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检索条件"任意字段=2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022"

共 33935 条记录，以下是1001-1010 订阅

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Deep and Collective Entity Resolution in Parallel 38

Deep and Collective Entity Resolution in Parallel

引用

38th IEEE international conference on data engineering (ICDE)

作者： Deng, Ting Fan, Wenfei Lu, Ping Luo, Xiaomeng Zhu, Xiaoke An, Wanhe Beihang Univ SKLSDE Beijing Peoples R China Univ Edinburgh Edinburgh Midlothian Scotland Shenzhen Inst Comp Sci Shenzhen Peoples R China

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

This paper studies deep and collective entity resolution (ER). As opposed to a single pass of pairwise comparison of tuples in a single table, deep ER recursively identifies tuples that refer to the same entity by making use of matches in the previous rounds, and collective ER determines matches by correlating information across multiple tables. We propose a fixpoint model for deep and collective ER, by chasing with logic rules that are collectively defined across multiple relations and may embed machine learning classifiers for ER as predicates. While powerful, we show that deep and collective ER is intractable. To scale with large datasets, we develop a data partitioning strategy and a parallel algorithm underlying the fixpoint model, which guarantee to reduce runtime when more processors are used. Using real-life data, we experimentally verify that the approach improves the ER accuracy and is parallelly scalable.

关键词： Runtime Program processors conferences Computational modeling machine learning data engineering data models

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Impact of Hyper Homocysteinemia on Endothelial Dysfunction and Cervical Cancer Recurrence Using machine learning Techniques 9

Impact of Hyper Homocysteinemia on Endothelial Dysfunction a...

引用

9th international conference on Communication and Electronics Systems, ICCES 2024

作者： Geeitha, S. Renuka, P. Kavitha, C. Umamaheswari, S. Dharani, R. Gurunathan, K. M.Kumarasamy College of Engineering Department of Information Technology Karur India Mahendra Engineering college for women Department of Cse Namakkal India Mahendra Engineering College Namakkal India St.Joseph's College of Engineering Chennai India Mount Zion College of Engineering and Technology Deparment of Cse Pudukottai India

ISBN: (纸本)9798350377972

The Research explores the correlation between endothelial dysfunction and Hyper Homocysteinemia (HHcy) with the possible relationship in the pathogenesis of cervical cancer. Extremely higher range of homocysteine (Hcy) are frequently linked with inflammatory damage, tumor metastasis, and vascular injury. The present study has also analyzed the impact of HHcy in the gene expression of human aortic tissue cells by investigating mRNA and microRNA expression profiles using Affymetrix GeneChip microarrays. Support vector machine, Linear regression and Random forest are the machine learning techniques which is used on clinical data in which random forest stands has lowest mean squared error then other two models. According to the findings, high Hcy values may lead to impaired endothelial function, which might be one of the reasons for cancer. And, on the other hand, low Hcy is related to high intake of essential minerals, which again establishes the fact that nutritional measurements can avert the chances of cancer. Also, prediction algorithms based on dietary as well as molecular information provides new insights into the future possibilities of cervical cancer along with modes of treatment. © 2024 IEEE.

关键词： Diseases

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Research on intelligent agricultural decision-making system based on Bayesian optimization 3

Research on intelligent agricultural decision-making system ...

引用

3rd international conference on Artificial Intelligence and Computer engineering, ICAICE 2022

作者： Yan, Xiaoying Software Engineering Institute of Guangzhou Guangdong Guangzhou510300 China

ISBN: (纸本)9781510663473

Along with social development, the problems of insufficient rural labor and mismatch between labor intensity and economic returns have become a greater obstacle to rural development;therefore, the development of agricultural intelligence to improve agricultural productivity will become the new direction of modern agricultural development. Based on Internet technology, intelligent agriculture adopts digital technologies such as intelligent perception, network transmission and big data processing to provide decision basis for agricultural planting, production and pest control, or directly deploy decision information to automated farm equipment, so as to use agricultural resources reasonably and efficiently. However, the development of smart agriculture is constrained by the low accuracy of automation control and decision information. In this paper, we optimize the threshold value of each agricultural production decision data through machine learning algorithm, use Bayesian optimization to learn the agricultural production environment and crop growth data, iteratively optimize the threshold value to get the global best value, improve the accuracy of automation control and reduce the risk of agricultural production decision, and effectively improve agricultural The economic returns of agriculture can be effectively improved. © 2023 SPIE.

关键词： machine learning

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Disease Prediction Using machine learning (Health Buddy) 2

Disease Prediction Using Machine Learning (Health Buddy)

引用

2nd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Pandey, Adarsh Kumar Tripathi, Adarsh Ranjan, Raju Galgotias University School of Computing Science and Engineering India

ISBN: (纸本)9781665437899

This is a paper on disease prediction using machine learning through a python graphical user interface application. The motivation behind this application is the pandemic (Covid- Situation) faced by the whole world and also the idea to robotize the current manual framework of initial diagnosis by the assistance of mechanized supplies and undeniable PC programming so that their important information/data can be put away for a more drawn out period and also for a more useful purpose. This paper introduces the field of diseases prediction, the treatment for the disease, and consulting with the doctors nearby through efficient programming using machine learning. It describes the need for a system of an online artificial doctor, which will not only help them in predicting and understanding the diseases, but it will also advise them of certain medicines that are necessary for controlling or curing those diseases. © 2022 IEEE.

关键词： Forecasting

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Investigating the Role of machine-learning Techniques in Performance Prediction of High Power Microwave Devices

Investigating the Role of Machine-Learning Techniques in Per...

引用

international conference on Recent Evolutions in Energy, Drives and E-Vehicles, REED-EV 2022

作者： Vengurlekar, Jyoti Dash, Rishav Saxena, Ayush Naik, Ashwini Petkar, Sharmila Department of Electronics and Telecommunication Engineering D Y Patil Deemed to be University Ramrao Adik Institute of Technology Maharashtra Navi Mumbai400706 India Department of Electronics Engineering D Y Patil Deemed to be University Ramrao Adik Institute of Technology Maharashtra Navi Mumbai400706 India

ISBN: (纸本)9789819707621

Particle-in-cell code-based finite-difference time domain (PIC-FDTD) simulations are widely used to design, simulate, and analyze high power microwave (HPM) devices. Although these simulations are quite accurate in simulating physics of the HPM devices and can generate huge amounts of data, they suffer from several limitations such as long runtimes and lack of optimization features. The aim of this paper is to apply machine learning techniques on the data generated by PIC-FDTD simulations in order to predict or forecast several performance parameters of HPM devices such as number of simulation particles, output power, current, efficiency, etc. The objective is to develop a forecasting algorithm which can be univariate or multivariate based upon the time series data generated by PIC simulations with intervals of several pico-seconds. Scope and performance of some of the popular machine learning algorithms such as ARIMA, VARIMA, Auto ARIMA, Fourier regression model, NBEATS model, Naive baselines, TCN model, Temporal fusion transformer models, etc., shall be compared. This work could be a starting point toward determination of optimum device design and performance parameters. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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Personalized Multi-document Text Summarization using Deep learning Techniques

Personalized Multi-document Text Summarization using Deep Le...

引用

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

作者： Veningston, K. Venkateswara Rao, P.V. Ronalda, M. Department of Computer Science and Engineering National Institute of Technology Jammu and Kashmir Srinagar190006 India Department of Computer Science and Engineering Madanapalle Institute of Technology and Science Andhra Pradesh 517325 India India

The summary of a large piece of text or a document is a concise description of the same thing. It must retain most of the important points from the document and remove any kind of verbosity. The text summarization task is an information processing scheme that given a document or a set of documents, mines the most essential content from the source by considering the user or task at hand and presents the summary in a well-formed and brief text. Summarization can be done not just on one document, it can be done on a set of documents as well, which is called multi-document summarization. The proposed approach considers and in-corporates user preferences for the summarization task. Depending on the current user or current task, the summary could differ essentially. For an instance, a summary on 'photosynthesis', would be very different for the kid in 4th Grade vs. a student doing a bachelor's in Science. Therefore, while writing a summary, the reading history of the user is employed as a source of personalizing text summarization tasks. The proposed method is experimentally evaluated in the domain of news articles and obtained better summaries capable of extracting important concepts based on user preferences explained in the document when considering the relevant domain terms in the process of multi-document text summarization. © 2023 The Authors. Published by Elsevier B.V.

关键词： Deep learning

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Survival Probability Assessment using machine learning Algorithms 2

Survival Probability Assessment using Machine Learning Algor...

引用

2nd international conference on machine learning and Intelligent Systems engineering, MLISE 2022

作者： Wang, Jingyi Zhang, Boyang Fuyang Normal University College Of Information Engineering Hefei China Shanxi University School Of Automation And Software Engineering Shanxi China

ISBN: (数字)9781665492461

ISBN: (纸本)9781665492461

As the largest passenger ship at that time, the sinking of the Titanic was a tragedy, many people died here. Of course, some people escaped successfully. What kind of people are more likely to survive this disaster? In this paper, we aim to predict which characteristic will affect the survive probability. First, we made statistics on the passenger in the accident and analyzed the characteristics of the survivors. The Decision Tree (DT) and K-Nearest Neighbor (KNN) algorithms were used to achieve. At last, the decision tree with cleaning data has higher accuracy, thus characterizing the survivor population. The Decision Tree with data preprocessing achieves the accuracy of 0.81716, which validate the effectiveness of the proposed method. © 2022 IEEE.

关键词： Decision trees

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A Brief Analysis of Remote Sensing Image Cloud Extraction Method Based on Deep learning 3

A Brief Analysis of Remote Sensing Image Cloud Extraction Me...

引用

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

作者： Gao, Lin Gai, Chenxi Shenyang Ligong University Shenyang China

ISBN: (纸本)9781510664814

In optical remote sensing images, different degrees of cloud cover will have different degrees of obscuration to ground information. Due to the special spectral and shape characteristics of clouds, cloud extraction makes the detection and extraction of cloud cover in the process of remote sensing data processing is one of the primary problems to be solved for further analysis and utilization of image information, and cloud extraction has gradually become a hot spot and frontier of current research in recent years. In view of this, this paper categorizes and summarizes the existing deep learning theories and methods based on a large amount of relevant literature in recent years, and classifies cloud extraction into three types of methods based on convolutional neural networks, recurrent neural networks, and generative adversarial neural networks by analyzing the network architectures adopted by different methods, briefly introduces the current development trends and representative works of cloud extraction methods based on remote sensing images at home and abroad, and finally gives an overview of the current Finally, we suggest and foresee the current problems and future development trend of cloud extraction. © 2023 SPIE.

关键词： Extraction

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Ethno medicine of Indigenous Communities: Tamil Traditional Medicinal Plants Leaf detection using Deep learning Models 2

Ethno medicine of Indigenous Communities: Tamil Traditional ...

引用

2nd international conference on machine learning and data engineering, icmlde 2023

作者： Govindaprabhu, G.B. Sumathi, M. Madurai Kamaraj University Madurai India PG & Research Department of Computer Science Sri Meenakshi Government Arts College for Women Madurai India

In indigenous communities, ethno medicine has played a prominent role in healing for centuries and provides valuable insight into the use of traditional medicinal plants. Using local plant leaves as therapeutic agents, this research explores the importance of traditional Tamil medicine. Traditional healers and practitioners can benefit from technological advances and collaborate with modern healthcare systems through these advances. Based on deep learning models, a novel approach for recognizing leaves of traditional Tamil medicinal plants is presented. Pharmaceutical companies are increasingly paying attention to plants that contain medicinal factors that have fewer side effects during treatment. By identifying plants with medicinal factors and identifying their medicinal uses, this work addresses the identification of plants with medicinal factors. To classify medicinal plants and their uses, we will use deep learning models and image processing techniques to identify Tamil Traditional Medical Plant (TTMP). The input dataset for this work includes 18 different traditional Tamil plant leaves such as Azadirachta Indica (Neem), Ocimum Tenuiflorum (Tulsi) and Trigonella Foenum-graecum (Fenugreek). Pre-processing steps are performed on the input plant image, including RBZR Augmentation, noise removal and grayscale conversion, to detect the image area that is important for plant type identification. To identify the core area of the plant, this work using the HGAW Segmentation algorithm. Deep learning model is trained with segmented plants and their labels as inputs. The aim is to digitize and make accessible ancestral medical knowledge by incorporating deep learning-based leaf recognition with 96.71% accuracy for the proposed EEXR model. © 2024 Elsevier B.V.. All rights reserved.

关键词： Plants (botany)

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Social Media Product data Integration with Product Lifecycle Management;Insights for Application of Artificial Intelligence and machine learning

Social Media Product Data Integration with Product Lifecycle...

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2022 IEEE international conference on Industrial engineering and engineering Management, IEEM 2022

作者： Mohammadian, N. Mechai, N. Valilai, O. Fatahi Jacobs University Bremen School of Business Social & Decision Sciences Bremen Germany

ISBN: (数字)9781665486873

ISBN: (纸本)9781665486873

Product Lifecycle Management (PLM) faces challenges for adaption to the global economy. These challenges range from strategic restructuring of product involved departments, to leveraging novel technologies like smart devices and machine learning. Social media on the other hand is recognized as a strong source for exploring knowledge from customers and product users. However, this domain is not explored for its potentials in relation with PLM framework. Today's state of Artificial Intelligence, machine learning techniques, in addition to ever falling computational costs, provide inevitable opportunities for mining customers' product related data. This paper aims to understand and analyze the patterns of PLM's evolution with social media integration. It is intended to identify a possible extension for PLM architectures enrichment by leveraging the social media domain. The paper will introduce some of the most relevant PLM definitions, followed by a literature review of domain experts. Consequently, it will analyze the findings and evaluate the possibility of social networks domain integration in the current PLM. Finally, it will highlight the evaluation perspectives for the benefits of proposed solutions against current literature solutions. © 2022 IEEE.

关键词： machine learning

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