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IAENG International Journal of computer Science 2025年第2期52卷 515-523页

作者： Li, Mei-Qi Zhou, Zi-Wei School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China

Image captioning is an interdisciplinary research hotspot at the intersection of computer vision and natural language processing, representing a multimodal task that integrates core technologies from both fields. This task requires the use of computer vision techniques to analyze and extract key visual features from images, followed by the application of natural language processing techniques to generate descriptive text that is syntactically and semantically aligned with human cognition. This process poses a significant challenge for computers. Existing models mostly ignore the relative positional information of visual objects and struggle to efficiently capture the complex relationships between visual and textual data. To address these challenges, we propose a vision-to-text bidirectional collaborative image captioning method. This approach extracts both visual features and positional information of objects, allowing the model to better understand the spatial relationships between objects. The CEW word embedding approach encodes textual information more profoundly, enhancing semantic expression and contextual understanding. In the decoding phase, a bidirectional cross-attention mechanism strengthens the interaction between vision and text, leading to improved accuracy in image understanding. The model is trained and tested on the MSCOCO 2014 dataset and compared with several popular models. Experimental results demonstrate that the proposed method achieves significant improvements on the CIDEr and BLEU-1 evaluation metrics with an increase of 1.5 and 1.1, respectively. In addition, we conduct ablation experiments, quantitative analysis, and qualitative analysis to comprehensively validate the effectiveness and stability of the proposed algorithm. © (2025), (International Association of Engineers). All rights reserved.

关键词： Embeddings

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Successive Interference Cancellation-Enabled Timely Status Update in Linear Multi-hop Wireless Networks

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IEEE Transactions on Mobile Computing 2025年第6期24卷 5298-5311页

作者： Han, Xinhui Pan, Haoyuan Wang, Zhaorui Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering Shenzhen China Future Network of Intelligence Institute The School of Science and Engineering Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology The College of Computer Science and Software Engineering Shenzhen China

We investigate the timely status update in linear multi-hop wireless networks, where a source tries to deliver status update packets to a destination through a sequence of half-duplex relays. Timeliness is measured by the age of information (AoI) metric. Maintaining a low AoI at the destination typically necessitates frequent transmission of update packets from the source. However, high packet transmission frequency in multi-hop scenarios can result in mutual wireless interference at intermediate relays. Specifically, when an intermediate relay receives wireless signals of a new packet from its previous node, simultaneous transmission of an old packet by its subsequent node to the next hop may cause wireless signals to interfere at the intermediate relay, conventionally leading to packet collision. A key motivation to solve this issue is that the intermediate relay has previously received the old packet (which can thus be forwarded to the subsequent node for further relaying). Hence, successive interference cancellation (SIC) can be employed to mitigate interference of the old packet and recover the new packet. This paper designs an SIC-enabled packet relaying scheme tailored to low AoI. Initially focusing on a three-hop network, we subsequently extend our approach to general multi-hop networks. We model the multi-hop relaying scheme using a Markov chain to derive the theoretical average AoI. Theoretical and simulation results indicate that the SIC-enabled packet relaying scheme significantly reduces the average AoI compared to the non-SIC approaches, owing to an increased packet transmission frequency at the source and the effectiveness of SIC techniques at the relays. © 2002-2012 IEEE.

关键词： Markov chains

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Reconfiguring Role-Based Access Control via Role Clustering

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IEEE Access 2025年 13卷 72984-72993页

作者： Durdag, Osman Coskuncay, Ahmet Ataturk University Department of Software Engineering Erzurum25240 Turkey Ataturk University Department of Computer Engineering Erzurum25240 Turkey

Role-based access control (RBAC) systems have become a widely used and accepted method by many organizations today due to their efficiency and ease of management. However, to truly benefit from RBAC systems, the roles in the relevant system must be well defined. Bottom-up, top-down, and hybrid approaches exist to accurately define the roles to be included in access control systems. Even if RBAC systems are designed carefully, the role privilege assignments tend to wear out and become chaotic after some time. This study demonstrates a role mining approach to reconfiguring such existing client specific RBAC systems by identifying clusters of similar roles using the assigned privileges. In this study, masked RBAC data of 10 clients of a software company are used. The approach creates role clusters via Agglomerative Hierarchical Clustering, an unsupervised clustering algorithm that works in a bottom-up technique. The experiment results show that it is better using the resulting candidate clusters as a reference in reconfiguring the RBAC systems, instead of using them to replace the existing roles. Additionally, it has been observed that in some client tables with a limited number of unique roles, the pairwise F1 score was remarkably high, indicating a strong correspondence between the candidate and expected roles. © 2013 IEEE.

关键词： Access control Data mining Companies software Complexity theory Hands Unified modeling language Usability System analysis and design Semantics

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A graph neural network technique for the prediction of cerebral stroke using an unbalanced medical dataset

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Multimedia Tools and Applications 2025年 1-37页

作者： Sha, Mohemmed Department of Software Engineering College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia

Cerebral stroke is a major health problem, and if not recognized and treated immediately, it can result in considerable morbidity and fatality. Predicting the possibility of a stroke can help with intervention, resulting in improved patient outcomes. However, constructing reliable stroke prediction algorithms can be difficult, especially when working with unbalanced medical data. Timely detection of the various warning signs of a stroke can significantly reduce its severity. To deal with unbalanced data, this research proposed the hybridization of two techniques Noise Reduction and oversampling technique (Noise Reduction-Border Line- Synthetic Minority Oversampling Technique (NR-BL-SMOTE) which only oversamples or strengthens the borderline minority class. It aims at eliminating noise from minority groups while preventing the making of noisy samples and BL-SMOTE is used to create synthetic data to balance the dataset. After that, feature extraction is done to extract features from the cerebral stroke prediction dataset using fine-tuned VGG19 architecture to improve the performance of the proposed model. The fine-tuned model uses an already trained network and re-trains the part of that by the new data set. Finally, classification is done using a graph neural network (GNN) to classify the prediction of normal or cerebral stroke (ischemic and hemorrhagic). Further, to optimize the accuracy of the classification performance;Improved Golden Jackal Optimization (IGJO) is done by repeating their iterations on the GNN hidden layer until we get the final optimal value. The proposed model will be simulated using the PYTHON tool. Then in the simulation results, the proposed approach achieves more powerful classification accuracy than established technologies, demonstrating its efficiency in stroke prediction and classification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Graph neural networks

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Stability Prediction in Smart Grid Using PSO Optimized XGBoost Algorithm with Dynamic Inertia Weight Updation

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computer Modeling in engineering & Sciences 2025年第1期142卷 909-931页

作者： Adel Binbusayyis Mohemmed Sha Department of Software Engineering College of Computer Engineering and Sciences in Al-Kharj Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid *** the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the *** also possesses a better impact on averting overloading and permitting effective energy *** though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized *** overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning *** accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data ***,the pre-processed data are taken for training and *** that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in *** PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed *** hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on *** results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.

关键词： Smart Grid machine learning particle swarm optimization XGBoost dynamic inertia weight update

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A mathematical multi-objective optimization model and metaheuristic algorithm for effective advertising in the social internet of things

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Neural Computing and Applications 2025年第6期37卷 4797-4821页

作者： Molaei, Reza Rahsepar Fard, Kheirollah Bouyer, Asgarali Department of Computer Engineering and Information Technology University of Qom Qom Iran Department of Software Engineering Azarbaijan Shahid Madani University Tabriz Iran Department of Software Engineering Faculty of Engineering and Natural Science Istinye University Istanbul Turkey

The integration of social networks with the Internet of Things (IoT) has been explored in recent research, giving rise to the Social Internet of Things (SIoT). One promising application of SIoT is viral marketing, which plays a critical role in modern advertising. However, current solutions proposed for viral marketing are often impractical in real-world scenarios, and there has been limited investigation into viral marketing within the context of SIoT. To address this issue, we present a multi-objective integer linear programming model named ILP-ASoT that facilitates advertising through SIoT. Our approach introduces two realistic parameters: the node sociality rate and the node appropriateness rate as an advertising destination. By balancing the cost of selecting a seed set with the number of users who receive the advertisement, our model offers an appropriate trade-off. Since this is proven as an NP-hard problem, A PSO-based metaheuristic algorithm is proposed to provide near-optimal solutions for advertising in SIoT, called PSO-ASoT. Through simulations, we show that our PSO-ASoT algorithm outperforms state-of-the-art algorithms in several metrics, such as influence spread, advertising cost, and duplicated advertising rate. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Heuristic algorithms

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Dynamic Certificateless Outsourced Data Auditing Mechanism Supporting Multi-Ownership Transfer via Blockchain Systems

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IEEE Transactions on Network and Service Management 2025年第2期22卷 2017-2030页

作者： Zhang, Xiaojun Liu, Qing Liu, Bingyun Zhang, Yuan Xue, Jingting Southwest Petroleum University School of Computer Science and Software Engineering Research Center for Cyber Security Chengdu610500 China Chengdu Neusoft University School of Computer and Software Chengdu611844 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu610054 China

Data auditing contributes to checking the integrity of outsourced data, promoting the vigorous development of cloud storage services. In actual scenarios, such as migration of electronic medical records or data transfer of enterprise mergers and acquisitions, it always require data auditing to help clients with dynamic data migration and integrity checking. In this paper, we present an efficient dynamic certificateless outsourced data auditing mechanism supporting multi-ownership transfer (CDA-MOT), addressing the issue of key escrow and without needing complex certificate management. By integrating a certificateless multi-signature on the same data file into the construction of a homomorphic authenticator based on the Lagrange inverse Multinomial theorem, CDA-MOT not only achieves integrity verification but also enables clients to transfer ownership rights and responsibilities for multi-ownership data in collaboration with cloud servers. Utilizing blockchain systems to store necessary data conversion and update records, as well as smart contracts to fulfill auditing tasks, CDA-MOT owns the characteristics of openness, transparency, accountability, and decentralized public auditing. Besides, CDA-MOT could be further applied in the extension of dynamic update operations, even if outsourced data have been transferred. The security analysis and performance evaluation have demonstrated the feasibility of CDA-MOT in the secure deployment of cloud storage. © 2004-2012 IEEE.

关键词： Data integrity

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Blended learning in software engineering education: The application lifecycle management experience with computer-supported collaborative learning

Blended learning in software engineering education: The appl...

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International Conference on Interactive Collaborative Learning, ICL 2015

作者： Maksimenkova, Olga Alexey, Neznanov School of Software Engineering Faculty of Computer Science National Research University Higher School of Economics Moscow Russia School of Data Analysis and Artificial Intelligence Faculty of Computer Science National Research University Higher School of Economics Moscow Russia

ISBN: (纸本)9781479987078

software engineering education (SEE) process simulates the main professional software lifecycle processes such as analysis, design, construction and maintenance (see SWEBoK, ITIL, etc.). The necessity of meeting both educational needs and requirements from industry explains that using Supported Collaborative Learning (CSCL) techniques in software engineering (SE) should be based on professional tools or on similar to them. The main purpose of this work is to fill the gap between the SEE needs and the current trends in CSCL development. We generalize world experience and suggest the framework of using industry approved methods and tools. We compare CSCL tools and the other collaborative services;analyze the teaching experience of several SE courses supported by different collaborative methods and collaborative web-services. Special attention is paid to formative feedback implementation. Following achieved result we suppose that using best practices from SE will enrich CSCL methodology and tools not only for SE field, but also for other areas of knowledge. © 2015 IEEE.

关键词： Application programs

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DevDynaP: A Dynamic Local Community Detection Algorithm

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SN computer Science 2025年第4期6卷 1-11页

作者： Bakhtar, Sahar Harutyunyan, Hovhannes A. Department of Computer Science and Software Engineering Concordia University Montreal Canada

Recent years have witnessed the rapid growth of social network services. Real-world social networks are huge and changing over time. Consequently, the problems in this area have become more complex. Community detection is one of the most important problems in social networks. A good community can be defined as a group of nodes that are highly connected to each other and loosely connected to the nodes outside the community. Regarding the fact that social networks are huge in size, having complete information about the whole network is almost impossible. As a result, the problem of local community detection has become more popular in recent years. However, the problem of community detection in dynamic networks is well-investigated, the local community detection is not widely addressed by researchers in dynamic networks. In this paper, a dynamic local community detection algorithm is proposed. The purpose of the proposed algorithm is to explore the network as fast as possible and detect communities as well. Experimental results show that the proposed algorithm discovers the unknown parts of networks faster than similar algorithms. Also, the detected communities by the proposed algorithm outperform that of the compared algorithm in different snapshots. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Dynamic social network Geodesic distance metric Local community detection Modularity

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Impact of optimizers functions on detection of Melanoma using transfer learning architectures

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Multimedia Tools and Applications 2025年第14期84卷 13787-13807页

作者： Kılıçarslan, Serhat Aydın, Hatice Aktas Adem, Kemal Yılmaz, Esra Kavalcı Software Engineering Bandırma Onyedi Eylul University Balıkesir Turkey Computer Engineering Sivas University of Science and Technology Sivas Turkey Computer Engineering Sivas Cumhuriyet University Sivas Turkey

Early diagnosis-treatment of melanoma is very important because of its dangerous nature and rapid spread. When diagnosed correctly and early, the recovery rate of patients increases significantly. Physical methods are not sufficient for diagnosis and classification. The aim of this study is to use a hybrid method that combines different deep learning methods in the classification of melanoma and to investigate the effect of optimizer methods used in deep learning methods on classification performance. In the study, Melanoma detection was carried out from the skin lesions image through a simulation created with the deep learning architectures DenseNet, InceptionV3, ResNet50, InceptionResNetV2 and MobileNet and seven optimizers: SGD, Adam, RmsProp, AdaDelta, AdaGrad, Adamax and Nadam. The results of the study show that SGD has better and more stable performance in terms of convergence rate, training speed and performance than other optimizers. In addition, the momentum parameter added to the structure of the SGD optimizer reduces the oscillation and training time compared to other functions. It was observed that the best melanoma detection among the combined methods was achieved using the DenseNet model and SGD optimizer with a test accuracy of 0.949, test sensitivity 0.9403, and test F score 0.9492. © The Author(s) 2024.

关键词： Deep learning

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