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

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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Aspect-oriented extraction and sentiment analysis using optimized hybrid deep learning approaches

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Multimedia Tools and Applications 2024年第41期83卷 88613-88644页

作者： Kotagiri, Srividya Sowjanya, A. Mary Anilkumar, B. Devi, N Lakshmi Computer Science and Engineering GMR Institute of Technology MR Nagar Andhra Pradesh Razam532127 India Andhra Pradesh Visakhapatnam530003 India Electronics and Communication Engineering GMR Institute of Technology MR Nagar Andhra Pradesh Razam532127 India

Aspect-oriented extraction involves the identification and extraction of specific aspects, features, or entities within a piece of text. Traditional methods often struggled with the complexity and variability of language, leading to the exploration of advanced deep learning approaches. In the realm of sentiment analysis, the conventional approaches often fall short when it comes to providing a nuanced understanding of sentiments expressed in textual data. Traditional sentiment analysis models often overlook the specific aspects or entities within the text that contribute to the overall sentiment. This limitation poses a significant challenge for businesses and organizations aiming to gain detailed insights into customer opinions, product reviews, and other forms of user-generated *** this research, we propose an innovative approach for aspect-oriented extraction and sentiment analysis leveraging optimized hybrid deep learning techniques. Our methodology integrates the powerful capabilities of deep learning models with the efficiency of Reptile Search Optimization. Furthermore, we introduce an advanced sentiment analysis framework employing the state-of-the-art Extreme Gradient Boosting Algorithm. The fusion of these techniques aims to enhance the precision and interpretability of aspect-oriented sentiment analysis. The proposed approach first utilizes deep learning architectures to extract and comprehend diverse aspects within textual data. Through the incorporation of Reptile Search Optimization, we optimize the learning process, ensuring adaptability and improved model generalization across various datasets. Subsequently, the sentiment analysis phase employs the robust Extreme Gradient Boosting Algorithm, known for its effectiveness in handling complex relationships and patterns within data. Our experiments, conducted on diverse datasets, demonstrate the superior performance of the proposed methodology in comparison to traditional approaches. The optimized

关键词： Extraction

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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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A fine-tuned transformer model for brain tumor detection and classification

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Multimedia Tools and Applications 2025年第15期84卷 15597-15621页

作者： Srinivas, B. Anilkumar, B. devi, NLakshmi Aruna, V.B.K.L. Andhra Pradesh Vizianagaram535005 India Electronics and Communication Engineering GMR Institute of Technology Andhra Pradesh Razam532127 India Computer Science and Engineering GMR Institute of Technology Andhra Pradesh Razam532127 India Electronics and Communication Engineering V R Siddhartha Engineering College Andhra Pradesh Vijayawada520007 India

The identification and classification of brain tumors from medical images is a challenging task, which plays a crucial role in treatment planning. In recent times, the transformer models earned greater attention in image classification tasks because of their tendency to learn complex and spatial interconnections in medical images. In this study, a novel fine-tuned transformer model was developed by combining the benefits of a data-efficient image transformer (DeiT) and the firefly algorithm (FA) for the precise classification of tumors. This framework utilizes the transformer's self-attention and feed-forward layers to learn and capture complex patterns and high-level features. It employs the Firefly fitness solution to fine-tune the DeiT architecture by updating its hyperparameters, thereby maximizing the system performance and reducing training loss. The novelty of the paper lies in its proposal and implementation of a fine-tuned transformer model specifically tailored for the detection and classification of brain tumors. By leveraging the capabilities of transformer architectures, such as the data-efficient image transformer (DeiT), the proposed model can effectively capture intricate spatial dependencies and complex patterns within brain MRI images. The experimental analysis utilizes the benchmark brain tumor MRI database from the Kaggle source and the developed model achieved greater classification accuracy of 99.7%. In addition, a comprehensive comparative analysis was performed with existing models to demonstrate the robustness of the proposed work. This intensive analysis verifies that this model facilitates the accurate and timely detection of brain tumors in medical diagnosis. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image classification

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Exploiting Cyber Threats and Protecting Cryptocurrencies Toward Bitcoin Exchanges 5

Exploiting Cyber Threats and Protecting Cryptocurrencies Tow...

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5th Biennial International Conference on Nascent Technologies in engineering, ICNTE 2023

作者： Vaddadi, Vasudha Rani Vardhan Raj Annepu, Punya Gollapalli, Nithin Challa, Abhishek Andhavarapu, Sai Bharath Kumar Botta, Prasanth GMR Institute of Technology Computer Science and Engineering India Computer Science and Engineering Rajam India

ISBN: (纸本)9781665465045

The concept of Bitcoin first came into creation in 2008, as a response to the Great Financial Crisis, and the world of finances relies upon banks for all kinds of financial transactions as intermediaries. Bitcoin is more than a cryptocurrency used as payment for investors to hold and hope for value;it is considered a digital asset these days. While Bitcoin transactions take place in the cyber network, possibilities are there for cyber-Attacks. To protect Bitcoins from illegal miners of Bitcoin data, it is required to find all possible intrusions, called vulnerabilities. If more vulnerabilities exist in the network that makes the transactions vulnerable. As of now, Bitcoin uses centralized Blockchain technology, where the transaction made by the user are managed by various blockchain methods like Merkelroot, Mining, Halving, Keys, and Wallets. These methods provide security to the cryptocurrencies online. Among all the four methods, Wallets are somewhat vulnerable due to their dual services i.e., custodians and non-custodians. Non-custodian Wallet type doesn't cause vulnerability due to not having any storage for keys. Whereas the Custodian Wallet type provides storage of keys for the customers which makes it as vulnerable. The techniques that lead to vulnerability in the custodian type are hot storage, cold storage, and deep cold storage. The exploitation of all these individual techniques is the main task of this work. When it comes to transactions of bitcoin along the network where the receiver needs to wait around 30 minutes due to mempool. This is also a loophole that may trigger the threat to the bitcoin exchange. These vulnerabilities can be reduced by regulatory oversight and reducing the mempool waiting time or it is possible by maintaining more legal crypto miners for every block of transaction. To verify the whole process of bitcoin transaction exchanges, few cloud services use blockchain technology for processing transactions. The proposed work is to impl

关键词： Digital storage

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Salt Segment Identification in Seismic Images of Earth Surface using Deep Learning Techniques 2

Salt Segment Identification in Seismic Images of Earth Surfa...

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Devi, N. Lakshmi Bochu, Rajasekhar Reddy Buddha, Naveen Kumar Gmr Institute of Technology Computer Science and Engineering Rajam India

ISBN: (纸本)9798350346640

Salt segmentation is the process of identifying whether a subsurface target is salt or not. There are several places on Earth where there are significant amounts of salt as well as oil and gas. For businesses engaged in oil and gas development, finding the exact locations of significant salt deposits is crucial. Also, lands that have been impacted by salt are not useful for farming. The absorption capacity of the plant reduces due to the presence of salt in the soil solution. So, in order to identify the land that contains salt, salt segmentation is being done. The seismic image of a particular pixel is analysed to classify it either as salt or sediment. TGS Salt Identification Challenge dataset is used which consists of 4,000 seismic image patches of size (101x101x3) and corresponding segmentation masks of size (101x101x1) in training set. 18,000 seismic image patches are present in the test set which are used for evaluation of the model. The existing models have less detection rate. So, this study has proposed two models for identifying the salt region with high detection rate. The primary model used here is a combination of UNET with ResNet-18 and ResNet-34. The secondary model achieves segmentation results by ensembling UNET with ResNet-34, VGG16 and Inceptionv3. Using these two models, the salt region can be determined from the seismic data. IoU is used as performance metric in order to evaluate the model. The outcomes demonstrate that the ensemble model outperforms individual network models and achieves better segmentation results. © 2023 IEEE.

关键词： Semantic Segmentation

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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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Autism spectrum disorder based on squeezenet with fractional tasmanian rat swarm optimization

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Multimedia Tools and Applications 2024年第41期83卷 89029-89054页

作者： Muppidi, Satish Anuradha, G. Valarmathi, K. Department of Computer Science and Engineering GMR Institute of Technology Andhra Pradesh Rajam India Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada Kanuru India Department of Computer Science and Engineering Panimalar Engineering College Bangalore Trunk Road Chennai Pooñamallee600123 India

A complicated neuro-developmental disorder called Autism Spectrum Disorder (ASD) is abnormal activities related to brain development. ASD generally affects the physical impression of the face as well as the growth of the brain in children. An early and proper medical diagnosis is essential for ASD affected children to enhance their quality of life. However, the clinical detection of ASD is a difficult task and time-consuming, hence it is essential to design an ASD detection approach for precise diagnosis of ASD children. In this research, an algorithmic approach called Fractional Tasmanian Rat Swarm Optimization driven SqueezeNet (FTRSO-SqueezeNet) is designed for the detection of ASD. The median filter and Region of Interest (RoI) extraction are used to de-noise the input image initially and extract a particular region from the filtered image. Later, the nub regions are extracted by choosing the optimal grid from the pre-processed image and using different feature extractors the features from the input images are determined. Finally, the detection of ASD is done by using SqueezeNet, which is trained using the FTRSO approach. The performance of the method is estimated, where the designed model achieved higher performance with an accuracy of 94.55%, sensitivity of 92.53%, and specificity of 95.22% than other prevailing approaches. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Rats

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An Analytical Review of Large Language Models Leveraging KDGI Fine-Tuning,Quantum Embedding’s,and Multimodal Architectures

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computers, Materials & Continua 2025年第6期83卷 4031-4059页

作者： Uddagiri Sirisha Chanumolu Kiran Kumar Revathi Durgam Poluru Eswaraiah G Muni Nagamani Department of Computer Science and Engineering Prasad V Potluri Siddhartha Institute of TechnologyVijayawada520007India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram522302India Department of Computer Science and Engineering(Data Science) AVN Institute of Engineering and TechnologyHyderabad501510India Department of Computer Science and Engineering(Data Science) Vignan’s Institute of Management and Technology forWomenHyderabad501301India Department of Computer Science and Engineering Andhra Loyola Institute of Engineering and TechnologyVijayawada520008India

A complete examination of Large Language Models’strengths,problems,and applications is needed due to their rising use across *** studies frequently focus on single-use situations and lack a comprehensive understanding of LLM architectural performance,strengths,and *** gap precludes finding the appropriate models for task-specific applications and limits awareness of emerging LLM optimization and deployment *** this research,50 studies on 25+LLMs,including GPT-3,GPT-4,Claude 3.5,DeepKet,and hybrid multimodal frameworks like ContextDET and GeoRSCLIP,are thoroughly *** propose LLM application taxonomy by grouping techniques by task focus—healthcare,chemistry,sentiment analysis,agent-based simulations,and multimodal *** methods like parameter-efficient tuning(LoRA),quantumenhanced embeddings(DeepKet),retrieval-augmented generation(RAG),and safety-focused models(GalaxyGPT)are evaluated for dataset requirements,computational efficiency,and performance *** for ethical issues,data limited hallucinations,and KDGI-enhanced fine-tuning like Woodpecker’s post-remedy corrections are *** investigation’s scope,mad,and methods are described,but the primary results are *** work reveals that domain-specialized fine-tuned LLMs employing RAG and quantum-enhanced embeddings performbetter for context-heavy *** medical text normalization,ChatGPT-4 outperforms previous models,while two multimodal frameworks,GeoRSCLIP,increase remote ***-efficient tuning technologies like LoRA have minimal computing cost and similar performance,demonstrating the necessity for adaptive models in multiple *** discover the optimum domain-specific models,explain domain-specific fine-tuning,and present quantum andmultimodal LLMs to address scalability and cross-domain *** framework helps academics and practitioners identify,adapt,and innovate LLMs for different *** work

关键词： Large languagemodels quantum embeddings fine-tuning techniques multimodal architectures ethical AI scenarios

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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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