The minimum independent dominance set(MIDS)problem is an important version of the dominating set with some other *** this work,we present an improved master-apprentice evolutionary algorithm for solving the MIDS probl...
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The minimum independent dominance set(MIDS)problem is an important version of the dominating set with some other *** this work,we present an improved master-apprentice evolutionary algorithm for solving the MIDS problem based on a path-breaking strategy called *** proposed MAE-PB algorithm combines a construction function for the initial solution generation and candidate solution *** is a multiple neighborhood-based local search algorithm that improves the quality of the solution using a path-breaking strategy for solution recombination based on master and apprentice solutions and a perturbation strategy for disturbing the solution when the algorithm cannot improve the solution quality within a certain number of *** show the competitiveness of the MAE-PB algorithm by presenting the computational results on classical benchmarks from the literature and a suite of massive graphs from real-world *** results show that the MAE-PB algorithm achieves high *** particular,for the classical benchmarks,the MAE-PB algorithm obtains the best-known results for seven instances,whereas for several massive graphs,it improves the best-known results for 62 *** investigate the proposed key ingredients to determine their impact on the performance of the proposed algorithm.
In this paper, combined with K-nearest neighbor classification and support vector machine classification, a predictive model on customer churn is designed. The goal of the model is to reduce the loss of innovative cus...
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Clinical auxiliary decision-making is related to life and health of patients, so the deep model needs to extract the personalised representation of patients to ensure high analysis and prediction accuracy;and provide ...
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Segmentation of brain tumors aids in diagnosing the disease early, planning treatment, and monitoring its progression in medical image analysis. Automation is necessary to eliminate the time and variability associated...
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There are many cloud data security techniques and algorithms available that can be used to detect attacks on cloud data,but these techniques and algorithms cannot be used to protect data from an *** cryptography is th...
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There are many cloud data security techniques and algorithms available that can be used to detect attacks on cloud data,but these techniques and algorithms cannot be used to protect data from an *** cryptography is the best way to transmit data in a secure and reliable *** researchers have developed various mechanisms to transfer data securely,which can convert data from readable to unreadable,but these algorithms are not sufficient to provide complete data *** algorithm has some data security *** some effective data protection techniques are used,the attacker will not be able to decipher the encrypted data,and even if the attacker tries to tamper with the data,the attacker will not have access to the original *** this paper,various data security techniques are developed,which can be used to protect the data from attackers ***,a customized American Standard Code for Information Interchange(ASCII)table is *** value of each Index is defined in a customized ASCII *** an attacker tries to decrypt the data,the attacker always tries to apply the predefined ASCII table on the Ciphertext,which in a way,can be helpful for the attacker to decrypt the *** that,a radix 64-bit encryption mechanism is used,with the help of which the number of cipher data is doubled from the original *** the number of cipher values is double the original data,the attacker tries to decrypt each *** of getting the original data,the attacker gets such data that has no relation to the original *** that,a Hill Matrix algorithm is created,with the help of which a key is generated that is used in the exact plain text for which it is created,and this Key cannot be used in any other plain *** boundaries of each Hill text work up to that *** techniques used in this paper are compared with those used in various papers and discussed that how far the current algorithm is better than all other algorit
In the realm of remote control, my preference is computer manipulation through hand gestures. Recent strides in technology showcase the feasibility of using manual gestures on Arduino platforms, facilitated by cloud c...
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computer-aided diagnosis based on image color rendering promotes medical image analysis and doctor-patient communication by highlighting important information of medical *** overcome the limitations of the color rende...
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computer-aided diagnosis based on image color rendering promotes medical image analysis and doctor-patient communication by highlighting important information of medical *** overcome the limitations of the color rendering method based on deep learning,such as poor model stability,poor rendering quality,fuzzy boundaries and crossed color boundaries,we propose a novel hinge-cross-entropy generative adversarial network(HCEGAN).The self-attention mechanism was added and improved to focus on the important information of the *** the hinge-cross-entropy loss function was used to stabilize the training process of GAN *** this study,we implement the HCEGAN model for image color rendering based on DIV2K and COCO datasets,and evaluate the results using SSIM and *** experimental results show that the proposed HCEGAN automatically re-renders images,significantly improves the quality of color rendering and greatly improves the stability of prior GAN models.
Block interception attack, also known as block withholding attack, is an attack method in the blockchain. The attacker penetrates the target mining pool for passive mining to destroy the target mining pool. This paper...
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Accurate prediction of mortality in nasopharyngeal carcinoma (NPC), a complex malignancy particularly challenging in advanced stages, is crucial for optimizing treatment strategies and improving patient outcomes. Howe...
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ISBN:
(纸本)9798350386226
Accurate prediction of mortality in nasopharyngeal carcinoma (NPC), a complex malignancy particularly challenging in advanced stages, is crucial for optimizing treatment strategies and improving patient outcomes. However, this predictive process is often compromised by the high-dimensional and heterogeneous nature of NPC-related data, coupled with the pervasive issue of incomplete multi-modal data, manifesting as missing radiological images or incomplete diagnostic reports. Traditional machine learning approaches suffer significant performance degradation when faced with such incomplete data, as they fail to effectively handle the high-dimensionality and intricate correlations across modalities. Even advanced multi-modal learning techniques like Transformers struggle to maintain robust performance in the presence of missing modalities, as they lack specialized mechanisms to adaptively integrate and align the diverse data types, while also capturing nuanced patterns and contextual relationships within the complex NPC data. To address these problem, we introduce IMAN: an adaptive network for robust NPC mortality prediction with missing modalities. IMAN features three integrated modules: the Dynamic Cross-Modal Calibration (DCMC) module employs adaptive, learnable parameters to scale and align medical images and field data;the Spatial-Contextual Attention Integration (SCAI) module enhances traditional Transformers by incorporating positional information within the self-attention mechanism, improving multi-modal feature integration;and the Context-Aware Feature Acquisition (CAFA) module adjusts convolution kernel positions through learnable offsets, allowing for adaptive feature capture across various scales and orientations in medical image modalities. Extensive experiments on our proprietary NPC dataset demonstrate IMAN's robustness and high predictive accuracy, even with missing data. Compared to existing methods, IMAN consistently outperforms in scenarios with incom
Recently, automatic segmentation algorithms based on deep learning have achieved promising results on various pathological image segmentation tasks. However, the inherent data-hungry nature of these methods and the hi...
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