Transfer learning as an AI-based solution to address limited datasets in space medicine

Waisberg, Ethan; Ong, Joshua; Kamran, Sharif Amit; Paladugu, Phani; Zaman, Nasif; Lee, Andrew G.; Tavakkoli, Alireza

doi:10.1016/j.lssr.2022.12.002

Cited by 19 publications

(6 citation statements)

References 14 publications

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“…Transfer learning is another recent AI method that leverages pre-trained models on large datasets to boost performance on new, related tasks with limited data [30]. This approach facilitates the transfer of knowledge from one domain to another, enabling more efficient training and improved generalization [31].…”

Section: Recent Ai Methodsmentioning

confidence: 99%

In-depth Analysis of Artificial Intelligence for Climate Change Mitigation

2024

Preprint

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Due to the major impact of climate change on the world's environment, political and economic systems, climate change mitigation has become a pressing priority for the international community and requires rapid action from the whole society. With the continuous advancement of artificial intelligence research, the integration of AI and other technologies makes it more and more used in the whole society. It has become a promising and innovative avenue in the field of climate change mitigation. This paper comprehensively considers the key role of AI technology in the field of climate change mitigation, such as climate modeling, the optimization of renewable energy systems, the development of intelligent solutions for sustainable practices and CSS technology, and affirms its future prospects. It also describes the challenges of AI in the field of climate change mitigation. As researchers, policymakers, and industries collaborate to refine AI methodologies and integrate them into practical applications, a concerted effort is required to establish ethical guidelines, transparency standards, and inclusive governance frameworks.

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Section: Recent Ai Methodsmentioning

confidence: 99%

In-depth Analysis of Artificial Intelligence for Climate Change Mitigation

2024

Preprint

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“…Transfer learning may potentially be employed to address this lack of relevant domain expertise. 10 While less of a concern for space medicine, ChatGPT self-copying when given a question many times, and a high degree of direct or “word-for-word” plagiarism from internet sources such as Wikipedia (San Francisco, California USA) and LinkedIn (Sunnyvale, California USA) exist. 9 There are several steps that may be taken to avoid or mitigate these issues.…”

Section: Limitationsmentioning

confidence: 99%

Generative Pre-Trained Transformers (GPT) and Space Health: A Potential Frontier in Astronaut Health During Exploration Missions

Waisberg

Ong

Masalkhi

et al. 2023

Prehosp. Disaster med.

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In anticipation of space exploration where astronauts are traveling away from Earth, and for longer durations with an increasing communication lag, artificial intelligence (AI) frameworks such as large language learning models (LLMs) that can be trained on Earth can provide real-time answers. This emerging technology may be helpful for acute medical emergencies, particularly in austere and distant space environments. In this manuscript, we provide an overview of generative pre-trained transformer (GPT) technology, a rapidly emerging AI technology, and implications, considerations, and limitations of such technology for space health.

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“…Nonetheless, the availability of diverse and high-quality data remains a common challenge in utilizing deep learning for plant disease detection. Leveraging pretrained models on extensive datasets can be instrumental in addressing data limitations [5]- [7].…”

Section: Introductionmentioning

confidence: 99%

Chili Leaf Health Classification using Xception Pretrained Model

Wulandari,

Munggaran,

Setiawan

et al. 2024

SISTEMASI

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As one of the high-demand horticultural crops, chili peppers have a significant impact on the economy of Indonesia. However, despite the growing demand and interest in chili peppers, their production often faces disruptions due to crop failures. One of the leading causes of such failures is pests and diseases. Among all parts of the chili plant, chili leaves are the most susceptible to damage. Distinguishing between healthy and unhealthy chili leaves can serve as an early detection step for chili diseases and preventive measures to contain their spread. Convolutional Neural Network (CNN) are effective algorithms for image classification. The development of CNN has led to the use of models previously trained on large datasets to accurately classify relatively small datasets. One such pretrained model known for its exceptional classification capabilities is Xception. By utilizing the pretrained Xception model trained on the ImageNet dataset for the classification of healthy and unhealthy chili leaf images, our model achieved an accuracy of 91% on a dataset containing 2136 images. Furthermore, the model achieved a 100% success rate by correctly predicting all 10 out of 10 given images.

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Transfer learning as an AI-based solution to address limited datasets in space medicine

Cited by 19 publications

References 14 publications

In-depth Analysis of Artificial Intelligence for Climate Change Mitigation

In-depth Analysis of Artificial Intelligence for Climate Change Mitigation

Generative Pre-Trained Transformers (GPT) and Space Health: A Potential Frontier in Astronaut Health During Exploration Missions

Chili Leaf Health Classification using Xception Pretrained Model

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