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Selective and Adaptive Incremental Transfer Learning with Multiple Datasets for Machine Fault Diagnosis

computers, Materials & Continua 2024年第1期78卷 1363-1379页

作者： Kwok Tai Chui Brij B.Gupta Varsha Arya Miguel Torres-Ruiz School of Science and Technology Hong Kong Metropolitan UniversityHong Kong999077China Department of Computer Science and Information Engineering Asia UniversityTaichung41354Taiwan Center for Advanced Information Technology Kyung Hee UniversitySeoul02447Korea Symbiosis Centre for Information Technology Symbiosis International UniversityPuneMaharashtra411042India Department of Electrical and Computer Engineering Lebanese American UniversityBeirut1102Lebanon School of Computing Skyline University CollegeSharjah1797United Arab Emirates Department of Business Administration Asia UniversityTaichung41354Taiwan Center for Interdisciplinary Research University of Petroleum and Energy StudiesDehradun248007India Department of Computer Science&Engineering Chandigarh UniversityChandigarh160036India Centro de Investigación en Computación Instituto Politécnico NacionalUPALM-ZacatencoMexico City07320Mexico

The visions of Industry 4.0 and 5.0 have reinforced the industrial *** have also made artificial intelligence incorporated as a major *** machine faults has become a solid foundation for automatically recognizing machine failure,and thus timely maintenance can ensure safe *** learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model,which typically involves two *** response to the availability of multiple datasets,this paper proposes using selective and adaptive incremental transfer learning(SA-ITL),which fuses three algorithms,namely,the hybrid selective algorithm,the transferability enhancement algorithm,and the incremental transfer learning *** is a selective algorithm that enables selecting and ordering appropriate datasets for transfer learning and selecting useful knowledge to avoid negative *** algorithm also adaptively adjusts the portion of training data to balance the learning rate and training *** proposed algorithm is evaluated and analyzed using ten benchmark *** with other algorithms from existing works,SA-ITL improves the accuracy of all *** studies present the accuracy enhancements of the SA-ITL,including the hybrid selective algorithm(1.22%-3.82%),transferability enhancement algorithm(1.91%-4.15%),and incremental transfer learning algorithm(0.605%-2.68%).These also show the benefits of enhancing the target model with heterogeneous image datasets that widen the range of domain selection between source and target domains.

关键词： Deep learning incremental learning machine fault diagnosis negative transfer transfer learning

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Reliable Fetal Health Assessment through Ensemble Techniques in Machine Learning

Reliable Fetal Health Assessment through Ensemble Techniques...

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Data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： M.Rajkumar E. S. Madhan Karthikeyan D Mohanraj Gopal Nithish G School of Computer Science Engineering and Information Systems Vellore Institute of Technology – Vellore Campus Tamil Nadu India School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Campus Tamil Nadu India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamil Nadu India Vellore Institute of Technology Vellore Campus Tamil Nadu India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

Ensuring the well-being of foetuses during pregnancy is crucial for preventing child and maternal mortality, aligning with the United Nations' Sustainable Development Goals. Utilizing a dataset of 2126 records from Cardiotocography (CTG) exams, obtained from the Kaggle repository, the study employs machine learning ensemble techniques, specifically stacking with 10x10 validations, to increase the classification of fetal health's precision and dependability. To address class imbalance, the dataset is resampled to 150%, and filter-based feature selection using the chi-squared statistical test is applied. Base models, including Decision Trees (C4.5), Random Forest (100 trees), Logistic Regression, and Sequential Minimal Optimization (SMO), are utilized. With the help of the Weka Machine Learning software tool, the ensemble model, after hyper parameter tuning and employing diverse techniques, with 98.68% accuracy, the model effectively classifies fetal health, indicating robust performance across various evaluation metrics.

关键词： Measurement Accuracy Soft sensors Stacking Solids Software reliability Sustainable development Software tools Random forests Tuning

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Integrating Consortium Blockchain and Attribute-Based Searchable Encryption for Automotive Threat Intelligence Sharing Model 6th

Integrating Consortium Blockchain and Attribute-Based Searc...

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6th International Conference on science of Cyber Security, SciSec 2024

作者： Xie, Tiange Liu, Feng Gao, Jiechao Wang, Yinghui Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Department of Computer Science University of Virginia CharlottesvilleVA United States School of Transportation Science and Engineering Beihang University Beijing China

ISBN: (纸本)9789819624164

Sharing cyber threat intelligence (CTI) enhances organizations’ abilities in threat detection and emergency response, fostering a proactive defense approach of "predict and prevent". Given the sensitive nature of automotive CTI and trust issues inherent in its sharing process, this paper presents an automotive CTI security sharing model that integrates blockchain and attribute-based searchable encryption. Leveraging consortium blockchain for CTI sharing, our model addresses the single point of failure and distrust in centralized systems through blockchain’s decentralization and immutability. Furthermore, by combining attribute-based searchable encryption algorithms with smart contracts, we achieve fine-grained access control and ciphertext retrieval for automotive CTI data. This enables data users to independently search CTI on the blockchain, mitigating the risk of sensitive information disclosure. Additionally, we store encrypted CTI data off-chain in the InterPlanetary File System (IPFS) to alleviate blockchain’s storage burden. Finally, we develop an automotive CTI sharing prototype system to demonstrate the feasibility and effectiveness of our proposed model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Access control

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Robust Multiresolution and Multistain Background Segmentation in Whole Slide Images 23rd

Robust Multiresolution and Multistain Background Segmentatio...

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Proceedings of the 23rd Polish Conference on Biocybernetics and Biomedical engineering, PCBEE 2023

作者： Jurgas, Artur Wodzinski, Marek Atzori, Manfredo Müller, Henning Information Systems Institute Sierre Switzerland Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Krakow Poland Department of Neuroscience University of Padova Padova Italy Medical Faculty University of Geneva Geneva Switzerland

ISBN: (纸本)9783031384295

Background segmentation is an important step in analysis of histopathological images. It allows one to remove irrelevant regions and focus on the tissue of interest. However, background segmentation is challenging due to the variability of stain colors and intensity levels across different images, modalities, and magnification levels. In this paper, we present a learning-based model for histopathology background segmentation based on convolutional neural networks. We compare two multiresolution approaches to deal with the variability of magnification in histopathology images: (i) model that uses upscaling of smaller patches of the image, and (ii) model simultaneously trained on multiple resolution levels. Our model is characterized by solid performance both in multiresolution and multistain dyes (H &E and IHC), achieving good performance on publicly available dataset. The quantitative scores are, in terms of the Dice score, close to 94.71. The qualitative analysis presents strong performance on previously unseen cases from different distributions and various dyes. We freely release the model, weights, and ground-truth annotations to promote the open science and reproducible research. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Pathology

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Urban-scale Point Cloud Semantic Segmentation with Transformer

Urban-scale Point Cloud Semantic Segmentation with Transform...

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2022 International Conference on Electronic information engineering and computer science, EIECS 2022

作者： Song, Jinge Cao, Zhenyuan Li, Xueyan Li, Xiuying Guo, Shuxu College of Electronic Science and Engineering Jilin University Changchun China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9781510663312

Semantic segmentation of urban-scale point clouds is widely used in aviation, unmanned aerial vehicles, and autonomous driving. However, owing to the many points in the urban-scale point cloud dataset and massive computation in the learning process, traditional networks often have poor segmentation performance and high costs. In this study, we adopted the Point Transformer network as the baseline and integrated random point sampling and attentive pooling into a new transition-down block, embedded in the encoder structure of the baseline to improve the speed and accuracy of semantic segmentation. On the challenging SensatUrban dataset, the Point Transformer network and the proposed network obtained mIoU values of 71.1% and 76.8%, respectively. The results show that the proposed network effectively improves the shortcomings of the Point Transformer network and achieves better semantic segmentation performance of urban-scale point clouds. © 2023 SPIE.

关键词： Semantic Segmentation

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Routing and Spectrum Assignment in Spatial Channel Network-Based Inter-Datacenter Networks

Routing and Spectrum Assignment in Spatial Channel Network-B...

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2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Zheng, Weichang Zheng, Yu Yang, Mingcong Yang, Kun School of Information and Communication Engineering Yangtze Delta Region Institute University of Electronic Science and Technology of China Quzhou China Graduate School of Systems and Information Engineering University of Tsukuba Tsukuba Japan Huawei Technologies Department of R&d Wuhan China School of Information and Communication Engineering University of Electronic Science and Technology of China Sichuan China

We introduce spatial channel networks (SCNs) to the interconnection of data center networks (DCNs). We propose an integer linear programming (ILP) model to solve the routing and spectrum assignment problem for SCN-bas... 详细信息

ISBN: (纸本)9798350312614

关键词： datacenter network (DCN) integer linear programming (ILP) routing and spectrum assignment Spatial channel network (SCN)

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Feasibility analysis of the Mamba-based landslide mapping from remote sensing images 24

Feasibility analysis of the Mamba-based landslide mapping fr...

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5th International Conference on Artificial Intelligence and computer engineering, ICAICE 2024

作者： Zhao, Chengqiang Chen, Shijie Wu, Fan Li, Huibin School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu China School of Engineering and Physics Science Herito-Watt University Edinburgh United Kingdom School of Computer and Network Security Chengdu University of Technology Sichuan Chengdu China

ISBN: (纸本)9798400718007

Mamba models based on the state space model can achieve the inference speed at a linear time in long-range contextual tasks, and their performance can match and even surpass existing Transformer models in some natural language processing and computer vision tasks. However, no Mamba-based image segmentation model has been discussed in the field of landslide mapping from remote sensing images (RSI). To fill this gap, we make efforts to explore the application of Mamba-based image segmentation models to landslide mapping from RSI. Besides, we employ three Mamba-based image segmentation models, namely, RS3Mamba, MiM-ISTD, and UltraLight VM-UNet, to analyze the performance of landslide segmentation and mapping on the dataset VHRRSLM that we have collated. Compared with the representative image segmentation models, Swin-UNet and Trans-UNet, the three comparative models achieve relatively close performance, and the parameters of the models and FLOPS have distinct superiorities. These superiorities reflect that the deployment of Mamba-based landslide mapping from RSI has promising potential for application in the case of limited remote sensing resources. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Mapping

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Hybrid TCN-transformer model for predicting sustainable food supply and ensuring resilience

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Alexandria engineering Journal 2025年 127卷 380-391页

作者： Alrashdi, Ibrahim Abd El-Aziz, Rasha M. Taloba, Ahmed I. Farsi, Mohammed Department of Computer Science College of Computer and Information Sciences Jouf University Al-Jouf Sakaka73211 Saudi Arabia Department of Information Systems College of Computer Science and Engineering Taibah University Yanbu966144 Saudi Arabia

Major concerns occur in maintaining a sustainable food supply due to population expansion, supply chain interruptions, and climate-related changes. Traditional forecasting models, such as ARIMA, LSTM, and GRU, fail to deal with the dynamic fluctuations and long-range correlations. These methods often have reduced accuracy, high computational cost, and poor generalization when applied to shifting patterns in the food supply system. Based on the abovementioned limitations, this study proposes a Hybrid TCN-Transformer Model by combining the strength of Temporal Convolutional Networks (TCN) with Transformer Attention to provide accurate and efficient food supply prediction. Thus, the project's key objective is to develop a deep learning system that effectively handles massive quantities of temporal data while preserving dependence in short and long durations. The proposed method is novel since it integrates TCN's causal convolutions for extracting sequential patterns. It uses a Transformer Model's self-attention mechanism to capture the complex interactions across time steps. Hybrid design enables faster training, increased interpretability, and better prediction accuracy than current methods. Results from experiments have revealed that the suggested model surpasses the performance of the stand-alone TCN, ARIMA, LSTM, and GRU models in terms of accuracy of predictions, efficiency of computations, and adaptability. Findings thus far suggest deep learning-based predictive analytics has helped improve the food supply chain management by mitigating food wastage, making it environmentally resilient. © 2025 The Authors

关键词： Deep learning

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Perceptual quantization parameter selection for crime scene investigation tool images

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Frontiers of computer science 2023年第1期17卷 241-243页

作者： Yanchao GONG Zhao LI Zhuang WANG Kaifang YANG Ying LIU Keng Pang LIM School of Communications and Information Engineering Xi’an University of Posts and TelecommunicationsXi’an 710121China Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai 201210China School of Electronic and Information Engineering Chongqing Three Gorges UniversityChongqing 404000China School of Computer Science Shaanxi Normal UniversityXi’an 710119China XsecPro Pte.Ltd. Singapore 787820Singapore

1 Introduction Crime scene investigation(CSI)is critical for solving criminal cases and court ***,all CSI data must be kept as electronic files in specific systems according to relevant *** files,including reconstructed CSI images,provides great convenience for the public security and court to store,manage,display,and analyze CSI data more *** coding which pursues higher reconstructed quality of CSI images using lower coding rate plays a very important role in the above *** with the previous image coding standards,high efficiency video coding(HEVC)still image coding technique significantly improves the image coding *** coding efficiency of HEVC is closely related to the selected quantization parameter(QP).

关键词： parameter image CSI

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Advanced VANET Technology for Emergency Vehicle Safety in Time-Critical Scenario 8

Advanced VANET Technology for Emergency Vehicle Safety in Ti...

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8th IEEE International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2023

作者： Murali, N. Beulah David, D. Saveetha School of Engineering Simats Department of Computer Science and Engineering Tamilnadu Chennai India Institute of Information Technology Saveetha School of Engineering Simats Department of Data Analytics Tamilnadu Chennai India

ISBN: (纸本)9798350347791

The most pressing issue with ITS is minimal latency in communication between vehicles, especially given that unstable traffic flow and road safety are two of their main concerns. Because to these problems, I have concentrated my study on employing a vehicular ad hoc network to track speedy object detection and provide the quickest warning to drivers. These systems require ongoing monitoring to function properly, making it possible to use machine learning techniques on vast amounts of data produced by various VANET applications. Machine learning is a technique used to prevent accidents for emergency vehicles by automatically learning and improving the system's rapid object detection stage in situations with high traffic. Our methods effectively implement the VANET aim by providing efficient unsupervised learning of the collected data. In this research, we suggested a unique real-time (DT-YOLO) algorithm-based automatic discovering vehicle gap approach for detecting and identifying objects in congested metropolitan regions. By using VANET technologies, we were able to address traffic, communication, and safety concerns in metropolitan areas. We investigated, using DT-YOLO technology, how these problems may be resolved in a transparent object-tracking system via machine learning techniques. Fast-moving EVs have a high probability of finding a gap to let the driver to make a decision. Moreover, a case study demonstrating a VANET-based scenario of nearest-object and gap-finding to flee from congested traffic with the ideal vehicle moment is shown. The paper examined current challenges and future directions. © 2023 IEEE.

关键词： Object detection

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