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

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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Enhancing Arabic Cyberbullying Detection with End-to-End Transformer Model

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computer Modeling in engineering & sciences 2024年第11期141卷 1651-1671页

作者： Mohamed A.Mahdi Suliman Mohamed Fati Mohamed A.G.Hazber Shahanawaj Ahamad Sawsan A.Saad Information and Computer Science Department College of Computer Science and EngineeringUniversity of Ha’ilHa’il55476Saudi Arabia Information Systems Department College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Software Engineering Department College of Computer Science and EngineeringUniversity of Ha’ilHa’il55476Saudi Arabia Computer Engineering Department College of Computer Science and EngineeringUniversity of Ha’ilHa’il55476Saudi Arabia

Cyberbullying,a critical concern for digital safety,necessitates effective linguistic analysis tools that can navigate the complexities of language use in online *** tackle this challenge,our study introduces a new approach employing Bidirectional Encoder Representations from the Transformers(BERT)base model(cased),originally pretrained in *** model is uniquely adapted to recognize the intricate nuances of Arabic online communication,a key aspect often overlooked in conventional cyberbullying detection *** model is an end-to-end solution that has been fine-tuned on a diverse dataset of Arabic social media(SM)tweets showing a notable increase in detection accuracy and sensitivity compared to existing *** results on a diverse Arabic dataset collected from the‘X platform’demonstrate a notable increase in detection accuracy and sensitivity compared to existing methods.E-BERT shows a substantial improvement in performance,evidenced by an accuracy of 98.45%,precision of 99.17%,recall of 99.10%,and an F1 score of 99.14%.The proposed E-BERT not only addresses a critical gap in cyberbullying detection in Arabic online forums but also sets a precedent for applying cross-lingual pretrained models in regional language applications,offering a scalable and effective framework for enhancing online safety across Arabic-speaking communities.

关键词： Cyberbullying offensive detection Bidirectional Encoder Representations from the Transformers(BERT) continuous bag of words Social Media natural language processing

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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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Advances in Manta Ray Foraging Optimization:A Comprehensive Survey

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Journal of Bionic engineering 2024年第2期21卷 953-990页

作者： Farhad Soleimanian Gharehchopogh Shafi Ghafouri Mohammad Namazi Bahman Arasteh Department of Computer Engineering Urmia BranchIslamic Azad UniversityUrmiaIran Department of Computer Engineering Tabriz BranchIslamic Azad UniversityTabrizIran Department of Computer Engineering Maybod BranchIslamic Azad UniversityMaybodIran Department of Software Engineering Faculty of Engineering and Natural ScienceIstinye UniversityIstanbulTurkey

This paper comprehensively analyzes the Manta Ray Foraging Optimization(MRFO)algorithm and its integration into diverse academic *** in 2020,the MRFO stands as a novel metaheuristic algorithm,drawing inspiration from manta rays’unique foraging behaviors—specifically cyclone,chain,and somersault *** biologically inspired strategies allow for effective solutions to intricate physical *** its potent exploitation and exploration capabilities,MRFO has emerged as a promising solution for complex optimization *** utility and benefits have found traction in numerous academic *** its inception in 2020,a plethora of MRFO-based research has been featured in esteemed international journals such as IEEE,Wiley,Elsevier,Springer,MDPI,Hindawi,and Taylor&Francis,as well as at international conference *** paper consolidates the available literature on MRFO applications,covering various adaptations like hybridized,improved,and other MRFO variants,alongside optimization *** trends indicate that 12%,31%,8%,and 49%of MRFO studies are distributed across these four categories respectively.

关键词： Manta ray foraging optimization Metaheuristic algorithms Hybridization Improved Optimization

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RiskProp: Account Risk Rating on Ethereum via De-anonymous Score and Network Propagation

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2132-2145页

作者： Lin, Dan Wu, Jiajing Fu, Qishuang Zheng, Zibin Chen, Ting Sun Yat-sen University School of Software Engineering Zhuhai519082 China Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu610051 China

As one of the most popular blockchain platforms supporting smart contracts, Ethereum has caught the interest of both investors and criminals. Differently from traditional financial scenarios, executing Know Your Customer verification on Ethereum is rather difficult due to its pseudonymous nature. Fortunately, as the transaction records stored in the Ethereum blockchain are publicly accessible, we can understand the behavior of accounts or detect illicit activities via transaction mining. Existing risk control techniques have primarily been developed from the perspectives of de-anonymizing address clustering and illicit account classification. However, these techniques cannot ascertain the potential risks for all accounts and are limited by specific heuristic strategies or insufficient label information. These constraints motivate us to seek an effective rating method for quantifying the spread of risk in a transaction network. To the best of our knowledge, we are the first to address the problem of account risk rating on Ethereum by proposing a novel model called RiskProp, which includes a de-anonymous score to measure transaction anonymity and a network propagation mechanism to formulate the relationships between accounts and transactions. Experimental results on a realistic Ethereum dataset demonstrate that proposed RiskProp newly discovered 63% of the Top 150 high-risk accounts as suspicious. The superior performance of risk score-based account classification experiments further verifies the effectiveness of our rating method (85.63% accuracy). © 2024 IEEE.

关键词： Ethereum

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A Lightweight Object Detection Algorithm for Remote Sensing Images

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engineering Letters 2025年第3期33卷 704-711页

作者： Hou, Donghao Zhang, Yujun Ren, Jia School of Computer and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China

With the continuous advancement of satellite technology, remote sensing images has been increasingly applied in fields such as urban planning, environmental monitoring, and disaster response. However, remote sensing images often feature small target sizes and complex backgrounds, posing significant computational challenges for object detection tasks. To address this issue, this paper proposes a lightweight remote sensing images object detection algorithm based on YOLOv9. The proposed algorithm incorporates the SimRMB module, which effectively reduces computational complexity while improving the efficiency and accuracy of feature extraction. Through a dynamic attention mechanism, SimRMB is capable of focusing on important regions while minimizing background interference, and by integrating residual learning and skip connections, it ensures the stability of deep networks. To further enhance detection performance, the FasterRepNCSPELAN4 module is introduced, which employs PConv operations to reduce computational load and memory usage. It also utilizes dilated convolutions and DFC attention mechanisms to strengthen feature extraction, thereby increasing the efficiency and accuracy of object detection. Additionally, this study integrates the GhostModuleV2 module, which generates core feature maps and employs lightweight operations to create redundant features, greatly reducing the computational complexity of *** results show that on the SIMD dataset, the improved YOLOv9 model has a parameter size of 167.88 MB and GFLOPs of 208.6. Compared to the baseline YOLOv9 model (parameter size: 194.57 MB, GFLOPs: 239.0), the parameter size is reduced by 13.71%, GFLOPs are reduced by 12.72%, and detection accuracy is improved by 1.4%. These results demonstrate that the proposed lightweight YOLOv9 model effectively reduces computational overhead while maintaining excellent detection performance, providing an efficient solution for object detection tasks in resou

关键词： Satellite imagery

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Steel Surface Defect Detection Based on YOLOv10

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engineering Letters 2025年第5期33卷 1220-1231页

作者： Sun, Luyu Zhang, Yujun School of Computer and Software Engineering University of Science and Technology Liaoning Anshan114051 China

To improve the accuracy of steel surface defect detection, this study proposes an improved multi-directional optimization model based on the YOLOv10n algorithm. First, we introduce innovations to the convolution (C2F) module in YOLOv10n with MSMHSA (Multi-Scale Multi-Head Self-Attention) and EMA (Enhanced Multi-Scale Attention) modules. These modules enhance learning and expression capabilities by reshaping across different scales, multiple attention heads, and channel dimensions. Second, a Multi-Dilated Channel Refinement (MDCR) module is employed. The MDCR module captures spatial features across various receptive field ranges through multiple depthwise separable convolutions, enabling more effective multi-level feature integration. Finally, a Context Aggregation module is embedded within the neck network as a generic building block for multi-head context aggregation, leveraging the inductive bias of local convolution operations to facilitate rapid convergence. Experimental results show that the improved model achieves a mean Average Precision (mAP) of 78.7% in steel surface defect detection tasks, marking a significant improvement of 4.3% over the original YOLOv10n model. In practical applications, the improved model can quickly and accurately locate and classify various steel surface defects, meeting the needs of steel surface defect detection in industrial production. © 2025, International Association of Engineers. All rights reserved.

关键词： Surface defects

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A multi-projection recurrent model for hypernym detection and discovery

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Frontiers of computer science 2025年第4期19卷 29-42页

作者： Xuefeng ZHANG Junfan CHEN Zheyan LUO Yuhang BAI Chunming HU Richong ZHANG School of Computer Science and Engineering Beihang UniversityBeijing 100191China School of Software Beihang UniversityBeijing 100191China Zhongguancun Laboratory Beijing 100094China

Hypernym detection and discovery are fundamental tasks in natural language *** former task aims to identify all possible hypernyms of a given hyponym term,whereas the latter attempts to determine whether the given two terms hold a hypernymy relation or *** research on hypernym detection and discovery tasks projects a term into various semantic spaces with single mapping *** their success,these methods may not be adequate in capturing complex semantic relevance between hyponym/hypernymy pairs in two ***,they may fall short in modeling the hierarchical structure in the hypernymy relations,which may help them learn better term ***,the polysemy phenomenon that hypernyms may express distinct senses is *** this paper,we propose a Multi-Projection Recurrent model(MPR)to simultaneously capture the hierarchical relationships between terms and deal with diverse senses caused by the polysemy ***,we build a multi-projection mapping block to deal with the polysemy phenomenon,which learns various word senses by multiple ***,we adopt a hierarchy-aware recurrent block with the recurrent operation followed by a multi-hop aggregation module to capture the hierarchical structure of hypernym *** on 11 benchmark datasets in various task settings illustrate that our multi-projection recurrent model outperforms the *** experimental analysis and case study demonstrate that our multi-projection module and the recurrent structure are effective for hypernym detection and discovery tasks.

关键词： natural language processing hypernym detection recurrent model

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Road Defect Detection Model Based on YOLOv8

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第4期52卷 986-994页

作者： Cong, Shengqiang Zhang, Chunna Yu, Yang Yue, Xiaoping Zhao, Jinchi Shen, Yuming School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning Anshan114051 China University of Science and Technology Liaoning Liaoning Anshan114051 China

The detection of road defects is crucial for ensuring vehicular safety and facilitating the prompt repair of roadway imperfections. Existing YOLOv8-based models face the following issues: extraction capabilities and insufficient feature representation in complex scenarios, slow bounding box regression speed. To address these challenges, we propose the YOLOv8-DSW model. Firstly, we incorporate the Dilation-wise Residual (DWR) module into the C2f module in the neck, improving the detection performance of multi-scale feature maps. Secondly, We incorporate the SENetv2 attention mechanism into the neck of the YOLOv8 model to augment feature expression capabilities. Finally, we introduce Wise-IoU (WIoU) to enhance the speed of bounding box regression. Experimental results indicate that YOLOv8-DSW enhances the mean Average Precision at 50% (mAP50) by 2.5% in comparison to the YOLOv8n, significantly improving detection accuracy and provide valuable method for the development of road defect detection. © (2025.0), (International Association of Engineers). All Rights Reserved.

关键词： Deep learning

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Advanced Optimized Anomaly Detection System for IoT Cyberattacks Using Artificial Intelligence

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computers, Materials & Continua 2024年第2期78卷 1525-1545页

作者： Ali Hamid Farea Omar H.Alhazmi Kerem Kucuk Department of Computer Science Taibah UniversityMedina42315Saudi Arabia Department of Software Engineering Kocaeli UniversityKocaeli41001Turkey

While emerging technologies such as the Internet of Things(IoT)have many benefits,they also pose considerable security challenges that require innovative solutions,including those based on artificial intelligence(AI),given that these techniques are increasingly being used by malicious actors to compromise IoT *** an ample body of research focusing on conventional AI methods exists,there is a paucity of studies related to advanced statistical and optimization approaches aimed at enhancing security *** contribute to this nascent research stream,a novel AI-driven security system denoted as“AI2AI”is presented in this ***2AI employs AI techniques to enhance the performance and optimize security mechanisms within the IoT *** also introduce the Genetic Algorithm Anomaly Detection and Prevention Deep Neural Networks(GAADPSDNN)sys-tem that can be implemented to effectively identify,detect,and prevent cyberattacks targeting IoT ***,this system demonstrates adaptability to both federated and centralized learning environments,accommodating a wide array of IoT *** evaluation of the GAADPSDNN system using the recently complied WUSTL-IIoT and Edge-IIoT datasets underscores its *** an impressive overall accuracy of 98.18%on the Edge-IIoT dataset,the GAADPSDNN outperforms the standard deep neural network(DNN)classifier with 94.11%***,with the proposed enhancements,the accuracy of the unoptimized random forest classifier(80.89%)is improved to 93.51%,while the overall accuracy(98.18%)surpasses the results(93.91%,94.67%,94.94%,and 94.96%)achieved when alternative systems based on diverse optimization techniques and the same dataset are *** proposed optimization techniques increase the effectiveness of the anomaly detection system by efficiently achieving high accuracy and reducing the computational load on IoT devices through the adaptive selection of active features.

关键词： Internet of Things security anomaly detection and prevention system artificial intelligence optimization techniques

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