世界中,由于缺乏数字资源,有些领域和领域仍然没有被技术所探索。现代机器学习技术,如深度学习方法,需要大量的数据,而这些数据并不总是可用的。这样一个案例就是马耳他植物区系分类的例子。马耳他是地中海中部的一个小岛,面积316公里。作为一个拥有独特和本土植物群的小岛,寻找已经可用的数字数据来进行技术研究是一项具有挑战性的壮举。出于这个原因,我们转向移动技术,以及它如何帮助收集这些数据,以增强现有的数据集,从而加强学术研究,并使分类更加有效和可行。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
USING MOBILE TECHNOLOGY TO CROWDSOURCE THE AUGMENTATION OF DEEP LEARNING DATASETS
In the past decade, mobile communications have seen drastic changes and improvements with an estimate of over 3.5 billion mobile phone users worldwide. In addition, the average mobile phone has gone from being a simple communication device to a smartphone device capable of web browsing, video conferencing, gaming, photography, and videography and intelligent applications. For this reason, companies and industries have been exploring this technology to create opportunities to enhance their communications with clients and to create further business opportunities. In this research, we analyze the approach of using mobile technologies as a technique to crowdsource data that would be used to enhance research by creating digital resources. In today’s modern and technological world there are areas and fields which are still unexplored by technology due to their lack of digital resources. Modern machine learning techniques such as deep learning methods, require a large volume of data that is not always available. Such a case is the example of classifying Maltese flora. Malta is a small island in the middle of the Mediterranean with an area of 316 km. Being such a small island with unique and indigenous flora makes it a challenging feat to find already available digital data to be able to conduct technological research. For this reason, we turn to mobile technology and how this can aid in the collection of such data to augment existing datasets that enhance academic research and render classification more effective and feasible.
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