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IAENG International Journal of computer Science 2024年第3期51卷 195-203页

作者： Hou, Yan-e Wang, Congran Zhang, Chunyang Dang, Lanxue Xiao, Chunjing Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China College of Computer and Information Engineering Henan University Kaifeng475004 China College of Computer and Information Engineering Henan University Kaifeng475004 China College of Computer and Information Engineering Henan University Kaifeng475004 China School of Computer and Information Engineering Henan University Kaifeng475004 China

Electric vehicles have become the first choice for products delivery in logistics, due to their environmental friendliness and low cost compared to the traditional fuel vehicles. This paper deals with an electric capacitated vehicle routing problem with variable energy consumption rate (E-CVRP), which aims to minimize the total cost including vehicle fixed cost, traveled distance cost and energy cost. First, a mixed integer linear programming model is constructed based on the fact that the energy consumption is affected by the vehicle load. Second, a hybrid Max-Min Ant System (HMMAS) algorithm is proposed to solve this problem. Three neighborhood structures are introduced and executed in a Variable Neighborhood Descent (VND) algorithm to improve the current solution. In addition, an effective charging station adjustment strategy is adopted to further improve the solution obtained by the local search process. Finally, some experiments have been conducted on public instances, and the results demonstrate the effectiveness and stability of the algorithm. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Energy utilization

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Identification of block cipher algorithms using multi-layer perception algorithm

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Soft Computing 2025年第8期29卷 3823-3834页

作者： Yuan, Ke Yu, Daoming Yang, Wei Du, Zhanfei Shen, Lin Li, Zheng School of Computer and Information Engineering Henan University Henan Kaifeng475004 China Henan Province Engineering Research Center of Spatial Information Processing Henan University Kaifeng475004 China

Cryptographic algorithm identification is the process of distinguishing or identifying the cryptographic algorithm by analysing the potential information of various features in the ciphertext under the condition of known ciphertext, which is the basis of cryptanalysis work. As the complexity of ciphertext increases, the interference between ciphertext data and encryption algorithms increases, and the identification effect of single-layer cryptographic algorithms in encryption algorithm identification work is poor. In order to solve the problems of low recognition accuracy and poor stability of the block cipher algorithm in the single-layer identification scheme, we propose a cryptographic algorithm identification scheme of block cipher algorithm using deep learning algorithm Multi-Layer Perception (MLP) in this paper. In this scheme, 15 NIST randomness test methods are used to extract ciphertext feature, and 10 meaningful features are selected as input to MLP classifier to make predictions. In the ciphertext-only scenario, five block cipher algorithms, AES, 3DES, Blowfish, CAST and RC2 are selected for the study of cryptographic algorithm identification, then binary classification and multiclassification identification are carried out. The experimental results demonstrate that, when the size of the ciphertext files and other experimental conditions are the same as for the five conventional machine learning models, the proposed scheme has superior accuracy and stability. Among them, the average identification accuracy of binary classification is 76.5% for ciphertext files of 1KB to 512KB size, which is 35.3%, 37.5%, 34.2%, 38.5%, and 41.6% higher than that of the traditional classical machine learning models SVM, GNB, KNN, RF, and LR respectively. The average identification accuracy of the multiclassification is 36.2%, which is significantly higher than the other five classical machine learning algorithms. When the ciphertext file sizes are different, among the ident

关键词： Learning algorithms

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YOLO-FNC: An Improved Method for Small Object Detection in Remote Sensing Images Based on YOLOv7

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2024年第9期51卷 1281-1290页

作者： Dang, Lanxue Liu, Gang Hou, Yan-e Han, Hongyu Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China School of Computer and Information Engineering Henan University Kaifeng475004 China School of Computer and Information Engineering Henan University Kaifeng475004 China

The detection algorithms of small objects in remote sensing images is often challenging due to the complex background and limited pixels. This can lead to reduced accuracy in detection and an increased number of missed small objects. So this paper introduces YOLOFNC, an enhanced network based on YOLOv7. To improve the model’s ability to capture features of small objects, an enhanced C3-Faster module based on the C3 module is designed and integrated into the YOLOv7 network. This module helps extract more features related to small objects. Additionally, we employ Normalized Wasserstein Distance (NWD) fusion GIoU as a novel loss function to refine the accuracy of network optimization weights and the small object regression framework. Furthermore, a coordinated attention (CA) mechanism is incorporated at strategic locations in the model to reduce redundant information in the feature layer and prevent the loss of important small object features. we conduct comparison experiments between YOLO-FNC and other commonly used object detection algorithms on DIOR, AITOD, and VisDrone datasets. The experimental results show that YOLO-FNC achieves 84.4% mAP on the DIOR dataset, 35.9% mAP on the AI-TOD dataset, and 52.6% mAP on the VisDrone dataset. Compared to YOLOv7 and other remote sensing object detection models, YOLO-FNC demonstrates better performance in object detection. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Proximity sensors

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Mapping the Knowledge Structure and Research Evolution of Deep Learning

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2024年第10期51卷 1404-1412页

作者： Yuan, Ke Zhao, Chenmeng Chen, Yayun Shen, Lin Tang, Qian Jia, Chunfu School of Computer and Information Engineering Henan University Kaifeng475004 China Henan Spatial Information Processing Engineering Research Center Henan University Kaifeng475004 China School of Computer and Information Engineering Henan University Kaifeng475004 China Yayun Chen is an undergraduate School of Computer and Information Engineering Henan University Kaifeng475004 China College of Geography and Environmental Science Henan University Kaifeng475004 China College of Cybersecurity Nankai University Tianjin 300350 China

Deep learning is a significant research field in today’s society, especially in the field of artificial intelligence. To gain a more comprehensive understanding of the current state of research in the field of deep learning and to identify areas of intense interest, this paper uses Citespace for research and learning, samples 1863 articles from the Web of Science (WoS) core dataset, which spans the years 2017 to 2023, and adopts the methods of co-occurrence analysis, keyword burst analysis, and co-citation analysis. information visualization is used to conduct bibliometric analysis, examining keywords, research institutions, authors, and countries. The analysis demonstrates a significant increase in the literature record from 2017 to 2022, followed by a decline from 2022 to 2023. The research on deep learning spans various fields, including computer science, engineering, and telecommunications, which are considered crucial areas of study. China, the USA, and England are leading the way in the field of deep learning research. These countries have established extensive cooperative relationships with many countries, with research institutions and authors playing a vital role in the cooperative network. The current theoretical research hotspots of deep learning mainly include "convolutional neural networks", "neural networks", "models", "classification", etc. The focus of research in deep learning has shifted from theoretical research to practical applications. It is now being applied in various areas, such as deep reinforcement learning, bearing fault, and semi-supervised learning. In the future, deep learning technology will be applied to more cutting-edge technologies. © (2024), (International Association of Engineers). All rights reserved.

关键词： Deep reinforcement learning

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An Improved Fire Detection Algorithm Based on YOLOv8 Integrated with DGIConv, FourBranchAttention and GSIoU

HighTech and Innovation Journal

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HighTech and Innovation Journal 2024年第3期5卷 677-689页

作者： Zhang, Muxiang School of Computer and Information Engineering Henan University Jinming Road Henan Kaifeng475004 China

Fire detection is highly important for people's lives and property, and enhancing its accuracy is essential. This study focused on utilizing and improving YOLOv8 to obtain higher detection accuracy for fire detection. Three methods were used. First, the newly designed DGIConv module replaces the original Conv module, thereby decreasing the computational complexity while enhancing the model's performance. Second, to enhance the recognition ability of flame targets, a new attention mechanism named FourBranchAttention was designed, and a comparison was made with other attention mechanisms. The experiments revealed that the newly designed attention mechanism performed best on the mAP50 and mAP50-95 metrics. Finally, to improve the convergence speed and localization ability of the model, the loss function is optimized by adopting better hyperparameters of the TaskAlignedAssigner and employing the newly designed GSIoU as an alternative to the original CIoU. Through ablation experiments, all three improvements improved the detection performance to a certain extent, and the model using the three improvements achieved the best performance. Compared with the baseline, the YOLOv8 model with DGIConv, FourBranchAttention, and the optimized loss function increased the mAP50 by 2.52% and the mAP50-95 by 3.37%. The mAP50 and mAP50-95 had reached 98.46% and 75.26%, respectively. Compared with previous models, such as SSD/YOLOv7, the performance metrics of enhanced YOLOv8 also exhibited significant enhancements, thereby augmenting the accuracy of fire detection. © 2024, Ital Publication. All rights reserved.

关键词： Premixed flames

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Authorized Protection Mechanism of Software Cloud Based on Time-Specific Encryption

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2024年第10期51卷 1474-1479页

作者： Yuan, Ke Wu, Junyi Zhang, Baolei Wang, Bozhen Wang, Yuye Jia, Chunfu School of Computer and Information Engineering Henan University Kaifeng475004 China Henan Province Engineering Research Center of Spatial Information Processing Henan University Kaifeng475004 China School of Computer and Information Engineering Henan University Kaifeng475004 China College of Cybersecurity Nankai University Tianjin 300350 China International BusinessSchool Henan University Zhengzhou450046 China

In order to improve the security, ease of use and flexibility of software products, this paper proposes a software cloud authorized protection mechanism based on specific time encryption. This mechanism sends the encrypted and packed executable file to the cloud server, so that only legitimate users can unpack, decrypt and run the software in the cloud within a specific time interval. On this basis, this article gives a formal definition of the solution model and constructs a specific implementation solution based on the random oracle model. Security analysis shows that this solution can not only prevent attackers from using cloud servers and time servers to illegally obtain software usage rights, but also reduce time overhead and costs, thereby effectively protecting the legitimate rights and interests of developers and users. © (2024), (International Association of Engineers). All rights reserved.

关键词： Cloud security

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KGECDR: drug response prediction based on element-aware knowledge graph attention network 24

KGECDR: drug response prediction based on element-aware know...

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5th International Symposium on Artificial Intelligence for Medicine Science, ISAIMS 2024

作者： Zhang, Ge Si, Qiqi Li, Xinxin Zhang, Lijun Yang, Hui School of Computer and Information Engineering Henan University Henan Kaifeng China

ISBN: (纸本)9798400717826

Rapid screening and accurate application of anticancer drugs is an important issue in the era of precision medicine, which is crucial for achieving personalized treatment. Many computational methods are currently being developed to predict drug responses. However, most methods ignore the richness of information about other biological entities (proteins, tissues, etc.) associated with drug-cell lines and lack interpretability. To address this problem, we propose a drug response prediction method based on an element-aware knowledge graph attention network. This method can model drugs and cell lines at a fine-grained level, thus identifying potential drug responses. First, KGECDR models the combination of relationships in the knowledge graph related to drugs and cell lines as elements related to drug cell line response (CDR). Second, KG-level embeddings of drugs and cell lines are learned through relation-aware aggregation. Third, element-level embeddings of drugs and cell lines are obtained for KG-level embeddings using element-aware message aggregation based on known CDR. Finally, the obtained embedding of drugs and cell lines can be used to predict the drug response to the cell line. Experiments demonstrated our method can be effective for anticancer drug response prediction. © 2024 Copyright held by the owner/author(s).

关键词： Knowledge graph

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Robust primary quantization step estimation on resized and double JPEG compressed images

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Multimedia Tools and Applications 2025年第12期84卷 11097-11118页

作者： Zhang, Lei Chen, XuGuang Niu, YaKun Zuo, XianYu Wang, Huaqing School of Computer and Information Engineering Henan Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Kaifeng Henan Henan Kaifeng475004 China National Internet Emergency Center Henan Zhengzhou450000 China

As one of the most important forensic tasks, reconstruction of the original information in tampered images is a key step for tampering detection and localization. Currently, a number of methods have been designed to estimate the primary quantization steps of double compressed JPEG images. However, the estimation in the presence of resizing operation remains a challenge. In this paper, we propose a robust primary quantization steps estimation method on resized and double JPEG compressed images. Specifically, the distribution of Discrete Cosine Transform (DCT) coefficients is firstly analyzed on the inverse resized image. Then, a maximum likelihood function together with a filtering strategy is designed to obtain the primary quantization step on Alternating Current (AC) bands. In addition, we find the prominent peak in the Discrete Fourier Transform (DFT) spectrum of the distribution of Direct Current (DC) coefficients is nonlinearly related to the step. Based on this observation, a mapping function derived from the geometric fitting is proposed to estimate the step on DC band. Experimental results demonstrate the proposed method provides superior estimation performance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image compression

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Analysis of the Impact of Artificial Intelligence-Driven Internet Celebrity Anchor Marketing on Consumer Psychological Health Through Big Data

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computer-Aided Design and Applications 2024年第S24期21卷 118-129页

作者： Zhang, Chao Jin, Di Zhang, Shuolin School of Journalism and Communication Henan University Kaifeng475000 China School of Psychology Henan University Kaifeng475000 China School of Information Engineering and Computer Science Hebei Finance University Baoding 071000 China

To explore the influence of Internet celebrity marketing behavior on consumers' mental health, this paper improves the algorithm based on extensive social media data analysis. It proposes a D2D data distribution method based on user fusion social relations. In data shunting, this paper considers the influence of two relationships: the trust between users and the user's preference rating for data content. In addition, this paper divides the Community into a composite network. This paper compares five seed node selection methods to find the seed users suitable for data sharing within the Community. The experimental research shows that the extensive data analysis method based on social media can play a specific role in analyzing the influence of Internet celebrity marketing behavior on consumers' mental health. At the same time, from the data point of view, Internet celebrity marketing can effectively drive consumers' useless consumption and promote the emergence of impulsive consumption behavior, which also has a positive effect. © 2024 U-turn Press LLC, http://***.

关键词： Big data

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An Adaptive Appearance Model Update Method for Target Tracking Based on Prior Confidence 4

An Adaptive Appearance Model Update Method for Target Tracki...

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4th International Symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2024

作者： Gong, Kaiting Xin, Ming Wang, Xin Wang, Bo School of Computer and Information Engineering Henan University Henan Kaifeng China

ISBN: (纸本)9798331541729

For the challenges posed by rapid changes in target scale and appearance due to the high-speed motion of aerial vehicles during the infrared imaging terminal guidance phase, an adaptive appearance model update method for target tracking based on prior confidence is proposed. This method uses OSTrack [1] as a baseline and comprehensively analyzes historical confidence variation information within a specified time window to assess the adaptability of the current appearance model and determine the timing for appearance updates. By analyzing the confidence performance of historical prediction results, reliable samples are selected to construct template information adaptively updating the target appearance model. This enhances the robustness and stability of the algorithm throughout the terminal guidance phase. Simulation results for infrared terminal guidance indicate that, compared to the baseline algorithm, the proposed method improves the success rate by 5% and increases accuracy by 4.8%. This approach provides an effective solution for enhancing the performance of infrared guidance systems. © 2024 IEEE.

关键词： Infrared imaging

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