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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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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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A Fast Algorithm for Computing the Deficiency Number of a Mahjong Hand

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Chinese Journal of Electronics 2024年第6期33卷 1383-1398页

作者： Xueqing YAN Yongming LI Sanjiang LI School of Computer Science Shaanxi Normal University School of Mathematics and Statistics Shaanxi Normal University Centre for Quantum Software and Information Faculty of Engineering and Information TechnologyUniversity of Technology Sydney

The tile-based multiplayer game Mahjong is widely played in Asia and has also become increasingly popular worldwide. Face-to-face or online, each player begins with a hand of 13 tiles and players draw and discard tiles in turn until they complete a winning hand. An important notion in Mahjong is the deficiency number(*** number in Japanese Mahjong) of a hand, which estimates how many tile changes are necessary to complete the hand into a winning hand. The deficiency number plays an essential role in major decision-making tasks such as selecting a tile to discard. This paper proposes a fast algorithm for computing the deficiency number of a Mahjong hand. Compared with the baseline algorithm, the new algorithm is usually 100 times faster and, more importantly,respects the agent's knowledge about available tiles. The algorithm can be used as a basic procedure in all Mahjong variants by both rule-based and machine learning-based Mahjong AI.

关键词： Machine learning algorithms Knowledge based systems Decision making Asia Games

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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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Diabetic Retinopathy Image Segmentation Method Based on Fusion DenseNet and U-Net Network

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engineering Letters 2025年第2期33卷 418-428页

作者： Zhang, Yaxuan Xu, Yang Zhai, Jinlong University of Science and Technology LiaoNing Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China

Diabetic Retinopathy (DR) is a common and significant complication in patients with diabetes, and severely affecting their quality of life. Image segmentation plays a crucial role in the early diagnosis and treatment of DR. However, traditional methods are limited in terms of segmentation accuracy and generalization capability. This paper proposes a novel image segmentation method for diabetic retinopathy based on the integration of DenseNet and U-Net networks. Firstly, DenseNet is utilized to replace the encoder part of U-Net. The dense connection mechanism enhances the efficiency of feature propagation and improves the feature extraction capability of the encoder. Secondly, we introduce Omni-Dimensional Dynamic Convolution (ODConv) to replace traditional convolutions. ODConv is used to handle diverse input features effectively. The model's adaptability and segmentation accuracy for various samples is improved. Finally, we integrate the Convolutional Block Attention Module (CBAM) into the decoder. By leveraging channel and spatial attention, the ability to capture key features is enhanced, and the segmentation accuracy and boundary recognition capabilities are improved. To validate the effectiveness and feasibility of the model. This paper conducts experiments on the DDR dataset and the IDRID dataset. The experimental results show that the proposed method improved the mDice score by 0.53% compared to traditional methods. It also increased the mIoU score by 1.12% over traditional methods. This indicates that the proposed method has better performance in segmenting diabetic retinopathy images. It also demonstrates better generalization ability. © 2025, International Association of Engineers. All rights reserved.

关键词： Image segmentation

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Spatial Correlation Module for Classification of Multi-Label Ocular Diseases Using Color Fundus Images

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computers, Materials & Continua 2023年第7期76卷 133-150页

作者： Ali Haider Khan Hassaan Malik Wajeeha Khalil Sayyid Kamran Hussain Tayyaba Anees Muzammil Hussain Department of Software Engineering Faculty of Computer SciencesLahore Garrison UniversityLahorePakistan Department of Computer Science School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan Department of Computer Science&Information Technology University of Engineering and Technology(UET)PeshawarPakistan Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan

To prevent irreversible damage to one’s eyesight,ocular diseases(ODs)need to be recognized and treated *** fundus imaging(CFI)is a screening technology that is both effective and *** to CFIs,the early stages of the disease are characterized by a paucity of observable symptoms,which necessitates the prompt creation of automated and robust diagnostic *** traditional research focuses on image-level diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of *** addition,they usually only target one or a few different kinds of eye diseases at the same *** this study,we design a patient-level multi-label OD(PLML_ODs)classification model that is based on a spatial correlation network(SCNet).This model takes into consideration the relevance of patient-level diagnosis combining bilateral eyes and multi-label ODs ***_ODs is made up of three parts:a backbone convolutional neural network(CNN)for feature extraction i.e.,DenseNet-169,a SCNet for feature correlation,and a classifier for the development of classification *** DenseNet-169 is responsible for retrieving two separate sets of attributes,one from each of the left and right *** then,the SCNet will record the correlations between the two feature sets on a pixel-by-pixel *** the attributes have been analyzed,they are integrated to provide a representation at the patient *** the whole process of ODs categorization,the patient-level representation will be *** efficacy of the PLML_ODs is examined using a soft margin loss on a dataset that is readily accessible to the public,and the results reveal that the classification performance is significantly improved when compared to several baseline approaches.

关键词： Ocular disease multi-label spatial correlation CNN eye disease

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Design of a ML-based trust prediction model using intelligent TrustBoxes in challenged networks

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International Journal of Systems, Control and Communications 2024年第3期15卷 209-234页

作者： Barai, Smritikona Kundu, Anindita Bhaumik, Parama Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu India Department of Information Technology Jadavpur University Kolkata India

Challenged networks (CNs) contain resource-constrained nodes deployed in regions where human intervention is difficult. Opportunistic networks (OppNets) are CNs with no predefined source-to-destination paths. Due to their inherent properties, CNs and OppNets are highly susceptible to black-hole (BH) attacks, resulting in degraded packet-delivery ratio. In this work, an ML-based trust prediction model (MLTPM) is proposed to identify potential BH nodes in OppNets. MLTPM uses a novel function to calculate the total-trust-value (TTV) of each node. Intelligent TrustBoxes are introduced in the network to identify possible BH nodes, using TTV, along with five more node-behaviour features. TrustBoxes reduce the computational overhead of the resource-constrained nodes. Three simulated scenarios are compared - no detection, non-ML-based detection, and MLTPM, each using epidemic, prophet, and spray-and-wait routing protocols. MLTPM performs best with spray-and-wait, exhibiting about 25.21% and 80% mean improvement in delivery-ratio and dropped-message numbers respectively, compared to non-ML-based detection. An overall 12.62% improvement in delivery-ratio and 26.7% improvement in dropped messages is observed using MLTPM, compared to the above-mentioned scenarios. © 2024 Inderscience Enterprises Ltd.

关键词： HTTP

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Finite-Time Sliding Mode Control of Vehicle Formation with Specified Performance

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engineering Letters 2025年第3期33卷 620-629页

作者： Guo, Dongyang 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

The current urban intelligent transportation is in a rapid development stage, and coherence control of vehicle formations has important implications in urban intelligent transportation research. This article focuses on the problem of urban intelligent transportation planning, and proposes a new control method for vehicle formation regulation tracking performance control with bounded disturbances and model uncertainty, combining the multiple advantages of sliding mode control and finite time control and utilizing the universal approximation of neural network. This method can ensure that the vehicle formation can achieve the specified tracking performance and be stable in finite time instead of asymptotically stable. First, the third-order dynamics model and spacing strategy of the vehicle are given. The tracking performance of the vehicle formation is specified. Then, the control objective of the system is transformed by using the error transformation to obtain a new system. An improved sliding mode surface is designed for the transformed system. Furthermore, the universal approximation property of the neural network is utilised to overcome the parameter uncertainty in the system. The reconstruction error of neural network is handled by a robust term. In addition, the jittery vibration phenomenon of sliding mode control has been overcome. Finally, the finite-time stability of the system is analysed by constructing the Lyapunov function, simulations were performed to validate and compare with conventional control methods, simulation results show that the novel control method proposed in this paper is significantly faster than the conventional control method, validated the effectiveness of the method proposed in this paper. © 2025, International Association of Engineers. All rights reserved.

关键词： Lyapunov functions

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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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Advancing Smart Sensor Networks and Carbon-Based Biosensors through Artificial Intelligence: A Deep Learning Approach to Optoelectronic Device Innovation

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IEEE Access 2025年 13卷 86083-86109页

作者： Luo, Keliang Putra University of Malaysia Software Engineering Faculty of Computer Science and Information Technology Kuala Lumpur43400 Malaysia

This research proposes a novel artificial decision-marking framework suitable for modern smart sensor networks and carbon-based biosensor systems which deals with uncertainty and the peculiarity of the data. To achieve the goals, the approach relies on the optoelectronic properties of carbon nanomaterials and already combines AI and deep learning. This raises the level of sensing, real-time data fusion, and adaptive decision-making within the environment to unprecedented levels. To enhance sensor evaluation in sensor manufacturing, we take a step further and implement Dombi Interval Valued Intuitionistic Fuzzy Sets (D-IVIFs) and work with Dombi Interval Valued Intuitionistic Fuzzy Dombi Bonferroni Mean (D-IVIFDBM) for hierarchical decision-making. Moreover, two Multi-Attribute Group Decision Making (MAGDM) methods IVIFWDBM and IVIFWDGBM are developed for expert evaluation aggregation in selection tasks of different criteria to enhance selection accuracy. These experiments did demonstrate improvements in decision accuracy as well as better overall performance than conventional models of comparison. The numerical experiments showed that these methods are more effective than traditional MAGDM models. Introducing such an advanced decision framework in deep learning systems enables improved adaptability, security, and resilience in next-generation sensor networks and biosensor devices. The new paradigm enables real-time signal interpretation and adaptive learning and provides effective solutions in harsh environments where severe fluctuations are common. This study assists in addressing the discrepancy between conceptual decision models and actual physical achievements in smart sensing, which will foster the development of more sophisticated and efficient optoelectronic devices. © 2013 IEEE.

关键词： Deep learning

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