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

共 33936 条记录，以下是1271-1280 订阅

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Received Path Power Prediction for Millimeter-wave Using machine learning

Received Path Power Prediction for Millimeter-wave Using Mac...

引用

2022 IEEE international conference on Consumer Electronics, ICCE 2022

作者： Yamazaki, Takato Shimaoka, Kohei Ohno, Kohei Johannsen, Ulf De Groot, Sonia Heemstra Meiji University School of Interdisciplinary Mathematical Sciences Department of Network Design Tokyo Japan Eindhoven University of Technology Department of Electrical Engineering Eindhoven Netherlands

ISBN: (纸本)9781665441544

This paper discusses the received power prediction of millimeter-wave by machine learning when a user moves simply like walking straight. In general, a large amount of data is required for the neural network to predict the received power. In this paper, the transfer function of the channel is divided into narrower bandwidths, and the received power obtained from the narrower channel is used for the learning data. The RMSE is evaluated to show the effectiveness of the proposed prediction scheme. © 2022 IEEE.

关键词： Millimeter waves

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DualGraph: Improving Semi-supervised Graph Classification via Dual Contrastive learning 38

DualGraph: Improving Semi-supervised Graph Classification vi...

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38th IEEE international conference on data engineering (ICDE)

作者： Luo, Xiao Ju, Wei Qu, Meng Chen, Chong Deng, Minghua Hua, Xian-Sheng Zhang, Ming Peking Univ Beijing Peoples R China Mila Quebec AI Inst Montreal PQ Canada Univ Montreal Montreal PQ Canada Alibaba Grp DAMO Acad Hangzhou Peoples R China

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

In this paper, we study semi-supervised graph classification, a fundamental problem in data mining and machine learning. The problem is typically solved by learning graph neural networks with pseudo-labeling or knowledge distillation to incorporate both labeled and unlabeled graphs. However, these methods usually either suffer from overconfident and biased pseudo-labels or suboptimal distillation caused by the insufficient use of unlabeled data. Inspired by the recent progress of contrastive learning and dual learning, we propose DualGraph, a principled framework to leverage unlabeled graphs more effectively for semi-supervised graph classification. DualGraph consists of a prediction module and a retrieval module to model graphs G and their labels y from opposite while complementary views (i.e., p(y vertical bar G) and p(G vertical bar y) respectively). The two modules are jointly trained via posterior regularization, which encourages their inter-module consistency on unlabeled graphs. Moreover, we improve model training for each module with a contrastive learning framework to encourage the intra-module consistency on unlabeled data. Experimental results on a range of publicly accessible datasets reveal the effectiveness of our DualGraph.

关键词： Graph Classification Graph Neural Networks Contrastive learning Semi-supervised learning

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Mobile application for foreign language learning by cognitively unimpaired seniors

Mobile application for foreign language learning by cognitiv...

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

作者： Klimova, Blanka University of Hradec Kralove Rokitanskeho 62 Kralove 3 Hradec500 03 Czech Republic

The goal of this paper is to analyze what features and content a mobile application (app) for foreign language learning, in this case, English, by cognitively unimpaired seniors should possess. The methodology was based on an online questionnaire survey, which was conducted among 113 cognitively healthy Czech seniors, who ranged in age, gender, and place of residence. The survey was conducted between 29 January 2021 and 31 March 2021 using Google forms. The findings reveal that the potential users of such a mobile app are seniors belonging to the age group between 50 and 69 years. They also indicate that a substantial majority of the elderly own a smartphone with the Android operating system. In addition, although some of them (39%) have already used mobile apps for learning English as a second language, there are those (27%) who have never exploited them. The positive features that seniors appreciate, in the tried educational language learning apps most often include clarity, ease of use, intuitiveness, the appearance of the user interface, and gamification features. On the contrary, they dislike a lack of clarity, lack of content, poor usability, and small fonts. Seniors generally use these apps once a week, and mostly at home. They usually use a mobile app for translating individual words, vocabulary learning, and practicing grammar. The findings of this survey study can be of interest to all stakeholders involved in the development of such mobile apps. In the future, there should be more research targeted at the examination and evaluation of mobile apps for elderly, taking in consideration their individual needs to improve quality of their life. © 2022 Elsevier B.V.. All rights reserved.

关键词： User interfaces

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A Review of machine learning Techniques for Tuberculosis Meningitis Diagnosis 4th

A Review of Machine Learning Techniques for Tuberculosis Men...

引用

4th international conference on Communication and Intelligent Systems, ICCIS 2022

作者： Ramteke, Monali Raut, Shital Kadam, Tejal Department of Computer Science and Engineering Visvesvaraya National Institute of Technology Nagpur India

ISBN: (纸本)9789819920990

TB has been considered to be a major global health hazard. With regard to early meningitis tuberculosis identification, numerous studies and research have been conducted in recent years. The most serious form of tuberculosis is tuberculous meningitis (TBM), although it can be challenging to distinguish TBM from other meningitis. Therefore, we reviewed papers related to machine learning models for identification and diagnosis of different forms of meningitis. In this review, the most recent research and methods for integrating medical data with machine learning models for diagnosis of meningitis and its respective outcomes are summarized. To achieve the best and most accurate results for detection and classification, authors have used a variety of strategies, beginning with preprocessing the data and partitioning the dataset into distinct training, validation, and test sets with varied ratios. This brings to attention that various machine learning tools and algorithms are useful in the analysis of medical data to diagnose meningitis. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Early detection of Parkinson’s disease based on IMU and machine learning

Early detection of Parkinson’s disease based on IMU and mac...

引用

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

作者： Zhao, Zhixu Yu, Hairong Wang, Feng Zheng, Huifen School of Biological Science and Medical Engineering Southeast University 2 Sipailou Nanjing210096 China Geriatric Hospital of Nanjing Medical University 65 Jiangsu Rd Nanjing210024 China

ISBN: (纸本)9781510663350

Parkinson’s disease (PD) is the second most neurodegenerative disease, which results in gradual loss of movement. Early diagnosis helps to control the disease in advance and prevent the disease from getting worse. The diagnosis of Parkinson’s disease in hospitals is cumbersome and expensive. IMU which is widely used in gait analysis is much cheaper and suitable for early detection. But pure gait analysis has limitations in detecting PD. In this paper, we selected a representative 10-meter folding task and a stationary standing task reflecting the gait domain, balance domain, and tremor domain from a scale commonly used in clinical Parkinson's examinations, and collected a total of 127 data from early PD patients and healthy individuals using IMU sensors. Fifteen valid features were selected using MRMR and a support vector machine was used to discriminate between early-stage patients and healthy individuals. Due to the introduction of balance and tremor features, the accuracy is improved from 88% to 96%, and reduced likelihood of healthy individuals being misclassified as PD patients. © 2023 SPIE.

关键词： Neurodegenerative diseases

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1st international conference on Computational Intelligence for engineering and Management Applications, CIEMA 2022

1st International Conference on Computational Intelligence f...

引用

1st international conference on Computational Intelligence for engineering and Management Applications, CIEMA 2022

ISBN: (纸本)9789811984921

The proceedings contain 68 papers. The special focus in this conference is on Computational Intelligence for engineering and Management Applications. The topics include: Strokes-Related Disease Prediction Using machine learning Classifiers and Deep Belief Network Model;analysis and Detection of Fraudulence Using machine learning Practices in Healthcare Using Digital Twin;prediction and Analysis of Polycystic Ovary Syndrome Using machine learning;feature Selection for Medical Diagnosis Using machine learning: A Review;convolutional Neural Network Architectures Comparison for X-Ray Image Classification for Disease Identification;Secure Shift-Invariant ED Mask-Based Encrypted Medical Image Watermarking;fusion-Based Feature Extraction Technique Using Representation learning for Content-Based Image Classification;a Comparative Study on Challenges and Solutions on Hand Gesture Recognition;design a Computer-Aided Diagnosis System to Find Out Tumor Portion in Mammogram Image with Classification Technique;a Novel Soft-Computing Technique in Hydroxyapatite Coating Selection for Orthopedic Prosthesis;performance Analysis of Panoramic Dental X-Ray Images Using Discrete Wavelet Transform and Unbiased Risk Estimation;efficient Image Retrieval Technique with Local Edge Binary Pattern Using Combined Color and Texture Features;texture and Deep Feature Extraction in Brain Tumor Segmentation Using Hybrid Ensemble Classifier;A Systematic Review on Sentiment Analysis for the Depression Detection During COVID-19 Pandemic;vehicular Adhoc Networks: A Review;the data Vortex Switch Architectures—A Review;survey on Genomic Prediction in Biomedical Using Artificial Intelligence;a Brief Review on Right to Recall Voting System Based on Performance Using machine learning and Blockchain Technology;sentiment Analysis Techniques: A Review;network Traffic Classification Techniques: A Review;remote Production Monitoring System.

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Detection of consumers with electric heating devices and temperature normalization of their electric energy consumption 18

Detection of consumers with electric heating devices and tem...

引用

18th international conference on the European Energy Market (EEM)

作者： Melink, Teja Prislan, Lev Ilic, Anja GEN I doo Krshko Slovenia

ISBN: (数字)9781665408967

ISBN: (纸本)9781665408967

In the present paper, the procedure to detect consumers with the electric heating devices is shown. In this procedure, the AdaBoost machine learning algorithm is used to detect heat pumps. For the input data, three main sources of data are used from which the telemetry of the 15-minute electric energy consumption shows the most informativeness. The accuracy of the results is estimated from the internal data of the consumer's market actions for heat pumps. Further, bilinear regression is used to detect consumers with any electric heating devices and the results of both analyses are compared. Finally, the algorithm of temperature normalization of energy consumption of these consumers is presented.

关键词： machine learning regression telemetry temperature normalization

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Specify Robust Causal Representation from Mixed Observations 23

Specify Robust Causal Representation from Mixed Observations

引用

29th ACM SIGKDD conference on Knowledge Discovery and data Mining (KDD)

作者： Yang, Mengyue Cai, Xinyu Liu, Furui Zhang, Weinan Wang, Jun UCL London England Nanyang Technol Univ Singapore Singapore Zhejiang Lab Hangzhou Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9798400701030

learning representations purely from observations concerns the problem of learning a low-dimensional, compact representation which is beneficial to prediction models. Under the hypothesis that the intrinsic latent factors follow some casual generative models, we argue that by learning a causal representation, which is the minimal sufficient causes of the whole system, we can improve the robustness and generalization performance of machine learning models. In this paper, we develop a learning method to learn such representation from observational data by regularizing the learning procedure with mutual information measures, according to the hypothetical factored causal graph. We theoretically and empirically show that the models trained with the learned causal representations are more robust under adversarial attacks and distribution shifts compared with baselines. The supplementary materials are available at https://***/ymy4323460/CaRI/.

关键词： Causal representation learning Robustness learning learning theory for Causal representation learning.

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Adaptive Prediction Of Online learning Based On machine learning Algorithms 12

Adaptive Prediction Of Online Learning Based On Machine Lear...

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12th IEEE international conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2024

作者： Yin, Qiang Yu, Xiaomei Zhao, Lixiang Li, Yongqin Shandong Normal University School of Information Science and Engineering Jinan250358 China China State Key Laboratory of High-end Server & Storage Technology Jinan250358 China Shandong Normal University Faculty of Education Jinan250014 China

ISBN: (纸本)9798331510633

In recent years, with the advancement of society and the rapid development of internet technology, the popularity of online education has been steadily increasing. Presently, nearly every educational institution worldwide, from elementary schools to universities, has incorporated some level of online teaching. While online instruction offers certain conveniences, it will also be accompanied by the emergence of some problems, given the substantial surge in data related to online learning among students, there is an urgent need for robust data processing methods to forecast their suitability for online education. Previous research underscores the effectiveness of machine learning in tackling diverse data classification tasks. However, there is a scarcity of studies comprehensively comparing various machine learning approaches in the context of online education data. This article utilizes seven distinct, parameter-optimized machine learning approaches to explore a range of performance predictions, spanning both binary and multi-class tasks. During the experimental process, four evaluation metrics and visual aids were employed to assess and compare the methods across these tasks, accompanied by a comprehensive discussion of the experimental results. The results of the experiments reveal that the random forest method demonstrates strong versatility across all selected datasets. © 2024 IEEE.

关键词： Adversarial machine learning

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Prediction of students' academic performance using machine learning Techniques

Prediction of students' academic performance using Machine L...

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2022 international Mobile and Embedded Technology conference, MECON 2022

作者： Verma, Utkarsh Garg, Chetna Bhushan, Megha Samant, Piyush Kumar, Ashok Negi, Arun DIT University School of Computing Dehradun India Chandigarh University Gharuan AIT-CSE Apex Punjab Mohali India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Deloitte USI Hyderabad India

ISBN: (纸本)9781665420204

The prediction of students' academic performance is a subset of Educational data Mining (EDM) which deals with the large-scale data gathered from an education system. EDM aims at deriving meaningful information from this data to facilitate the stakeholders of the education system. The proposed work uses various machine learning (ML) techniques to predict the student's academic performance using the real data collected (comprising the academic history and personal habits of the students). Furthermore, a comparison of ML techniques on different evaluation metrics has been presented. It will assist the students to keep a track of their academic performance and accordingly, manage their study pattern to help them perform well in future. © 2022 IEEE.

关键词： Forecasting

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