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An Innovative K-Anonymity Privacy-Preserving Algorithm to Improve data Availability in the Context of Big data

computers, Materials & Continua 2024年第4期79卷 1561-1579页

作者： Linlin Yuan Tiantian Zhang Yuling Chen Yuxiang Yang Huang Li State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China College of Information Engineering Guizhou Open UniversityGuiyang550025China Guizhou Academy of Tobacco Science Guiyang550025China

The development of technologies such as big data and blockchain has brought convenience to life,but at the same time,privacy and security issues are becoming more and more *** K-anonymity algorithm is an effective and low computational complexity privacy-preserving algorithm that can safeguard users’privacy by anonymizing big ***,the algorithm currently suffers from the problem of focusing only on improving user privacy while ignoring data *** addition,ignoring the impact of quasi-identified attributes on sensitive attributes causes the usability of the processed data on statistical analysis to be *** on this,we propose a new K-anonymity algorithm to solve the privacy security problem in the context of big data,while guaranteeing improved data ***,we construct a new information loss function based on the information quantity *** that different quasi-identification attributes have different impacts on sensitive attributes,we set weights for each quasi-identification attribute when designing the information loss *** addition,to reduce information loss,we improve K-anonymity in two ***,we make the loss of information smaller than in the original table while guaranteeing privacy based on common artificial intelligence algorithms,i.e.,greedy algorithm and 2-means clustering *** addition,we improve the 2-means clustering algorithm by designing a mean-center method to select the initial center of ***,we design the K-anonymity algorithm of this scheme based on the constructed information loss function,the improved 2-means clustering algorithm,and the greedy algorithm,which reduces the information ***,we experimentally demonstrate the effectiveness of the algorithm in improving the effect of 2-means clustering and reducing information loss.

关键词： Blockchain big data K-anonymity 2-means clustering greedy algorithm mean-center method

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Application of the Deep Convolutional Neural Network for the Classification of Auto Immune Diseases

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computers, Materials & Continua 2023年第10期77卷 647-664页

作者： Fayaz Muhammad Jahangir Khan Asad Ullah Fasee Ullah Razaullah Khan Inayat Khan Mohammed ElAffendi Gauhar Ali Department of Computer Science and Information Technology Sarhad University of Science and Information TechnologyPeshawar25000Pakistan Department of Computer Science University of Engineering and TechnologyMardan23200Pakistan EIAS Data Science and Blockchain Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

IIF(Indirect Immune Florescence)has gained much attention recently due to its importance in medical *** primary purpose of this work is to highlight a step-by-step methodology for detecting autoimmune *** use of IIF for detecting autoimmune diseases is widespread in different medical *** 80 different types of autoimmune diseases have existed in various body *** IIF has been used for image classification in both ways,manually and by using the computer-Aided Detection(CAD)*** data scientists conducted various research works using an automatic CAD system with low *** diseases in the human body can be detected with the help of Transfer Learning(TL),an advanced Convolutional Neural Network(CNN)*** baseline paper applied the manual classification to the MIVIA dataset of Human Epithelial cells(HEP)type II cells and the Sub Class Discriminant(SDA)analysis technique used to detect autoimmune *** technique yielded an accuracy of up to 90.03%,which was not reliable for detecting autoimmune disease in the mitotic cells of the *** the current research,the work has been performed on the MIVIA data set of HEP type II cells by using four well-known models of *** augmentation and normalization have been applied to the dataset to overcome the problem of overfitting and are also used to improve the performance of TL *** models are named Inception V3,Dens Net 121,VGG-16,and Mobile Net,and their performance can be calculated through parameters of the confusion matrix(accuracy,precision,recall,and F1 measures).The results show that the accuracy value of VGG-16 is 78.00%,Inception V3 is 92.00%,Dense Net 121 is 95.00%,and Mobile Net shows 88.00%accuracy,***,DenseNet-121 shows the highest performance with suitable analysis of autoimmune *** overall performance highlighted that TL is a suitable and enhanced technique compared to its ***,the proposed technique is used

关键词： Indirect immune fluorescence computer-aided diagnosis transfer learning confusion matrix

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OSFS-Vague: Online streaming feature selection algorithm based on vague set

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1451-1466页

作者： Jie Yang Zhijun Wang Guoyin Wang Yanmin Liu Yi He Di Wu School of Physics and Electronic Science Zunyi Normal UniversityZunyiChina Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqingChina Department of Computer Science Old Dominion UniversityNorfolkVirginiaUSA College of Computer and Information Science Southwest UniversityChongqingChina

Online streaming feature selection(OSFS),as an online learning manner to handle streaming features,is critical in addressing high-dimensional *** real big data-related applications,the patterns and distributions of streaming features constantly change over time due to dynamic data generation ***,existing OSFS methods rely on presented and fixed hyperparameters,which undoubtedly lead to poor selection performance when encountering dynamic *** make up for the existing shortcomings,the authors propose a novel OSFS algorithm based on vague set,named *** main idea is to combine uncertainty and three-way decision theories to improve feature selection from the traditional dichotomous method to the trichotomous ***-Vague also improves the calculation method of correlation between features and ***,OSFS-Vague uses the distance correlation coefficient to classify streaming features into relevant features,weakly redundant features,and redundant ***,the relevant features and weakly redundant features are filtered for an optimal feature *** evaluate the proposed OSFS-Vague,extensive empirical experiments have been conducted on 11 *** results demonstrate that OSFS-Vague outperforms six state-of-the-art OSFS algorithms in terms of selection accuracy and computational efficiency.

关键词： feature selection online feature selection three-way decision vague set

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TRAINING GRAPH TRANSFORMERS VIA CURRICULUM-ENHANCED ATTENTION DISTILLATION 12

TRAINING GRAPH TRANSFORMERS VIA CURRICULUM-ENHANCED ATTENTIO...

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12th International Conference on Learning Representations, ICLR 2024

作者： Huang, Yisong Li, Jin Chen, Xinlong Fu, Yang-Geng College of Computer and Data Science Fuzhou University Fuzhou China Guangzhou China

Recent studies have shown that Graph Transformers (GTs) can be effective for specific graph-level tasks. However, when it comes to node classification, training GTs remains challenging, especially in semi-supervised settings with a severe scarcity of labeled data. Our paper aims to address this research gap by focusing on semi-supervised node classification. To accomplish this, we develop a curriculum-enhanced attention distillation method that involves utilizing a Local GT teacher and a Global GT student. Additionally, we introduce the concepts of in-class and out-of-class and then propose two improvements, out-of-class entropy and top-k pruning, to facilitate the student's out-of-class exploration under the teacher's in-class guidance. Taking inspiration from human learning, our method involves a curriculum mechanism for distillation that initially provides strict guidance to the student and gradually allows for more out-of-class exploration by a dynamic balance. Extensive experiments show that our method outperforms many state-of-the-art methods on seven public graph benchmarks, proving its effectiveness. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Curricula

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Thalaseemia Prediction Using Deep Neural Networks: A Step Towards Personalized Medicine 2

Thalaseemia Prediction Using Deep Neural Networks: A Step To...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Pream Sudha, V. Karpagavalli, S. Sathiyakumari, K. Sountharraj, A.C. Dr.N. G.P Arts and Science College Department of Computer Science with Data Analytics Coimbatore India PSGR Krishnammal College for Women Department of Computer Science Coimbatore India

ISBN: (纸本)9798331505745

Thalaseemia is a series of genetic hemolytic diseases brought on by defective hemoglobin synthesis. It is prevalent in many Asian, African, and Mediterranean nations. Four million Indians are Thalaseemia carriers, and more than 100,000 are actively combating the condition, according to the World Health Organization. It is specifically taken under investigation because cases of Thalassemia that have clinical, hematologic characteristics cannot be effectively separated from cases of sickle cell anemia and are misdiagnosed as such. The precise profiles of the genetic and pathophysiological mechanisms of patients with Thalassemia are not yet well defined, despite the fact that they are clinically treated identically. Even among people with the same genotype, there are differences in the severity of the clinical phenotype. The frequency of complications changes over time, both within and between individuals. Patients display a striking variation in severity, from a milder clinical course to severe transfusion dependency and progressive organ destruction. This study focuses on the creation of a discriminative model for Thalaseemia classification. In this study, the prediction of Thalassemia is modeled as a pattern classification issue, and the models are constructed using deep learning approaches. The hybrid CNN-Long Short Term Memory (LSTM) based Thalaseemia prediction model has demonstrated improved performance, according to the performance evaluation of these models. © 2025 IEEE.

关键词： Theranostics

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SMMGCL: A novel multi-level graph contrastive learning framework for integrating spatial multi-omics data

SMMGCL: A novel multi-level graph contrastive learning frame...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Bo Liu, Wei Luo, Jiawei Chen, Xiangtao Kwoh, Chee Keong Hunan University College of Computer Science and Electronic Engineering China Nanyang Technological University College of Computing and Data Science Singapore

ISBN: (纸本)9798350386226

Recent advances in spatial omics technologies have allowed various omics data to be obtained from a single tissue section. To fully explore the relationships among these different types of omics data, it is urgent to develop more effective methods for spatial multi-omics data integration. In this work, we propose a novel Multi-level Graph Contrastive Learning framework, named SMMGCL, to simultaneously mine complementary information at both spot and graph levels for integrating Spatial Multi-omics data. Specifically, to adaptively fuse multi-omics modalities, we first design a multi-modality autoencoder that integrates spatial locations with spot omic expressions to extract modality-specific embeddings. These embeddings are then fused into a consensus representation using an attention mechanism to capture spot-level cross-omics representations. Next, to explore the complex inter-omic structural information, we connect corresponding spots across different omics adjacency graphs into a heterogeneous graph. We then employ a graph convolutional network (GCN) to extract spatial correlations across the omics, learning a graph-level cross-omics global representation. Finally, SMMGCL aligns feature similarity matrixes between spot-level and graph-level representations with their pseudo-label similarity matrix, ensuring multi-level clustering consistency and leading to more accurate spatial multi-omics integration. Experimental results on simulated and real datasets from across tissues show that SMMGCL consistently outperforms other state-of-the-art methods in spatial multi-omics integration performance. The code for SMMGCL is available for download from the GitHub repository at https://***/cs-wangbo/SMMGCL. © 2024 IEEE.

关键词： Contrastive Learning

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Feature Contrast Difference and Enhanced Network for RGB-D Indoor Scene Classification in Internet of Things

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IEEE Internet of Things Journal 2025年第11期12卷 17610-17621页

作者： Zhou, Wujie Jian, Bitao Liu, Yuanyuan Zhejiang University of Science and Technology School of Information and Electronic Engineering Hangzhou310023 China China University of Geosciences School of Computer Science and Technology Wuhan430074 China Nanyang Technological University College of Computing and Data Science 308232 Singapore

The era of smart connectivity spawned by the Internet of Things (IoT) has made the need to achieve environmental perception and understanding of different scenarios increasingly urgent. Among the many scenarios, indoor scene classification has attracted much attention because of its its relevance to the daily lives of people, ranging from comfort regulation in living spaces to the optimal allocation of resources in offices, and a variety of approaches for this task have emerged. However, increasing accuracy remains a crucial objective due to the complexity and disorder of indoor scenes. Therefore, we propose a feature contrast difference and enhanced network for RGB-D indoor scene classification, FCDENet. First, the RGB and Depth images express different information. Therefore, we built a feature contrast difference module for the first two low-level features to extend the receptive fields of the different features, utilizing differential contrast to complement each other. Second, the high-level feature semantic information is abstract. Therefore, we introduced information cluster blocks, which are used to aggregate feature points with similar attributes into compact clusters after being parsed by an initial frequency transform, enabling instantiated representations of the semantic information. Finally, to further enhance the integrated features, we introduced a wavelet transform block in the cross-layer decoding process. In contrast to conventional decoding methods, we employed a wavelet transform for initial denoising cross-layer features and used multiple pooling structures to supplement local information, gradually weighting to achieve higher prediction accuracy. Extensive experiments on two typical indoor datasets, NYUDv2 and SUN RGB-D, show that our results exhibit excellent performance. In addition, to better demonstrate the reliability of the method, we conducted generalizability experiments on other datasets, and the proposed method provides a robust soluti

关键词： Wavelet decomposition

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Proximal Policy Optimization-Based Intrusion Detection Model for Satellite Networks 5

Proximal Policy Optimization-Based Intrusion Detection Model...

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5th International Symposium on computer Engineering and Intelligent Communications, ISCEIC 2024

作者： Liang, Shuaishuai Feng, Huibin Wu, Jiawei He, Canfei College of Computer and Data Science Fuzhou University Fuzhou China School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (纸本)9798331518677

As satellite network communication systems become an increasingly pivotal role in modern life, The routine maintenance of satellite networks is challenging due to limited resources and their susceptibility to interference. Satellite networks are more reliant on interruption detection systems than terrestrial networks. The importance of interruption detection systems in satellite networks is becoming increasingly evident. In this paper, we propose a novel intrusion detection model based on Proximal Policy Optimization (PPO). This method interacts with the environment to learn and optimize detection strategies, allowing for dynamic adaptation to environmental changes and attack patterns. We evaluated our model on benchmark datasets and compared its performance metrics against existing models. The results demonstrate that our proposed model significantly enhances overall accuracy and precision, and outperforms baseline models in terms of training convergence speed. © 2024 IEEE.

关键词： Network intrusion

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Few-shot Object Counting and Detection with Query-Guided Attention 23

Few-shot Object Counting and Detection with Query-Guided Att...

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4th International Conference on Computing, Networks and Internet of Things, CNIOT 2023

作者： Lin, Yuhao College of Computer and Data Science Fuzhou University China

ISBN: (纸本)9798400700705

The focus of this paper is on Few-Shot Counting and Detection (FSCD), a task that involves counting and localizing target objects based on a few exemplar bounding boxes. In particular, we address two major challenges in developing a FSCD model: the high cost of bounding box labeling and the large variations in object appearance. To mitigate the former issue, we propose a neighbor distance-aware mechanism for generating pseudo bounding boxes. This mechanism utilizes neighboring objects as context to estimate the location and size of the target object without requiring training. To address the challenge of appearance variation, we introduce a novel query-guided attention module that enhances the visual features of the search image by employing multi-head cross attention with query features. The module is designed to encourage attentive inspection of the search image by directing the model to focus more on regions that share similarities with the target objects. We integrate the query-guided attention module into the Faster-RCNN object detection model, resulting in a new few-shot object detector named Counting-RCNN. The proposed approach outperforms the state-of-the-art method on a large-scale FSCD147 dataset, achieving 0.60 MAE, 5.36 RMSE, and 13.01% AP50 improvement. © 2023 ACM.

关键词： Object detection

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EEG time-frequency correlation analysis of different decision stages in the ultimatum game 16

EEG time-frequency correlation analysis of different decisio...

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16th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2023

作者： Jin, Zhan Gao, Xiang Huang, Zhihua Fuzhou University College of Computer and Data Science China

ISBN: (纸本)9798350330755

The cognitive decision-making process in the ultimatum game (UG) paradigm consists of three stages: 1) option evaluation stage;2) action selection and execution stage;3) outcome evaluation stage. EEG correlation between different stages of UG is an important issue in exploring cognitive decision-making process. However, the ERP-based analysis is not sufficient to reveal EEG correlations at different stages. This study proposes an EEG time-frequency correlation analysis method. This method first calculates Event-Related Spectral Perturbations (ERSPs), and then uses non-parametric statistical tests with the Pearson correlation coefficient as the first-order statistical test quantity to calculate the EEG correlates of different decision stages in different decision states. It was found that there is a correlation between the first and second stages of the decision-making process when accepting a decision. These findings support the division of the decision-making process and indicate differences in different decision states, demonstrating that ERSP is a useful tool for decision analysis. © 2023 IEEE.

关键词： Decision making

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