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arXiv 2021年

作者： Ding, Xiaoou Wang, Hongzhi Wang, Chen Li, Zijue Liang, Zheng School of Computer Science and Technology Harbin Institute of Technology China National Engineering Laboratory for Big Data Software EIRI Tsinghua University China

—The demand for high-performance anomaly detection techniques of IoT data becomes urgent, especially in industry field. The anomaly identification and explanation in time series data is one essential task in IoT data mining. Since that the existing anomaly detection techniques focus on the identification of anomalies, the explanation of anomalies is not well-solved. We address the anomaly explanation problem for multivariate IoT data and propose a 3-step self-contained method in this paper. We formalize and utilize the domain knowledge in our method, and identify the anomalies by the violation of constraints. We propose set-cover-based anomaly explanation algorithms to discover the anomaly events reflected by violation features, and further develop knowledge update algorithms to improve the original knowledge set. Experimental results on real datasets from large-scale IoT systems verify that our method computes high-quality explanation solutions of anomalies. Our work provides a guide to navigate the explicable anomaly detection in both IoT fault diagnosis and temporal data cleaning. Copyright © 2021, The Authors. All rights reserved.

关键词： Internet of things

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Remote Sensing Scene Classification Based on Improved GhostNet

Remote Sensing Scene Classification Based on Improved GhostN...

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2020 International Conference on computer science and Communication Technology, ICCSCT 2020

作者： Wei, Biyun Shen, Xiaole Yuan, Yule College of Big Data and Internet Shenzhen Technology University Shenzhen China College of Applied Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Nowadays, the design of convolutional neural network (CNN) models is getting deeper and wider. When traditional CNN is used to process limited data of remote sensing images, it will lead to overfitting. We will use lightweight and efficient models to classify remote sensing images. In order to improve the classification accuracy and reduce the intermediate parameters, we improved GhostNet and proposed a smaller CNN named Improved GhostNet. Meanwhile, we use image enhancement methods to enlarge the datasets and dropout, it will reduce the amount of parameters. We experimented on three datasets, such as AID, UC Merced, NWPU-RESISC45. Then, we used MobileNetV3-Small and GhostNet to compare with our CNN model. The classification accuracy of improved GhostNet achieves more than 91%, and the accuracy on the AID is improved by 2.05% compared to the original GhostNet. These results demonstrate the effectiveness and efficiency of improved GhostNet. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Image enhancement

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Code-Aware Fault Localization with Pre-Training and Interpretable Machine Learning

SSRN

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SSRN 2022年

作者： Zhang, Zhuo Li, Ya Yang, Sha Zhang, Zhanjun Lei, Yan School of Information Technology and Engineering Guangzhou College of Commerce Guangzhou China Ningbo Artificial Intelligence Institute Shanghai Jiaotong University Ningbo China College of Computer National University of Defense Technology Changsha China School of Big Data and Software Engineering Chongqing University Chongqing China

Following the rapid development of deep learning, many studies in the field of fault localization (FL) have utilized deep learning to analyze statements coverage information (i.e., executed or not executed) and test cases' results (i.e., failing or passing), which have shown dramatic ability in identifying suspicious statements potentially responsible for failures. However, they mainly pay attention to the binary information of executing test cases but ignore incorporating code snippets and their inner relationships into the learning process. Furthermore, how a complex deep learning model for FL achieves a particular decision is not transparent. These drawbacks may limit the effectiveness of FL. Recently, graph-based pre-training techniques have dramatically improved the state-of-the-art in a variety of code-related tasks such as natural language code search, clone detection, code translation, code refinement, etc. And interpretable machine learning tackles the problem of non-transparency and enables learning models to explain or present their behaviours to humans in an understandable *** this paper, our insight is to select a candidate solution that leverages the promising learning ability of graph-based pre-training techniques to learn a feasible model for incorporating code snippets as well as their inner relationships into fault localization, and then uses interpretable machine learning to localize faulty statements. Thus, we propose CodeAwareFL, a code-aware fault localization technique with pre-training and interpretable machine learning. Concretely, CodeAwareFL constructs a variety of code snippets through executing test cases. Next, CodeAwareFL utilizes the code snippets to extract propagation chains which could show a set of variables interact with each other to cause a failure. After that, a graph-based pre-trained model is customized for fault localization. CodeAwareFL takes the code snippets and their corresponding propagation chains as inputs with te

关键词： Deep learning

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Hierarchical Context Modeling Network for Landmark Recognition

Hierarchical Context Modeling Network for Landmark Recogniti...

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IEEE International Conference on data Mining (ICDM)

作者： Xing Bao Huan Zheng Zhao Zhang Zhengjun Zha Meng Wang School of Information Science and Technology University of Science and Technology of China Hefei China Aerospace Information Research Institute Suzhou China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Kunming China

Landmark recognition stands as a prominent classification challenge within the domain of vision and perception, involving the identification and localization of landmarks in images. However, existing landmark recognition methods often fall short in delivering satisfactory performance. A critical issue lies in the lack of simultaneous exploration of local, regional, and global modeling. Intuitively, the local details, regional features, and global structure within an image, especially of a building, significantly contribute to landmark recognition. To address this issue, we propose a novel approach, termed Hierarchical Context Modeling Network (HCMNet), for landmark recognition. In our methodology, we introduce a Hierarchical Context Modeling Block (HCMB) designed with a triplet-branch structure to model image context in a hierarchical manner. These branches consist of the local branch, which leverages a convolutional layer to capture local details, the regional branch focused on extracting regional features through large-kernel asymmetrical convolutional layers, and the global branch aims at encompassing the broader context by incorporating channel attention. To enhance the modeling capacity, we assemble multiple HCMBs into the HCMNet, creating a hierarchical structure. Comprehensive experiments validate the efficacy of the proposed HCMNet for landmark recognition, showcasing its superior performance compared to current methods. The proposed hierarchical context modeling approach proves instrumental in capturing the intricate features essential for accurate landmark recognition.

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Predictive Analytics for Ear Health Management Using IoT and Ensemble Learning

Predictive Analytics for Ear Health Management Using IoT and...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： S.K. Saravanan Palaniraj Rajidurai Parvathy R. Meenakshi Prabu K T. R. GaneshBabu B Meenakshi Department of Big Data and Network Security Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Mphasis Corporation Chandler Arizona USA Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Gojan School of Business and Technology Redhills Chennai Tamil Nadu India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Electrical and Electronics Engineering Sri Sairam Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Proactive health monitoring solutions are becoming more important due to a growing population of older people. To better manage the ear health of the elderly, this research investigates ensemble learning methods within the domain of predictive analytics using the Internet of Things (IoT). It provides an ensemble learning approach that uses IoT capabilities to predict potential ear health problems in this population. It evaluates the efficacy of many ensembles learning approaches, including bagging, boosting, and stacking, using real-world sensor data acquired from IoT devices. Our results show that ensemble learning methods considerably improve prediction accuracy compared to standalone models. Ensemble learning overcomes the limitations of individual algorithms and maximizes their combined prediction potential by efficiently combining varied models. Our findings have important implications for the management of healthcare for the elderly, providing new opportunities to enhance early diagnosis and intervention techniques for ear health concerns via the use of predictive analytics provided by the IoT. It highlights the potential of ensemble learning approaches to improve predictive analytics in the senior healthcare sector, especially with IoT-driven solutions.

关键词： Accuracy Stacking Ear Medical services Predictive models Ensemble learning Internet of Things Older adults Predictive analytics Monitoring

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Construction Cost Index Forecasting: A Multi-feature Fusion Approach

arXiv

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arXiv 2021年

作者： Zhan, Tianxiang He, Yuanpeng Xiao, Fuyuan College of Computer and Information Science College of Software Southwest University Chongqing400715 China School of Big Data and Software Engineering Chongqing University Chongqing401331 China Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China

The construction cost index is an important indicator of the construction industry. Predicting CCI has important practical significance. This paper combines information fusion with machine learning, and proposes a multi-feature fusion (MFF) module for time series forecasting. Compared with the convolution module, the MFF module is a module that extracts certain features. Experiments have proved that the combination of MFF module and multi-layer perceptron has a relatively good prediction effect. The MFF neural network model has high prediction accuracy and efficient prediction efficiency. At the same time, MFF continues to improve the potential of prediction accuracy, which is a study of continuous attention. Copyright © 2021, The Authors. All rights reserved.

关键词： Forecasting

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Detection of Misinformation Related to Pandemic Diseases using Machine Learning Techniques in Social Media Platforms

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EAI Endorsed Transactions on Pervasive Health and Technology 2024年 10卷

作者： Naeem, J. Gul, O.M. Parlak, I.B. Karpouzis, K. Salman, Y.B. Kadry, S.N. Department of Computer Engineering Bahcesehir University Istanbul Turkey Informatics Institute Istanbul Technical University Istanbul Turkey Department of Computer Engineering Galatasaray University Istanbul Turkey Panteion University of Social and Political Sciences Greece Department of Software Engineering Bahcesehir University Istanbul Turkey Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon MEU Research Unit Middle East University Amman11831 Jordan

INTRODUCTION: The advent of the COVID-19 pandemic has brought with it not only a global health crisis but also an infodemic characterized by the rampant spread of misinformation on social media platforms. OBJECTIVES: In response to the urgent need for effective misinformation detection, this study presents a comprehensive approach harnessing machine learning and deep learning techniques, culminating in ensemble methods, to combat the proliferation of COVID-19 misinformation on Facebook, Twitter, Instagram, and YouTube. METHODS: Drawing from a rich dataset comprising user comments on these platforms, encompassing diverse COVID-19-related discussions, our research applies Support Vector Machine (SVM), Decision tree, logistic regression, and neural networks to perform in depth analysis and classification of comments into two categories: positive and negative information. The innovation of our approach lies in the final phase, where we employ ensemble methods to consolidate the strengths of various machine learning and deep learning algorithms. This ensemble approach significantly improves the model’s overall accuracy and adaptability. RESULTS: Experimental results underscore the efficacy of our methodology, showcasing marked improvements in detection performance compared to individual models. After applying ensemble learning, we achieve an accuracy of 91% for Facebook data, 79% for Instagram data, 80% for Twitter data and 95% for YouTube data. CONCLUSION: Our system not only aids in curbing the dissemination of COVID-19 misinformation but also provides a robust framework for addressing misinformation across various contexts on social media platforms. © 2024 J. Naeem et al., licensed to EAI.

关键词： COVID-19

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Speech emotion recognition using emotion perception spectral feature

Speech emotion recognition using emotion perception spectral...

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作者： Jiang, Lin Tan, Ping Yang, Junfeng Liu, Xingbao Wang, Chao Institute of Big Data and Internet Innovation College of Computer and Information Engineering Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China College of Software East China University of Technology Nanchang China Collaborative Innovation Center for Economics Crime Investigation and Prevention Technology Jiangxi Police College Nanchang China National Engineering Research Center for Multimedia Software Wuhan University Wuhan China

Speech emotion recognition is an important technique for human-computer interface applications. Due to contain rich information of emotion, the spectral feature is widely used for emotion recognition. However, the recognition performance is limited because of imprecise extracted rule and uncertain size of resolution of spectral feature. To address this issue, motivated by speech coding, we introduced psychoacoustics model, provided a perception spectral subband partition method for obtaining more precise frequency resolution. Moreover, we also provided a new spectral feature on the divided subband frequency signals. The proposed feature includes emotional perception entropy, spectral inclination, and spectral flatness. Then, a Support Vector Machine classifier is used to recognize emotion categories. The experiment results show that the proposed spectral feature is superior to the traditional MFCC feature, and also better than the state-of-the-art Fourier feature and multi-resolution amplitude feature. © 2019 John Wiley & Sons, Ltd.

关键词： Emotion Recognition

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Code-Aware Fault Localization with Pre-Training and Interpretable Machine Learning

SSRN

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SSRN 2022年

关键词： Deep learning

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DP-GAN+B: A lightweight generative adversarial network based on depthwise separable convolutions for generating CT volumes

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computers in Biology and Medicine 2024年 174卷 108393-108393页

作者： Xing, Xinlong Li, Xiaosen Wei, Chaoyi Zhang, Zhantian Liu, Ou Xie, Senmiao Chen, Haoman Quan, Shichao Wang, Cong Yang, Xin Jiang, Xiaoming Shuai, Jianwei Postgraduate Training Base Alliance of Wenzhou Medical University Zhejiang Wenzhou325000 China Wenzhou Institute University of Chinese Academy of Sciences Zhejiang Wenzhou325000 China School of Artificial Intelligence Guangxi Minzu University Nanning530006 China Department of Radiology The First Affiliated Hospital of Wenzhou Medical University Wenzhou325000 China Department of Big Data in Health Science The First Affiliated Hospital of Wenzhou Medical University China Department of Mathematics and Statistics Carleton College 300 N College St NorthfieldMN55057 United States School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

X-rays, commonly used in clinical settings, offer advantages such as low radiation and cost-efficiency. However, their limitation lies in the inability to distinctly visualize overlapping organs. In contrast, Computed Tomography (CT) scans provide a three-dimensional view, overcoming this drawback but at the expense of higher radiation doses and increased costs. Hence, from both the patient's and hospital's standpoints, there is substantial medical and practical value in attempting the reconstruction from two-dimensional X-ray images to three-dimensional CT images. In this paper, we introduce DP-GAN+B as a pioneering approach for transforming two-dimensional frontal and lateral lung X-rays into three-dimensional lung CT volumes. Our method innovatively employs depthwise separable convolutions instead of traditional convolutions and introduces vector and fusion loss for superior performance. Compared to prior models, DP-GAN+B significantly reduces the generator network parameters by 21.104 M and the discriminator network parameters by 10.82 M, resulting in a total reduction of 31.924 M (44.17%). Experimental results demonstrate that our network can effectively generate clinically relevant, high-quality CT images from X-ray data, presenting a promising solution for enhancing diagnostic imaging while mitigating cost and radiation concerns. © 2024 Elsevier Ltd

关键词： Convolution

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