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Enhancing Cybersecurity in the Big Data Era: A GA-Optimized Fuzzy Clustering Approach

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 2025年第3期33卷 353-377页

作者： Xu, Tieguang Ma, Can Su, Zhaozhao Su, Jingqiong Ma, Zhiming Wang, Jianzhen Yang, Zhaolong Nanxun Rnnovation Research Institute Zhejiang University of Water Resources and Electric Power Hangzhou310008 China Zhejiang Industry Polytechnic College ShaoXing312000 China Department of Information Engineering Jinzhong University of Information Technology Shanxi Jinzhong030800 China School of Computer Science and Technology Xinjiang Normal University Xinjiang Urumqi830054 China

Research Highlights • This study proposes a novel approach for enhancing cybersecurity through the integration of GA-AFCM. This method significantly improves the accuracy, efficiency, and adaptability of IDS. • The GA-AFCM technique demonstrates superior performance compared to conventional methods such as K-Means, MKKM-IC, Density Peaks, and GMM. • The proposed method effectively addresses the challenges of security of information in the period of Big Data, achieving the highest detection rate while significantly reducing false positives, thereby enhancing overall system reliability and efficiency. Cybersecurity includes protecting computer networks and systems from unauthorized access, harm, and fraud, employing various techniques and technologies such as barriers, antivirus software, and cryptography. Regular system updates, employee training, and adherence to best practices are crucial for maintaining confidentiality and ensuring reliable IT services in both corporate and public sectors. This paper introduces a GA-AFCM technique, which enhances intrusion detection and cybersecurity tasks by combining the strengths of Genetic Algorithms and Adaptive Fuzzy C-Means Clustering. The study began with data collection and preprocessing using Z-score normalization, followed by feature extraction through Linear Discrimination Analysis (LDA). The GA-AFCM approach was compared with traditional methods such as K-Means, Density Peaks, GMM, and MKKM-IC. The results demonstrate the TPR (91%), FPR (4%) precision (83.56%), accuracy (95.6%), and F1-score (87%) are used to examining and interpreting quickly and dynamically generated data streams efficiently solved by the proposed approach. The GA-AFCM method significantly enhances detection rates to over 95% while substantially reducing false positives, establishing it as a robust solution for cybersecurity in the big data era. © 2025 World Scientific Publishing Company.

关键词： HTTP

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Fact relation and Keywords fusion Abstractive Summarization 3

Fact relation and Keywords fusion Abstractive Summarization

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3rd International Conference on Artificial Intelligence and computer Engineering, ICAICE 2022

作者： Tian, Shihao Zhang, Long Zheng, Qiusheng Henan Key Laboratory on Public Opinion Intelligent Analysis Zhengzhou China School of Computer Science Zhongyuan University of Technology Zhengzhou China

ISBN: (纸本)9781510663473

With the wide application of deep learning, the abstractive text summary has become an important research topic in natural language processing. The abstractive text summary has high flexibility and can generate words that have not appeared in the text. However, the generated summary model will have factual errors, which significantly affect the usability of the summary. Therefore, this paper proposes a text summary model based on fact relationships and keyword fusion. We extract the fact relation triplet in the input text and automatically extract the keywords in the text to assist in the generation of the abstract. The fusion of fact relations and keywords can effectively alleviate the problem of factual errors in the abstract. Many experiments show that compared with other baseline models, our model (FRKFS) improves the performance of summaries generated on the data sets CNN/Daily Mail and XSum and alleviates the problem of factual errors. © 2023 SPIE.

关键词： Errors

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A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

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CAAI Transactions on Intelligence technology 2024年第4期9卷 790-804页

作者： Nechirvan Asaad Zebari Chira Nadheef Mohammed Dilovan Asaad Zebari Mazin Abed Mohammed Diyar Qader Zeebaree Haydar Abdulameer Marhoon Karrar Hameed Abdulkareem Seifedine Kadry Wattana Viriyasitavat Jan Nedoma Radek Martinek Department of Information Technology Lebanese French UniversityErbilIraq Department of Computer Science University of ZakhoZakhoKurdistan RegionIraq Department of Computer Science College of ScienceNawroz UniversityDuhokKurdistan RegionIraq Department of Artificial Intelligence College of Computer Science and Information TechnologyUniversity of AnbarRamadiIraq Department of Telecommunications VSB‐Technical University of OstravaOstravaCzech Republic Department of Cybernetics and Biomedical Engineering VSB‐Technical University of OstravaOstravaCzech Republic Department of Computer Network and Information Security Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq Information and Communication Technology Research Group Scientific Research CenterAl‐Ayen UniversityThi‐QarIraq College of Computer Sciences and Information Technology University of KerbalaKarbalaIraq College of Agriculture Al‐Muthanna UniversitySamawahIraq Department of Applied Data Science Noroff University CollegeKristiansandNorway Faculty of Commerce and Accountancy Chulalongkorn Business SchoolChulalongkorn UniversityBangkokThailand

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

关键词： brain tumour deep learning feature fusion model MRI images multi‐classification

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Chinese named entity recognition incorporating syntactic information 3

Chinese named entity recognition incorporating syntactic inf...

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3rd International Conference on Artificial Intelligence and computer Engineering, ICAICE 2022

作者： Li, Jiahao Zhang, Long Zheng, Qiusheng Yu, Kaige Henan Key Laboratory on Public Opinion Intelligent Analysis Zhengzhou China School of Computer Science Zhongyuan University of Technology Zhengzhou China

ISBN: (纸本)9781510663473

At present, Chinese named entity recognition techniques incorporating lexical knowledge have gained good development on graph neural networks. However, since there is still a deficiency in graph neural networks for longer distance semantic information and its pointing and localization information. In this task, we try to extract the missing pointing and locating information by employing relative position encoding techniques;it deals with long-distance dependencies by obtaining lexical information through dependency parsing. It is also able to deal more effectively with long-distribution relations and missing data in terms of direction and location through interactions with words in relative location coding. The experimental results on three NER datasets show that the proposed model is improved compared with other governor models. © 2023 SPIE.

关键词： Graph neural networks

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A Variant of the Sine Cosine Algorithm for Engineering Optimization Design 4

A Variant of the Sine Cosine Algorithm for Engineering Optim...

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4th International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2023

作者： Jia, Fuyun Ye, Yin Henan Medical College Department of Public Infrastructure Henan Zhengzhou451191 China Wuhan University of Technology School of Computer Science and Artificial Intelligence Hubei Wuhan430070 China

ISBN: (纸本)9798350343618

The design of pressure vessels is a constrained optimization problem that is influenced by many real-world standards and specifications. It involves multiple variables and multiple constraints. The existence of real constraints can make the solution space complex, leading to multiple local optima and making the search for the global optimum very difficult. Metaheuristic algorithms provide effective solutions for constrained optimization problems. The Sine Cosine Algorithm (SCA) is an optimization algorithm based on sine and cosine functions. However, like most metaheuristic algorithms, the traditional SCA has some shortcomings, such as high dependence on the initial solution and the tendency to get trapped in local optima. In this paper, we address these issues by using the Singer chaotic mapping to initialize the solution space of the population and introducing quantum theory to improve the search mechanism of the SCA algorithm. We propose a variant of SCA called CQ-SCA for optimizing the weight design of pressure vessels. Experimental results show that the design scheme obtained by CQ-SCA is more suitable than other algorithms. © 2023 IEEE.

关键词： Chaos theory

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Autoregressive-incorporated Non-negative Latent Factorization of Tensors for Temporal Link Prediction in Cryptocurrency Transaction Network 21

Autoregressive-incorporated Non-negative Latent Factorizatio...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Zhou, Yue Ding, YuTing Xia, Yan School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China School of Cyber Science and Engineering Southeast University Nanjing211102 China School of Electronic Information and Communication Engineering Chongqing Aerospace Polytechnic Chongqing400021 China

ISBN: (纸本)9798350365221

Cryptocurrency transaction networks (CTNs) are frequently encountered in real-world applications. Due to practical constraints, it is generally not feasible to observe complete interactions among all nodes at every time slot, leading to numerous missing links in CTNs. A link prediction model based on Non-negative Latent Factorization of Tensors (NLFT) has demonstrated effectiveness in predicting these missing links within a temporal network. However, existing NLFT models do not adequately capture the evolving relationships within a temporal network, limiting their ability to predict temporal links effectively. To address this limitation, this paper proposes an Autoregressive-incorporated Non-negative Latent-Factorization of Tensors (ArNLFT) model. The core idea is to adopt an autoregressive model to represent the evolving relationships in temporal networks, thereby constructing an autoregressive-incorporated objective function. Additionally, a non-negative parameter learning scheme, based on a single latent factor-dependent, non-negative, and multiplicative update rule, is designed to ensure the non-negativity of the proposed model. The effectiveness of the ArNLFT model is ultimately verified through temporal link prediction tasks on two real CTNs, with results showing that ArNLFT achieves a significant accuracy improvement compared to its peers. © 2024 IEEE.

关键词： Prediction models

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Dimensionality Reduction for Classification Using Divide-and-Conquer Based Genetic Programming 13

Dimensionality Reduction for Classification Using Divide-and...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wang, Peng Xue, Bing Liang, Jing Zhang, Mengjie School of Electrical and Information Engineering Zhengzhou University Zhengzhou China Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand School of Electrical Engineering and Automation Henan Institute of Technology Xinxiang China

ISBN: (纸本)9798350308365

Dimensionality reduction (DR) is to obtain meaningful low-dimensional representation concealed within high-dimensional data. Genetic programming (GP) has been used to achieve DR for classification because of the flexible representations and promising performance. However, one drawback of many current GP-based DR methods is the high computational demands, potentially limiting their utility in high-dimensional scenarios. To tackle this issue, this work develops a new GP algorithm to achieve DR for classification. A divide-and-conquer strategy based on feature clustering is proposed to split the original feature set into multiple small feature groups. Each tree in the multi-tree GP is used to learn one high-level feature from each feature group. The performance of the proposed method is examined on 10 classification datasets with varying difficulty. The results indicate that the new GP method achieves significantly better classification performance than commonly used feature construction and feature selection methods. More importantly, the proposed GP method uses fewer nodes but achieves better or comparable classification accuracy than the baseline multi-tree GP method. © 2024 IEEE.

关键词： Dimensionality reduction

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High-Fidelity Document Stain Removal via A Large-Scale Real-World Dataset and A Memory-Augmented Transformer

High-Fidelity Document Stain Removal via A Large-Scale Real-...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Li, Mingxian Sun, Hao Lei, Yingtie Zhang, Xiaofeng Dong, Yihang Zhou, Yilin Li, Zimeng Chen, Xuhang School of Computer Science and Engineering Huizhou University China The Ohio State University Department of Computer Science and Engineering United States School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China School of Electronic and Communication Engineering Shenzhen Polytechnic University China

ISBN: (纸本)9798331510831

Document images are often degraded by various stains, significantly impacting their readability and hindering downstream applications such as document digitization and analysis. The absence of a comprehensive stained document dataset has limited the effectiveness of existing document enhancement methods in removing stains while preserving fine-grained details. To address this challenge, we construct StainDoc, the first large-scale, high-resolution (2145 x 2245) dataset specifically designed for document stain removal. StainDoc comprises over 5,000 pairs of stained and clean document images across multiple scenes. This dataset encompasses a diverse range of stain types, severities, and document backgrounds, facilitating robust training and evaluation of document stain removal algorithms. Furthermore, we propose StainRestorer, a Transformer-based document stain removal approach. StainRestorer employs a memory-augmented Transformer architecture that captures hierarchical stain representations at part, instance, and semantic levels via the DocMemory module. The Stain Removal Transformer (SRTransformer) leverages these feature representations through a dual attention mechanism: an enhanced spatial attention with an expanded receptive field, and a channel attention captures channel-wise feature importance. This combination enables precise stain removal while preserving document content integrity. Extensive experiments demonstrate StainRestorer's superior performance over state-of-the-art methods on the Stain-Doc dataset and its variants *** and ***, establishing a new benchmark for document stain removal. Our work highlights the potential of memory-augmented Transformers for this task and contributes a valuable dataset to advance future research. © 2025 IEEE.

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A Short Text Classification Method Based on N-Gram and CNN

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Chinese Journal of Electronics 2020年第2期29卷 248-254页

作者： WANG Haitao HE Jie ZHANG Xiaohong LIU Shufen College of Computer Science and Technology Henan Polytechnic University College of Computer Science and Technology Jilin University

Text classification is a fundamental task in Nature language process(NLP) application. Most existing research work relied on either explicate or implicit text representation to settle this kind of problems, while these techniques work well for sentence and can not simply apply to short text because of its shortness and sparseness feature. Given these facts that obtaining the simple word vector feature and ignoring the important feature by utilizing the traditional multi-size filter Convolution neural network(CNN) during the course of text classification task, we offer a kind of short text classification model by CNN, which can obtain the abundant text feature by adopting none linear sliding method and N-gram language model, and picks out the key features by using the concentration mechanism, in addition employing the pooling operation can preserve the text features at the most certain as far as possible. The experiment shows that this method we offered, comparing the traditional machine learning algorithm and convolutional neural network, can markedly improve the classification result during the short text classification.

关键词： Short text Classification Convolution neural network N-gram Concentration mechanism

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Car Lock Control System Using Radio Frequency Identification(RFID)

Car Lock Control System Using Radio Frequency Identification...

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2024 IEEE SmartBlock4Africa, SmartBlock4Africa 2024

作者： Izang Aaron, A. Jonathan, Erihri O. Oyerinde Emmanuel, I. Ominike, Akpovi Chukwu Esther, C. Omodara Oluwademilade, E. Micheal Goldrich, C. School of Computing and Engineering Sciences Babcock University Department of Information Technology Ogun State Ilishan-Remo Nigeria School of Information and Communication Technology Delta State Polytechnic Department of computer Science Ogwashiuku Nigeria Petroleum Training Institute Computer Science and Information Technology Department Delta State Effurun Nigeria School of Computing and Engineering Sciences Babcock University Department of Computer Science Ogun State Ilishan-Remo Nigeria

ISBN: (纸本)9798350376838

Car Lock controlled system addresses the critical issue of vehicle security by utilizing RFID technology to create a more secure, passive, and wireless vehicle access system. This innovation surpasses traditional key-based systems, offering enhanced convenience and security. The project involves the development of a digital door locking mechanism integrated with RFID for secure vehicle identification. The system's efficacy was validated through rigorous testing, showcasing its potential to significantly deter vehicle theft. Despite challenges such as signal interference, the outcomes suggest a promising future for RFID in vehicle security. The study concludes with recommendations for extending RF communication range and exploring emerging technologies for further enhancement. This research marks a significant step towards redefining vehicle security, demonstrating the feasibility and effectiveness of a radio frequency-controlled car lock system. © 2024 IEEE.

关键词： Radio frequency identification (RFID)

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