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8th international conference on Electronic information Technology and computer engineering, EITCE 2024

作者： Qu, Zhiyu Wu, Xu Geng, Zhenya College of Information Science and Engineering Hohai University Jiangsu Nanjing China College of Computer Science and Software Engineering Hohai University Jiangsu Nanjing China The 49th Research Institute of China Electronics Technology Group Corporation Heilongjiang Harbin China

ISBN: (纸本)9798400710094

Traditional Direction of Arrival (DOA) estimation algorithms for coherent signals in uniform circular array (UCA), such as mode space transformation, allow for the adaptation of advanced algorithms initially developed for uniform linear arrays to be applied effectively in UCA. However, after mode space transformation, the array will encounter aperture loss and will also be unable to perform two-dimensional DOA estimation effectively. In this paper, we propose a new method that that integrates covariance matrix element transformation with the Multiple Signal Classification (MUSIC) to estimate the DOA of coherent signals in various symmetric antenna arrays, including UCA. The algorithm leverages the phase difference symmetry of the antenna array to decorrelate the incoming signals, effectively restoring the full rank of the covariance matrix. Subsequently, it employs two-dimensional MUSIC for DOA estimation. Simulation results demonstrate the effectiveness of the proposed algorithm. © 2024 Copyright held by the owner/author(s).

关键词： Covariance matrix

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advanced perspectives on big data security: Navigating challenges in cloud computing 4th

Advanced perspectives on big data security: Navigating chall...

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4th international conference on Computational Methods in science and Technology, ICCMST 2024

作者： Verma, Richa Bhatt, Ravindara Department of Computer Engineering Jaypee University of Information Technology Solan India

ISBN: (纸本)9781032911571

Recent years have seen the rise of big data workflow management solutions as widespread data analytic platforms for handling massive amounts of data in the cloud. However, keeping information private and ensuring the integrity of workflow applications is still a major and difficult issue. This document reviews and organizes research on large data workflow management privacy and security breaches and remedies. In this case, data encryption is utilized to safeguard information at rest and in transit. These points of view would allow researchers to better comprehend crucial research areas and create novel approaches. according to research question improve parameters of security by proposed approach. In this paper review of different workflows and security issue in cloud and other frame works © 2025 the Author(s).

关键词： Cloud security

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research on Pupil Segmentation Network Based on Improved TransUNet 8

Research on Pupil Segmentation Network Based on Improved Tra...

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8th international conference on computer Graphics and Virtuality, ICCGV 2025

作者： Jianting, Liu Huazhong, Xiang Zexi, Zheng Dawei, Zhang Songlin, Zhuang College of Health Sciences and Engineering University of Shanghai for Science and Technology Shanghai200093 China College of Mechanical Engineering University of Shanghai for Science and Technology Shanghai200093 China College of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9781510689213

To address the critical challenge of pupil segmentation accuracy in medical image analysis, this paper proposes an innovative pupil segmentation method based on an enhanced TransUNet deep neural network. The proposed architecture introduces three key improvements: a novel multi-kernel self-selective attention mechanism that optimizes local feature extraction through weighted fusion of different convolution modules;a modified cross-attention Transformer that enables efficient parallel processing of spatial information in both horizontal and vertical dimensions;and an advanced feature fusion module that effectively integrates global and local information through multi-scale feature weighting. Extensive experiments on the OpenEDS dataset demonstrate the method's superior performance, achieving state-of-the-art results with average precision of 97.7%, average recall of 97.41%, and average Dice coefficient of 98.15%. These significant improvements in segmentation accuracy not only advance the field of medical image analysis but also provide a robust foundation for applications in ophthalmological diagnosis, human-computer interaction, and cognitive science research. The proposed methodology offers valuable insights into designing efficient attention mechanisms for medical image segmentation tasks, contributing to both theoretical understanding and practical applications in healthcare imaging systems. © 2025 SPIE.

关键词： Deep neural networks

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Revolutionizing Learning: The Role of AR/VR Technologies in Educational Delivery and Student Engagement in Higher Studies 2

Revolutionizing Learning: The Role of AR/VR Technologies in ...

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2nd international conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Patel, Krisha R. Brahmbhatt, Vishva B. Dubey, Nilesh Jayswal, Hardik Devang Patel Institute of Advanced Technology and Research Department of Computer Engineering Vadodara India Devang Patel Institute of Advanced Technology and Research Department of Computer Science and Engineering Vadodara India Devang Patel Institute of Advanced Technology and Research Department of Infromation Technology Vadodara India

ISBN: (纸本)9798331505745

Augmented Reality (AR) and Virtual Reality (VR) are emerging technologies that combine digital and physical worlds to provide immersive and interactive experiences. Their use in education has been revolutionary, particularly in terms of increasing student engagement, grasp of complicated concepts, and encouraging experiential learning. AR superimposes virtual things on real-world surroundings, whereas VR immerses viewers in a completely digital environment. These tools gained traction during the COVID-19 epidemic, demonstrating their potential for remote learning and increasing student enthusiasm and information retention. While AR and VR are commonly employed in higher education, particularly in sectors such as healthcare and engineering, there are still barriers to their complete adoption, including cost, accessibility, and ethical concerns. This study explores the impactful uses of AR and VR in education, shedding light on their potential to modernize traditional teaching methods and improve student outcomes. © 2025 IEEE.

关键词： Students

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Brillouin Frequency Shift Extraction for BOTDA Based on Gramian Angular Fields 4

Brillouin Frequency Shift Extraction for BOTDA Based on Gram...

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4th international Computational Imaging conference, CITA 2024

作者： Zhu, Borong Zhang, Zhihao Qian, Yuhao Ma, Xiaole Wang, Liang Yu, Kuanglu Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Institute of Information Science School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China National Engineering Laboratory for Next Generation Internet Access System School of Optics and Electronic Information Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781510688834

To accurately extract Brillouin frequency shift of BOTDA with large sweeping step sizes, a novel structure of GAFCNN is proposed, combining time series coding with convolutional neural networks. The experimental data validation results demonstrate that the GAFCNN is capable of obtaining more accurate results under large sweep steps and low signal-to-noise ratios (SNRs) than other current methods. © 2025 SPIE.

关键词： Convolutional neural networks

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Big Data Privacy Protection Technology Integrating CNN and Differential Privacy

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international Journal of advanced computer science and Applications 2025年第3期16卷 407-415页

作者： Liu, Yanfeng Li, Ping Zhang, Min Liu, Qinggang School of Information Engineering Shaanxi Xueqian Normal University XI’an710100 China

To solve the difficulty of balancing privacy and availability in big data privacy protection technology, this study integrates the powerful feature extraction ability of convolutional neural network models with the efficiency of differential privacy technology in data privacy protection. An innovative privacy protection method combining gradient adaptive noise and adaptive step size control is proposed. The experiment findings denote that the research method outperforms existing advanced privacy protection technologies in terms of performance, with an average accuracy of 97.68% and a performance improvement of about 20% to 30%. In addition, for larger privacy budgets, increasing the threshold appropriately can further optimize the effectiveness of research methods. This indicates that through refined noise control and step size adjustment, not only can the privacy protection process be optimized, but also the high efficiency and accuracy of data processing can be maintained. In summary, while ensuring data utility, research methods can not only significantly reduce the risk of privacy breaches, but also optimize privacy protection mechanisms, achieving an ideal balance between protecting personal privacy and maximizing data utility. This innovative approach provides an efficient probability distribution function solution for the field of privacy protection, with the potential to promote further development of related technologies and applications. © (2025), (science and information Organization). All Rights Reserved.

关键词： Differential privacy

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Enhanced Emotion Recognition Using a Hybrid Autoencoder-LSTM Model Optimized with a Hybrid ACO-WOA Algorithm for Hyperparameter Tuning

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international Journal of advanced computer science and Applications 2025年第4期16卷 861-876页

作者： Waiker, Vinod Ramesh, Janjhyam Venkata Naga Bala, Ms Kiran Krishnaiah, V.V. Jaya Rama Jackulin, T. Muniyandy, Elangovan Shahin, Osama R. Datta Meghe Institute of Management Studies Maharashtra Nagpur India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed To Be University Uttarakhand Dehradun248002 India Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of CSE Panimalar Engineering College Tamil Nadu Chennai India Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science College of Computer and Information Sciences Jouf University Saudi Arabia Physics and Mathematics Department Faculty of Engineering Helwan University Helwan Egypt

Emotion recognition is vital in the human computer interaction because it improves interaction. Therefore, this paper proposes an improved method for emotion recognition regarding the Hybrid Autoencoder-Long Short-Term Memory (LSTM) model and the newly developed hybrid approach of the Ant Colony Optimization (ACO) and Whale Optimization Algorithm (WOA) for hyperparameters tuning. In this case, Autoencoder can reduce input data dimensionality for input data and find the features relevant for the model’s work. In addition, LSTM is able to work with temporal structures of sequential inputs like speech and videos. The contribution of this research lies in the novel combination method of ACO-WOA which aims at tweaking hyperparameters of Autoencoder-LSTM model. Global aspect of ACO and WOA thereby improve the search efficiency and the accuracy of the proposed emotion recognition system and its generalization capacity. In context with the benchmark dataset for the experimentations of emotion recognition, it has established the efficiency of the proposed model in terms of the conventional methods. Recall rates in recognitive intended various emotions and different modalities were also higher in the hybrid Autoencoder-LSTM model. The optimization algorithms like the ACO-WOA also supported in reducing the computational cost which arose due to hyperparameters tuning. The implementation of this paper is done through Python Software. This implementation shows a high accuracy of 94.12% and 95.94% for audio datasets and image datasets respectively when compared with other deep learning models of Conv LSTM and VGG16. Therefore, the research shows that the presented hybrid approach can be a useful solution for successfully employing emotion recognition for enhancing the creation of the empathetic AI systems and for improving user interactions within various fields including healthcare, entertainment, and customer support. © (2025), (science and information Organization). All rights

关键词： Ant colony optimization

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Modularity-Driven Influence: An Enhanced LPA Framework for Community Detection and Maximization in Social Networks

Modularity-Driven Influence: An Enhanced LPA Framework for C...

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2025 international conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Manoj Jayanth, N. Nithin, M. Kushwanth, M.V. Reddy, K. Akash Shukla, Ankita Nagarjuna College of Engineering and Technology Department of Information Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798331513894

It is quite an uphill task to identify social network communities as well as to diffuse impact because of their complex architecture as well as evolving nature of their relationships. In this paper, we present an advanced version of the Modularity-Driven Label Propagation Algorithm, Enhanced Label Propagation Algorithm (ELPA) which has provided solutions to these issues through the adoption of modularity optimization and the local influence effectiveness measure. The modularity component ensures accurate and coherent community discovery, while the Local Influence Effectiveness (LIE) metric optimizes effective node selection and network impact propagation. The productivity and the impact distribution of the method have surpassed the results of the Louvain method which is often used on large databases of networks. The findings suggest that this new LPA is of a linearly increasing runtime with better impact diffusion making it appropriate for the use in viral marketing, public health activities and the dissemination of specific information. By integrating computational scale with analytical precision, the methodology advanced in this study offers a robust and efficient strategy for the current evolution of social networks research. © 2025 IEEE.

关键词： Expectation maximization algorithm

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Optimization and Deployment of Joint Source-Channel Coding for Enhance Future 6G Network Performance 1

Optimization and Deployment of Joint Source-Channel Coding f...

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1st international conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Acharjya, Kalyan Thimmappa, Arunkumar Devalapura Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Ramnagar District Karnataka India

ISBN: (纸本)9798331518578

research on semantic communication has advanced because of deep learning in the construction for collaborative (joint) resource-channel coding (JSCC) project. Deep learning technology (DL)-based JSCC systems currently in practice are developed based upon a preset signal-to-noise ratio (SNR);though, such design is not intended in situations when SNR is changing. Thus, new semantic adaptive system with the semantic adaption modal selectioning (SAMS) modules is developed for semantic communication (SC). It is named collaborative (joint) resource-channel coding with adaptive models (AMJSCC). Both the real-time channel circumstances as well as the model's computing capabilities are taken into account by collaborative (joint) resource-channel encoding (JSCE) & collaborative (joint) resource-channel decoding (JSCD) models. In order to further increase the precision of information recovery, residual networks with various layers are also being studied. Simulation findings show that the model is flexible with the SNR as well as communication services, and it can achieve increased recovery similarity. Meanwhile, it uses less store space and communication resources than the most advanced deep JSCC techniques. © 2025 IEEE.

关键词： Signal to noise ratio

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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international Journal of advanced computer science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (science and information Organization). All rights reserved.

关键词： Quantum cryptography

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