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检索条件"任意字段=4th International Multi-Conference on Computing in the Global Information Technology"

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WOCP: Controller Placement using Whale Optimization in SDN-WAN 4

WOCP: Controller Placement using Whale Optimization in SDN-W...

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4th international conference on Evolutionary computing and Mobile Sustainable Networks, ICECMSN 2024

作者： thalapala, Vidya Sagar Guravaiah, Koppala Department of Computer Science & Engineering Indian Institute of Information Technology Kerala Kottayam686635 India

the established SDN multi-controller architecture faces the challenge of locating controllers to boost the scalability, reliability, and security of the network. Reliability is one of the important metric to handle when a failure event occurs in a network. In this paper, a Whale Optimization technique is used for controller placement and proposed Whale Optimization based Controller Placement (WOCP), that places the controllers in an optimized and efficient manner. the adequate placement of controllers minimizes the overall average latency with a high reliability index. the WOCP method resemblances with Minimizing Latency for Controller Placement (MLCP), resulting in an 18% reduction in latency in the Geant network topology. © 2025 the Authors. Published by Elsevier B.V.

关键词： Software reliability

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New era of gene classification methods using machine learning 4th

New era of gene classification methods using machine learnin...

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4th international conference on Computational Methods in Science and technology, ICCMST 2024

作者： Kaur, Amanpreet Kaur, Harpreet Singh, Amandeep Punjabi University Patiala Department of Information Technology Chandigarh Engg. College-CGC India Punjabi University Patiala India Department of Mechanical Engineering BBSBEC Fatehgarh Sahib India

ISBN: (纸本)9781032911571

New diagnostic technologies like DNA microarray technology have changed illness detection in the fast-changing technological age. this work focuses DNA microarray technology in gene expression research to find and understand genetic anomalies that cause illnesses, including cancer. Gene expression analysis is critical for cancer diagnosis to uncover underlying problems and create novel treatments. the number and size of data make human analysis difficult, requiring automation. Machine learning and statistics are used for data categorization, focusing on cancer prediction. this article discusses various feature selection approaches for large data sets. the related work section discusses KNN-based, MLDA, signal-to-noise ratio, and ensemble algorithms. Sample size, computing costs, and classification accuracy are addressed by each technique. the study emphasizes feature selection and the range of machine learning algorithms. this article proposes a gene classification research, encompassing current gene categorization approaches, feature extraction methods, datasets, result analysis, and conclusion. the article seeks to improve gene expression analysis for illness diagnosis and therapy. © 2025 the Author(s).

关键词： Microarrays

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Smart Irrigation Systems: An Overview of Current Trends and Technologies 4

Smart Irrigation Systems: An Overview of Current Trends and ...

引用

4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Habib, Rawshan Al-Amin Alrashed, Abdelrhman Mouly, Fariha Jahan Avi, Shuva Dasgupta School of Engineering and Information Technology Murdoch University Murdoch Australia Ahsanullah University of Science & Technology Department of Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798331523923

Nowadays, irrigation accounts for almost 70% of global water usage. therefore, it is essential to design irrigation systems in such a way that it can reduce water usage as well as the expense. Making eco-friendly irrigation systems is another challenge for researchers. To overcome these problems, a variety of appliances and systems combining state-of-the-art software and hardware features have been developed. Yet, research on smart irrigation systems is ongoing. To have a comprehensive understanding of the prospective and continuing instance, this study presents an overview of recent advancements in smart irrigation systems. A variety of irrigation system designs are addressed, including solar-powered, Wireless Sensor Network (WSN)-based, and Internet of things (IoT)-based systems. the authors firmly expect that this research will significantly influence the most recent developments in smart irrigation systems. © 2025 IEEE.

关键词： Smart agriculture

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An efficient formal verification method for concurrent programs 4

An efficient formal verification method for concurrent progr...

引用

4th international conference on Network Communication and information Security, ICNCIS 2024

作者： Zhang, Jianyu Zhang, Long Zhang, Jingjing Yang, Feng University of Electronic Science and Technology of China 2006 Xiyuan Avenue Western District High-Tech Zone Chengdu611731 China National Key Laboratory of Science and Technology on Information System Security No. 13 Dachen Road Fengtai District Beijing100141 China

ISBN: (数字)9781510688261

ISBN: (纸本)9781510688254

Formal methods (FM) are a pivotal technology for ensuring the safety and reliability of software systems. these methods are centered around the application of precise mathematical principles to verify the security and dependability of software. Formal methods have been widely employed in the verification of critical systems, with the increasing significance of concurrent programs in the context of the growing prevalence of parallel computing architectures like multi-core processors. However, the efficiency of formal methods in verifying concurrent programs is often low, which hinders their broader adoption and application. Addressing this issue, this study proposes an efficient formal verification method tailored for concurrent programs. this method bifurcates the verification process of concurrent programs into two stages: proving sequential correctness and verifying other attributes. In the first stage, leveraging theorem-proving strategies and large language model (LLM) technology, the method assists in proving the sequential correctness of concurrent programs. the second stage, based on model checking technology, involves transforming the program to narrow down the verification scope before using model checking tools to verify other security attributes of the program. Experimental results have shown that the proposed method significantly reduces the state space that needs to be searched during verification, thereby enhancing the efficiency of concurrent program verification. © 2025 SPIE.

关键词： Model checking

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Application of Intelligent technology in Electronic information Engineering with multi-information Algorithm Fusion 14th

Application of Intelligent Technology in Electronic Informat...

引用

14th international conference on Frontier computing, FC 2024

作者： Yang, Luyuan Wang, Zhouxing School of Electronic Engineering Xidian University Shaanxi Xi’an710000 China School of Microelectronics Xidian University Shaanxi Xi’an710000 China

ISBN: (纸本)9789819623907

the intelligent technology of multi-information algorithm fusion can combine multiple information sources to improve the accuracy and efficiency of data processing and analysis. this research adopts comprehensive research methods, including literature review, experimental design and data analysis. First of all, the research status of intelligent technology integrated with multi-information algorithms in the field of electronic information engineering is comprehensively reviewed and analyzed in the literature. then, based on the literature information obtained, the experiment was designed and relevant data was collected. the performance and effect of intelligent technology integrated with multi-information algorithm in electronic information engineering are evaluated through data analysis and experimental results. the results show that the resource utilization rate of this method is 18%–37%. the intelligent technology of multi-information algorithm fusion has a broad prospect and excellent application value. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data accuracy

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Hybridization of DNA computing and Chaotic System for Ensuring the Randomness and Integrity of Image Data 4

Hybridization of DNA Computing and Chaotic System for Ensuri...

引用

4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Dhakal, Madhav Shakya, Subarna Tribhuvan University Central Department of Computer Sci. & Information Technology Kathmandu Nepal Pulchowk Campus Tribhuvan University Department of Electronics and Computer Engineering Institute of Engineering Kathmandu Nepal

ISBN: (纸本)9798331523923

In this paper, the concept of DNA computing and 3-D chaotic system is implemented to maintain the security of grayscale image while transmitting through the publicly available network. Here, random selection technique for sequencing of DNA bases with secret key generated through chaotic sequence encryption technique is purposed. Generation of permutation box for diffusion of image pixels carried out on the basis of chaotic system, which create more randomness and difficult to retrieve the secret key and maintain confidentiality from the intruder. System parameter and initial values of chaotic system work as the private key. In this proposed concept, first, plain image is divided in to various block of 512 bit size and padding is necessary if the last block does not contain the block of 512 sizes and then data obtained from the blocks are XOR with the key value obtained through chaotic system. After that, results obtained from this operation are transformed in to the cipher image with using the concept of Central Dogma of Molecular Biology. Experimental result was thoroughly examined using various techniques including histogram analysis, chi-square test, information entropy assessment, differential attack and the result shows that the proposed concept is secure and maintain the randomness, confidentiality and integrity of image data. © 2025 IEEE.

关键词： Gene encoding

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Enhanced Quantum Key Distribution Protocol for Secure Smart Grid Communication using Quantum Gates 4

Enhanced Quantum Key Distribution Protocol for Secure Smart ...

引用

4th international conference on Power, Control and computing Technologies, ICPC2T 2025

作者： Sree, Anagani Navya Lachure, Jaykumar Doriya, Rajesh Jatoth, Chandra Shekar Department of Information Technology National Institute of Technology Raipur Raipur India

ISBN: (纸本)9798331530402

this paper presents an enhanced Quantum Key Distribution (QKD) protocol to strengthen the security of smart grid systems through secure communication and signature verification. As smart grids increasingly depend on digital communication, safeguarding them against cyber threats is paramount. Our approach incorporates Hadamard, SWAP, and rotation gates to fortify the BB84 protocol, ensuring secure key exchange while verifying the integrity and authenticity of transmitted data between smart meters and grid operators. Experimental results demonstrate that the protocol provides efficient key generation, encryption, and decryption while improving eavesdropping detection. With 10 qubits, the protocol achieved a key generation rate of 957 bits and successfully encrypted messages. these findings suggest that QKD offers a robust security framework for smart grid environments, providing superior resistance to both quantum and classical attacks. © 2025 IEEE.

关键词： Cyber attacks

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Point cloud classification method based on local dynamic edge graph convolution and global information fusion 4

Point cloud classification method based on local dynamic edg...

引用

4th international conference on Computer Vision, Application, and Algorithm, CVAA 2024

作者： Du, Jie Sheng, Zhiwei Jin, Xingyu Huang, Yuanyuan Tian, Junbiao Chengdu University of Information Technology Chengdu610225 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

the development of automatic driving, unmanned aerial vehicle detection and virtual reality cannot be separated from the research of 3D point cloud. Existing studies either lack of local shape capture and local spatial feature description of point cloud, resulting in poor accuracy of point cloud classification network, or pursue fine and complex local feature extractors, sacrificing extraction time and memory overhead to extract classification progress, but the results show that the effect is not significantly improved. therefore, this paper proposes a Point cloud classification method based on Local Point Dynamic Edge Graph Convolution and global information Fusion. Among them, the local point dynamic edge graph convolution module improves the edge graph convolution, which can improve the local extraction feature effect without deepening the local feature extractor, and reduce the time and memory overhead. the global information fusion block concatenates multi-layer local features to extract and fuse feature information to improve the classification accuracy of the network. Our network is tested on the public datasets ModelNet40 dataset and ScanObjectNN dataset. the results show that compared with the current mainstream point cloud classification algorithms, the point cloud classification accuracy of the proposed classification method is improved, and it has good robustness. © 2025 SPIE.

关键词： Point clouds Feature extraction Convolution Data modeling information fusion Data conversion Education and training Transformers Deep learning Statistical modeling

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Advanced Neural Network MRI Evaluation for Automated Knee Trauma Detection 4th

Advanced Neural Network MRI Evaluation for Automated Knee Tr...

引用

4th international conference on Emerging Trends and Technologies on Intelligent Systems, ETTIS 2024

作者： Kumar, Simran Salvi, Bhavika Limbachiya, Krina Varma, Hemanth Department of Mathematics and Computing National Institute of Technology Himachal Pradesh Hamirpur India Fr Conceicao Rodrigues College of Engineering Mumbai India Malaviya National Institute of Technology Jaipur India Indian Institute of Information Technology Bhagalpur India

ISBN: (纸本)9789819757022

the paper introduces a method for automated diagnosis of knee injuries using magnetic resonance imaging (MRI) sequences. MRI examinations serve as a crucial function in detecting knee ailments, yet the procedure is lengthy and susceptible to inaccuracies. the proposed approach utilizes sophisticated deep learning methodologies, particularly convolutional neural networks (CNNs) like AlexNet and SqueezeNet, to scrutinize MRI sequences. Different techniques, including attention mechanisms, are employed to reduce sequence information and predict injuries such as ACL and meniscus tears. the study demonstrates that a multi-model ensemble, combining the strengths of various networks, surpasses solitary frameworks concerning the Area Beneath the Receiver Operating Characteristic (ROC) Curve for varied categories of harm. Future work could explore diverse feature extraction methods and more advanced ensemble techniques for better performance. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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GSEA: global Structure-Aware Graph Neural Networks for Entity Alignment 13th

GSEA: Global Structure-Aware Graph Neural Networks for Entit...

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13th international conference on Natural Language Processing and Chinese computing

作者： Wang, Cunda Wang, Weihua Liang, Qiuyu Yu, Jie Gao, Guanglai Inner Mongolia Univ Coll Comp Sci Hohhot Peoples R China Natl & Local Joint Engn Res Ctr Intelligent Infor Hohhot Peoples R China Inner Mongolia Key Lab Multilingual Artificial In Hohhot Peoples R China Natl Univ Def Technol Coll Comp Sci Changsha Peoples R China

ISBN: (纸本)9789819794331;9789819794348

Entity alignment (EA) aims to identify the same real-world entities presented in different knowledge graphs (KGs), which is the most crucial step in integrating multi-source KGs. Existing entity alignment methods based on Graph Neural Networks mainly focus on aggregating neighborhood structure information within the original graph to generate entity embeddings. However, these methods fail to effectively leverage the global structural information of entities. In this paper, we propose a novel method based on global Structure-Aware Graph Neural Networks to capture the global structure between entities. Specifically, our method learns global embeddings from the augmented graph reconstructed by Singular Value Decomposition. Moreover, we employ contrastive learning to maximize the consistency between global and local embeddings while mitigating the over-smoothing problem. Extensive experimental results on benchmark datasets, along with further analysis, demonstrate the superiority and effectiveness of our method. the source code is available at https://***/wonderCS1213/GSEA.

关键词： Knowledge graph Entity alignment Graph neural networks Contrastive learning

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