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3rd international conference on machine learning and Computer Application, ICMLCA 2022

作者： Sun, Jiyu Zhang, Shengnan School of Software Shenyang University of Technology Shenyang China

ISBN: (纸本)9781510664814

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 competitiveness of a country. As patent output continues to increase, the quality and value of patents become increasingly important. In this paper, the machine learning method is used to evaluate the quality of electronic information patents and identify high-quality patents from many patents, which can promote patent transfer and transformation, acquisition and custody, transaction flow, pledge financing, and other economic activities. machine learning is more reliable and scientific than other methods. It is the task of learning corresponding rules and patterns from data and applying them to new data to make predictions. © 2023 SPIE.

关键词： Support vector machines

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Application of LSTM Algorithm in Vocabulary Analysis in English Education 24

Application of LSTM Algorithm in Vocabulary Analysis in Engl...

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international conference on machine Intelligence and Digital Applications (MIDA)

作者： Han, Jing Maanshan Teachers Coll Sch Foreign Languages Maanshan 243041 Anhui Peoples R China

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 learning data 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.

关键词： Vocabulary Analysis LSTM Algorithm English Education Word learning Efficiency learning Performance Evaluation

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A Deep learning Method for Autism Spectrum Disorder 1

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2nd international conference on Computational Intelligence in machine learning, ICCIML 2022

作者： George, Bindu Chandra Blessie, E. Resmi, K.R. Santhigiri College Vazhithala India Department of Artificial Intelligence and Machine Learning Coimbatore Institute of Technology Coimbatore India University Karnataka Bengaluru India

ISBN: (数字)9789819979547

ISBN: (纸本)9789819979530

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 neurological condition marked by repetitive behaviours and social difficulties. A deep learning-based approach using transfer learning for automatic detection of ASD is proposed in this study, which uses characteristics retrieved from the intracranial brain volume and corpus callosum from the ABIDE data set. T1-weighted MRI scans provide information on the intracranial brain volume and corpus callosum. ASD is detected using VGG-16 based on transfer learning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electronic data interchange

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Prediction and comparison of psychological health during COVID-19 among Indian population and Rajyoga meditators using machine learning algorithms

Prediction and comparison of psychological health during COV...

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2022 international conference on machine learning and data engineering, icmlde 2022

作者： Shobhika Kumar, Prashant Chandra, Sushil Academy of Scientific and Innovative Research CSIO Chandigarh160030 India CSIR Central Scientific Instruments Organisation Chandigarh160030 India Institute of Nuclear Medicine and Allied Sciences DRDO New Delhi110054 India

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 COVID-19 is its capacity for early identification and forecasting of a person's mental health decline resulting in chronic mental health issues. Therefore, through this study aims at addressing the hological problems by identifying people who are more likely to acquire mental health issues induced by COVID-19 epidemic. To achieve this goal, this study includes 1) Rajyoga practitioners' perceptions of psychological effects, levels of anxiety, stress, and depression are compared to those of the non practitioners 2) Predictions of mental health disorders such as stress, anxiety and depression using machine learning algorithms using the online survey data collected from Rajyoga meditators and general the population. Decision tree, random forest, naive bayeBayespport vector machine and K nearest neighbor algorithms were used for the prediction as they have been shown to be more accurate for predicting psychological disorders. The support vector machine showed the highest accuracy among all other algorithms. The f1 score was also the highest for support vector machine. © 2022 Elsevier B.V.. All rights reserved.

关键词： COVID-19

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GeoAI for Natural Disaster Assessment 24

GeoAI for Natural Disaster Assessment

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33rd ACM international conference on Information and Knowledge Management (CIKM)

作者： Adhikari, Saugat Indiana Univ Bloomington Bloomington IN 47405 USA

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 machine learning, and by developing physics-guided machine learning models for accurate natural disaster assessment.

关键词： Flood Extent Mapping Digital Elevation Model Active learning Parallelization

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BIM and real estate valuation: challenges, potentials and lessons for future directions

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engineering CONSTRUCTION AND ARCHITECTURAL MANAGEMENT 2024年第4期31卷 1642-1677页

作者： Jafary, Peyman Shojaei, Davood Rajabifard, Abbas Ngo, Tuan Bldg 4 0 CRC Melbourne Australia Univ Melbourne Ctr Spatial Data Infrastruct & Land Adm Dept Infrastruct Engn Melbourne Australia Univ Melbourne Dept Infrastruct Engn Melbourne Australia

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 machine learning-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

关键词： Building information modeling (BIM) Real estate valuation Artificial intelligence (AI) machine learning (ML) Three-dimensional (3D) data

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machine learning-Based Disease Diagnosis Using Body Signals: A Review 4th

Machine Learning-Based Disease Diagnosis Using Body Signals:...

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4th international conference on data, engineering, and Applications, IDEA 2022

作者： Dangi, Jyoti Arya, Rakesh Kumar Agrawal, Shikha UIT RGPV Bhopal India MPCST Bhopal India

ISBN: (纸本)9789819700363

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 diseases and core symptoms of the patient group. Some of these problems can be resolved by researchers, doctors, and patients thanks to machine learning (ML), a branch of artificial intelligence (AI). Based on relevant research, this review describes the use of machine learning in the early identification of several diseases. The paper then provides a summary of the most recent developments in the field of machine learning-based disease detection and telemedicine, taking into account the algorithm, various disease kinds, various body signals, various applications, and various assessment metrics. Finally, we summarize the main results and provide some perspective on possible future trends and research directions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Jadeite Origin Recognition Based on Ensemble learning

Jadeite Origin Recognition Based on Ensemble Learning

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2022 international conference on Computer Graphics, Artificial Intelligence, and data Processing, ICCAID 2022

作者： Wang, Lingling Tu, Jiahai Li, Yuan Li, Mingyi School of Electronics and Information Engineering Hubei Open University Wuhan China

ISBN: (纸本)9781510663350

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, under the same quality, the prices of Guatemalan jade and Russian jade are generally lower than those of Myanmar jade, so some illegal merchants will use Guatemalan jade to impersonate Myanmar jade. Due to the continuous improvement of jade counterfeiting technology, traditional identification methods can no longer meet the demand. In order to protect the rights and interests of consumers need to establish a rapid and effective jade origin traceability method. In this paper, through the (LA-ICP-MS) trace element data set and the method based on weighted extreme learning machine, AdaBoost and incremental learning fusion, the jadeite discrimination model of different producing areas is established to realize the intelligent discrimination of jadeite producing areas. The recognition accuracy of integrated learning algorithm is more than 80 %. Compared with the basic extreme learning machine and weighted extreme learning machine, it can be found that the classification accuracy of integrated learning algorithm is higher and more stable. © 2023 SPIE.

关键词： Trace elements

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Incentivizing Exploration with Linear Contexts and Combinatorial Actions 40

Incentivizing Exploration with Linear Contexts and Combinato...

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40th international conference on machine learning

作者： Sellke, Mark Amazon Core AI Cambridge MA 02142 USA

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.

关键词： machine learning

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Systematic review on machine learning and computer vision in precision agriculture: Applications, trends, and emerging techniques

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engineering APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 148卷

作者： Kuan, Y. N. Goh, K. M. Lim, L. L. Tunku Abdul Rahman Univ Coll Fac Engn & Technol Dept Elect & Elect Engn Jalan Genting Klang Kuala Lumpur 53300 Malaysia

machine learning (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.

关键词： machine learning Computer vision Precision agriculture Deep learning Convolutional neural networks

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