This paper investigates classical imageprocessing techniques and unsupervised deep learning algorithms for segmenting images with high variance for an under researched industrial problem, focusing on beam burns gener...
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Artificial Intelligence systems have become useful in many sectors, including healthcare. In this paper, we focus on detecting signs of mental health disorders from social media texts. This can be used to direct patie...
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ISBN:
(纸本)9798350380170;9798350380163
Artificial Intelligence systems have become useful in many sectors, including healthcare. In this paper, we focus on detecting signs of mental health disorders from social media texts. This can be used to direct patients to consult a healthcare professional, while on waiting lists, or for post-monitoring. The performance of the current algorithms for detecting multiple disorders is limited. We propose a new method to increase performance by including target-domain knowledge for each type of mental health disorder (for nine disorders). We used this information to select the best training examples to include in our carefully engineered prompts for performing few-shot learning. We show that the results improved when compared to zero-shot learning based on Large Language Models and when compared to state-of-the-art results on the same test set.
Watermelon is a commonly cultivated fruit worldwide, especially in Southeast Asia. As one of the top exports in Asia, the commercialization of watermelon has its market. Its consumer appeal makes it one of the most so...
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ISBN:
(纸本)9798350372113;9798350372106
Watermelon is a commonly cultivated fruit worldwide, especially in Southeast Asia. As one of the top exports in Asia, the commercialization of watermelon has its market. Its consumer appeal makes it one of the most sought-after fruits globally. With the watermelon fruit consisting of different varieties, consumers usually have difficulty in classifying watermelons due to their similar external appearances, especially when labeled under the same name. This study is conducted to implement a system that detects and classifies three red watermelon varieties, Red Export, Orchid Sweet, and Dixie Queen, with imageprocessingalgorithms. The researchers utilized Canny Edge Detection for the dataset's preprocessing phase and Convolutional Neural Network (CNN) for its classification. The Raspberry Pi is also applied to this study. Moreover, the researchers created and collected their datasets for testing and validation data. The model used in this study has acquired 84.71% overall accuracy.
This article explores the application of deep learning (DL) algorithms in power system load forecasting. With the continuous advancement of the construction of new power systems, traditional load forecasting models de...
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The development of computer vision systems stimulates the development of various applications in the field of image recognition. Methods and algorithms for image recognition in document processingsystems play a cruci...
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Multispectral (MS) imaging systems have a wide range of applications for computer vision and computational photography tasks, but do not yet enjoy widespread adoption due to their prohibitive costs. Recently, advances...
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ISBN:
(纸本)9798350318920;9798350318937
Multispectral (MS) imaging systems have a wide range of applications for computer vision and computational photography tasks, but do not yet enjoy widespread adoption due to their prohibitive costs. Recently, advances in the design and fabrication of photonic metamaterials have enabled the development of MS sensors suitable for integration into consumer grade mobile devices. Augmenting existing RGB cameras and their processingalgorithms with richer spectral information has the potential to be utilized in many steps of the imageprocessing pipeline, but diverse real world datasets suitable for conducting such research are not freely available. We introduce Beyond RGB(1), a real-world dataset comprising thousands of multispectral and RGB images in diverse real world and lab conditions that is suitable for the development and evaluation of algorithms utilizing multispectral and RGB data. All the scenes in our dataset include a colorimetric reference and a measurement of the spectrum of the scene illuminant. Additionally, we demonstrate the practical use of our dataset through the introduction of a novel illuminant spectral estimation (ISE) algorithm. Our algorithm surpasses the current state-of-the-art (SoTA) by up to 58.6% on established benchmarks and sets a baseline performance on our own dataset.
This paper aims to investigate a system that uses various machine-learning algorithms to predict symptoms and deep-learning techniques for imageprocessing that leads to early disease prediction, an essential aspect o...
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In the era of rapidly expanding image data, the demand for improved image compression algorithms has grown significantly, particularly with the integration of deep learning approaches into traditional imageprocessing...
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ISBN:
(纸本)9781510673854;9781510673847
In the era of rapidly expanding image data, the demand for improved image compression algorithms has grown significantly, particularly with the integration of deep learning approaches into traditional imageprocessing tasks. However, many of the existing solutions in this domain are burdened by computational complexity, rendering them unsuitable for real-time deployment on standard devices as they often necessitate complex systems and substantial energy consumption. This work addresses the growing paradigm of edge computing for real-time applications by introducing a novel, on-edge device solution. This innovative approach aims to strike a balance between efficiency and accuracy, adhering to the practical constraints of real-world deployment. By presenting demonstrations of the proposed solution's performance on readily available devices, we provide tangible evidence of its applicability and viability in real-world scenarios. This advance contributes to the ongoing dialogue about the need for accessible and efficient image compression algorithms that can be deployed real-time applications on edge devices, bridging the gap between the demanding computational requirements of deep learning and the practical limitations of everyday hardware. As data continues to surge, solutions like this become ever more critical in ensuring effective image compression, aligning with on-edge computing within AI. This research paves the way for improved imageprocessing in real-time applications while conserving computational resources and energy consumption.
Biomedical image analysis has benefited tremendously from the advent of artificial intelligence. Machine learning and deep learning-based algorithms are increasingly utilized in real time to assist clinicians with mak...
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ISBN:
(纸本)9798350325744
Biomedical image analysis has benefited tremendously from the advent of artificial intelligence. Machine learning and deep learning-based algorithms are increasingly utilized in real time to assist clinicians with making crucial decisions for patients. Explainability and interpretability of these algorithms are critical for doctors and patients to develop trust in the automated decision making-process. Furthermore, designing specialized solutions based on clinical applications of these algorithms is non-trivial, due to the heterogeneity of imaging data available. Therefore, we propose an adaptive and interpretable framework for biomedical image analysis with novel applications to transcranial magnetic resonance-guided focused ultrasound thalamotomy for the treatment of essential tremor. The algorithm automatically configures itself to analyze brain lesions based on heterogeneous magnetic resonance images and subsequently predicts short-term clinical outcomes utilizing random forest and SHAP values, while ensuring interpretability for this process.
Obtaining satellite images has created the need to process these images and turn them into meaningful data. Every day, new methods and algorithms are designed in the literature to meet this demand. These allow the imp...
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ISBN:
(数字)9798350388961
ISBN:
(纸本)9798350388978;9798350388961
Obtaining satellite images has created the need to process these images and turn them into meaningful data. Every day, new methods and algorithms are designed in the literature to meet this demand. These allow the improvement of parameters that evaluate the performance of imaging systems, such as the Modulation Transfer Function (MTF). Within the scope of this study, the MTF value was calculated by means of edge detection filters and mathematical operators. The algorithm was developed using the Python 3.11.1 programming language. The developed algorithm was tested for two different reference electro-optical satellite images. In this study, a literature search on the subject headings is included, and the methods and purposes of the procedures are explained.
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