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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Prakash, Ravi Thomas, Tony Kerala University of Digital Sciences Innovation and Technology School of Computer Science and Engineering Kerala India

ISBN: (纸本)9798331531195

The rise of the industrial metaverse has brought digital twins (DTs) to the forefront. Blockchain-powered non-fungible tokens (NFTs) offer a decentralized approach to creating and owning these cloneable DTs. However, the potential for unauthorized duplication, or counterfeiting, poses a significant threat to the security of NFT-DTs. Existing NFT clone detection methods often rely on static information like metadata and images, which can be easily manipulated. To address these limitations, we propose a novel deep-learning-based solution as a combination of an autoencoder and RNN-based classifier. This solution enables real-time pattern recognition to detect fake NFT-DTs. Additionally, we introduce the concept of dynamic metadata, providing a more reliable way to verify authenticity through AI-integrated smart contracts. By effectively identifying counterfeit DTs, our system contributes to strengthening the security of NFT-based assets in the metaverse. © 2025 IEEE.

关键词： Smart contract

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A Parallel Color Image Encryption Algorithm Based on a 2D Logistic-Rulkov Neuron Map

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IEEE Internet of Things Journal 2025年第11期12卷 18115-18124页

作者： Gao, Suo Zhang, Zheyi Ho-Ching Iu, Herbert Ding, Siqi Mou, Jun Erkan, Uğur Toktas, Abdurrahim Li, Qi Wang, Chunpeng Cao, Yinghong The School of Information Science and Engineering Dalian Polytechnic University Dalian116034 China School of Electrical Electronic and Computer Engineering The University of Western Australia Perth6009 Australia School of Mathematical Sciences Dalian University of Technology Dalian116024 China Department of Computer Engineering Faculty of Engineering Karamanoglu Mehmetbey University Karaman70200 Turkey Department of Artificial Intelligence and Data Engineering Engineering Faculty Ankara University Ankara06830 Turkey Jinan250000 China

Images are widely used in social networks, necessitating efficient and secure transmission, especially in bandwidth-constrained environments. This paper aims to develop a color image encryption algorithm that enhances security while optimizing computational efficiency. A novel parallel color image encryption algorithm based on the 2D Logistic-Rulkov neuron map (2D-LRNM) is proposed. In this approach, the three channels of the color image are first separated. Cross-channel information interaction is introduced to form three new channels, which are then processed in parallel. During the encryption process of each channel, a block-wise parallel encryption mechanism is applied, ensuring simultaneous encryption of each block. This block-wise strategy effectively leverages parallel computing resources and balances the task load. To meet the demand for a large number of keystreams during encryption, the 2D-LRNM is introduced. It combines the simplicity and chaotic properties of the Logistic map with the multi-timescale dynamics and neurodynamic behaviors of the Rulkov map. By overcoming the dimensional limitations inherent in the single Logistic map, this approach extends the system to a two-dimensional framework, significantly increasing the complexity of chaotic behavior and improving its unpredictability. Experimental results demonstrate that the proposed encryption algorithm achieves high security and reduces computation time by approximately 83.3%. © 2014 IEEE.

关键词： Encryption algorithms

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Grape Guard:A YOLO-based mobile application for detecting grape leaf diseases

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Journal of Electronic science and Technology 2025年第1期23卷 60-75页

作者： Sajib Bin Mamun Israt Jahan Payel Md.Taimur Ahad Anthony S.Atkins Bo Song Yan Li Department of Computer Science and Engineering Daffodil International UniversityDhaka1207Bangladesh Department of Computer Science University of Southern QueenslandToowoomba4350Australia Faculty of Digital TechnologyInnovationand BusinessStaffordshire UniversityStoke-on-TrentST42DEUnited Kingdom School of Engineering University of Southern QueenslandToowoomba4350Australia School of Mathematics Physics and ComputingUniversity of Southern QueenslandToowoomba4350Australia

Grape crops are a great source of income for *** yield and quality of grapes can be improved by preventing and treating *** farmer’s yield will be dramatically impacted if diseases are found on grape *** detection can reduce the chances of leaf diseases affecting other healthy *** studies have been conducted to detect grape leaf diseases,but most fail to engage with end users and integrate the model with real-time mobile *** study developed a mobile-based grape leaf disease detection(GLDD)application to identify infected leaves,Grape Guard,based on a TensorFlow Lite(TFLite)model generated from the You Only Look Once(YOLO)v8 model.A public grape leaf disease dataset containing four classes was used to train the *** results of this study were relied on the YOLO architecture,specifically YOLOv5 and *** extensive experiments with different image sizes,YOLOv8 performed better than ***8 achieved 99.9%precision,100%recall,99.5%mean average precision(mAP),and 88%mAP50-95 for all classes to detect grape leaf *** Grape Guard android mobile application can accurately detect the grape leaf disease by capturing images from grape vines.

关键词： Bacterial diseases Grape Guard Mobile-based application YOLOv5 YOLOv8

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Zero-dynamics attack detection based on data association in feedback pathway

Cognitive Robotics

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Cognitive Robotics 2025年 5卷 126-139页

作者： Zhang, Zeyu Li, Hongran Todo, Yuki Division of Electrical Engineering and Computer Science Kanazawa University Ishikawa Kanazawa9201192 Japan School of Computer Engineering Jiangsu Ocean University Jiangsu Lianyungang222005 China Faculty of Electrical Information and Communication Engineering Kanazawa University Ishikawa Kanazawa9201192 Japan

This paper considers the security of non-minimum phase systems, a typical kind of cyber-physical systems. Non-minimum phase systems are characterized by unstable zeros in their transfer functions, making them particularly susceptible to disturbances and attacks. The non-minimum phase systems are more vulnerable to zero-dynamics attack (ZDA) than minimum phase systems. ZDA is a stealthy attack strategy that exploits the internal dynamics of a system, remaining undetectable while causing gradual system destabilization. Recent cyber incidents have demonstrated the increasing risk of such hidden attacks in critical infrastructures, such as power grids and transportation systems. This paper first demonstrates that the existing ZDA has the limitation of falling into local convergence, and then proposes an enhanced zero-dynamics attack (EZDA), which overcomes local convergence by diverging system data. Furthermore, this paper presents an autoregressive model which can build the data association between the original data and the forged data. By observing the fluctuations in state values, the presented model can detect not only ZDA, but also EZDA. Finally, numerical simulations and an application example are provided to verify the theoretical results. © 2025

关键词： Cyber attacks

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DiffuSAR: Frequency Domain-Aware Diffusion Model for SAR Image Generation

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 11851-11866页

作者： Ying, Zilu Ke, Wenyu Zhai, Yikui Long, Zhihao Zhou, Jianhong Zhu, Hufei Chen, C. L. Philip Wuyi University School of Electronics and Information Engineering Jiangmen529020 China South China University of Technology School of Electronics and Information Engineering Guangzhou510000 China South China University of Technology Faculty of Computer Science and Engineering Guangzhou510000 China

In recent years, deep learning-based synthetic aperture radar (SAR) image detection, recognition, and segmentation models achieve remarkable accuracy when trained on large amounts of SAR image samples. However, the acquisition of SAR images tends to be costly. A practical approach to address this issue involves the use of artificially generated training samples to supplement the dataset. Current SAR image generation methods based on generative adversarial networks have issues with generation fidelity and are challenge to converge. Moreover, most of these methods only consider the spatial domain of SAR images without exploring the characteristics of their frequency domain. To tackle the aforementioned problems, we proposed a lightweight frequency domain-aware SAR image generation model based on the denoising diffusion probabilistic model. The proposed generative model is capable of producing highly realistic artificial SAR image samples while converging stably. Meanwhile, our research reveals that high-frequency (HF) components of SAR images play a crucial role in the generation process, and a frequency adjustment module (FAM) based on the fast Fourier transform is implemented in our generation model to prevent interference of HF components by low-frequency components. Tests on the MSTAR dataset highlight our model's advantages in both generation quality and parameter efficiency compared to other cutting-edge image generation models. An additional ablation study also confirms the effectiveness of our proposed FAM, which reduces the Fréchet inception distance by 2.85%, confirming our hypothesis that HF components are more crucial to SAR image generation. © 2008-2012 IEEE.

关键词： Fast Fourier transforms

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An Automated Approach to Discovering Software Refactorings by Comparing Successive Versions

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IEEE Transactions on Software engineering 2025年第5期51卷 1358-1380页

作者： Liu, Bo Liu, Hui Niu, Nan Zhang, Yuxia Li, Guangjie Jiang, He Jiang, Yanjie University of Cincinnati Department of Electrical Engineering and Computer Science CincinnatiOH United States National Innovation Institute of Defense Technology Beijing China School of Software Dalian University of Technology Dalian China Beijing Institute of Technology School of Computer Science and Technology Beijing China Peking University School of Computer Science Beijing China

Software developers and maintainers frequently conduct software refactorings to improve software quality. Identifying the conducted software refactorings may significantly facilitate the comprehension of software evolution, and thus facilitate software maintenance and evolution. Besides that, the identified refactorings are also valuable for data-driven approaches in software refactoring. To this end, researchers have proposed a few approaches to identifying software refactorings automatically. However, the performance (especially precision) of such approaches deserves substantial improvement. To this end, in this paper, we propose a novel refactoring detection approach, called REEXTRACTOR+. At the heart of REEXTRACTOR+ is a reference-based entity matching algorithm that matches coarse-grained code entities (e.g., classes and methods) between two successive versions, and a context-aware statement matching algorithm that matches statements within a pair of matched methods. We evaluated REEXTRACTOR+ on a benchmark consisting of 400 commits from 20 real-world projects. The evaluation results suggested that REEXTRACTOR+ significantly outperformed the state of the art in refactoring detection, reducing the number of false positives by 59.6% and improving recall by 19.2%. We also evaluated the performance of the proposed matching algorithms that serve as the cornerstone of refactoring detection. The evaluation results suggested that the proposed algorithms excel in matching code entities, substantially reducing the number of mistakes (false positives plus false negatives) by 67% compared to the state-of-the-art approaches. © 2025 IEEE.

关键词： Software quality

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A Dual-transformer Series Resonant Converter with Wide ZVS Range and Minimized RMS Current Operation

IEEE Open Journal of Industry Applications

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IEEE Open Journal of Industry Applications 2025年 6卷 281-294页

作者： Li, Yinan Hu, Song Sun, Chuan Li, Peiwen Nie, Wanlin Wen, Huiqing Rathore, Akshay Li, Xiaodong Macau University of Science and Technology Faculty of Innovation Engineering China Changshu Institute of Technology School of Electrical Engineering and Automation Suzhou China Xi'an Jiaotong-Liverpool University Suzhou China Singapore Institute of Technology Electric Power Engineering Division Singapore

To improve the operation efficiency under various conversion gains, a series resonant DC-DC converter is proposed in this article for renewable power generation application, which adopts a dual-transformer structure and employs an asymmetrical modulation scheme. Firstly, the operation principle of the converter and the modulation scheme is analyzed. As a result, the steady-state characteristics expressions are solved, including the resonant tank current and transmission power. To reduce the high-frequency switching loss, the additional parameter of dual-transformer structure, which is defined as the ratio of two high-frequency transformers, can be tuned to widen zero voltage switching (ZVS) range on both sides. Furthermore, Lagrange Multiplier Method (LMM) is applied to minimize the rms current to reduce the conduction loss. With full ZVS range operation and minimized rms current, the overall efficiency of the proposed converter can be enhanced. Finally, the performance of the proposed converter with the adopted control scheme is evaluated with the experimental results on a 400W lab prototype converter © 2020 IEEE.

关键词： DC transformers

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A hybrid CNN-SVM model optimized with PSO for accurate and non-invasive brain tumor classification

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Neural Computing and Applications 2025年 1-30页

作者： Semwal, Tanay Jain, Sania Mohanta, Agradeep Jain, Ankur School of Computer Science and Engineering VIT Bhopal University Madhya Pradesh Kothri Kalan India Faculty of Science The Maharaja Sayajirao University of Baroda Gujarat Vadodara India

Brain and central nervous system (CNS) cancers are the leading cause of cancer-related mortality, presenting significant diagnostic challenges due to their aggressive nature and diverse manifestations. While biopsies are the gold standard for diagnosis, their invasiveness and associated risks necessitate the development of noninvasive, automated tools for accurate and rapid tumor classification. This research presents a hybrid approach that combines a convolutional neural network (CNN) for feature extraction with a support vector machine (SVM) classifier, optimized by particle swarm optimization (PSO). The proposed model achieves multiclass classification across four tumor categories: glioma, meningioma, pituitary, and no tumor. The model incorporates data augmentation, dropout layers, pooling, batch-normalization, and hyperparameter tuning via PSO, to enhance generalization and minimize overfitting. The experimental analysis on the brain tumor MRI dataset highlights the model's strong performance, with an overall accuracy of 84.77%, a macro-average F1-score of 0.80, and a macro-average precision of 0.91. The performance of the PSO-optimized CNN-SVM model was benchmarked against traditional machine learning (ML) and deep learning (DL) methods, which demonstrated mean accuracies between 48 and 72%. The proposed hybrid model significantly surpasses these conventional approaches, establishing its superiority in classification accuracy and reliability. The results highlight the proposed model's reliability as a noninvasive diagnostic solution, offering considerable upside for enhancing early brain tumor detection and aiding in treatment planning. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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Exploring Meta Evidence for Prompt Optimization

Exploring Meta Evidence for Prompt Optimization

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： He, Yao Liu, Jiajun Ke, Wenjun Wang, Peng Xiao, Congda Li, Yining School of Institute of Collaborate Innovation University of Macau China School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9798350368741

Prompt optimization aims to construct effective prompts to fully leverage the capabilities of large language models (LLMs). The key challenge lies in analyzing the outputs of LLMs, identifying fine-grained errors as explicit evidence and guiding reverse prompt correction. However, existing dominant methods feed all evidence into LLMs, leading to excessive redundancy that misleads the models. Moreover, this redundant input could increase computational costs. To address the issue, we introduce a novel meta evidence-aware prompt optimization method (MEPO), which performs fine-grained analysis of the causal relationships within evidence using clustering and Bayesian inference. Initially, MEPO generates preliminary evidence to describe the drawbacks of the given prompt. Subsequently, the evidence is clustered and constructed into a directed acyclic graph (DAG) to produce meta evidence. Finally, we edit the initial prompt based on the obtained meta evidence and employ the beam search to select the best-performing prompt. Experimental results show that MEPO outperforms state-of-the-art baselines, achieving absolute F1 score improvements of 5.8% and 5.0% on two common-used Liar and Jailbreak datasets, respectively. The datasets and codes are available at https://***/r/MEPO-E1A0. © 2025 IEEE.

关键词： Large Language Models Prompt Optimization

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Comprehensive multimodal approach for Parkinson’s disease classification using artificial intelligence: insights and model explainability

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Soft Computing 2025年第3期29卷 1845-1877页

作者： Balaha, Hossam Magdy Hassan, Asmaa El-Sayed Ahmed, Rawan Ayman Balaha, Magdy Hassan Bioengineering Department J.B. Speed School of Engineering University of Louisville LouisvilleKY United States Computer Science and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Mathematics and Engineering Physics Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Obstetrics and Gynecology Faculty of Medicine Tanta University Tanta Egypt

Parkinson’s disease (PD) is a debilitating neurodegenerative disorder affecting millions worldwide. Early detection is vital for effective management, yet remains challenging. In this study, we investigated four distinct datasets for PD detection. Through comprehensive experimentation employing ensemble methods and feature selection, we achieved high classification accuracies across the datasets. For the Oxford Parkinson’s Disease Detection Dataset, an accuracy of 95.67%, precision of 97.59%, recall of 84.5%, specificity of 99.32%, and F1-score of 90.57% were achieved. For the Alzheimer Parkinson Diseases 3 Class Dataset, the "Stacking" approach surpasses individual models, reaching an accuracy of 99.85%, precision of 99.81%, recall of 99.81%, specificity of 99.86%, and F1 of 99.81%. For the NewHandPD dataset, Regarding the Spiral category, The "Base-P32-384" model surpasses others with an accuracy of 97.35%, precision of 96.50%, recall of 98.57%, and F1-score of 97.53%. The collective "Stacking" approach proves highly effective regarding the Circle category, achieving 100% across all performance metrics. Regarding the Meander category, the "Base-P16-224" model achieves an accuracy of 97.35%, precision of 99.26%, recall of 95.71%, specificity of 99.19%, and F1 of 97.45%. The Mobile Device Voice Recordings at King’s College London (MDVR-KCL) dataset contains two datasets. Regarding the "SpontaneousDialogue" dataset, accuracy, BAC, precision, recall, specificity, and F1-score were computed, resulting in values of 94.03%, 92.83%, 90.78%, 100.0%, and 85.67%, respectively. Regarding the "ReadText" dataset, accuracy, BAC, precision, recall, specificity, and F1-score were computed, resulting in values of 91.89%, 90.62%, 87.5%, 100.0%, and 81.25%, respectively. Our findings highlight the efficacy of leveraging diverse data sources and advanced machine learning techniques to enhance PD detection accuracy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germa

关键词： Neurodegenerative diseases

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