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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

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Long-term structural health monitoring of long-span suspension bridges and anomaly detection using statistical indicators 11

Long-term structural health monitoring of long-span suspensi...

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11th international conference on Structural Health Monitoring of intelligent Infrastructure, SHMII 2022

作者： Çetindemir, Oğuzhan Tepe, Esra Zülfikar, Abdullah Can Yesilyurt, Ali Apaydin, Nurdan Memişoğlu Department of Civil Engineering Gebze Technical University Kocaeli Gebze41400 Turkey Department of Computer Engineering Istanbul University-Cerrahpaşa Istanbul34320 Turkey Department of Civil Engineering Istanbul University-Cerrahpaşa Avcilar Istanbul34320 Turkey Ministry of Transport and Infrastructure Uskudar Istanbul34668 Turkey

Some research has been conducted on structural health monitoring (SHM) utilizing raw data to discover structural behavior changes, anomalies, or damage assessment. However, few of them employed statistical indicators represented as input data. Long-term monitoring has several advantages (before/after event comparison, detecting/tracking abrupt changes or aging effects), while the data collection process may have disadvantages (data storing issues, impractical computationally expensive analyses). thus, the current work proposes two unsupervised machine learning techniques for detecting anomalies using statistical indicators extracted from long-term raw dynamic measurements recorded from a three-dimensional accelerometer installed on the midspan of the Osman Gazi Bridge built in Turkey. For this purpose, one-class Support Vector Machine (SVM) and Local Outlier Factor (LOF) are used as machine learning algorithms to detect anomalous instances. Comparison between these two machine learning revealed similar anomalies. the acquired results support the development of computational methods for assessing structural anomalies based on statistical indicators of acceleration measurements. © 2022 international conference on Structural Health Monitoring of intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.

关键词： Suspension bridges

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Efficient Abnormal Building Consumption Detection by Deep learning LSTM IOT data Classification 11

Efficient Abnormal Building Consumption Detection by Deep Le...

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11th IEEE international conference on Renewable Energy Research and Applications, ICRERA 2022

作者： Arkhangelski, Jura Abdou-Tankari, Mahamadou Lefebvre, Gilles University of Paris-Est Creteil Certes Laboratory Creteil France

ISBN: (数字)9781665471404

ISBN: (纸本)9781665471404

this research deals with the method of efficient detection of abnormal building energy consumption and energy losses for further intelligent building energy management and respect of energy transition obligations. Automatic energy loss detection has been proposed using Deep learning (DL) Long Short Term Memory (LSTM) networks specially dedicated for building data provided by Internet of things (IoT) objects. the article presents the proposed neural network, a description of the data classification, as well as features of processing the input and output data of the classification. A practical evaluation of the classification method was made on a real IoT building sensors dataset provided by the multifunctional administrative building in the Paris area, France. To train the neural network for accurate classification, the process of initial manual input data processing was presented. A two-month dataset was used for training, and a two-week dataset was used to test the efficiency of the obtained neural network. the subsequent in-depth data analysis showed the high efficiency and significant accuracy of the obtained method in the classification and research of abnormal building energy consumption. © 2022 IEEE.

关键词： Internet of things

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Using Grasshopper Optimization in Big data 11th

Using Grasshopper Optimization in Big Data

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11th and 12th EAI international conference on Big data Technologies and Applications, BDTA 2021 and BDTA 2022

作者： Khalf, Asmaa G. Ghany, Kareem Kamal A. Faculty of Computers and Information Beni-Seuf University Beni Suef Egypt Faculty of Computers and Information Sadat Academy Cairo Egypt

ISBN: (纸本)9783031336133

From algorithms that have been popular and presently used in meta-heuristic is the grasshopper optimization method, which has made many theoretical breakthroughs and is widely applied in numerous optimization issues across different fields such as image processing, machine learning, engineering design, control over wireless networking, power systems, and other things. In this study, we review the literature that is currently accessible on the grasshopper optimization technique and its extensions to the chaotic, binary, multi-objective scenarios and hybrid. Finally, the grasshopper optimization algorithm has proven superior to other optimization algorithms in most literature. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： data mining

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Rich Information Driven Popularity Prediction on Weibo 11

Rich Information Driven Popularity Prediction on Weibo

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11th international conference on Advanced Cloud and Big data, CBD 2023

作者： Shen, Xinyang Wu, Zhen Zhou, Jingya Zhu, Yujian Xu, Weiqing Soochow University School of Future Science and Engineering Suzhou China Soochow University School of Computer Science and Technology Suzhou China

ISBN: (纸本)9798350345346

Popularity prediction is to predict the number of social network users involved in information diffusion. Recently, deep learning methods have become mainstream explorations for popularity prediction. However, most existing research is limited to making popularity predictions from temporal and spatial (user interaction) data. In addition to temporal and spatial data, there are also widespread types of data such as text (such as information content posted by users and user profiles), geographical location (such as user residence), discrete tags (such as user authentication) in social networks, and these types of rich data have not been fully used. To this end, we propose a novel framework, called Rich Information heterogeneous graph-based popularity Predictor (RIP). RIP refines the granularity of information diffusion across users on Weibo and constructs a rich information Weibo forwarding heterogeneous graph containing multiple types of data. Our experimental results on real-world Weibo datasets including Wb-SSC and Wb-MSC demonstrate significant gains for RIP over state-of-the-art information diffusion prediction models. © 2023 IEEE.

关键词： User profile

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Measuring Acceptance of Adoption Metaverse in elearning by Using TAM Model 11

Measuring Acceptance of Adoption Metaverse in eLearning by U...

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11th international conference on Information Technology, ICIT 2023

作者： Alhalaybeh, Awf Althunibat, Ahmad Software Engineering Al-Zaytoonah University of Jordan Madaba Jordan Software Engineering Al-Zaytoonah University of Jordan Amman Jordan

ISBN: (纸本)9798350320060

this paper examines the Acceptance of Adoption Metaverse (AAM) in elearning by utilizing the Technology Acceptance Model (TAM) framework. the TAM model is a widely recognized theoretical framework that is used to understand the factors that influence users' acceptance of technology. In this study, we explore the relationship between AAM and elearning adoption by investigating the key factors that influence users' behavioral intention towards AAM. the Metaverse is a virtual world that simulates the real-world environment and offers learners an interactive, immersive, and collaborative learning experience. It posits that perceived usefulness and perceived ease of use are the key factors that influence users' behavioral intention to use technology. the study employs a quantitative research approach to collect data from students through a survey questionnaire. the data collected will be analyzed using both multiple linear regression model and simple linear regression model to examine the relationship between Model variables for the acceptance of AAM in elearning. © 2023 IEEE.

关键词： Linear regression

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Visible-Infrared Pedestrian Re-Identification via Heterogeneity Augmented Cross-Modality learning 11

Visible-Infrared Pedestrian Re-Identification via Heterogene...

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11th international conference on Information Systems and Computing Technology, ISCTech 2023

作者： Xi, Zhengjie Liu, Jin Li, Xingye Zhou, Yujie Shanghai Maritime University College of Information Engineering Shanghai China

ISBN: (纸本)9798350342406

Visible-Infrared Cross Modality pedestrian Re-identification (CmRe-id) focuses on mapping pedestrian photographs from different cameras of the same identity. Despite significant achievements in previous works, it remains restriction: cross-modality feature mismatch caused by data heterogeneity. To overcome this restriction, in this paper, a heterogeneity augmented cross-modality learning is proposed to handle feature representations among modalities. Specifically, to cope with cross-modality feature mismatch, we design a Heterogeneity Augmented Module. It utilizes channel attention mechanism, extract heterogenous features of different modalities from feature maps. With channel attention mechanism, the HAM can better perceive identity information on both modalities. Comprehensive experiments on the public SYSU-MM01 and RegDB datasets reveal the proposed method's superiority over current practices. © 2023 IEEE.

关键词： Computer vision

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intelligent Algorithm Big data Analysis in Personalized Film and Television Recommendation Algorithms 11th

Intelligent Algorithm Big Data Analysis in Personalized Film...

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11th international conference on Applications and Techniques in Cyber Intelligence, ATCI 2023

作者： Lyu, Lin Ma, Chuanming School of Media&communication Changchun Humanities and Sciences College Jilin Changchun130117 China School of Creative Industry Jilin Animation Institute Jilin Changchun130012 China Department of Integrative Arts Silla University Busan46958 Korea Republic of

Many people can't receive accurate push according to their personal preferences when watching movies and TV (television) plays like Tiktok, and the recommendation accuracy of the existing movie and TV recommendation systems in the market is not high. this article comprehensively analyzed the relevant theories and methods of intelligent algorithms and big data analysis, and explored their specific applications in personalized film and television recommendations. Emphasis was placed on recommendation algorithms based on Generative Adversarial Networks (GANs), and big data analysis techniques such as user behavior mining and association analysis were introduced. this research found that the algorithm achieved the highest accuracy of 99% in personalized film and television recommendation, and could provide more accurate and personalized recommendation results by learning user behavior and film and television content characteristics. Big data analysis technology can explore user interests and preferences, discover associations and similarities between users, and build more accurate and personalized recommendation models. © 2024 the Authors. Published by Elsevier B.V.

关键词： Generative adversarial networks

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intelligent Bearing Fault Diagnosis With a Lightweight Neural Network 11

Intelligent Bearing Fault Diagnosis With a Lightweight Neura...

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11th international conference on Control, Automation and Information Sciences (ICCAIS)

作者： Nguyen Duc thuan Nguyen thi Hue Pham Quang Vuong Hoang Si Hong Hanoi Univ Sci & Technol Sch Elect & Elect Engn Hanoi Vietnam Phenikaa Univ Fac Elect & Elect Engn Hanoi Vietnam

ISBN: (数字)9781665452489

ISBN: (纸本)9781665452489

Bearing fault diagnosis plays a vital role in machine health monitoring. With the support of deep learning methods, bearing fault diagnosis based on neural networks using vibration signals has achieved excellent results. Compact models with high accuracy that can be embedded in handheld devices are in high demand. this research proposes a novel method for bearing fault diagnosis based on vibration signals using a low-computation-cost deep learning model. Generally, the vibration signal collected from the bearing of the electrical motor is transformed into the spectrogram images by Constant-Q nonstationary Gabor transform, in which these images are used as the training data. Subsequently, the proposed method uses a compact convolutional neural network, called MobileNetV3, combined with AutoCompress pruning method to decrease the number of required parameters and multiply-accumulate operations. Experimental results manifest that the nominated method gains an accuracy of up to 99.34%. Compared with other deep learning models with similar accuracy, the proposed model has a 2-times less number of parameters and approximately 8-times less number of multiply-accumulate operations.

关键词： fault diagnosis bearing fault machine health monitoring vibration signals convolutional neural network compression model

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Deep learning-based Anomaly Detection in Structural Health Monitoring 11

Deep Learning-based Anomaly Detection in Structural Health M...

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11th international conference on Structural Health Monitoring of intelligent Infrastructure, SHMII 2022

作者： Zhao, Mengchen Sadhu, Ayan Capretz, Miriam Department of Electrical and Computer Engineering Western University LondonONN6A 3K7 Canada Department of Civil and Environmental Engineering Western University LondonONN6A 3K7 Canada

A structural health monitoring (SHM) system involves the collection of large amounts of data and data transmission. However, sensors installed on civil infrastructures can get malfunctioned due to normal aging, exposure to outdoor weather conditions, and delayed replacements, resulting in the collection of multiclass anomalies in measured data. It is often combined with imbalance issues where most of the collected data remained biased to a certain type of anomaly. this paper proposes a convolutional neural network (CNN) with hyperparameter tuning using random search to optimize the performance. the proposed model is examined with a multiclass time-series of anomaly data obtained from a real-life cable-stayed bridge. the study concludes that balancing the database with a time shift window to increase the database has generated the optimum results, with an overall accuracy of 97.74%. © 2022 international conference on Structural Health Monitoring of intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.

关键词： Convolutional neural networks

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Machine learning Model for Medical data Classification for Accurate Brain Tumor Cell Detection 4th

Machine Learning Model for Medical Data Classification for A...

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4th international conference on Innovative Product Design and intelligent Manufacturing System, IPDIMS 2022

作者： Navabothu, Gnana Sri Sai Sujith Sakode, Himanshu Gottipati, Jagathi Rama Devi, Polagani Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada520007 India

ISBN: (纸本)9789819916641

the health industry has grown into a large organization, and a vast number of medical data can be produced every day by the healthcare industry to collect information for the prediction of future diseases that can be produced by a patient using treatment history and health information. In a variety of medical diagnostic applications, automated defect identification in medical imaging has become an arising sector. automated brain tumor identification and categorization are the focus of our work. In most cases, CT or MRI scans are used to examine the anatomy of the brain. the main objective of brain tumor detection is to help in clinical diagnosis. the goal is to design a machine learning method that ensures the existence of a tumor and to provide a reliable technique of tumor diagnosis that aids medical staff to provide proper medication to the patient. We get deep convolutional neural network (CNN) models that have already been trained using the transfer learning method to extract features from brain MRI scans. the Visual Geometry Group (VGG 16) is the most notable model when it comes to image recognition architectures as it surpasses and outperforms other traditional convolutional neural network models. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural network models

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