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HASHL: Dynamic Hash Verification for Detecting and Preventing Eclipse Attacks

IEEE Internet of Things Journal 2025年第13期12卷 23524-23535页

作者： He, Daojing Gong, Wei Tu, Chen Chan, Sammy Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Jiangxi University of Science and Technology School of Information Engineering Jiangxi Ganzhou China City University of Hong Kong Department of Electrical Engineering Hong Kong

With the rapid development of blockchain technology, P2P networks are facing increasing security threats, among which Eclipse attacks, as a type of network isolation attack, have seriously affected the normal operation of the network and the integrity of data. To address this challenge, this study implements node authentication and dynamic reputation evaluation by leveraging a dynamic hash computation mechanism that integrates challenge strings, node identifiers, and the latest active time, ensuring the uniqueness of node identities and the authenticity of operations. Based on a dynamic hash chain behavior evaluation mechanism, node behaviors are quantified across three dimensions: integrity, consistency, and temporal consistency, enabling precise identification of anomalous nodes. Furthermore, a network prevention repository framework is proposed, which dynamically adjusts the trust index of nodes by combining historical behavior with real-time data, effectively detecting and defending against stealthy Eclipse attacks. In addition, extensive testing on both Bitcoin and Ethereum platforms has shown that the method proposed in this study not only can effectively coexist on these two platforms, but also significantly improves the security and stability of the network, effectively reducing the occurrence of Eclipse attacks. © 2014 IEEE.

关键词： Blockchain

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A novel approach for image retrieval in remote sensing using vision-language-based image caption generation

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Multimedia Tools and Applications 2025年第6期84卷 2985-3014页

作者： Yadav, Prem Shanker Tyagi, Dinesh Kumar Vipparthi, Santosh Kumar Department of Computer Science and Engineering Malaviya National Institute of Technology Rajasthan Jaipur302017 India School of Artificial Intelligence and Data Engineering Indian Institute of Technology Ropar Punjab Rupnagar140001 India

Recent advancements in satellite technologies have resulted in the emergence of Remote Sensing (RS) images. Hence, the primary imperative research domain is designing a precise retrieval model for retrieving the most pertinent images based on the query. Present Remote Sensing Image Retrieval (RSIR) systems use visual descriptors to characterize the primitives (such as various land-cover types) that are visible in the images. However, the visual descriptors are inadequate for defining the complicated content of RS images. To solve this problem, a new model is devised for image retrieval based on image captions. The goal is to generate textual illustrations with captions to define relations amongst objects precisely. Here, image captioning is attained based on the vision-language pre-training model. The image captions are utilized for generating features like term frequency-inverse document frequency (TF-IDF), length of text, and Bag of Words. Meanwhile, query text is utilized wherein features like TF-IDF, text length, and Bag of Words are obtained. The similarity between query text features and the image captions features has been computed on the basis of a hybrid similarity measure wherein weights are tuned with the proposed Honey Badger Political Optimizer (HBPO) to retrieve the image. The proposed HBPO provided enhanced efficiency with elevated precision of 93.3%, recall of 93.7%, F1-score of 93.5%, and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) of 0.441. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Visual languages

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A perspective to ammonia-hydrogen energy industry in China

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science China Chemistry 2024年第6期67卷 1765-1775页

作者： Baoshun Zhang Wangyang Ji Jiewei Zhu Baian Shen Menghao Lv Bin Zhang Tihu Wang Yifu Yu Department of Chemistry Institute of Molecular PlusSchool of ScienceTianjin UniversityTianjin300072China Tianjin University-Asia Silicon Joint Research Center of Ammonia-Hydrogen New Energy Asia Silicon(Qinghai)Co.Ltd.Xining810007China Department of Catalysis Science and Engineering School of Chemical Engineering and TechnologyTianjin UniversityTianjin300072China

1 *** meet the 1.5℃ climate goal in our planet,the world is making a hard work for decarbonization[1-5].Constructing a clean energy system based on green energy is considered as the most promising way to achieve the ... 详细信息

关键词： ammonia planet energy

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Deep learning based RAGAE-SVM for Chronic kidney disease diagnosis on internet of health things platform

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Multimedia Tools and Applications 2024年 1-39页

作者： Kandukuri, Prabhakar Abdul, Ashu Kumar, Kuchipudi Prasanth Sreenivas, Velagapudi Ramesh, G. Gundu, Venkateswarlu Department of Artificial Intelligence and Machine Learning Chaitanya Bharathi Institute of Technology Telangana Hyderabad500075 India Department of Computer Science and Engineering SRM University Andhra PradeshNeerukonda Mangalagiri 522503 India Department of Computer Science & amp Engineering Koneru Lakshmaiah Education Foundation Andhra PradeshVaddeswaram 522302 India Department of Computer Science and Engineering SRK Institute of Technology Andhra Pradesh Vijayawada521108 India Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology TelanganaBachupally Hyderabad500090 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra PradeshVaddeswaram 522302 India

Chronic kidney disease (CKD) is a prominent disease that causes loss of functionality in the kidney. Doctors can now more easily gather patient health status data due to the growth of the Internet of Health Things (IoHT). The IoHT data contains a huge number of redundant data, making it challenging to predict CKD disease quickly and accurately. In healthcare applications like feature-based classification, a variety of disease diagnosis systems were used to address this problem. Current disease detection algorithms suffer from imbalanced dataset processing, low-accuracy feature learning, and high computational power requirements. Thus, deep learning-based clinical decision support systems have been developed to solve these complexities. To remove outliers from medical data, data collected with IoHT devices is first pre-processed using an enhanced K-means clustering technique. The Synthetic Minority over Sampling Technique is used to balance data because the IoHT dataset is highly imbalanced. The classifier detects anomalies in less time because of the implemented processing step. The balanced CKD dataset is then presented for use with a novel classifier called Residual Attention-Gated Autoencoder with Support Vector Machine. In order to improve accurate detection, the adopted classifier can learn and extract features. The proposed method results in an increased accuracy of 99.43% within 70.1 s of computation time. The mean intersection of the union and kappa coefficient of the proposed method is 98.15% and 98.1%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Classification (of information)

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Ensemble activation enabled deep classifier for Alzheimer's disease detection in the blockchain-enabled distributed edge environment

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International Journal of Information technology (Singapore) 2024年 1-18页

作者： Mandawkar, Umakant Diwan, Tausif Department of Computer Science and Engineering Indian Institute of Information Technology Nagpur India Department of Computer Engineering SVKM’s Institute of Technology Maharashtra Dhule India

Alzheimer's disease (AD) is the most well-known cause of dementia that affects memory. Alzheimer's patients have a neurodegenerative disorder that results in the loss of many brain functions. Today’s researchers have determined that the key to enhancing patient care and treatment is early disease diagnosis. Traditional techniques for diagnosing AD are difficult ineffective, and take a lot of time to learn and execute. In this research, a disease detection system is developed using the proposed tawny flamingo-based federated deep CNN (Convolutional Neural Networks) (TF based-deep CNN) which can manage accurate patient diagnosis in dispersed contexts with blockchain support. The collected data is maintained and structured in a blockchain-based database, which upholds a high level of data security and increases the detection accuracy. After the medical professional receives the data, disease detection is done using the proposed federated learning framework, which makes it feasible to manage remote data effectively. The features are then extracted from the segments created by the CUF-GW (Cuttlefish-GreyWolf) algorithm. The detection is then carried out by the proposed ensemble activation-based deep CNN classifier, with the ensemble process being assisted by the TF (Tawny Flamingo) approach. According to AD accomplishments, at TP (Training Percentage) 80, acc (Accuracy), sen (Sensitivity), and spe (Specificity) are respectively 99.19%, 99.99%, and 98.87%;similarly, based on the k-fold 10, acc, sen, and spe are respectively 99.45%, 99.99%, and 98.45%. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Alzheimer's disease CUF-GW algorithm Deep CNN Federated learning TF

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Transformers@DravidianLangTech-EACL2024: Sentiment Analysis of Code-Mixed Tamil Using RoBERTa 4

Transformers@DravidianLangTech-EACL2024: Sentiment Analysis ...

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4th Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, DravidianLangTech 2024

作者： Singhal, Kriti Bedi, Jatin Computer Science and Engineering Department Thapar Institute of Engineering and Technology India

ISBN: (纸本)9798891760783

Sentiment analysis has been an active field of research for over 20 years and has gained immense popularity due to its applications in both academia and industry. Sentiment Analysis of code-mixed posts and comments on social media, especially in Dravidian languages, is gaining more and more traction. This paper describes the team Transformers’ submission to the Sentiment Analysis in Tamil shared task organized by DravidianLangTech 2024 workshop at EACL 2024. A BERT-based architecture, RoBERTa was used for the shared task. The best macro average F1-score achieved was 0.212. We secured the 5th rank in the Sentiment Analysis shared task in Tamil. © 2024 Association for Computational Linguistics.

关键词： Sentiment analysis

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Google pi using raspberry pi for visually impaired 3

Google pi using raspberry pi for visually impaired

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Katkar, Gowthami Vinod Posonia, A. Mary Ushanandini, D. Abirami, K. Ankayarkanni, B. Sathyabama Institute of Science and Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798350372120

This article introduces Google Pi, a new voice assistant system aimed at improving the lives of visually challenged people. Leveraging the affordability and versatility of the Raspberry Pi single-board computer, Google Pi integrates Google's voice assistant API with deep learning algorithms for object and currency *** voice assistants lack important features for visually impaired users. Although they provide voice-based control, they do not meet important requirements such as object identification and financial *** Pi bridges this gap with real-time object and currency recognition, increasing user independence and security. This paper details the technical implementation of Google Pi and highlights its user-friendly design and accessibility considerations. A hybrid approach that leverages Google's APIs and deep learning algorithms to address the challenges of providing efficient user interactions and immediate responses. Additionally, this paper highlights the benefits and importance of Google Pi for visually impaired people by promoting accessibility and promoting a safer and more independent lifestyle. © 2024 IEEE.

关键词： Object recognition

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An Empirical Survey on the Classification Strategies for Automated Alzheimer’s Disease

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SN computer science 2024年第8期5卷 1140页

The early Alzheimer’s disease (AD) classification is highly important as this neuro-degenerative disease causes severe problems particularly, loss of memory among the patients. In addition, classifying Normal Control (NC), Mild Cognitive Impairment (MCI) and AD in time assists the patient to undergo treatments in the initial period of the disease. Hence, it is necessary to develop a trust worthy categorization model to classify the patients with or without AD. One of principal modalities commonly used for AD classification is the magnetic resonance imaging (MRI), the analysis of which achieved improved classification performance in categorizing the stages of AD. Hence, this review paper presents the detailed survey of 55 research articles showing different strategies to classify AD. The two important categories of the research papers are the methods using machine learning strategies, and the methods using deep learning strategies. The papers are classified based on various modalities, datasets, performance indices, and the software tools used for implementation. In addition to this, various research gaps and the challenges associated with the existing works of AD classification are discussed. In addition, this review provides the future scope for researchers with the analysis of research issues seen in the literary works. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Alzheimer’s disease Deep learning and machine learning Magnetic resonance imaging Mild cognitive impairment

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Recommendation algorithm for agricultural and sideline products on e-commerce platforms based on deep learning

Recommendation algorithm for agricultural and sideline produ...

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2024 International Conference on Artificial Intelligence, Deep Learning and Neural Networks, AIDLNN 2024

作者： Chen, Lisi School of Computer Science and Engineering Guangzhou Institute of Science and Technology China

ISBN: (纸本)9798331520816

Aiming at the problem of low accuracy in e-commerce platform recommendation of agricultural and sideline products, an agricultural and sideline product recommendation algorithm based on deep learning was studied. Fully analyze the problems existing in the process of recommending agricultural and sideline products, study the mechanism of item-based collaborative filtering algorithm, and combine the document topic and matrix decomposition hybrid algorithm. Highlighting the advantages of both, a topic weighted fusion collaborative filtering algorithm was formed. Through experiments, this algorithm can effectively improve the recommendation effect of agricultural and sideline products on e-commerce platforms. Compared with the simple item collaborative filtering algorithm, the algorithm studied in this study can effectively improve the accuracy of recommended targets by about 5%, and also has a 2% improvement in the diversity of recommended targets. In terms of accuracy of prediction results, through experimental comparison, the root mean square error of this algorithm is also lower than other comparative algorithms, which proves the advantages of this algorithm. © 2024 IEEE.

关键词： Agricultural and sideline products E-commerce platform New media Recommendation algorithm

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A Semantically Driven Model for Web Image Tagging Using Diverse Tag Selection 15th

A Semantically Driven Model for Web Image Tagging Using Dive...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Roy, Abhijith Deepak, Gerard Department of Computer Science and Engineering National Institute of Technology Tiruchirapalli India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Institute of Higher Education Manipal Bengaluru India

ISBN: (纸本)9783031889912

There is a need for tagging of Multimedia content on the Web, specifically images, in addition, most of these images are from the medical domain continue to be neglected over the Web 3.0 and they need to be annotated and tagged in this era of Web 3.0 for easing the process of image retrieval and annotation based applications. This paper proposes a strategic model for Web Image Tagging of Medical Images using semantics and artificial intelligence techniques. The proposed framework facilitates Web Image Tagging with formalization of Term Sets by accepting the categories and annotations from the image dataset. A very strong learning infrastructure in terms of CNNs to classify the medical image dataset is encompassed, the presence of strategic domain relevant ebooks helps in curating the knowledge graph, hence furthering the auxiliary knowledge and thereby fact based reasoning. The phenomenon of ontology generation also aggregates auxiliary knowledge and populates quiz knowledge which are highly relevant to the proposed framework. The Jiang-Conrath Similarity Index along with the Normalized Information Distance and the Horn’s Index provides a very strong ecosystem for semantics similarity based reasoning and overall precision _ %, with an F-measure of _ %, lowest measure of FDR of __ has been achieved by the proposed framework for tagging of Web images pertaining to Medical domain. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Content based retrieval

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