Solar power generation has emerged as a significant source of renewable energy, emphasizing the importance of precise analysis and prediction of solar generation data. In this study, we focus on enhancing the accuracy...
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The utilization of computer vision technology is of the utmost significance in the examination of plant diseases. Research utilizing image processing to investigate plant diseases necessitates the analysis of discerni...
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The utilization of computer vision technology is of the utmost significance in the examination of plant diseases. Research utilizing image processing to investigate plant diseases necessitates the analysis of discernible patterns on plants. Recently, numerous image processing and pattern classification techniques have been employed in the construction of a digital vision system capable of recognizing and categorizing the visual manifestations of plant diseases. Given the abundance of algorithms formulated for the purpose of plant leaf image classification for the detection of plant diseases, it is imperative to assess the accuracy of each algorithm, as well as its potential to identify diverse disease types. The main objective of this study is to explore accurate deep learning architectures that are more effective in deploying and detecting tomato diseases, thus eliminating human error when identifying tomato diseases through visual observation. and get more widespread use. An initial model was constructed from the ground up using a convolutional neural network (CNN), which was trained with 22930 tomato leaf images, and then compared to VGG16, Mobile Net, and Inceptionv3 architectures through a fine-tuning process. The basic CNN model achieved a training accuracy of 90%, whereas the training accuracies of VGG16, Mobile Net, and Inceptionv3 were respectively observed to be 89%, 91%, and 87%. The VGG16 model has a greater computational complexity than other approaches due to its considerable quantity of predefined parameters. Despite to be simpler, MobileNet proved to be the most efficient in terms of accuracy and thus is the most suitable for this research, due to its lightweight structure, fast functioning and adaptability for mobile devices. In contrast to other architectures, the suggested CNN architecture exhibits shallower characteristics, facilitating faster training on the same dataset. This research will provide a solid foundation for future scholars to easil
On massively parallel computing systems, applications involving many small message transfers suffer from degradation in performance and scalability. Typically, applications built on OpenSHMEM, Chapel, and Unified Para...
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Advances in machine learning and computer vision have significantly improved the diagnostic capabilities of medical imaging. Convolutional Neural Networks (CNNs) have emerged as a crucial tool for image classification...
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This paper describes the image selection method for an efficient three-dimensional (3D) reconstruction computation from an image sequence. Adequate images must be selected from the image sequence to improve the comput...
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ISBN:
(纸本)9781665445405
This paper describes the image selection method for an efficient three-dimensional (3D) reconstruction computation from an image sequence. Adequate images must be selected from the image sequence to improve the computational efficiency of the 3D reconstruction. Thus, we investigated a threshold based on the displacement among images obtained from a camera mounted on a remotely operated robot. The results confirmed that the proposed method can select adequate images for efficient 3D reconstruction by the threshold based on the optical flow from the image sequence. Therefore, the computational cost could be reduced by eliminating the duplicate and high-similarity images to perform the efficient 3D reconstruction.
The integration of artificial intelligence with antenna design and optimization represents a promising research frontier. This paper provides a concise overview of current research, emphasizing the computational cost ...
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Every individual exhibits a different cognitive load for a particular task of the same level of difficulty. Measuring the cognitive load of an individual can help to develop an effective learning environment, design p...
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Color conversion media (CCM) are crucial for realizing cost-effective, high-resolution micro-LED and OLED displays. In this work, we developed green and red organic-perovskite bilayer CCM that combine the PLQY and col...
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The vibration signals collected by sensors can be regarded as the convolution result of multiple interference factors and fault source signals, so the deconvolution method has certain advantages in signal processing. ...
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Poverty, a pervasive and multidimensional societal issue, continues challenging policymakers, researchers, and humanitarian organizations. In the quest to address this complex problem, leveraging advancements in data ...
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ISBN:
(纸本)9798350348460
Poverty, a pervasive and multidimensional societal issue, continues challenging policymakers, researchers, and humanitarian organizations. In the quest to address this complex problem, leveraging advancements in data science and computer vision has emerged as a promising avenue. This review paper delves into the fusion of multimodal data - comprising images and text - as a transformative approach for poverty prediction, focusing on India, a nation marked by its rich diversity and unique challenges. The paper elucidates poverty's multidimensional nature, emphasizing its economic, social, and spatial dimensions. Traditional poverty assessment methods are scrutinized, revealing their inherent limitations, particularly in a dynamic and rapidly evolving nation like India. Recognizing the limitations of these conventional approaches, the paper sets the stage for exploring the novel paradigm of multimodal data analysis. The article surveys prominent datasets employed in the field, illustrating how these information repositories empower researchers to build predictive models capable of dissecting the intricate nuances of poverty. The core of this review revolves around a comprehensive evaluation of cutting-edge methodologies and techniques underpinning poverty prediction, particularly in the Indian context, revealing how they unlock insights into housing conditions, agricultural productivity, healthcare access, and socio-economic disparities. This review paper transcends the boundaries of traditional poverty assessment methodologies by venturing into the realm of multimodal data analysis. By weaving together, the intricate threads of images and text, it unravels fresh insights into poverty dynamics, particularly within the unique socio-economic landscape of India. This synthesis of multidisciplinary knowledge serves as a guiding beacon for researchers, practitioners, and policymakers alike, beckoning them to harness the transformative power of data science in the relentless
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