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Two-Way Neural Network Performance PredictionModel Based onKnowledge Evolution and Individual Similarity

computer Modeling in engineering & Sciences 2024年第2期138卷 1183-1206页

作者： Xinzheng Wang Bing Guo Yan Shen College of Computer Science Sichuan UniversityChengdu610065China School of Information Science and Engineering Guilin University of TechnologyGuilin541004China School of Control Engineering Chengdu University of Information TechnologyChengdu610225China

Predicting students’academic achievements is an essential issue in education,which can benefit many stakeholders,for instance,students,teachers,managers,*** with online courses such asMOOCs,students’academicrelateddata in the face-to-face physical teaching environment is usually sparsity,and the sample size is *** makes building models to predict students’performance accurately in such an environment even *** paper proposes a Two-WayNeuralNetwork(TWNN)model based on the bidirectional recurrentneural network and graph neural network to predict students’next semester’s course performance using only theirprevious course *** experiments on a real dataset show that our model performs better thanthe baselines in many indicators.

关键词： computer education performance prediction deep learning

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Color Image Compression and Encryption Algorithm Based on 2D Compressed Sensing and Hyperchaotic System

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computers, Materials & Continua 2024年第2期78卷 1977-1993页

作者： Zhiqing Dong Zhao Zhang Hongyan Zhou Xuebo Chen School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan114051China School of Electronic and Information Engineering University of Science and Technology LiaoningAnshan114051China

With the advent of the information security era,it is necessary to guarantee the privacy,accuracy,and dependable transfer of *** study presents a new approach to the encryption and compression of color *** is predicated on 2D compressed sensing(CS)and the hyperchaotic ***,an optimized Arnold scrambling algorithm is applied to the initial color images to ensure strong ***,the processed images are con-currently encrypted and compressed using 2D *** them,chaotic sequences replace traditional random measurement matrices to increase the system’s ***,the processed images are re-encrypted using a combination of permutation and diffusion *** addition,the 2D projected gradient with an embedding decryption(2DPG-ED)algorithm is used to reconstruct *** with the traditional reconstruction algorithm,the 2DPG-ED algorithm can improve security and reduce computational ***,it has better *** experimental outcome and the performance analysis indicate that this algorithm can withstand malicious attacks and prove the method is effective.

关键词： Image encryption image compression hyperchaotic system compressed sensing

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A secure sharing model of environmental monitoring data for remote environmental pollution control

Journal of Biotech Research

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Journal of Biotech Research 2024年 18卷 15-25页

作者： Liu, Yunzhu School of Computer Information Engineering Nanchang Institute of Technology Jiangxi Nanchang China

The rapid development of industrialization and urbanization has made the environmental pollution problem in remote areas more prominent. However, this problem is often overlooked. This study attempted to construct an environmental monitoring data security sharing model for remote environmental pollution control to address this issue. A novel environmental monitoring model was proposed by introducing blockchain technology and the concept of cloud storage. A new data sharing model was proposed by combining symmetric encryption and asymmetric encryption algorithms. The results demonstrated that the maximum total storage capacity of the alliance chain + cloud storage node was 8.96 gigabytes with the minimum daily storage capacity of a single node reaching 0.47 megabytes. The maximum data transmission error of the data sharing model did not exceed 1 unit. The maximum throughputs of the carbon monoxide index and chemical oxygen demand were 9, 400 and 1, 950 kilobytes, respectively. Its latency data could reach a minimum of 24, 117 milliseconds with the transmission energy consumption reaching a minimum of 30.7%. The data security and data integrity could reach the maximum of 92.4% and 99.7%. The proposed model had demonstrated excellent data transmission and security performance, which could provide a new technical reference for environmental monitoring and data security sharing in remote areas. © (2024), (Bio Tech System). All rights reserved.

关键词： Cryptography

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Enhancing Data Forwarding Efficiency in SIoT with Multidimensional Social Relations

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computers, Materials & Continua 2024年第1期78卷 1095-1113页

作者： Fang Xu Songhao Jiang Yi Ma Manzoor Ahmed Zenggang Xiong Yuanlin Lyu School of Computer and Information Science Hubei Engineering UniversityXiaogan432000China School of Computer Science and Information Engineering Hubei UniversityWuhan430062China Artificial Intelligence Industrial Technology Research Institute Hubei Engineering UniversityXiaogan432000China

Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research *** paper proposes a data forwarding algorithm based on Multidimensional Social Relations(MSRR)in SIoT to solve this *** proposed algorithm separates message forwarding into intra-and cross-community forwarding by analyzing interest traits and social connections among *** new metrics are defined:the intensity of node social relationships,node activity,and community *** the community,messages are sent by determining which node is most similar to the sender by weighing the strength of social connections and node *** a node performs cross-community forwarding,the message is forwarded to the most reasonable relay community by measuring the node activity and the connection between *** proposed algorithm was compared to three existing routing algorithms in simulation *** indicate that the proposed algorithmsubstantially improves message delivery efficiency while lessening network overhead and enhancing connectivity and coordination in the SIoT context.

关键词： SIoT data forwarding social attributes social relations community

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A Dual-Layer Attention Based CAPTCHA Recognition Approach with Guided Visual Attention

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computer Modeling in engineering & Sciences 2025年第3期142卷 2841-2867页

作者： Zaid Derea Beiji Zou Xiaoyan Kui Alaa Thobhani Amr Abdussalam School of Computer Science and Engineering Central South UniversityChangsha410083China College of Computer Science and Information Technology Wasit UniversityWasit52001Iraq Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China

Enhancing website security is crucial to combat malicious activities,and CAPTCHA(Completely Automated Public Turing tests to tell computers and Humans Apart)has become a key method to distinguish humans from *** text-based CAPTCHAs are designed to challenge machines while remaining human-readable,recent advances in deep learning have enabled models to recognize them with remarkable *** this regard,we propose a novel two-layer visual attention framework for CAPTCHA recognition that builds on traditional attention mechanisms by incorporating Guided Visual Attention(GVA),which sharpens focus on relevant visual *** have specifically adapted the well-established image captioning task to address this *** approach utilizes the first-level attention module as guidance to the second-level attention component,incorporating two LSTM(Long Short-Term Memory)layers to enhance CAPTCHA *** extensive evaluation across four diverse datasets—Weibo,BoC(Bank of China),Gregwar,and Captcha 0.3—shows the adaptability and efficacy of our *** approach demonstrated impressive performance,achieving an accuracy of 96.70%for BoC and 95.92%for *** results underscore the effectiveness of our method in accurately recognizing and processing CAPTCHA datasets,showcasing its robustness,reliability,and ability to handle varied challenges in CAPTCHA recognition.

关键词： Text-based CAPTCHA image recognition guided visual attention web security computer vision

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Mitigation of Optical Multipath Interference Algorithms in IM-DD Transmission System

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China Communications 2024年第4期21卷 1-9页

作者： Huo Jiahao Zhu Jin Zhu Zuqing Zhang Xiaoying Liu Shaonan Tao Jianlong Wei Huangfu School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China School of Information Science and Technology University of Science and Technology of ChinaHefei 230026China

This paper aimed to propose two algorithms,DA-M and RF-M,of reducing the impact of multipath interference(MPI)on intensity modulation direct detection(IM-DD)systems,particularly for four-level pulse amplitude modulation(PAM4)***-M reduced the fluctuation by averaging the signal in blocks,RF-M estimated MPI by subtracting the decision value of the corresponding block from the mean value of a signal block,and then generated interference-reduced samples by subtracting the interference signal from the product of the corresponding MPI estimate and then weighting *** paper firstly proposed to separate the signal before decision-making into multiple blocks,which significantly reduced the complexity of DA-M and *** results showed that the MPI noise of 28 GBaud IMDD system under the linewidths of 1e5 Hz,1e6 Hz and 10e6 Hz can be effectively alleviated.

关键词： IM-DD multipath interference PAM4

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Cooperative-Guided Ant Colony Optimization with Knowledge Learning for Job Shop Scheduling Problem

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Tsinghua Science and technology 2024年第5期29卷 1283-1299页

作者： Wei Li Xiangfang Yan Ying Huang School of Information Engineering Jiangxi University of Science and TechnologyGanzhou 341000China School of Mathematics and Computer Science Gannan Normal UniversityGanzhou 341000China

With the advancement of the manufacturing industry,the investigation of the shop floor scheduling problem has gained increasing *** Job shop Scheduling Problem(JSP),as a fundamental scheduling problem,holds considerable theoretical research ***,finding a satisfactory solution within a given time is difficult due to the NP-hard nature of the JSP.A co-operative-guided ant colony optimization algorithm with knowledge learning(namely KLCACO)is proposed to address this *** algorithm integrates a data-based swarm intelligence optimization algorithm with model-based JSP schedule knowledge.A solution construction scheme based on scheduling knowledge learning is proposed for *** problem model and algorithm data are fused by merging scheduling and planning knowledge with individual scheme construction to enhance the quality of the generated individual solutions.A pheromone guidance mechanism,which is based on a collaborative machine strategy,is used to simplify information learning and the problem space by collaborating with different machine processing ***,the KLCACO algorithm utilizes the classical neighborhood structure to optimize the solution,expanding the search space of the algorithm and accelerating its *** KLCACO algorithm is compared with other highperformance intelligent optimization algorithms on four public benchmark datasets,comprising 48 benchmark test cases in *** effectiveness of the proposed algorithm in addressing JSPs is validated,demonstrating the feasibility of the KLCACO algorithm for knowledge and data fusion in complex combinatorial optimization problems.

关键词： Ant Colony Optimization(ACO) Job shop Scheduling Problem(JSP) knowledge learning cooperative guidance

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Enhanced Multi-Scale Object Detection Algorithm for Foggy Traffic Scenarios

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computers, Materials & Continua 2025年第2期82卷 2451-2474页

作者： Honglin Wang Zitong Shi Cheng Zhu School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China School of Electrical&Computer Engineering University of Illinois at Urbana ChampaignUrbanaIL 61801USA

In foggy traffic scenarios, existing object detection algorithms face challenges such as low detection accuracy, poor robustness, occlusion, missed detections, and false detections. To address this issue, a multi-scale object detection algorithm based on an improved YOLOv8 has been proposed. Firstly, a lightweight attention mechanism, Triplet Attention, is introduced to enhance the algorithm’s ability to extract multi-dimensional and multi-scale features, thereby improving the receptive capability of the feature maps. Secondly, the Diverse Branch Block (DBB) is integrated into the CSP Bottleneck with two Convolutions (C2F) module to strengthen the fusion of semantic information across different layers. Thirdly, a new decoupled detection head is proposed by redesigning the original network head based on the Diverse Branch Block module to improve detection accuracy and reduce missed and false detections. Finally, the Minimum Point Distance based Intersection-over-Union (MPDIoU) is used to replace the original YOLOv8 Complete Intersection-over-Union (CIoU) to accelerate the network’s training convergence. Comparative experiments and dehazing pre-processing tests were conducted on the RTTS and VOC-Fog datasets. Compared to the baseline YOLOv8 model, the improved algorithm achieved mean Average Precision (mAP) improvements of 4.6% and 3.8%, respectively. After defogging pre-processing, the mAP increased by 5.3% and 4.4%, respectively. The experimental results demonstrate that the improved algorithm exhibits high practicality and effectiveness in foggy traffic scenarios.

关键词： Deep learning object detection foggy scenes traffic detection YOLOv8

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Surface Defect Detection Algorithm of Hot-Rolled Strip Based on Improved YOLOv7

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2024年第4期51卷 345-354页

作者： Shen, Lijia Cui, Wenhua Tao, Ye Shi, Tianwei Liao, Jinzhen School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China

To enhance the capability of classifying and localizing defects on the surface of hot-rolled strips, this paper proposed an algorithm based on YOLOv7 to improve defect detection. The BI-SPPFCSPC structure was incorporated into the feature pyramid in this algorithm, enabling enhanced extraction of features from small objects and improved accuracy in network model positioning. Additionally, a small object detection layer was introduced to enhance shallow feature capture. Then the CARAFE sampling operator was used for up-sampling to reduce the feature loss problem of small objects. Finally, the WIoU served as the loss function for network model training to expedite convergence. The NEU-DET dataset was utilized for ablation and comparison tests. The findings indicated that the enhanced YOLOv7 model's mAP value had increased to 80.7%. The detection impact was much enhanced in comparison to other traditional models, and the frequency of false and missing detections was also decreased. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Surface defects

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Incremental Weighted Ensemble for Data Streams With Concept Drift

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 92-103页

作者： Jiao, Botao Guo, Yinan Yang, Cuie Pu, Jiayang Zheng, Zhiji Gong, Dunwei School of Electromechanical and Information Engineering Beijing100083 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Victoria University of Wellington School of Engineering and Computer Wellington6140 New Zealand Electromechanical and Information Engineering Beijing100083 China Qingdao University of Science and Technology School of Information Science and Technology Qingdao266061 China

As a popular strategy to tackle concept drift, chunk-based ensemble method adapts a new concept by adjusting the weights of historical classifiers. However, most previous approaches normally evaluate the historical classifier based on an entire chunk newly arrived, which may cause delayed adaptation. To address the issue, two novel ensemble models, named incremental weighted ensemble (IWE) and incremental weighted ensemble for multi-classification (IWE-M), are proposed. At each time step, all base classifiers are incrementally updated on a newly arrived instance. Following that, the instance is collected into a cache array. Once a data chunk is formed, a new base classifier is created. More specially, a forgetting mechanism based on variable-size window is designed to adjust the weight of each base classifier in IWE in terms of its classification accuracy on the latest instances in an online manner. IWE-M, an extension of IWE, aims to solve multiclass problems with local concept drifts. In IWE-M, the weight of a base classifier is expanded to a weight vector. In this way, this ensemble model can retain specific historical information about nondrift regions from a local drift. Experimental results show that the proposed ensemble frameworks outperform six competitive approaches on accuracy and G-mean. © 2020 IEEE.

关键词： Classifiers

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