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Real-time earthquake magnitude prediction using designed machine learning ensemble trained on real and CTGAN generated synthetic data

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Geodesy and Geodynamics 2025年第3期16卷 350-368页

作者： Anushka Joshi Balasubramanian Raman C.Krishna Mohan Department of Computer Science and Engineering Indian Institute of Technology

The earthquake early warning(EEW) system provides advance notice of potentially damaging ground shaking. In EEW, early estimation of magnitude is crucial for timely rescue operations. A set of thirty-four features is extracted using the primary wave earthquake precursor signal and site-specific *** Japan's earthquake magnitude dataset, there is a chance of a high imbalance concerning the earthquakes above strong impact. This imbalance causes a high prediction error while training advanced machine learning or deep learning models. In this work, Conditional Tabular Generative Adversarial Networks(CTGAN), a deep machine learning tool, is utilized to learn the characteristics of the first arrival of earthquake P-waves and generate a synthetic dataset based on this information. The result obtained using actual and mixed(synthetic and actual) datasets will be used for training the stacked ensemble magnitude prediction model, MagPred, designed specifically for this study. There are 13295, 3989, and1710 records designated for training, testing, and validation. The mean absolute error of the test dataset for single station magnitude detection using early three, four, and five seconds of P wave are 0.41, 0.40,and 0.38 MJMA. The study demonstrates that the Generative Adversarial Networks(GANs) can provide a good result for single-station magnitude prediction. The study can be effective where less seismic data is available. The study shows that the machine learning method yields better magnitude detection results compared with the several regression models. The multi-station magnitude prediction study has been conducted on prominent Osaka, Off Fukushima, and Kumamoto earthquakes. Furthermore, to validate the performance of the model, an inter-region study has been performed on the earthquakes of the India or Nepal region. The study demonstrates that GANs can discover effective magnitude estimation compared with non-GAN-based methods. This has a high potential for wid

关键词： Magnitude Synthetic data Machine learning Earthquake CTGAN

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PriChain: Efficient Privacy-Preserving Fine-Grained Redactable Blockchains in Decentralized Settings

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Chinese Journal of Electronics 2025年第1期34卷 82-97页

作者： Hongchen Guo Weilin Gan Mingyang Zhao Chuan Zhang Tong Wu Liehuang Zhu Jingfeng Xue School of Computer Science and Technology Beijing Institute of Technology School of Cyberspace Science and Technology Beijing Institute of Technology School of Computer and Communication Engineering University of Science and Technology Beijing

Recently, redactable blockchain has been proposed and leveraged in a wide range of real systems for its unique properties of decentralization, traceability, and transparency while ensuring controllable on-chain data redaction. However, the development of redactable blockchain is now obstructed by three limitations, which are data privacy breaches, high communication overhead, and low searching efficiency, respectively. In this paper, we propose PriChain, the first efficient privacy-preserving fine-grained redactable blockchain in decentralized settings. PriChain provides data owners with rights to control who can read and redact on-chain data while maintaining downward compatibility, ensuring the one who can redact will be able to read. Specifically, inspired by the concept of multi-authority attribute-based encryption, we utilize the isomorphism of the access control tree, realizing fine-grained redaction mechanism, downward compatibility, and collusion resistance. With the newly designed structure, PriChain can realize O（n） communication and storage overhead compared to prior O（n2） schemes. Furthermore, we integrate multiple access trees into a tree-based dictionary, optimizing searching efficiency. Theoretical analysis proves that PriChain is secure against the chosen-plaintext attack and has competitive complexity. The experimental evaluations show that PriChain realizes 10× efficiency improvement of searching and 100× lower communication and storage overhead on average compared with existing schemes.

关键词： Resistance Access control Data privacy Dictionaries Blockchains Encryption Complexity theory Usability Protection

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A recover-then-discriminate framework for robust anomaly detection

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science China(Information sciences) 2025年第4期68卷 300-318页

作者： Peng XING Dong ZHANG Jinhui TANG Zechao LI School of Computer Science and Engineering Nanjing University of Science and Technology Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology

Anomaly detection(AD) has been extensively studied and applied across various scenarios in recent years. However, gaps remain between the current performance and the desired recognition accuracy required for practical *** paper analyzes two fundamental failure cases in the baseline AD model and identifies key reasons that limit the recognition accuracy of existing approaches. Specifically, by Case-1, we found that the main reason detrimental to current AD methods is that the inputs to the recovery model contain a large number of detailed features to be recovered, which leads to the normal/abnormal area has not/has been recovered into its original state. By Case-2, we surprisingly found that the abnormal area that cannot be recognized in image-level representations can be easily recognized in the feature-level representation. Based on the above observations, we propose a novel recover-then-discriminate(ReDi) framework for *** takes a self-generated feature map(e.g., histogram of oriented gradients) and a selected prompted image as explicit input information to address the identified in Case-1. Additionally, a feature-level discriminative network is introduced to amplify abnormal differences between the recovered and input representations. Extensive experiments on two widely used yet challenging AD datasets demonstrate that ReDi achieves state-of-the-art recognition accuracy.

关键词： recovery network HOG prompt discriminative network self-correlation loss anomaly detection

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MLRT-UNet:An Efficient Multi-Level Relation Transformer Based U-Net for Thyroid Nodule Segmentation

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computer Modeling in engineering & sciences 2025年第4期143卷 413-448页

作者： Kaku Haribabu Prasath R Praveen Joe IR Department of Computer Science and Engineering RMK College of Engineering and TechnologyTiruvallur601206India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and ***,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound *** existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,*** address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule *** MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding *** transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the *** approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the ***,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation *** results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)*** findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.

关键词： Thyroid nodules endocrine system multi-level relation transformer U-Net self-attention external attention co-operative transformer fusion thyroid nodules segmentation

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DeepGAN: Utilizing generative adversarial networks for improved deep learning

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International Journal of Knowledge-Based and Intelligent engineering Systems 2024年第4期28卷 732-748页

作者： V, Edward Naveen A, Jenefa T.M, Thiyagu A, Lincy Taurshia, Antony Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and Technology India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences India Division of Computer Science and Engineering Karunya Institute of Technology and Sciences India Department of Computer Science and Engineering National Engineering College India

In the realm of deep learning, Generative Adversarial Networks (GANs) have emerged as a topic of significant interest for their potential to enhance model performance and enable effective data augmentation. This paper addresses the existing challenges in synthesizing high-quality data and harnessing the capabilities of GANs for improved deep learning outcomes. Unlike traditional approaches that heavily rely on manually engineered data augmentation techniques, our work introduces a novel framework that leverages DeepGANs to autonomously generate diverse and high-fidelity data. Our experiments encompass a diverse spectrum of datasets, including images, text, and time series data. In the context of image classification tasks, we conduct experiments on the widely recognized CIFAR-10 dataset, which consists of 50,000 image samples. Our results demonstrate the remarkable efficacy of DeepGANs in enhancing model performance across various data domains. Notably, in image classification using the CIFAR-10 dataset, our innovative approach achieves an impressive accuracy of 97.2%. This represents a substantial advancement beyond conventional CNN models, underscoring the profound impact of DeepGANs in the realm of deep learning. In summary, this research sheds light on DeepGANs as a fundamental component in the pursuit of enhanced deep learning performance. Our framework not only overcomes existing limitations but also heralds a new era of data augmentation, with generative adversarial networks leading the way. The attainment of an accuracy rate of 97.2% on CIFAR-10 serves as a compelling testament to the transformative potential of DeepGANs, solidifying their pivotal role in the future of deep learning. This promises the development of more robust, adaptive, and accurate models across a myriad of applications, marking a significant contribution to the field. © 2024 – IOS Press. All rights reserved.

关键词： Generative adversarial networks

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Hyperspectral image restoration using noise gradient and dual priors under mixed noise conditions

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CAAI Transactions on Intelligence technology 2025年第1期10卷 72-93页

作者： Hazique Aetesam Suman Kumar Maji V.B.Surya Prasath Computer Science and Engineering Birla Institute of Technology MesraBiharIndia Computer Science and Engineering Indian Institute of Technology PatnaBiharIndia Department of Computer Science University of CincinnatiCincinnatiOhioUSA

Images obtained from hyperspectral sensors provide information about the target area that extends beyond the visible portions of the electromagnetic ***,due to sensor limitations and imperfections during the image acquisition and transmission phases,noise is introduced into the acquired image,which can have a negative impact on downstream analyses such as classification,target tracking,and spectral *** in hyperspectral images(HSI)is modelled as a combination from several sources,including Gaussian/impulse noise,stripes,and *** HSI restoration method for such a mixed noise model is ***,a joint optimisation framework is proposed for recovering hyperspectral data corrupted by mixed Gaussian-impulse noise by estimating both the clean data as well as the sparse/impulse noise ***,a hyper-Laplacian prior is used along both the spatial and spectral dimensions to express sparsity in clean image ***,to model the sparse nature of impulse noise,anℓ_(1)−norm over the impulse noise gradient is *** the proposed methodology employs two distinct priors,the authors refer to it as the hyperspectral dual prior(HySpDualP)*** the best of authors'knowledge,this joint optimisation framework is the first attempt in this *** handle the non-smooth and nonconvex nature of the generalℓ_(p)−norm-based regularisation term,a generalised shrinkage/thresholding(GST)solver is ***,an efficient split-Bregman approach is used to solve the resulting optimisation *** results on synthetic data and real HSI datacube obtained from hyperspectral sensors demonstrate that the authors’proposed model outperforms state-of-the-art methods,both visually and in terms of various image quality assessment metrics.

关键词： hyper-laplacian prior hyperspectral images image restoration mixed noise variational approach

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Transfer Learning-Based Class Imbalance-Aware Shoulder Implant Classification from X-Ray Images

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Journal of Bionic engineering 2024年第2期21卷 892-912页

作者： Marut Jindal Birmohan Singh Department of Computer Science and Engineering Sant Longowal Institute of Engineering and TechnologySangrur148106PunjabIndia

Total shoulder arthroplasty is a standard restorative procedure practiced by orthopedists to diagnose shoulder arthritis in which a prosthesis replaces the whole joint or a part of the *** is often challenging for doctors to identify the exact model and manufacturer of the prosthesis when it is *** paper proposes a transfer learning-based class imbalance-aware prosthesis detection method to detect the implant’s manufacturer automatically from shoulder X-ray *** framework of the method proposes a novel training approach and a new set of batch-normalization,dropout,and fully convolutional layers in the head *** employs cyclical learning rates and weighting-based loss calculation *** modifications aid in faster convergence,avoid local-minima stagnation,and remove the training bias caused by imbalanced *** proposed method is evaluated using seven well-known pre-trained models of VGGNet,ResNet,and DenseNet *** is performed on a shoulder implant benchmark dataset consisting of 597 shoulder X-ray *** proposed method improves the classification performance of all pre-trained models by 10–12%.The DenseNet-201-based variant has achieved the highest classification accuracy of 89.5%,which is 10%higher than existing ***,to validate and generalize the proposed method,the existing baseline dataset is supplemented to six classes,including samples of two more implant *** results have shown average accuracy of 86.7%for the extended dataset and show the preeminence of the proposed method.

关键词： Biomedical engineering Artificial intelligence Total shoulder arthroplasty Prosthesis identification Biomedical image classification

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DNACDS:Cloud IoE big data security and accessing scheme based on DNA cryptography

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Frontiers of computer science 2024年第1期18卷 157-170页

作者： Ashish SINGH Abhinav KUMAR Suyel NAMASUDRA School of Computer Engineering KIIT Deemed to be UniversityBhubaneshwar 751024India Department of Computer Science and Engineering Indian Institute of Information Technology SuratSurat 394190India Department of Computer Science and Engineering National Institute of Technology AgartalaAgartala 799046India

The Internet of Everything(IoE)based cloud computing is one of the most prominent areas in the digital big data *** approach allows efficient infrastructure to store and access big real-time data and smart IoE services from the *** IoE-based cloud computing services are located at remote locations without the control of the data *** data owners mostly depend on the untrusted Cloud Service Provider(CSP)and do not know the implemented security *** lack of knowledge about security capabilities and control over data raises several security *** Acid(DNA)computing is a biological concept that can improve the security of IoE big *** IoE big data security scheme consists of the Station-to-Station Key Agreement Protocol(StS KAP)and Feistel cipher *** paper proposed a DNA-based cryptographic scheme and access control model(DNACDS)to solve IoE big data security and access *** experimental results illustrated that DNACDS performs better than other DNA-based security *** theoretical security analysis of the DNACDS shows better resistance capabilities.

关键词： IoE based cloud computing DNA cryptography IoE big data security StS KAP feistel cipher IoE big data access

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Enhanced Acceleration for Generalized Nonconvex Low-Rank Matrix Learning

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Chinese Journal of Electronics 2025年第1期34卷 98-113页

作者： Hengmin Zhang Jian Yang Wenli Du Bob Zhang Zhiyuan Zha Bihan Wen School of Electrical and Electronic Engineering Nanyang Technological University School of Computer Science and Engineering Nanjing University of Science and Technology School of Information Science and Engineering East China University of Science and Technology Department of Electrical and Computer Engineering University of Macau

Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inherent biases and computational burdens, especially when used to relax the rank function, making them less effective and efficient in real-world scenarios. To address these challenges, our research focuses on generalized nonconvex rank regularization problems in robust matrix completion, low-rank representation, and robust matrix regression. We introduce innovative approaches for effective and efficient low-rank matrix learning, grounded in generalized nonconvex rank relaxations inspired by various substitutes for the ?0-norm relaxed functions. These relaxations allow us to more accurately capture low-rank structures. Our optimization strategy employs a nonconvex and multi-variable alternating direction method of multipliers, backed by rigorous theoretical analysis for complexity and *** algorithm iteratively updates blocks of variables, ensuring efficient convergence. Additionally, we incorporate the randomized singular value decomposition technique and/or other acceleration strategies to enhance the computational efficiency of our approach, particularly for large-scale constrained minimization problems. In conclusion, our experimental results across a variety of image vision-related application tasks unequivocally demonstrate the superiority of our proposed methodologies in terms of both efficacy and efficiency when compared to most other related learning methods.

关键词： Learning systems Image recognition Minimization Computational efficiency Complexity theory Matrix decomposition Optimization Image reconstruction Singular value decomposition Convergence

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Fine Tuned Hybrid Deep Learning Model for Effective Judgment Prediction

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computer Modeling in engineering & sciences 2025年第3期142卷 2925-2958页

作者： G.Sukanya J.Priyadarshini School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

Advancements in Natural Language Processing and Deep Learning techniques have significantly pro-pelled the automation of Legal Judgment Prediction,achieving remarkable progress in legal *** of the existing research works on Legal Judgment Prediction(LJP)use traditional optimization algorithms in deep learning techniques falling into local *** research article focuses on using the modified Pelican Optimization method which mimics the collective behavior of Pelicans in the exploration and exploitation phase during cooperative food ***,the selection of search agents within a boundary is done randomly,which increases the time required to achieve global *** address this,the proposed Chaotic Opposition Learning-based Pelican Optimization(COLPO)method incorporates the concept of Opposition-Based Learning combined with a chaotic cubic function,enabling deterministic selection of random numbers and reducing the number of iterations needed to reach global ***,the LJP approach in this work uses improved semantic similarity and entropy features to train a hybrid classifier combining Bi-GRU and Deep *** output scores are fused using improved score level fusion to boost prediction *** proposed COLPO method experiments with real-time Madras High Court criminal cases(Dataset 1)and the Supreme Court of India database(Dataset 2),and its performance is compared with nature-inspired algorithms such as Sparrow Search Algorithm(SSA),COOT,Spider Monkey Optimization(SMO),Pelican Optimization Algorithm(POA),as well as baseline classifier models and transformer neural *** results show that the proposed hybrid classifier with COLPO outperforms other cutting-edge LJP algorithms achieving 93.4%and 94.24%accuracy,respectively.

关键词： Bi-GRU deep maxout semantic similarity legal judgment prediction opposition based learning pelican optimization

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