The development of today's society is inseparable from the continuous renewal of science and technology. As an important indicator of scientific and technological innovation, patents reflect the core competitivene...
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In response to the limited data coverage and lack of personalized learning among students in English education, vocabulary analysis was conducted utilizing the Long Short-Term Memory (LSTM) algorithm. By improving the...
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ISBN:
(纸本)9798400718144
In response to the limited data coverage and lack of personalized learning among students in English education, vocabulary analysis was conducted utilizing the Long Short-Term Memory (LSTM) algorithm. By improving the accuracy and efficiency of vocabulary analysis, deepening the understanding of student learning processes, and other methods, traditional research problems were solved, thereby improving the quality and effectiveness of English education. This paper analyzed and modeled the learningdata of students, and combined the advantages of LSTM algorithm to achieve personalized learning paths and guidance for different students, better meeting the learning needs and levels of different students;in order to better understand how students learn vocabulary and to offer more useful advice and support for teaching practices, the LSTM algorithm was applied. The training loss obtained by using grid search method was 0.65, and the validation loss was 0.75;the training loss obtained by the random search method was 0.7, and the validation loss was 0.85;the training loss obtained by Bayesian optimization method was 0.8, and the validation loss was 0.9;the training loss obtained by the genetic algorithm method was 0.85, and the validation loss was 1.1. The model obtained by the grid search method performed well on both the training and validation sets, with good fitting and generalization abilities.
The present study uses deep learning methods to detect autism spectrum disorder (ASD) in patients from global multi-site database Autism Brain Imaging data Exchange (ABIDE) based on brain activity patterns. ASD is a n...
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Issues of providing mental health support to people with emerging or current mental health disorders are becoming a significant concern throughout the world. One of the biggest effects of digital psychiatry during COV...
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Climate change has led to a sharp increase in the number and severity of extreme events, such as floods, tornados and wildfires. These events have resulted in adverse effects on human lives and the infrastructure. Swi...
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ISBN:
(纸本)9798400704369
Climate change has led to a sharp increase in the number and severity of extreme events, such as floods, tornados and wildfires. These events have resulted in adverse effects on human lives and the infrastructure. Swift disaster assessment is crucial for the effective planning of disaster response and relief efforts. AI and big data have provided unprecedented opportunities to enable swift disaster assessment, but two significant hurdles exist: (1) the scarcity of annotated geospatial data to train AI models, and (2) the lack of AI solutions that encode physics knowledge in a geospatial context. My research aims to address both challenges by developing an active-learning-based annotation platform that improves the annotation productivity of geospatial data for geospatial machinelearning, and by developing physics-guided machinelearning models for accurate natural disaster assessment.
Purpose - Building information modeling (BIM) is a striking development in the architecture, engineering and construction (AEC) industry, which provides in-depth information on different stages of the building lifecyc...
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Purpose - Building information modeling (BIM) is a striking development in the architecture, engineering and construction (AEC) industry, which provides in-depth information on different stages of the building lifecycle. Real estate valuation, as a fully interconnected field with the AEC industry, can benefit from 3D technical achievements in BIM technologies. Some studies have attempted to use BIM for real estate valuation procedures. However, there is still a limited understanding of appropriate mechanisms to utilize BIM for valuation purposes and the consequent impact that BIM can have on decreasing the existing uncertainties in the valuation methods. Therefore, the paper aims to analyze the literature on BIM for real estate valuation practices. Design/methodology/approach - This paper presents a systematic review to analyze existing utilizations of BIM for real estate valuation practices, discovers the challenges, limitations and gaps of the current applications and presents potential domains for future investigations. Research was conducted on the Web of Science, Scopus and Google Scholar databases to find relevant references that could contribute to the study. A total of 52 publications including journal papers, conference papers and proceedings, book chapters and PhD and master's theses were identified and thoroughly reviewed. There was no limitation on the starting date of research, but the end date was May 2022. Findings - Four domains of application have been identified: (1) developing machinelearning-based valuation models using the variables that could directly be captured through BIM and industry foundation classes (IFC) data instances of building objects and their attributes;(2) evaluating the capacity of 3D factors extractable from BIM and 3D GIS in increasing the accuracy of existing valuation models;(3) employing BIM for accurate estimation of components of cost approach-based valuation practices;and (4) extraction of useful visual features for rea
Effective illness diagnosis is an unmet clinical need on a global scale. Building a tool for early diagnosis and an efficient course of treatment is particularly challenging due to the complexity of many complex disea...
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Most of the jade on the market now comes from Myanmar, Guatemala, and a few from Russia. The gemological properties of jadeite from different producing areas are consistent. However, in the middle-end jade market, und...
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We advance the study of incentivized bandit exploration, in which arm choices are viewed as recommendations and are required to be Bayesian incentive compatible. Recent work of (Sellke & Slivkins, 2022) has shown ...
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We advance the study of incentivized bandit exploration, in which arm choices are viewed as recommendations and are required to be Bayesian incentive compatible. Recent work of (Sellke & Slivkins, 2022) has shown that for the special case of independent arms, after collecting enough initial samples, the popular Thompson sampling algorithm becomes incentive compatible. This was generalized to the combinatorial semibandit in (Hu et al., 2022). We give an analog of this result for linear bandits, where the independence of the prior is replaced by a natural convexity condition. This opens up the possibility of efficient and regret-optimal incentivized exploration in high-dimensional action spaces. In the semibandit model, we also improve the sample complexity for the pre-Thompson sampling phase of initial data collection.
machinelearning (ML) and computer vision (CV) play a crucial role in precision agriculture (PA) by enabling data-driven solutions that enhance crop yields, resource efficiency, and sustainability. These technologies ...
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machinelearning (ML) and computer vision (CV) play a crucial role in precision agriculture (PA) by enabling data-driven solutions that enhance crop yields, resource efficiency, and sustainability. These technologies have transformed PA by addressing complex agricultural challenges through real-time monitoring and predictive analytics. This systematic review aims to investigate the recent advancements in ML and CV methodologies within PA, focusing on crop, harvesting, soil and water management. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, the study analyzes 213 articles from the past five years, providing insights into publication trends, key application area, and the distribution of research efforts across various crops and vision data types. The review highlights the predominant use of Convolutional Neural Networks (CNNs) in PA, particularly in crop detection, disease and pest detection, and weed detection. Emerging deep learning techniques, such as Vision Transformers (ViTs) and Generative Adversarial Networks (GANs), are also explored for their potential in visual data analysis. Findings show that maize is the most frequently studied crop, Red-Green-Blue (RGB) imagery is the primary vision data type and self-collected datasets are preferred over public ones. Key challenges include limited access to high-quality and diverse datasets, and the need for adaptable solutions across diverse agricultural contexts. By summarizing these advancements and challenges, this review provides direction for future research and highlights opportunities to expand ML and CV applications in sustainable and efficient agricultural practices.
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