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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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Early Detection of Autism Spectrum Disorder (ASD)-A Deep Learning Approach 1st

Early Detection of Autism Spectrum Disorder (ASD)-A Deep Lea...

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1st International Conference on Intelligent Computing, Smart Communication, and Network Technologies, ICICSCNT 2023

作者： Nirmalapriya, G. Ravikumar, K. Jayasri, M. PrasannaKumar, V. Department of ECE Rajalakshmi Institute of Technology Chennai India Department of Computer and Communication Engineering Rajalakshmi Institute of Technology Chennai India Computer Science Engineering Rajalakshmi Institute of Technology Chennai India

ISBN: (纸本)9783031759567

In this research, we put together a deep learning strategy that brings together the medical biomarker data from EEG tests and information from both clinical observations and inquiries to recognize the children with a Autism Spectrum Disorder (ASD). We expect that it can improve the effectiveness of identification significantly in addition to minimizing the expenditure. To begin with, our approach employed a pioneering tactic for selecting a neuronal signal landmarks derived from an eye movement, facial expression, and EEG results. This research showed that using a combination of Local Binary Pattern algorithm and deep learning classification led to a 87.50% accurate classification rate for multi-modal data merging. Finding a point to this method of detection as highly useful and effective for diagnosing ASD in young children. Graphs and confusion matrices unveiled different discrimination methods of EEG, personal behavior, and questionaries’ for discrepancy discrimination between ASD individuals and typically developing kids, with EEG being the most useful in drawing distinctions. Studies portray the physiological and behavioral important complementary characteristics. The combination of complementary information in this study proposed the deep learning approach has led to a significant improvement in classification accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Diseases

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An Intelligent System to Predict Diabetic Retinal Diseases Based on Diabetic Attributes

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SN computer science 2024年第6期5卷 755页

作者： Eswari, M. Shanmuga Balamurali, S. Ramkumar, K. Department of Computer Applications Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Rajalakshmi Institute of Technology

The most common illness among individuals and the general population in the medical field is diabetes. This is coupled with a careful diabetic retinal that has no signal. The historical record offers unrecoverable insight. To avoid a hallucinatory dispute at the time, early detection of retinal illness is necessary. Positively, the results of diabetic patients have been able to foretell the threat of diabetic-related eyeailments such as Diabetic Retinopathy, Glaucoma, and Diabetic Macular Edema.A diabetic retinal disease prediction exemplary have been created by via system learning rules, such as narrow neural network, bilayered neural network, wide neural network and trilayer neural network, in close proximity to the onset of the disease. The trilayer neural network with various activation functions accuracy based results servedas the foundation for the final model that evaluates real time data to predict the likelihood of *** learner representation accuracy of 0.95and the evaluation phase accuracy of 0.94 are *** ideal diabetic features-based diabetic retinal system outcome occupies a crucial place in the medical decision support system to make prompt assessments in the field of E-healthcare system to connect ophthalmology field to eradicate insulin based eye problems at the time of regular assessment. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Boosting computer-aided Design Diabetic Mellitus Ensemble Machine Learner Retinal eye Disease

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Deep Fish: An Approach to Fish Species Identification Through Deep Learning Techniques 3rd

Deep Fish: An Approach to Fish Species Identification Throug...

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3rd International Conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2024

作者： Reddy, Penumalli Jithya Malathi, M. Julaiha, AG. Noorul Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India Department Computer Science Engineering Rajalakshmi Institute of Technology Chennai India

ISBN: (纸本)9789819739905

This analysis introduces a system for automated identification of fish species and classification based on deep learning. This system can provide valuable insights to marine biologists, with better understanding on fish species habitats. The developed system relies on deep learning neural networks (DNNs) to extract features from underwater images and classify fish into different species. Classifying underwater images of fish species is crucial for conducting fish surveys, maintaining ecological balance, monitoring populations, and conserving endangered species. Yet, it proves challenging due to optical obstacles like smattering and raptness in ocean water, leading to dark and low images. This research delves into the application of deep learning in diverse aquatic tasks, encompassing fish identification, classification, feeding decisions and behavior analysis, estimation of its size, and quality of water prediction. Using a sizable labeled dataset from Kaggle, the proposed system employs a hierarchical CNN classification approach, achieving notable recognition rates and accuracy. The study highlights the efficacy of deep learning in fish species classification, emphasizing its automatic feature extraction capability, and removing the necessity for labor-intensive manual work when engineering features. Furthermore, the advantage of deep learning models lies in their capacity to attain high accuracy with extensive datasets, exemplified by the successful training on the Kaggle dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Performance Evaluation of Natural Language Processing Algorithms for Sentiment Analysis

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SN computer science 2024年第6期5卷 724页

作者： Silviya, S. H. Annie Faith, S. Julia Seetha, R. Hemalatha, M. Department of Computer Science and Engineering Rajalakshmi Institute of Technology (RIT) Department of Information Technology S.A.Engineering College Department of Information Technology Vellore Institute of Technology Department of Computer Science and Engineering RMD Engineering College

Sentiment analysis can be used to identify if a text’s sentiment is neutral, positive, or negative. One type of natural language processing is sentiment analysis. An interdisciplinary field encompassing linguistics, computer science, and artificial intelligence called “natural language processing” studies how computers and human language interact, with a focus on how to teach computers to process and analyze vast volumes of natural language data. Sentiment analysis, sometimes referred to as opinion mining or emotion artificial intelligence, is the methodical identification, extraction, quantification, and study of affective states and subjective data using natural language processing, text analysis, computational linguistics, and biometrics. Sentiment analysis is frequently used in online and social media platforms, voice of the customer materials (such as reviews and survey replies), and healthcare materials for marketing, customer service, and clinical medicine purposes. This paper discusses different sentiment analysis methods and selects the best approach for a certain task. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Biometrics Computational linguistics Natural language processing Sentiment analysis Text analysis

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Efficient Text Compression Algorithms: Principles, Performance, and Applications 5

Efficient Text Compression Algorithms: Principles, Performan...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Sriman, B. Stephi, Adlin Annie Silviya, S.H. Sangeetharani, M. Shreya, B. Sivakumar, S. Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Sree Sastha Institute of Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350385649

Textual data is a fundamental element of human communication and information exchange, playing a pivotal role in a wide array of applications across various domains. However, the digital age has ushered in an era of unprecedented data growth, resulting in the need for efficient storage, transmission, and processing of vast amount of text. Lossless text compression techniques emerge as a vital solution to this challenge, offering the ability to compactly represent textual content without compromising any information. This research study explores the lossless text compression algorithms by investigating the underlying principles, performance, and applications. © 2024 IEEE.

关键词： Digital storage

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Optimal Deep Belief Network Based Lung Cancer Detection and Survival Rate Prediction

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computer Systems science & engineering 2023年第4期45卷 939-953页

作者： Sindhuja Manickavasagam Poonkuzhali Sugumaran Department of Information Technology Rajalakshmi Engineering CollegeChennai600125TamilnaduIndia Department of Computer Science and Engineering Rajalakshmi Engineering CollegeChennai600125TamilnaduIndia

The combination of machine learning(ML)approaches in healthcare is a massive advantage designed at curing illness of millions of *** efforts are used by researchers for detecting and providing primary phase insights as to cancer *** cancer remained the essential source of disease connected mortality for both men as well as women and their frequency was increasing around the *** disease is the unrestrained progress of irregular cells which begin off in one or both *** previous detection of cancer is not simpler procedure however if it can be detected,it can be curable,also finding the survival rate is a major challenging *** study develops an Ant lion Optimization(ALO)with Deep Belief Network(DBN)for Lung Cancer Detection and Classification with survival rate *** proposed model aims to identify and classify the presence of lung ***,the proposed model undergoes min-max data normalization approach to preprocess the input ***,the ALO algorithm gets executed to choose an optimal subset of *** addition,the DBN model receives the chosen features and performs lung cancer ***,the optimizer is utilized for hyperparameter optimization of the DBN *** order to report the enhanced performance of the proposed model,a wide-ranging experimental analysis is performed and the results reported the supremacy of the proposed model.

关键词： Lung cancer feature selection ant lion optimization classification disease diagnosis metaheuristics

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Combatting Counterfeit Currency: The Power of EURion Constellation and Other Anti-Fraud Measures 2

Combatting Counterfeit Currency: The Power of EURion Constel...

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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Sriman, B. Silviya, S.H. Annie Anitha, R. Pandithurai, O. Shriram, S.K. Sathishkumar, R. Rajalakshmi Institute of Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798350315905

This paper depicts the analysis and survey done on banknotes of various foreign currencies. Without permission and in violation of the legislation of the country, it is strictly forbidden to photocopy and reproduce bank notes. EURion constellation and Counterfeit Deterrence System (CDS) are the key methodologies used in fake currency detection and photocopy prevention. Photocopying the banknotes and currency might result you involving in criminal endeavour. All modern photocopy machines and printers have this built-in firmware detection mechanism which detects the presence of EURion constellation when they try to copy. Some machines resulted in printing white lines across the printing area and some leave empty white spaces. After the invention of EURion constellation, almost all the countries started to use this technique in their currency notes in order to prevent the reproducing of banknotes. But some countries use this to detect fake currencies and some use this to prevent photocopy of notes. In this study, we offers a system that prevents photocopying of Indian currencies, which already uses the EURion constellation and Counterfeit Deterrence System to detect the fake notes. © 2023 IEEE.

关键词： Photocopying

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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 Detection using InceptionV3 and Security using Blockchain 8

Autism Detection using InceptionV3 and Security using Blockc...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Annie Silviya, S.H. Seetha, R. Raja, S. Sriman, B. Rupesh, S. Gopala Krishnan, J. Rajalakshmi Institute of Technology Computer Science Engineering Chennai India Vellore Institute of Technology Computer Science Engineering and Information Systems Vellore India Panimalar Engineering College Computer Science and Engineering Chennai India Institute of Science and Technology Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&d Chennai India Rajalakshmi Institute of Technology Computer and Communication Engineering Chennai India

ISBN: (纸本)9798350367904

The mental health and well-being of children are critical components of their overall development and future success. In India, only 1 in 6 8 children are diagnosed with autism, since monitoring and addressing the mental health issues among children is a challenge because of various factors. Studies have proven that autism detection can be made possible with the assessment of specific facial features that are used as primary markers for the proposed model. The proposed solution aims to leverage technological advancements, such as deep learning, for detecting autism in children using the aforementioned markers. The model gathers data on the children's facial features and predicts any underlying mental disorder. Since the application collects and analyzes sensitive information from the users, an additional security feature is implemented in the proposed solution using blockchain. Key features of the solution include standardized assessment tools, real-time tracking capabilities, personalized interventions, and seamless communication channels between parents, caregivers, and mental health professionals. © 2024 IEEE.

关键词： Diseases

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