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31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Zhang, Shigeng Xiao, Kai Yu, Jianjiang Liu, Xuan Wang, Weiping School of Computer Science and Engineering Central South University China Zhengzhou Xinda Institute of Advanced Technology China College of Computer Science and Electronic Engineering Hunan University China

ISBN: (纸本)9798350399738

The number of devices connected to the Internet has been exploding in recent years, and the wide range of device types poses a serious challenge for asset management and maintenance. We need to know if IoT devices are under cyberattack and if there are devices that violate our privacy, such as pinhole cameras. Traffic-oriented IoT device type identification has become an effective method to prevent cyberattacks and manage assets, but at this stage, in the face of the proliferation of novel IoT devices, the current mainstream IoT device type identification methods are difficult to identify them successfully. At the same time, for a significant number of lightweight IoT devices, most identification methods are simply unable to make correct identifications because the traffic generated by these devices is too little. In this paper, we propose IoT-Siamese, a type identification method for IoT devices based on few-shot traffic, which mainly relies on Siamese network to solve the problem of few samples. Experiments show that our proposed identification method has high identification accuracy for those devices that generate a small volume of traffic, and effectively identify novel devices that join the network. © 2023 IEEE.

关键词： Internet of things

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An Ontology-based Three-Stage Approach to Medical Text Classification with Feature Selection by Particle Swarm Optimisation 28th

An Ontology-based Three-Stage Approach to Medical Text Class...

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28th International Conference on Knowledge Based and Intelligent information and engineering Systems, KES 2024

作者： Abdollahi, Mahdi Gao, Xiaoying Mei, Yi Ghosh, Shameek Li, Jinyan Narag, Michael School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand Founder & CTO Medius Health Sydney Australia Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518055 China

The document classification (DC) task assigns predefined classes to unlabeled documents using trained models. In the medical field, DC is crucial for tasks like categorizing risk factors and classifying electronic health records. This paper addresses challenges in medical document analysis, such as the prevalence of abbreviations and acronyms. Existing classification performance in medical documents is suboptimal. The paper introduces novel feature engineering methods leveraging domain-specific knowledge to enhance classification performance. Results indicate that the Three-Stage approach surpasses related works, showcasing improved medical document classification performance. © 2024 The Authors.

关键词： Particle swarm optimization (PSO)

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Leveraging Artificial Neural Networks for Breast Cancer Detection and Prognosis

Leveraging Artificial Neural Networks for Breast Cancer Dete...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Goel, Aadya Mishra, Pallavi Bhatia, Rachna School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Advanced Sciences Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331512965

Breast cancer remains one of the leading causes of mortality among women worldwide, making early detection and accurate classification critical for improving patient out-comes. This study explores the application of Artificial Neural Networks (ANN) for breast cancer multiclass classification and prediction using the CBIS-DDSM: Breast Cancer Image Dataset. The model was trained to classify mammographic images into three categories: benign, malignant, and benign without callback, achieving an impressive accuracy of 99.6%. The choice of ANN was driven by its proven capability to model complex, nonlinear relationships within data, offering a significant improvement over traditional machine learning technique. Despite the challenges posed by the limited size and heterogeneity of the dataset, the ANN demonstrated robust performance, highlighting its potential for clinical applications. The high accuracy of the model underscores its value as a reliable tool for aiding radiologists in the early detection and prognosis of breast cancer, which could lead to more effective treatment planning and better patient outcomes. This study lays the foundation for integrating advanced AI technologies into clinical settings, offering promise for more precise, timely breast cancer diagnoses. © 2025 IEEE.

关键词： Lung cancer

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Deep learning-based classification,detection,and segmentation of tomato leaf diseases:A state-of-the-art review

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Artificial Intelligence in Agriculture 2025年第2期15卷 192-220页

作者： Aritra Das Fahad Pathan Jamin Rahman Jim Md Mohsin Kabir M.F.Mridha Department of Computer Science and Engineering American International University-BangladeshDhaka 1229Bangladesh Institute of Information and Communication Technology Shahjalal University of Science and TechnologySylhet 3114Bangladesh Advanced Machine Intelligence Research Lab Dhaka 1207Bangladesh Bangladesh University of Business and Technology DhakaBangladesh

The early identification and treatment of tomato leaf diseases are crucial for optimizing plant productivity,efficiency and *** by the farmers poses the risk of inadequate treatments,harming both tomato plants and *** of disease diagnosis is essential,necessitating a swift and accurate response to misdiagnosis for early *** regions are ideal for tomato plants,but there are inherent concerns,such as weather-related *** diseases largely cause financial losses in crop *** slow detection periods of conventional approaches are insufficient for the timely detection of tomato *** learning has emerged as a promising avenue for early disease *** study comprehensively analyzed techniques for classifying and detecting tomato leaf diseases and evaluating their strengths and *** study delves into various diagnostic procedures,including image pre-processing,localization and *** conclusion,applying deep learning algorithms holds great promise for enhancing the accuracy and efficiency of tomato leaf disease diagnosis by offering faster and more effective results.

关键词： Deep learning Tomato leaf disease Classification Detection Segmentation Imaging

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Observer-Structured PID Controller: Flight Controller Design Examples 4

Observer-Structured PID Controller: Flight Controller Design...

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4th International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2024

作者： Sato, Masayuki Sebe, Noboru Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan

ISBN: (纸本)9798350391183

This note shows two design examples of PID con-Trollers, whose state variables estimate the selected state vari-Ables of the controlled plant systems, for LTI Parametrically Dependent (LTIPD) systems. The authors have already proposed observer-structured controllers, whose structure is not the same as but is very similar to the well-established Luenberger observer-based controllers, and have also proposed a conversion method from a priori designed unstructured LTI or Linear Parameter-Varying (LPV) controllers to observer-structured LTI/LPV con-Trollers with the plant state estimation ability implemented. How-ever, the successful conversion has been reported only for full-order controllers. In this note, we show two examples, in which a priori designed LTI PID controllers are successfully converted to observer-structured PID controllers, and the designed observer-structured PID controllers well estimate the selected plant state variables in numerical simulations. © 2024 IEEE.

关键词： Three term control systems

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Federated Ensemble for Lung Pneumonia Detection 15

Federated Ensemble for Lung Pneumonia Detection

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sharma, Vishwash Challa, VenkataHemant Kumar Reddy Pranavi, Pasupuleti Singh, Tripty Nair, Rekha R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India Alliance University Alliance School of Advanced Computing Department of Computer Science and Enginnering Bengaluru India

ISBN: (纸本)9798350370249

In the realm of medical imaging, the detection of lung pneumonia through automated methods has garnered significant attention due to its potential to augment diagnostic processes. This study proposes a novel approach, termed Federated CNN Ensemble, aiming to enhance the accuracy and robustness of pneumonia detection from chest X -ray images through federated learning and ensemble techniques. The proposed method leverages convolutional neural networks trained locally at distributed medical institutions, thereby preserving data privacy and security while harnessing collectiveintelligence. By aggregating the learned models through an ensemble strategy, our approach achieves superior performance in pneumonia detection compared to standalone CNN models. This research contributes to the advancement of automated medical diagnosis systems, offering a promising avenue for scalable and collaborative disease detection in healthcare settings. © 2024 IEEE.

关键词： DeepLearning Ensemble models Federated Learning

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The fNIRS-based Emotion Recognition by Spatial Transformer and WGAN Data Augmentation Towards Developing A Novel Affective BCI

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IEEE Transactions on Affective Computing 2024年第2期16卷 875-890页

作者： Si, Xiaopeng Huang, He Yu, Jiayue Ming, Dong Tianjin University Academy of Medical Engineering and Translational Medicine State Key Laboratory of Advanced Medical Materials and Devices Haihe Laboratory of Brain-computer Interaction and Human-machine Integration Tianjin Key Laboratory of Brain Science and Neural Engineering Institute of Applied Psychology Tianjin300072 China Tianjin University Academy of Medical Engineering and Translational Medicine State Key Laboratory of Advanced Medical Materials and Devices Haihe Laboratory of Brain-computer Interaction and Human-machine Integration Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin300072 China

The affective brain-computer interface (aBCI) facilitates the objective identification or regulation of human emotions. Current aBCI mainly relies on electroencephalography (EEG). However, research shows that emotions involve a large-scale distributed brain network. Compared to electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS) offers a higher spatial resolution. It holds greater potential in capturing emotional spatial information, which may foster the development of new affective Brain-computer Interfaces (aBCI). We proposed a novel self-attention-based deep-learning transformer language model for fNIRS cross-subject emotion recognition, which could automatically learn the emotion's spatial attention weight information with strong interpretability. Besides, we performed data augmentation by introducing the wasserstein generative adversarial networks (WGAN). Results showed: (1) We achieved 84% three-category cross-subject emotion decoding accuracy. The spatial transformer module and WGAN improved the accuracy by 12.8% and 4.3%, respectively. (2) Compared with cutting-edge fNIRS research, we led by 10% in three-category decoding accuracy. (3) Compared with cutting-edge EEG research, we lead by 28% in arousal decoding accuracy, 10% in valence decoding accuracy, and 2% in three-category decoding accuracy. (4) Besides, our approach holds the potential to uncover the brain's spatial encoding mechanism of human emotion processing, providing a new direction for building interpretable artificial intelligence models. © 2010-2012 IEEE.

关键词： Near infrared spectroscopy

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A Robust Deep Feature Fusion Model for Skin Cancer Classification 3rd

A Robust Deep Feature Fusion Model for Skin Cancer Classific...

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3rd International Conference on advanced Computing and Applications, ICACA 2024

作者： Mandal, Diptarka Ray, Amartya Sarkar, Sujan Ojjela, Odelu Sarkar, Ram School of Computer Engineering and Mathematical Sciences Defence Institute of Advanced Technology Maharashtra Pune411025 India Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata700032 India

ISBN: (纸本)9789819747986

Skin cancer ranks among the deadliest forms of cancer worldwide, leading to numerous fatalities annually. Hence, the development of efficient computer-aided diagnosis (CAD) systems has become a pressing need to help medical professionals classify various types of skin cancer. This paper introduces a novel method which combines the outcomes of two deep learning models, leveraging the feature fusion strategy. Our approach utilizes Xception and Google’s Big Transfer (BiT-M) model as base learners, complemented by a squeeze and excitation attention module to enhance the fused feature maps. We also establish a combined loss function to optimize our model. We have evaluated the model on the challenging ISIC 2017 dataset and obtained an impressive accuracy of 0.795, surpassing the performance of many recently published methods within the literature. The source code of the proposed model is available at https://***/Cmatermedicalimageanalysis/ISIC2017. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Lung cancer

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Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 686-698页

作者： Zhaolin Yuan Xiaorui Li Di Wu Xiaojuan Ban Nai-Qi Wu Hong-Ning Dai Hao Wang the Beijing Advanced Innovation Center for Materials Genome Engineering Institute of Artificial IntelligenceBeijing Key Laboratory of Knowledge Engineering for Materials ScienceSchool of Computer and Communication EngineeringUniversity of Science and Technology Beijing the Department of ICT and Natural Science Norwegian University of Science and Technology IEEE the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and Technology the Department of Computing and Decision Sciences Lingnan University the Department of Computer Science Norwegian University of Science and Technology

It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the *** proliferation of industrial sensors and the availability of thickening-system data make this ***,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive *** address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening *** a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental *** results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system *** proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.

关键词： Industrial 24 paste thickener ordinary differential equation(ODE)-net recurrent neural network time series prediction

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WindVibraTransformer: A Foundational Model for Precise and Robust Wind Turbine Condition Monitoring via Vibration Signals 23

WindVibraTransformer: A Foundational Model for Precise and R...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Wakayama, Takuya Inoue, Taiki Ogata, Jun Iida, Makoto Ogawa, Tetsuji Waseda University Department of Communications and Computer Engineering Japan Japan Research Center for Advanced Science and Technology The University of Tokyo Japan

ISBN: (纸本)9798350374889

We aimed to develop WindVibraTransformer, a foundational model based on Transformers applied to vibration spectrograms, to derive precise and robust feature representations for wind turbine condition monitoring. In anomaly detection for wind turbine equipment, it is common to employ inlier modeling, where models are built solely on data from normal operation, with deviations classified as anomalies. Acquiring data for wind turbines, especially from the specific turbine being monitored, is challenging, often resulting in limited availability. However, this limitation can increase susceptibility to false alarms due to environmental variations. Thus, in this study, we explored the use of a vision Transformer trained with self-supervised learning to predict masked spectra conditioned on unmasked ones from vibration spectrograms, serving as the precise and robust feature extractor. Since this model aims to capture relationships within the context of the surroundings rather than solely the overview of the spectrogram, the resulting feature representations are expected to be complex yet less directly influenced by environmental variations. The complexity of these feature representations is addressed by constructing the normal state model using highly expressive normalizing flows. Experimental comparisons conducted on anomaly detection using real wind turbine vibration signals demonstrated that our approach enables highly reliable detection compared to existing unsupervised auto-encoder-based and supervised wind turbine classifier-based feature extraction methods. © 2024 IEEE.

关键词： Spectrographs

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