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检索条件"任意字段=International Conference on Advanced Research on Computer Science and Information Engineering"

共 160469 条记录，以下是281-290 订阅

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A Scrutiny of AES-Based Image Encryption and Decryption for Fraudulent Data Security and Defense Against Intrusion Attacks 12th

A Scrutiny of AES-Based Image Encryption and Decryption for ...

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12th international conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Nayak, Himanshu Pravalika, G. Maheswari, K. Pavan, G. Tulasi, P. Pavani, M. CMR Engineering College Telangana Hyderabad India Department of Electronics and Communication Engineering CMR Engineering College Telangana Hyderabad India Department of Computer Science and Engineering CMR Technical Campus Telangana Hyderabad India CMR Technical Campus Telangana Hyderabad India Department of Computer Science and Engineering CMR College of Engineering and Technology Telangana Hyderabad India CMR College of Engineering and Technology Telangana Hyderabad India

ISBN: (纸本)9789819601387

Image data are becoming the most common type of information sent over networks, thanks to advancements in information technology. The advancement of picture encryption technology is occurring concurrently with the growth of photographic data theft technologies. In order to stay up with the growing array of methods used for information theft, we must investigate more robust image encryption strategies. information can now be transmitted quickly and reliably because of advancements in communication technology, which has increased its application across a range of industries, including business, science, the military, and the civilian sector. In computer science, cryptography is the process of transforming readable data, such as plain text, into an encrypted form which can only be decoded by another party with the decryption key. It strengthens security and aids in safeguarding private and sensitive data. Encryption and decryption safeguard data against loss, alteration, or compromise. Numerous technologies, including RSA, BLOW FISH, DES, TRIPLE DES, genetic algorithms, and chaotic maps, have been created. For generations, the security of data has also grown in importance. This solution suggested employing AES (advanced Encryption Standard) technology for image encryption and decryption in order to address these issues. This technique guarantees secrecy, integrity, and authentication. These methods aid in preventing intrusion attempts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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Analysis of tree obstacle prediction technology for power lines based on laser point cloud 24

Analysis of tree obstacle prediction technology for power li...

引用

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

作者： Yin, Weiwei Tu, Gao Li, Rui Ultra High Voltage Company State Grid Jilinsheng Electric Power Supply Company Jilin Changchun China School of Electrical and Information Engineering Changchun Institute of Technology Jilin Changchun China

ISBN: (纸本)9798400710094

In order to solve the problem of incomplete tree hazard inspection in UAV laser ranging, it is proposed to use laser point cloud data to predict tree hazard of power line. This paper uses the improved KDTree to automatically extract and classify the power line point cloud data and complete the single tree segmentation and classification management. Finally, a model is established to predict the data growth trend, which can solve the problem of tree hazard inspection caused by tree growth. The research results show that the use of theoretical models to scientifically predict the growth trend of trees and accurately assess the risk of tree hazard according to the distance between trees and ground wires can provide an effective technical solution for solving the problem of tree hazard inspection. © 2024 Copyright held by the owner/author(s).

关键词： Power distribution lines

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Differential Protection for Distribution Grids Based on Dual Compressed Sensing Theory

Differential Protection for Distribution Grids Based on Dual...

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2024 international conference Optoelectronic information and Optical engineering, OIOE 2024

作者： Duan, Yahui Wu, Tonghua Chen, Wei Dai, Wei Cao, Yuhan Cao, Wenjun School of China EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan430074 China The State Key Laboratory of Technology and Equipment for Defense against Power System Operational Risks Nari Technology Co. Ltd. Jiangsu211106 China The State Key Laboratory of Advanced Electromagnetic Technology Huazhong University of Science and Technology Wuhan430074 China State Grid Hebei Electronic Power Research Institute Hebei250002 China State Grid Shandong Electronic Power Research Institute Shandong050011 China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

With the increasing integration of distributed renewable energy sources into distribution networks, the characteristics of fault currents in distribution networks have undergone significant changes. Traditional three-stage current protection is no longer sufficient to meet the protection requirements. In theory, a differential protection scheme can better fulfill the requirements of rapid and accurate fault detection and clearance in distribution networks. Differential protection relies on real-time communication for information exchange between devices, and many researchers have incorporated wireless networks such as 5G into distribution network differential protection. Given the exponential growth of data in distribution networks and the security considerations of wireless communication like 5G, this paper proposes a secure protection technique for distribution network differential protection based on dual compressed sensing theory. This technique employs two compressed sensing schemes to compress the data before transmission, reducing the amount of data transferred while enhancing security. Subsequently, secure determinations are made based on the data at both ends processed through the compressed sensing algorithm, ensuring the security of communication in distribution network differential protection while achieving the same level of protection performance as traditional differential protection schemes. © 2025 SPIE.

关键词： Compressed sensing Data transmission Matrices Data communications Wireless communications Network security Reconstruction algorithms computer security Signal processing Signal to noise ratio

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Discussion on the use of LoRa wireless communication technology in building electrification fire supervision system 5

Discussion on the use of LoRa wireless communication technol...

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5th international conference on Signal Processing and computer science, SPCS 2024

作者： Wu, Xiaochuan Shenyang Fire Science and Technology Research Institute of MEM Shenyang110034 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

The electrical fire monitoring mode is mostly set to one-way mode, which can achieve the expected monitoring task, but lacks stability and reliability, and it is difficult to achieve coordination and positioning monitoring in different communication background environments, with low controllability. Therefore, the design and verification of electrical fire monitoring method based on Lo Ra wireless communication technology are proposed. The fire monitoring model of LoRa wireless communication is constructed. After analysis, it is found that the application of LoRa wireless communication technology has long transmission distance, good wall-penetrating effect and low power consumption, so it can be widely used in the fire supervision system of building electrification. © 2025 SPIE.

关键词： Fires

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Adaptive Quantum and Graph-Attentive Security Framework for DoS Attack Mitigation in VANETs 2

Adaptive Quantum and Graph-Attentive Security Framework for ...

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

作者： Anjum, Asma Rao, P. S. V. Srinivasa Sindhu, M. Bhatt, Pooja Priya, M.R. Maranan, Ramya HKBK College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru560045 India Joginpally B R Engineering College Department of Computer Science and Engineering Telangana Hyderabad500075 India R.M.K Engineering College Department of Computer Science and Business Systems Tamil Nadu Thiruvallur601206 India Parul institute of Engineering and Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology Post Limda Gujarat 391760 India NIE First Grade College Department of Computer Science Karnataka Mysuru570008 India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331505745

Vehicular Ad Hoc Networks (VANETs) are considered crucial for real-time vehicle-to-vehicle communication, which in turn enhances the efficiency of traffic and road safety. VANETs are very vulnerable to Denial-of-Service (DoS) attacks, which seriously disrupt communication and degrade network performance. This paper introduces a high-performance neural network-based detection and prevention mechanism that can effectively counter DoS attacks. The proposed method is initiated with the real-time monitoring of the network. Data collection and preprocessing through the Adaptive Self-Guided Loop Filter (ASGLF) are used to filter out noise from the data. The Fast Hybrid Vision Transformer (FHVT) conducts feature extraction for network traffic to effectively preserve the spatial and temporal relationships. Using the Reflecting Equivariant Quantum Neural Network (REQNN) integrated with Heterogeneous Edge-Enhanced Graph Attention Network (HEEGAT) ensures timely anomaly detection accuracy of attacks. Subtraction-Average-Based Optimizer (SAO) optimizes the detection parameters to effectively utilize resources. The experimental results have shown accuracy in the detection of DoS attacks as 99.9% with a PDR of 99.9% and also improved throughput efficiency by 99.8%. These results validate the effectiveness of the proposed model in securing VANETs from DoS attacks, offering uninterrupted network availability and improved vehicular communication security for intelligent transportation systems. © 2025 IEEE.

关键词： advanced driver assistance systems

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Robust audio watermarking based on a multi-band masking model using a DNN 5

Robust audio watermarking based on a multi-band masking mode...

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5th international conference on Signal Processing and computer science, SPCS 2024

作者： Zhu, Jiji School of Information Science and Engineering Chongqing Jiaotong University Chongqing400074 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

The technique of embedding the owner’s valid copyright information in an audio signal is known as digital audio watermarking. research in this field has primarily focused on the trade-off between imperceptibility, payload, and robustness. Traditionally, audio watermarking algorithms have been implemented using signal processing techniques, such as Least Significant Bit (LSB) Coding. In this study, we propose a deep learning-based approach for embedding image watermarking information into audio clips of finite length. We utilize Short-Time Fourier Transform (STFT) and Inverse Short-Time Fourier Transform (ISTFT) as differentiable layers within the network. Additionally, we design a multi-band masking model that integrates a psychoacoustic model-based masking threshold to achieve covert watermarking. To enhance the algorithm’s resilience against various attacks, we incorporate multiple differentiable distortions during the training process to simulate realistic attack scenarios. We evaluate the model’s performance through experimental analysis under both attack-free conditions and various channel distortions. The experimental results demonstrate that our method achieves a Signal-to-Distortion Ratio (SDR) of up to 44 dB and an extremely low Bit Error Rate (BER). Comprehensive comparative experiments and ablation studies further validate the effectiveness of our proposed method. © 2025 SPIE.

关键词： Inverse problems

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Federated learning techniques in the healthcare area 4th

Federated learning techniques in the healthcare area

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

作者： Kumar, Sanjay Virk, Ishpreet Singh Puri, Renu Department of Computer Science and Engineering Chandigarh Engineering College-CGC Landran India

ISBN: (纸本)9781032911571

Federated understanding techniques have actually shown prospective, in the medical care sector allowing cooperation as well as information sharing while promoting personal privacy and also safety and security steps. This evaluation explores making uses of federated knowing in health care showcasing its capacity to boost results development medical study and also sustain tailored medication. The evaluation covers the advantages along with barriers of incorporating federated discovering in medical care together with opportunities for expedition as well as development in this area. Furthermore it deals with difficulties as well as chances for research study on medical care applications. The goal is to inspire a journey of linked expertise in health care contexts for it to come to be an approach for securing fragile professional information. The future pattern suggests an expanding fostering of linked knowing in setups, causing sophisticated procedures that focus on personal privacy and also safety and security while successfully using this innovation to resolve crucial problems within the medical care field. © 2025 the Author(s).

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research on Large-scale Ocean Date Analysis based on Machine Learning 24

Research on Large-scale Ocean Date Analysis based on Machine...

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

作者： Zhou, Tong Meng, Wenzheng Yu, Dingfeng Wuhan Second Ship Design and Research Institute Hubei Wuhan China School of Energy and Power Engineering Huazhong University of Science and Technology Hubei Wuhan China

ISBN: (纸本)9798400710094

The rapid development of electronic information and communication technology has led to the widespread use of a variety of marine monitoring technologies, including aerospace remote sensors, automatic buoys, multi-beam echo-sounders, and underwater detection equipment. This has resulted in a significant increase in the volume of marine big data. This work puts forth an innovative model that is based on the most recent machine learning techniques. In consideration of the distinctive attributes of high-dimensional, nonlinear, and strongly spatiotemporally correlated marine data, the model employs an enhanced deep neural network architecture. Initially, the Variational Autoencoder (VAE) was employed for the purpose of reducing the dimensionality of the data set and extracting the most pertinent features, thus enabling the effective handling of the noise and redundant information inherent to marine data. Subsequently, the spatiotemporal dependencies in the data were mined using a spatiotemporal convolutional neural network (ST-CNN) with a self-attention mechanism, enabling high-precision prediction and classification of ocean parameters. The model has been optimised to account for the distinctive characteristics of marine data, thereby enhancing its capacity to adapt to complex marine environments. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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An Enhanced Intrusion Detection Model with FeedForward Neural Network Classifier 27

An Enhanced Intrusion Detection Model with FeedForward Neura...

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27th international conference on advanced Communications Technology, ICACT 2025

作者： Amirjon, Asadov Gain, Mrityunjoy Oh Kim, Keon Hong, Choong Seon Department of Computer Science and Engineering Kyung Hee University Yongin Korea Republic of Department of Artificial Intelligence Kyung Hee University Yongin Korea Republic of

ISBN: (纸本)9791188428137

A major obstacle in the face of increasingly complex cyberattacks is network security. Proactive security measures require effective intrusion detection systems (IDS) that can precisely classify and categorize network threats. In order to improve network attack detection and classification, this paper proposes a reliable method utilizing a Feedforward Neural Network (FFNN) supplemented with Adaptive Synthetic (ADASYN) sampling. We created a model using the UNSWNB15 dataset that efficiently handles high-dimensional datasets by preprocessing data using a combination of polynomial feature transformation and one-hot encoding. The FFNN model is optimized for binary and multi-class classification tasks. It consists of layers of dense units with dropout and batch normalization. Our method’s efficacy is proven by rigorous training and validation procedures, where the model significantly increased its ability to handle class imbalances and improve classification accuracy. The synthesis of new training data by ADASYN was crucial in improving model performance, especially in underrepresented classes. Evaluation measures that highlight the potential of deep learning in network security applications are ROC-AUC scores and classification reports, which show a notable improvement in our IDS’s detection capabilities. The results show that advanced machine learning techniques can be used to enhance conventional intrusion detection systems and provide a means to build stronger network security designs. Copyright 2025 Global IT research Institute (GIRI). All rights reserved.

关键词： Network intrusion

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Intelligent Dengue Prediction and Management: a Companied-Optimized Ensemble Approach With advanced Feature Selection and Dynamic Recommendations 2

Intelligent Dengue Prediction and Management: a Companied-Op...

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

作者： Muthuselvi, P. Dhanasekaran, S. Vasudevan, V. Kalasalingam Academy of Research and Education Deemed to be university Department of Computer Science and Engineering Tamil Nadu Krishnankoil India Kalasalingam Academy of Research and Education Deemed to be university Department of Information Technology Taminadu Krishnankoil India

ISBN: (纸本)9798331505745

Dengue fever poses a significant health challenge, necessitating efficient resource allocation. Complex internal and external factors contribute to non-linear fluctuations in dengue fever occurrences, making resource planning challenging. While machine learning (ML), as well as deep learning (DL) algorithms, are pivotal in healthcare analysis, there's a need for enhanced predictive capabilities and intelligent recommendations. This research aims to execute an advanced early-warning system for dengue fever forecasting, emphasizing improved prediction accuracy and refined recommendation strategies. In data pre-processing phase, techniques like outlier removal as well as missing data imputation have been employed to elevate data quality. To optimize feature selection, we introduce an advanced Neighbour Count-based Dragonfly Electric Fish Optimization (ANC-DEFO) algorithm. This method aims to determine most relevant features for accurate dengue fever prediction. Subsequently, selected features undergo an Optimized Ensemble Classifier (OEC), leveraging a combination of 'Convolutional Neural Network, Artificial Neural Network, and Support Vector Machine' based on their high-ranking performance. Beyond prediction, our model contributes to public health by offering tailored medical recommendations in areas where dengue fever is predicted. Recommendations span precautionary measures, immunity-boosting remedies, drug prescriptions, and strategies for minimizing disease spread. The research evaluates the model against various comparative algorithms, demonstrating its efficiency and superiority in early dengue fever diagnosis. © 2025 IEEE.

关键词： Health insurance

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