Blockchain as a decentralized storage technology is widely used in many *** has extremely strict requirements for reliability because there are many potentially malicious ***,blockchain is a chain storage structure fo...
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Blockchain as a decentralized storage technology is widely used in many *** has extremely strict requirements for reliability because there are many potentially malicious ***,blockchain is a chain storage structure formed by interconnecting blocks1),which are stored by full replication method,where each node stores a replica of all blocks and the data consistency is maintained by the consensus *** decrease the storage overhead,previous approaches such as BFT-Store and Partition Chain store blocks via erasure ***,existing erasure coding based methods utilize static encoding schema to tolerant f malicious nodes,but in the typical cases,the number of malicious nodes is much smaller than f as described in previous *** redundant parities to tolerate excessive malicious nodes introduces unnecessary storage *** solve the above problem,we propose Dynamic-EC,which is a Dynamic Erasure Coding method in permissioned blockchain *** key idea of Dynamic-EC is to reduce the storage overhead by dynamically adjusting the total number of parities according to the risk level of the whole system,which is determined by the number of perceived malicious nodes,while ensuring the system *** demonstrate the effectiveness of Dynamic-EC,we conduct several experiments on an open source blockchain software *** results show that,compared to the state-of-the-art erasure coding methods,Dynamic-EC reduces the storage overhead by up to 42%,and decreases the average write latency of blocks by up to 25%,respectively.
Online shopping platforms are experiencing rapid growth, necessitating effective product recommendation systems to enhance customer satisfaction by recommending visually similar products. Traditional statistical techn...
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In today's dynamic and highly competitive market, brand differentiation has become both essential and complex. The growth of social media and enhanced digital accessibility have transformed brand promotion into a ...
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The life expectancy of a population is a vital measure of its overall health and healthcare quality. This study use machine learning methods, notably XGBoost, to predict life expectancy in industrialized and emerging ...
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The Internet has transformed into a hub for a wide array of illegal activities, ranging from annoying spam ads to financial scams, all thanks to advancements in modern technology. With the constant enhancements in net...
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With the advancing technology, the increase in threats has been exponential. These technologies have led to the production of huge amounts of network traffic data. Therefore, it is of immense importance for the compan...
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Lightweight cryptography is now emerging as a new method for providing security to resource-constrained devices. Securing those devices from external hackers during data transmission is now becoming an important issue...
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Over the past two decades, the rise in video streaming has been driven by internet accessibility and the demand for high-quality video. To meet this demand across varying network speeds and devices, transcoding is ess...
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In the area of data hiding and information security, the greater need is to ensure high embedding capacity of the stego media without hampering the visual quality while ensuring the tightest possible security. Now, it...
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Parkinson’s Disease (PD) is a neurodegenerative disorder that requires correct diagnosis and continuous monitoring of the disease severity. The state-of-the-art methods tend to be unimodal or lack robustness in gener...
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Parkinson’s Disease (PD) is a neurodegenerative disorder that requires correct diagnosis and continuous monitoring of the disease severity. The state-of-the-art methods tend to be unimodal or lack robustness in generalizing between modalities, and hence cannot be applied clinically in diverse populations. A comprehensive approach is a multi-modal framework that overcomes these limitations by integration of brain Magnetic Resonance Imaging (MRI) data, gait analysis, and speech signals for enhanced classification and severity estimation of PD. A Hierarchical Attention-based Multi-modal Fusion (HAMF) model is developed in this paper to employ hierarchical attention mechanism at feature and decision levels to help the model learn representations at various levels. This leads to richer feature extraction, besides fusing different data modalities with accurate integration. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods are used in optimizing the model, by which the convergence speed raised by 15–20 %. An accuracy of 94.2 % was achieved, thus improving by 4–5 %, compared to the existing methodologies. Temporal Convolutional Network (TCN) which can capture long-range temporal dependencies, was used in the longitudinal severity estimation task, achieving a Mean Squared Error (MSE) of 0.12 in disease progression forecasting. Beyond this, Domain-Adversarial Neural Network (DANN) enables improved cross-domain generalization and maintains a consistent classification accuracy of 90-93% on diversified datasets. Finally, SHapley Additive exPlanations - Class Activation Maps (SHAP-CAM) further enhanced the model explainability. During the conduct of this work, 85% of all cases provided clinically interpretable insights that allowed clinicians to conduct personalized treatment planning in a more robust and interpretable way. This work substantially extends current multi-modal diagnosis and analysis of PD progression by offering a robust and interpretable tool to
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