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C3shartMark: A Chart Watermarking Scheme With Consecutive-Encoding and Concurrent-Decoding

IEEE Transactions on Circuits and Systems for Video technology 2025年第1期35卷 492-507页

作者： Ma, Linfeng Fang, Han Ma, Zehua Jia, Zhaoyang Zhang, Weiming Yu, Nenghai University of Science and Technology of China Anhui Province Key Laboratory of Digital Security The Cas Key Laboratory of Electromagnetic Space Information Hefei230026 China National University of Singapore School of Computing Queenstown Singapore117417 Singapore

Chart images are widely employed as the intuitive form to express information, which renders them highly valuable. Consequently, there is an urgent demand to develop a watermarking algorithm for copyright protection and leakage prevention of chart images. Nevertheless, existing chart watermarking methods fail to thoroughly consider the chart image's special characteristics and simply rely on the previous natural image-based watermarking framework. Compared to natural images, the chart image generally exhibits relatively simple layouts and textures, containing fewer complex texture regions that watermarks are typically embedded in. Therefore, the embedding locations of watermarks for different distortions can be relatively dispersed in natural images, while for chart images, watermark embedding regions under various distortion conditions tend to be relatively concentrated and share more overlaps. Inspired by the above special characteristics of chart images, to sufficiently leverage them and design a better framework, this paper proposes C3hartMark, a chart watermarking scheme with consecutive-encoding and concurrent-decoding. Instead of using the combined noise layer as existing methods to ensure multiple robustness, a novel consecutive training framework is introduced in this paper, which efficiently utilizes the overlapping of embedded watermark features in chart images, and simultaneously, mitigates the poor convergence brought by the combined noise layer. During the extraction stage, multiple concurrent decoders are introduced to extract the potential embedded watermarks for different distortions independently. Moreover, we also incorporate two special noise layers, namely Captioning and Fusion, to address the corresponding realistic distortions in chart images, and an agnostic noise layer to accommodate potential channel transmission distortions unknown during training. Through extensive experiments, we demonstrate that with the better visual quality, C3hartMar

关键词： Image texture

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AOBL-IPACO: A novel and optimized algorithm to mitigate losses in electrical grid systems

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International Journal of Information technology (Singapore) 2025年第3期17卷 1919-1927页

作者： Gupta, Rahul Khanna, Ashish Virdee, Bal School of Electrical and Electronics Engineering Maharaja Agrasen Institute of Technology Himachal Pradesh India School of Computer Science Engineering & amp Technology Maharaja Agrasen Institute of Technology Delhi India School of Computing & amp Digital London Metropolitian University London United Kingdom

This paper shows the framework for solving the economic optimization problem in electrical grid system using Advanced Oppositional Based Learning (AOBL) technique with Invasive Plant Ant Colony Optimization (IPACO) algorithm. The work focuses on application of newly developed algorithm called Advance Oppositional Invasive Plant Ant Colony Optimization (AOIPACO) to solve the various constraints of single objective problem. The artificial algorithm leads to direction of exploration for new space of ant colonies with process of threats elimination from native candidates in energy management. During simulation process, the proposed algorithm reduces the power loss along with improvement of cost factor and computational time. The proposed AOIPACO approach is implemented on standard IEEE-5, 13 and 40 bus systems and generates optimal results as compared to other conventional techniques while solving the complex problem of optimization. Further, this meta heuristic technique help to improve the quality of power market system with minimum computation error at fast convergence rate. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Cost function Economic and Emission Dispatch (EED) Invasive Ant Colony Optimization Invasive Plant Optimization Oppositional Base Learning (OBL)

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An Efficient Automated Technique for Classification of Breast Cancer Using Deep Ensemble Model

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Computer Systems Science & Engineering 2023年第7期46卷 897-911页

作者： Muhammad Zia Ur Rehman Jawad Ahmad Emad Sami Jaha Abdullah Marish Ali Mohammed A.Alzain Faisal Saeed Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City University15 Bartholomew RowBirminghamB55JUUK

Breast cancer is one of the leading cancers among *** has the second-highest mortality rate in women after lung *** detection,especially in the early stages,can help increase survival ***,manual diagnosis of breast cancer is a tedious and time-consuming process,and the accuracy of detection is reliant on the quality of the images and the radiologist’s ***,computer-aided medical diagnosis has recently shown promising results,leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early *** research presented in this paper is focused on the multi-class classification of breast *** deep transfer learning approach has been utilized to train the deep learning models,and a pre-processing technique has been used to improve the quality of the ultrasound *** proposed technique utilizes two deep learning models,Mobile-NetV2 and DenseNet201,for the composition of the deep ensemble *** learning models are fine-tuned along with hyperparameter tuning to achieve better ***,entropy-based feature selection is *** cancer identification using the proposed classification approach was found to attain an accuracy of 97.04%,while the sensitivity and F1 score were 96.87%and 96.76%,*** performance of the proposed model is very effective and outperforms other state-of-the-art techniques presented in the literature.

关键词： Breast cancer image enhancement ensemble model transfer learning feature selection

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K∞ Function-Based Consensus Tracking Control for Multi-Agent Systems With Unknown Disturbances 50

K∞ Function-Based Consensus Tracking Control for Multi-Agen...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Ning, Boda Han, Qing-Long Centre for Sustainable Infrastructure and Digital Construction Swinburne University of Technology MelbourneVIC3122 Australia School of Science Computing and Engineering Technologies Swinburne University of Technology MelbourneVIC3122 Australia

ISBN: (纸本)9781665464543

In this paper, consensus tracking is investigated for multi-agent systems (MASs) with unknown disturbances. A K∞ function-based control strategy is proposed to guarantee that the tracking errors between each follower and the leader are converged to a small neighborhood of zero in finite-time. To be more specific, by introducing an error auxiliary considering local interactions, a K∞ function-based adaptive controller is designed such that the error auxiliary is constrained to an adjustable region. Then, through a matrix-based rigorous proof, practical consensus tracking is realized in finite-time for MASs. A distinguished feature of the K∞ function-based control is its independence on the use of the bound information of unknown disturbances. Another advantage of the proposed controller is its capability of alleviating the input chattering issue. Finally, numerical examples are presented to validate the effectiveness of the K∞ function-based controller. © 2024 IEEE.

关键词： Multi agent systems

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Novel Non-Linear Optimized Control Techniques for Conversion of Hybrid Renewable Energy 11

Novel Non-Linear Optimized Control Techniques for Conversion...

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11th International Conference on Electrical and Electronics Engineering, ICEEE 2024

作者： Sabeel, Khursheed Aggarwal, Geetika Ali, Usama School of Computing Engineering and Digital Technology Teesside University Middlesbrough United Kingdom Glassgow Coleading University Department of Electrical Power Engineering Glasgow United Kingdom

ISBN: (纸本)9798350362541

The increasing demand for sustainable and reliable energy sources has spurred significant interest in renewable energy systems. The paper introduces a novel design for a grid-connected hybrid renewable energy system, integrating a permanent magnet synchronous generator (PMSG) wind turbine and a solar photovoltaic (PV) conversion system. The core focus is enhancing energy conversion efficiency using advanced components, including inverters and rectifiers. A significant contribution of this work is the application of the Maximum Power Point Tracking (MPPT) algorithm, which optimises solar power output and wind speed, ensuring maximal energy extraction from both wind and solar sources. Furthermore, the paper presents innovative control techniques tailored to the nonlinear model of the converter-generator system. These techniques are pivotal in achieving the dual objectives of maximising energy conversion efficiency and ensuring system stability. The robustness and strength of the proposed approach are rigorously analysed using backstepping control methods and Lyapunov theory. The results of this analysis confirm the effectiveness and high efficiency of the proposed control strategy. This research contributes to the field of renewable energy by providing a more efficient and stable approach to hybrid energy conversion, which is vital for advancing sustainable and reliable energy systems. © 2024 IEEE.

关键词： Energy conversion efficiency

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Toward Practical Entity Alignment Method Design: Insights from New Highly Heterogeneous Knowledge Graph Datasets 24

Toward Practical Entity Alignment Method Design: Insights fr...

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33rd ACM Web Conference, WWW 2024

作者： Jiang, Xuhui Xu, Chengjin Shen, Yinghan Wang, Yuanzhuo Su, Fenglong Shi, Zhichao Sun, Fei Li, Zixuan Guo, Jian Shen, Huawei Institute of Computing Technology Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Science Beijing China IDEA Research International Digital Economy Academy Guangdong Shenzhen China Institute of Computing Technology Chinese Academy of Sciences Beijing China National University of Defense Technology Hunan Changsha China

ISBN: (纸本)9798400701719

The flourishing of knowledge graph (KG) applications has driven the need for entity alignment (EA) across KGs. However, the heterogeneity of practical KGs, characterized by differing scales, structures, and limited overlapping entities, greatly surpasses that of existing EA datasets. This discrepancy highlights an oversimplified heterogeneity in current EA datasets, which obstructs the exploration of the EA application. In this paper, we study the performance of EA methods on the alignment of highly heterogeneous KGs (HHKGs). Firstly, we address the oversimplified heterogeneity settings of current datasets and propose two new HHKG datasets that closely mimic practical EA scenarios. Then, based on these datasets, we conduct extensive experiments to evaluate previous representative EA methods. Our findings reveal that, in aligning HHKGs, valuable structure information can hardly be exploited, which leads to inferior performance of existing EA methods, especially those based on GNNs. These findings shed light on the potential problems associated with the conventional application of GNN-based methods as a panacea for all EA datasets. Consequently, to elucidate what EA methodology is genuinely beneficial in practical scenarios, we undertake an in-depth analysis by implementing a simple but effective approach: Simple-HHEA. Our experiment results conclude that the key to the future EA model design in practice lies in their adaptability and efficiency to varying information quality conditions, as well as their capability to capture patterns across HHKGs. The datasets and source code are available at https://***/IDEA-FinAI/Simple-HHEA. © 2024 Owner/Author.

关键词： Alignment

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Risk Management for 5G-Enabled Internet of Things by Using Machine Learning: A Survey

Risk Management for 5G-Enabled Internet of Things by Using M...

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2024 Arab ICT Conference, AICTC 2024

作者： Alfaw, Aysha Elmedany, Wael Sharif, Mhd Saeed College of Information Technology University of Bahrain Sakhir Bahrain School of Architecture Computing and Engineering Department Computer Science and Digital Technologies UEL London United Kingdom

ISBN: (纸本)9798350342086

Nowadays, there is a noticeable increase in the development and use of the Internet of Things(loT). With this rapid increase, IoT devices may face several challenges when used in the real world through applications. Using 5G with large numbers of Internet-connected devices of IoT puts many devices at risk and requires risk management. The growing of IoT leads to the growth of cyber-attackers and allow them to expose the vulnerabilities. This paper will present the risk management of 5G-enabled IoT technology and the use of machine learning to mitigate the risk and reduce the attacks on these technologies, and finding solutions through previous researches. This research aims to identify and eliminate potential risks in the IoT based 5G by machine learning. The paper also aims to present a solution to the security problems and risks faced when integrating the fifth-generation network and the Internet of Things. With 5G-enabled IoT, the risk management helps organizations use emerging technologies effectively while mitigating potential underlying risks such as security breaches, and data loss. Authentication,encryption, access control, and communication security are essential for making security. Machine learning algorithms have the potential to remove many obstacles to implementing the security of the Internet of Things, paving the door for the use of sophisticated technology like 5G. With new 5G networks, it is expected that the current IoT will be significantly expanded, which will improve cellular operations and the security of IoT, as well as push the future of the Internet to its edges. Machine learning (ML) creates a secure and intelligent system and provides a robust security mechanism and dynamic for 5G networks. Therefore, this time will also present previous solutions with machine learning against the risks to IoT and 5G. This paper will present a set of previous studies related to insurance of risk management for the 5G-enabled IoT, Which aims to find previ

关键词： Adversarial machine learning

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Deep Learning Techniques for Tuberculosis Detection on Chest X-Rays in Low-Resource Settings: A Survey of Opportunities and Challenges

Deep Learning Techniques for Tuberculosis Detection on Chest...

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2024 International Conference on Electrical and Computer Engineering Researches, ICECER 2024

作者： Sidume, Freedmore Muchuchuti, Stewart Chengetenai, Gibson Tamukate, Rabson Ntwaetsile, Kushata Ntekeng, Patience School of Computing & Information Systems Botswana Accountancy College Gaborone Botswana Botswana International University of Sciences and Technology Computing & Information Systems Palapye Botswana Botswana Teaching Professionals Council Digital Transformation and Innovation Gaborone Botswana

ISBN: (纸本)9798331539733

Tuberculosis (TB) remains a significant public health challenge, particularly in low-resource settings, where limited access to diagnostic tools and healthcare infrastructure exacerbates the burden of the disease. This survey critically examined the application of deep learning techniques for TB detection using chest X-rays, focusing on the opportunities and challenges specific to low-resource environments. This study reviews state-of-the-art deep learning models, such as Convolutional Neural Networks (CNNs), transfer learning frameworks, and hybrid approaches, to assess their effectiveness in enhancing diagnostic accuracy, scalability, and cost-efficiency. The key challenges in implementing these models include the scarcity of annotated data, limited computational resources, and the need for model adaptability across diverse populations. These findings underscore the transformative potential of deep learning in improving TB detection, particularly through automation, integration with telemedicine, and deployment of lightweight, efficient models suitable for resource-constrained settings. This survey highlights the critical need for continued research and innovation to overcome existing barriers and fully realize the benefits of deep learning in TB diagnostics. © 2024 IEEE.

关键词： Deep learning

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Recommendation Algorithm Based on Wide&Deep and FM

Recommendation Algorithm Based on Wide&Deep and FM

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International Conference on Artificial Intelligence and Communication technology, ICAICT 2023

作者： Zheng, Songkun Li, Xian Chen, Xueliang Li, Xu Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Beijing Zhongke Zhihe Digital Technology Co. Ltd Beijing China

ISBN: (纸本)9789819966400

Online learning is more and more popular because it is not limited by time and space. How to choose a suitable course from thousands of online courses is a great challenge faced by online learners, and online course recommendation came into being. The personalized recommendation algorithm analyzes the user’s preferences by collecting some previous historical records of the user and other information, and generates recommendations for the user. Since Wide&Deep was proposed, due to its inherent ease of implementation, adaptability, and versatility, this approach has gained significant traction across various industry sectors. But its feature intersection method is not efficient. Sufficient feature engineering is required to provide informative features that can effectively distinguish objects. In this paper, the WD-FM model is proposed by combining Wide&Deep and factorization machine, and good results have been achieved through experimental demonstration. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Frequency modulation

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A classifier design for micro bubble generators based on deep learning technique

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Materials Today: Proceedings 2023年 80卷 2684-2696页

作者： Qaddoori, Ayman S. Saud, Jamila H. Hamad, F.A. Informatics Institute for Postgraduate Studies Iraqi Commission for Computer and Informatics Baghdad Iraq College of Science Mustansiriyah University Baghdad Iraq School of computing Engineering and Digital Technology Teesside University Middlesbrough United Kingdom

This paper aims to study the sizes of bubbles generated by the micro-bubble generator device (MBGs) in the water because of its impact on the dissolved oxygen percentage in the water and we find this in aquaculture where oxygen is important for marine life and in many applications. Where we have designed a classifier for micro-bubbles of different sizes and the effectiveness of the device has been proven in generating bubbles whose size ranges from 20 to 50 and calculating their numbers in the image by converting the image from (RGB) to (HSV), it has proven its effectiveness in maintaining lighting and neglecting color information Which is not important in identifying bubbles in the image, as well as increasing the processing speed, and then finding edges in the image, using canny edge detection, which has proven its great effectiveness in finding weak and strong edges. Where we then used the (Circular Hough Transform) algorithm to identify the tiny bubbles in the image, calculate their average diameter and numbers in the image, and find the overlapping bubbles between them by using (Two Thresholds) one for the edge and the other for the center of the bubble and then making (Segmentation) for these bubbles and inserting them on (Gaussian Normal Distribution) Do not neglect the dark bubbles, because in most cases they are unreal bubbles and may be distortions in the image or reflections in the lighting. And then make (zero paddings) for the extracted images to make them equal in size and avoid the distortion that will happen in the image if another method is used to make all bubbles of equal dimensions to prepare them for classification. And then we calculated the (mean square error) of the resulting bubble images to avoid repeating the same bubble in (datasets). The efficiency of the classifier was calculated by using deep learning technology. Where we built a (Convolution Neural Network) consisting of (15) layers and then we trained these layers on the images resul

关键词： Feature extraction

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