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A fuzzy zeroing neural network and its application on dynamic Hill cipher

Neural Computing and Applications 2025年第17期37卷 10605-10619页

作者： Jin, Jie Lei, Xiaoyang Chen, Chaoyang Lu, Ming Wu, Lianghong Li, Zhijing School of Information Engineering Changsha Medical University Hunan Changsha410219 China School of Information and Electrical Engineering Hunan University of Science and Technology Hunan Xiangtan411201 China

Cryptography is the core of information security, and the Hill cipher is one of the most important methods for cryptography. For the purpose of the improvement in the security of traditional Hill cipher (THC) with time-invariant key, a new dynamic Hill cipher (DHC) method with time-varying key is proposed in this paper, which replaced the time-invariant matrix key of the THC with a time-varying matrix key for encryption. In order to ensure the efficiency and accuracy of the proposed DHC, the zeroing neural network (ZNN) is adopted to solve the time-varying key inversion (TVKI) matrix of the DHC decryption. Generally, conventional zeroing neural network (C-ZNN) models cannot effectively deal with the noises, and their convergence and robustness cannot be guaranteed in noisy environments. For the purpose of ensuring the robustness of the ZNN model for solving the TVKI matrix of the DHC decryption, a fuzzy activation function is designed, and a new fuzzy ZNN (FZNN) is constructed to solve the TVKI matrix of the DHC decryption. Comparative simulation results of the FZNN model with other C-ZNN models for solving the TVKI matrix of the DHC demonstrate that the FZNN model converges to the theoretical solution of the TVKI matrix within 0.8s in noisy environment, while other C-ZNN models take more than 0.8s or fail due to noises, which further validates the superior performances of the FZNN model. In addition, the encryption and decryption experiments on strings and RGB images are provided to prove the security of the proposed DHC, and comparative simulation results show that DHC has high feasibility and security in information encryption. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Covariance matrix

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Underwater Biological Target Detection Algorithm and Research Based on YOLOv7 Algorithm

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第6期51卷 594-601页

作者： Zhuang, Hongwei Liu, Weisheng School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China College of Computer Science and Software Engineering University of Science and Technology Liaoning CO Anshan114051 China

Underwater target detection is an important method for detecting marine organisms. However, due to the image occlusion of underwater targets, blurred water quality, poor lighting conditions, small targets, and complex backgrounds, the detection of underwater biological targets has posed significant challenges. In the intricate underwater environment, the conventional feature extraction method has a few drawbacks, including imprecise feature extraction, sluggish detection speed, and inadequate robustness. Consequently, an underwater target detection method based on the enhanced You Only Look Once 7 (YOLOv7) is proposed in this study. The network architecture is reconstructed, and the Deformable Convolutional Network (DCN) modules replace some 3×3 convolutional blocks in the ELAN structure to offset sampling points and reduce background interference. Skip connections and 1× 1 convolutional architecture are added to the DCN module to improve the model’s perception of image details. In addition, Contextual Transformer 3 (COT3) is also incorporated to improve visual performance. Finally, to improve the detection efficiency of small objects, the CIoU loss function is finally replaced by the Normalized Wasserstein Distance (NWD) algorithm. The mAP of DCCN-YOLOv7 on the URPC dataset is 80.4%, according to the experimental results, 2.8% higher than the YOLOv7 network model that is used as a baseline. Furthermore, in contrast to the original YOLOv7 algorithm, the detection speed and accuracy are higher, making it more appropriate for target recognition underwater. © (2024), (International Association of Engineers). All rights reserved.

关键词： Feature extraction

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Multi-label, Classification-based Prediction of Breast Cancer Metastasis Directions

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第1期52卷 1-10页

作者： Wang, Tingting Fan, Qi Tan, Liang Zhang, Beier School of Computer and Software Engineering Anhui Institute of Information Technology China School of Computer Science and Technology Huaibei Normal University China School of Computer and Software Engineering Anhui Institute of Information Technology China School of Computer Science and Technology Huaibei Normal University China

Predicting the metastatic direction of primary breast cancer (BC), thus assisting physicians in precise treatment, strict follow-up, and effectively improving the prognosis. The clinical data of 293,946 patients with primary BC diagnosed between 2010 and 2015 were collected from the Surveillance, Epidemiology, and End Results database. Multiple interpolations and Multi-label Synthetic Minority Over-sampling Technique methods were used for data analysis, and machine learning model was established for multi-label classification. Finally, Surgical information, lymph node status, distant metastasis, tumor size, chemotherapy, histological type, and radiotherapy had significant influence as inputs. Compared with the k-nearest neighbor model, average accuracies of the decision tree and random forest (RF) models increased from 88.84% to 93.59% and 94.14%, respectively. Their average precision, recall rate, F1 score, area under the receiver operating characteristic curve and weighted-F1 increased from 87.24% to 95.85% and 94.74%, 87.73% to 90.40% and 91.76%, 87.07% to 92.16% and 93.45%, 97.11% to 99.53% and 99.95%, 82.13% to 89.44% and 90.48%, respectively. In conclusion, the RF model, which showed the best performance, can be used in multi-label prediction of BC metastasis directions, and can assist physicians in diagnosing and treating patients with primary BC. © (2025), (International Association of Engineers). All rights reserved.

关键词： Lung cancer

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Secure vehicular data communication in Named Data Networking

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Digital Communications and Networks 2023年第1期9卷 203-210页

作者： Xiaonan Wang Xilan Chen Xingwei Wang School of Computer Science and Engineering Changshu Institute of TechnologyChangshu215500China School of Computer Science and Technology Northeastern UniversityShenyang110169China

Vehicular data misuse may lead to traffic accidents and even loss of life,so it is crucial to achieve secure vehicular data *** paper focuses on secure vehicular data communications in the Named Data Networking(NDN).In NDN,names,provider IDs and data are transmitted in plaintext,which exposes vehicular data to security threats and leads to considerable data communication costs and failure *** paper proposes a Secure vehicular Data Communication(SDC)approach in NDN to supress data communication costs and failure *** constructs a vehicular backbone to reduce the number of authenticated nodes involved in reverse *** the ciphtertext of the name and data is included in the signed Interest and Data and transmitted along the backbone,so the secure data communications are *** is evaluated,and the data results demonstrate that SCD achieves the above objectives.

关键词： Named data networking Reverse path Secure data communication

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CR2-Net: Component Relationship Reasoning Network for Traffic Text Detection

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Wang, Runmin Hei, Jielei Liu, Minghao Wan, Zukun Xu, Juan Cao, Xiaofei He, Xuan Ding, Yajun Gao, Changxin Sang, Nong School of Information Science and Engineering Hunan Normal University Changsha China College of Information Science and Engineering Hunan University Changsha China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

Traffic text, as a special type of natural scene text, plays an important role in intelligent transportation. Apart from the challenges faced by general natural scene text detection, the presence of a large amount of non-traffic text which leads to false alarms further complicates the problem of traffic text detection, e.g., billboards, building signs, etc. In this work, we infer text component relationships via a component relationship reasoning network (CR2-Net) for traffic text detection. The method contains main contributions as follows: (1) explored a GCN-based component-relational reasoning traffic text detection network by leveraging global contextual information and reasoning about intrinsic connections between text components to improve traffic text detection performance. (2) designed a Group Deformable Convolution Network (GDCN) and adaptively adjusting the convolutional manner to enhance the feature extraction capability of the network for text regions. (3) proposed a Fusion Attention Module (FAM) to enhance the network adaptive ability by assigning feature map weights to different channels. To demonstrate the effectiveness, we evaluate CR2-Net on four standard benchmarks. Experimental results demonstrate that our method achieves remarkable performance on two traffic text datasets, i.e., CTST-1600, and TPD. Especially, our results on TPD dataset surpass all previously reported results to the best of our knowledge. Additionally, our approach exhibits competitive results on general natural scene text datasets, such as ICDAR 2015, and SCUT-CTW1500, thereby fully demonstrating the superiority and adaptability of our method. The code will be publicly available at https://***/runminwang/CR2-Net. IEEE

关键词： Convolution

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Clustering and Artificial Intelligence-based Prediction of Ecologically Sustainable Species Introductions

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第4期52卷 1159-1168页

作者： Liu, Shuqiao Zhang, Zhao Zhou, Hongyan Chen, Xue-Bo School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

There is a growing interest in sustainable ecosystem development, which includes methods such as scientific modeling, environmental assessment, and development forecasting and planning. However, due to insufficient survey data in many current development areas, development progress is delayed and stagnant. To address this situation, this paper proposes a SWOT-TOPSIS-K-Means (STK) data analysis and evaluation model to analyze ecological factors, which can realize a comprehensive and complete data analysis with fewer samples. Decision tree (DT), random forest (RF), and multilayer perceptron (MLP) neural network models were constructed from the results of this analysis, and statistical tests such as r-squared, mean absolute error, and cross-validation are used to further confirm the performance efficiency of the computational prediction models to provide real-time prediction research solutions. For this purpose, data from research scholars on species introduction in ecosystem development were selected for testing. The results show that the proposed assessment model and modeling results satisfy all accuracy-related acceptance requirements. Among them, MLP is better than DT and RF. In summary, the STK assessment model and the MLP prediction model can provide a basis for the selection and development of ecological factors. © (2025), (International Association of Engineers). All rights reserved.

关键词： Prediction models

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Algorithms and statistical analysis for linear structured weighted total least squares problem

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Geodesy and Geodynamics 2024年第2期15卷 177-188页

作者： Jian Xie Tianwei Qiu Cui Zhou Dongfang Lin Sichun Long School of Earth Science and Spatial Information Engineering Hunan University of Science and TechnologyXiangtan 411201China College of Science Central South University of Forestry and TechnologyChangsha 410018China National-Local Joint Engineering Laboratory of Geo-spatial Information Technology Hunan University of Science and TechnologyXiangtan 411201China

Weighted total least squares(WTLS)have been regarded as the standard tool for the errors-in-variables(EIV)model in which all the elements in the observation vector and the coefficient matrix are contaminated with random ***,in many geodetic applications,some elements are error-free and some random observations appear repeatedly in different positions in the augmented coefficient *** is called the linear structured EIV(LSEIV)*** kinds of methods are proposed for the LSEIV model from functional and stochastic *** the one hand,the functional part of the LSEIV model is modified into the errors-in-observations(EIO)*** the other hand,the stochastic model is modified by applying the Moore-Penrose inverse of the cofactor *** algorithms are derived through the Lagrange multipliers method and linear *** estimation principles and iterative formula of the parameters are proven to be *** first-order approximate variance-covariance matrix(VCM)of the parameters is also derived.A numerical example is given to compare the performances of our proposed three algorithms with the STLS ***,the least squares(LS),total least squares(TLS)and linear structured weighted total least squares(LSWTLS)solutions are compared and the accuracy evaluation formula is proven to be feasible and ***,the LSWTLS is applied to the field of deformation analysis,which yields a better result than the traditional LS and TLS estimations.

关键词： Linear structured weighted total least squares Errors-in-variables Errors-in-observations Functional modelmodification Stochastic model modification Accuracyevaluation

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Explainable Business Process Remaining Time Prediction Using Reachability Graph

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Chinese Journal of Electronics 2023年第3期32卷 625-639页

作者： CAO Rui ZENG Qingtian NI Weijian LU Faming LIU Cong DUAN Hua College of Computer Science and Engineering Shandong University of Science and Technology School of Computer Science and Technology Shandong University of Technology

With the recent advances in the field of deep learning, an increasing number of deep neural networks have been applied to business process prediction tasks, remaining time prediction, to obtain more accurate predictive results. However, existing time prediction methods based on deep learning have poor interpretability, an explainable business process remaining time prediction method is proposed using reachability graph,which consists of prediction model construction and visualization. For prediction models, a Petri net is mined and the reachability graph is constructed to obtain the transition occurrence vector. Then, prefixes and corresponding suffixes are generated to cluster into different transition partitions according to transition occurrence vector. Next,the bidirectional recurrent neural network with attention is applied to each transition partition to encode the prefixes, and the deep transfer learning between different transition partitions is performed. For the visualization of prediction models, the evaluation values are added to the sub-processes of a Petri net to realize the visualization of the prediction models. Finally, the proposed method is validated by publicly available event logs.

关键词： Deep learning Training Visualization Recurrent neural networks Petri nets Transfer learning Process control

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CMKD-Net: a cross-modal knowledge distillation method for remote sensing image classification

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Advances in Space Research 2025年第12期75卷 8515-8534页

作者： Song, Huaxiang Xie, Junping Duan, Yingying Xie, Xinyi Zhou, Yang Wang, Wenhui School of Geography Science and Tourism Hunan University of Arts and Science Hunan Changde415000 China Faculty of Transportation Engineering Kunming University of Science and Technology Yunnan Kunming650500 China

Cross-modal knowledge distillation (KD) offers the potential to synergize the strengths of Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs) in remote sensing image (RSI) classification. However, existing KD techniques in this field are frequently ineffective and time-consuming. We contend that this inefficiency stems from the data sparsity inherent in RSI samples—a challenge long overlooked in previous studies. To address this issue, we propose a novel algorithm designed to alleviate data sparsity and enhance the quality of the training data. Building upon this, we introduce CMKD-Net, a KD framework that facilitates knowledge transfer from a ViT teacher to a CNN student model. Experimental evaluations on three RSI datasets demonstrate that CMKD-Net outperforms 17 state-of-the-art models published since 2022 on classification accuracy and model compactness. Furthermore, our method cuts down training time by at least 83% compared to current KD methods, making cross-modal KD for RSI classification much more effective. © 2025 COSPAR

关键词： Convolutional neural networks

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SA-Model:Multi-Feature Fusion Poetic Sentiment Analysis Based on a Hybrid Word Vector Model

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computer Modeling in engineering & sciences 2023年第10期137卷 631-645页

作者： Lingli Zhang Yadong Wu Qikai Chu Pan Li Guijuan Wang Weihan Zhang Yu Qiu Yi Li School of Computer Science and Engineering Sichuan University of Science and EngineeringZigong643000China School of Automation and Information Engineering Sichuan University of Science and EngineeringZigong643000China School of Computer Science and Technology Southwest University of Science and TechnologyMianyang621000China School of Information Engineering Southwest University of Science and TechnologyMianyang621000China

Sentiment analysis in Chinese classical poetry has become a prominent topic in historical and cultural tracing,ancient literature research,***,the existing research on sentiment analysis is relatively *** does not effectively solve the problems such as the weak feature extraction ability of poetry text,which leads to the low performance of the model on sentiment analysis for Chinese classical *** this research,we offer the SA-Model,a poetic sentiment analysis ***-Model firstly extracts text vector information and fuses it through Bidirectional encoder representation from transformers-Whole word masking-extension(BERT-wwmext)and Enhanced representation through knowledge integration(ERNIE)to enrich text vector information;Secondly,it incorporates numerous encoders to remove text features at multiple levels,thereby increasing text feature information,improving text semantics accuracy,and enhancing the model’s learning and generalization capabilities;finally,multi-feature fusion poetry sentiment analysis model is *** feasibility and accuracy of the model are validated through the ancient poetry sentiment *** with other baseline models,the experimental findings indicate that SA-Model may increase the accuracy of text semantics and hence improve the capability of poetry sentiment analysis.

关键词： Sentiment analysis Chinese classical poetry natural language processing BERT-wwm-ext ERNIE multi-feature fusion

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