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检索条件"任意字段=8th IEEE International Conference on Software Engineering and Computer Systems, ICSECS 2023"

共 1532 条记录，以下是671-680 订阅

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Hybrid Stochastic Genetic Evolution-Based Prediction Model of Received Input Voltage for Underground Imaging Applications 8

Hybrid Stochastic Genetic Evolution-Based Prediction Model o...

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8th international conference on Business and Industrial Research, ICBIR 2023

作者： Baun, Jonah Jahara Janairo, Adrian Genevie Concepcion, Ronnie Francisco, Kate Enriquez, Mike Louie Relano, R-Jay De Leon, Joseph Aristotle Bandala, Argel Vicerra, Ryan Rhay Dungca, Jonathan De la Salle University Department of Electronics and Computer Engineering Manila Philippines De la Salle University Center for Engineering and Sustainability Development Research Manila Philippines De la Salle University Department of Manufacturing Engineering and Management Manila Philippines De la Salle University Department of Civil Engineering Manila Philippines

ISBN: (纸本)9798350399646

the capacitive resistivity technique in underground object detection comprises configured transmitter and receiver antennas that are capacitively coupled to the ground. However, underground imaging lacks a basis for determining the received voltage for precise data analysis. this study aimed to develop a prediction model of the received input voltage signal amplitude from the ground of a single-pair antenna underground imaging system. the receiver antenna circuit for this application is designed and simulated in Proteus software. Genetic Programming (GP) is applied to predict the received input signal based on the shape of the received waveform signal, operating frequency, resistance of the waveform shaping circuit, and buffer amplifier output signal. the resulting fitness function of GP (4) is acceptable as it scored an R2 of 99.38% with a negligible MSE of 0.0059 and an MAE of 12.3423. then, the GP fitness function is optimized through Genetic Algorithm (GA), Differential Evolution (DE), and Evolutionary Strategy (ES) in which the GP-GA model outperformed the two hybrid models providing fast convergence and 2.49e-8 best fitness value. this study proved that GP can be effectively combined with stochastic genetic evolution algorithms to avoid lengthy mathematical calculations and accurately estimate the natural voltage received from the ground. © 2023 ieee.

关键词： Genetic programming

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Knowledge Aware Embedding for Distantly Supervised Relation Extraction 8

Knowledge Aware Embedding for Distantly Supervised Relation ...

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8th international conference on computer and Communication systems, ICCCS 2023

作者： Zhang, Runmei Liu, Jingjing Li, Lulu Yin, Lei Xu, Weiyi Cao, Weiwei School of Mechanical and Electrical Engineering Anhui Jianzhu University Hefei China School of Electronic and Information Engineering Anhui Jianzhu University Hefei China School of Flight Technology Civil Aviation Flight University of China Sichuan Guanghan China

ISBN: (纸本)9781665456128

Relation extraction intrinsically suffers from noisy labels due to the strong assumption of distant supervision. Most prior works adopt a selective attention mechanism over sentences in a bag to denoise from wrongly labeled data, which however could be incompetent when there is only one sentence in a bag. In this paper, we propose a distantly supervised relation extraction method based on knowledge aware embedding to address the distantly supervised relation extraction problem and alleviate the defects in previous selective attention framework, which uses entity type and relation alias information in the knowledge base to improve relation extraction. Firstly, use an entity-aware word embedding method is used to fuse the entity type information to highlight the essence of entities for this task, and then capturing rich contextual dependencies with bidirectional gated recurrent units, for noisy data, improved intrabag attention using relational alias information to generate match relation embedding, while using interbag attention aggregation bag representation for final relationship classification. the model achieves the F1 score of 57.2 on the NYT dataset, which is 1.7 higher than the state-of-the-art baselines. the experimental results verified the effectiveness of the model in improving the accuracy of relation extraction. © 2023 ieee.

关键词： Embeddings

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Weak Network Oriented Mobile Distributed Storage: A Hybrid Fault-Tolerance Scheme Based on Potential Replicas 24

Weak Network Oriented Mobile Distributed Storage: A Hybrid F...

引用

24th ieee international conference on High Performance Computing and Communications, 8th ieee international conference on Data Science and systems, 20th ieee international conference on Smart City and 8th ieee international conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Wei, Xinlei Tan, Yujuan Liu, Duo Wu, Daitao Wu, Yu Chen, Xianzhang Li, Jian College of Computer Science Chongqing University Chongqing China School of Big Data and Software Engineering Chongqing University Chongqing China School of Electrical Engineering Chongqing University Chongqing China

ISBN: (纸本)9798350319934

Node failure is one of the most typical issues in distributed storage systems. the classic fault-tolerance methods can meet the fault tolerance needs of systems deployed in edge storage, 5G IoT, and other high-performance data centers. However, mobile distributed systems contend with inconsistent network signals and relatively low bandwidth in weak network environments. the traditional fault-tolerance schemes have difficulty ensuring distributed storage and data repair simultaneously, posing a considerable challenge to data storage reliability. To address this problem, we proposed a hybrid fault-tolerance scheme based on potential replicas, called HFPR, which trades off storage overhead and performance for weak network mobile distributed storage systems. HFPR first stores data with the erasure code redundancy and then gradually increases data redundancy by reserving potential replicas, enabling the system to disperse network transmission pressure and reduce network transmission. To maximize replica utilization and control the storage overhead of potential replicas, we design a node state prediction mechanism and a file lifecycle management mechanism. Evaluation results show that, compared with the traditional hybrid fault-tolerant scheme with the same storage overhead, HFPR achieves better replica-level repair efficiency in data repair and a reduced probability of degraded reads by approximately 50%. During data distribution, HFPR can minimize data write amplification by up to 1.1× and additional network transfers by up to 77.5%. © 2022 ieee.

关键词： Fault tolerance

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Rank Attack (RA) Detection in RPL Protocol based on Network Characteristics 8

Rank Attack (RA) Detection in RPL Protocol based on Network ...

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8th international conference on Communication and Electronics systems, ICCES 2023

作者： Chaitanya, Kosaraju Yasaswini, K. Kumari, Nandini Rajeswari, Ch. Kavya, E. Vignan's Nirula Institute of Technology and Science for Women Department of Computer Sc. and Engineering Guntur India

ISBN: (纸本)9798350396638

In this technological world, Internet of things (IoT) dominates other technologies. IoT security concerns have rapidly increased in recent years. In IoT network, these security challenges are unavoidable. Attacks specifically targeting the IoT network layer take advantage of the entire IoT system. the protocol that was designed for low power and lossy networks in IoT provides a minimal level of defense against various attack types. A rank attack be the extremely common attack that significantly damages an IoT network employing RPL. By tricking neighbours into routing packets via them, malicious nodes can create false routing by using this rank value. the effects of this barrage are much more damaging. throughput is decreased, and communication delays are increased. there is a need to solve rank attack right away because IoT devices are resource-constrained. By developing effective methods for identifying and mitigating RA, the effects of these attacks could be lessened. To identify rank attacks, the rank and the level of the nodes are examined to determine if the rank matches the level. When compared to the current system, Sink Based Intrusion Detection System (SBIDS), the suggested model provides more accuracy. LEACE is put forth in this research to recognize and counteract rank attacks based on the network node levels. the Cooja Simulator is a tool designed to identify the Rank attack. © 2023 ieee.

关键词： Intrusion detection

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Decentralized Blockchain based Online Voting System with Biometric Authentication 8

Decentralized Blockchain based Online Voting System with Bio...

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8th international conference on Communication and Electronics systems, ICCES 2023

作者： Waniya, Joel Sanjeev Palmer, Matthew Kathrine, G Jaspher W. Basil Xavier, S. Aarthi, Shilpa Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350396638

Online voting system using blockchain with integration of biometric authentication have emerged as a promising solution to address the challenges of online voting such as preventing fraud and maintaining the integrity of the voting results. Blockchain technology provides a high level of security and transparency, making it difficult for hackers to manipulate or alter the voting results. Biometric authentication, on the other hand, ensures that only eligible voters can cast their votes, preventing double voting and other forms of fraud. Despite the potential benefits of using blockchain technology and biometric authentication for online voting, there are still challenges that need to be addressed. these challenges include ensuring that the system is accessible and user-friendly to all voters, protecting against cyberattacks, ensuring compliance with legal and regulatory requirements, and maintaining voter privacy. the objective of this proposed online voting system using blockchain with biometric authentication is to create a secure and reliable voting system that is accessible and user-friendly to all voters, while maintaining the privacy and integrity of the voting process. the system will use blockchain technology to create a tamper-proof and auditable record of the votes, and biometric authentication to ensure that only eligible voters can cast their votes. the system will also incorporate additional security measures such as encryption and multifactor authentication to protect against cyberattacks and ensure compliance with legal and regulatory requirements. By addressing these challenges, this proposed online voting system using blockchain with biometric authentication has the potential to increase voter confidence and trust in the election process. © 2023 ieee.

关键词： Cryptography

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A Two-Sided Stable Matching Method in Ridesharing 8

A Two-Sided Stable Matching Method in Ridesharing

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8th ieee international conference on Cloud Computing and Intelligence systems, CCIS 2022

作者： Lv, Jingwei Hao, Jiannan Yao, Shuzhen Tan, Huobin Beihang University School of Computer Science and Engineering Beijing100191 China Beihang University School of Physics Beijing100191 China Beihang University School of Software Beijing100191 China

ISBN: (纸本)9781665477352

Many researchers have studied optimization methods for ridesharing. However, the individual interests of passengers and drivers are not considered enough. So we propose a two-sided stable matching method according to the actual preferences of passengers(requesters) and drivers(workers). We also design a pruning algorithm based on Euclidean distance to speed up the matching process. Experiments based on real data show that our method can perform well. © 2022 ieee.

关键词： Dynamic Ridesharing Pruning Single-worker multiple-requesters Stable Matching

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Identification of Building Damages using Satellite Images based on CNN- Recurrent Neural Network Approach 8

Identification of Building Damages using Satellite Images ba...

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8th international conference on Communication and Electronics systems, ICCES 2023

作者： Chavhan, Ganesh K. Singh, Sumeshwar Sahebrao Doke, Ankush Pilli, Daniel Kailasavalli, S. Vyankatesh Ghamande, Manasi K. J. Somaiya College of Arts Commerce and Science Department of Geography Ahmednagar Maharashtra Kopargaon India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India Mahatma Phule Nutan Maha Vidyalaya Department of Geography Maharashtra Mirajgaon India Klbs Koneru Lakshmaiah Educational Foundation Mba Department Andhra Pradesh Vijayawada India Psna College of Engineering and Technology Mathematics Tamilnadu Dindigul India Vishwakarma Institute of Information Technology Engineering and Applied Science Maharashtra Pune India

ISBN: (纸本)9798350396638

Recently, numerous disaster-related studies have integrated Remote-Sensing (RS) technology alongside picture processing and conventional methods. One example is identifying earthquake damage to buildings in orthophotos. Common approaches to creating damage maps from RS pictures include automatic and visual methods. However, because of the need for manual sampling, the visual method is laborious. By extracting the defect features, the automatic technique can identify the damaged structure. Challenges to the widespread use of automatic methods are presented by the great variety of design approaches and the dynamic nature of real-world situations, such as variations in shadow and light. To better train neural networks with less interference from measurement noise, principal component analysis (PCA) is used. After PCA is applied to the DI values, an ensemble of neural networks is utilized to provide predictions about the location and severity of damage. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are two widely used methods in machine learning, and the CRNN approach combines the two (RNNs). As compared to the CNN and RNN models, the proposed technique performs admirably. © 2023 ieee.

关键词： Principal component analysis

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Enhancing IoT Security with Federated Deep Learning Techniques 8

Enhancing IoT Security with Federated Deep Learning Techniqu...

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8th international conference on Communication and Electronics systems, ICCES 2023

作者： Muhaimeen, A. Aadithiyaprasana, K. Ranjith, A. Sasirekha, S.P. Reshma, R. Mekala, N. Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350396638

In addition to the most recent advances in machine learning-based IoT security techniques, Machine learning algorithms can learn from data and adapt to new security risks, allowing IoT systems to defend against unanticipated cyber-attacks. ML-based security solutions are flexible and dynamically updateable. this is critical in dealing with emerging IoT based security threats. In IoT security, there are two types of ML: supervised learning and unsupervised learning. To detect anomalies and categorize security risks, the supervised learning technique employs various algorithms such as decision trees, support vector machines, and neural networks. On the other hand, the unsupervised learning approach includes clustering techniques, such as k-means clustering and hierarchical clustering to detect anomalies and identify unknown security threats. In addition, reinforcement learning (RL) has been employed to enhance the security of IoT systems by training the system to identify and respond to cyber-attacks in real time. Additionally, these solutions can provide real-time security monitoring and incident response, allowing for quicker and more effective mitigation of cyber-physical security risks in IoT systems. Henceforth ML-based security solutions have shown promise in addressing the cyber-physical security challenges that arise in IoT systems. these solutions can learn from data, adapt to new and evolving threats, and provide real-time security monitoring and incident response. As the number of connected devices in IoT systems continues to grow, it is essential to adopt modern and innovative security solutions to ensure the safety and privacy of IoT users. © 2023 ieee.

关键词： Deep learning

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Intelligent System with the IoT: A survey on techniques of Artificial Intelligence over the field of Internet of things 8

Intelligent System with the IoT: A survey on techniques of A...

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8th international conference on Advanced Computing and Communication systems, ICACCS 2022

作者： Willson Joseph, C. Willsie Kathrine, G. Jaspher Karunya Institute of Technology and Sciences India Sahrdaya College of Engineering and Technology Dept. Computer Science and Engineering India Karunya Institute of Technology and Sciences Dept. Computer Science and Engineering India

ISBN: (纸本)9781665408165

Internet of things (IoT) is a network of various devices connected via the Internet, and they are capable of collecting and trading information with one another. these IoT devices create a lot of information that should be collected and mined for worthy outcomes by utilizing Artificial Intelligence (AI) to manage tremendous information flow and storage in the IoT network. In this paper, we quickly examined what IoT and AI are, challenges in AI with IoT and applications of the artificial system in the field of the IoT. this paper illustrates the methods of intelligence, to optimize the Internet of things domain and summarize the various securities mechanisms for IoT networks. the automation network and software characterized network are portions of the significant boundaries in the Artificial Intelligence IoT System. the hereafter IoT will surely experience a profound reasonable business and social impacts on our survival. Artificial Intelligence (Machine Learning & Deep Learning) methods that can embed knowledge in the IoT gadgets and networks can be utilized to adapt to various security issues. © 2022 ieee.

关键词： Internet of things

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Accurate Classroom Person Detection Based on Multi-Frame Feature Fusion with Attention 8

Accurate Classroom Person Detection Based on Multi-Frame Fea...

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8th international conference on Image, Vision and Computing, ICIVC 2023

作者： Cheng, Guoan Ren, Jiandong Li, Yuping Wang, Shengke Ding, Yougang Wang, Hao School of Information & Electrical Engineering Qingdao Harbour Vocational & Technical College Qingdao China Qingdao Harbour Vocational & Technical College Student Work Department Qingdao China College of Computer Science and Engineering Ocean University of China Qingdao China Experimental School Affiliated to Shandong University Mathematics Teaching and Research Group Heze China

ISBN: (纸本)9798350335231

In recent years, object detection technology has developed rapidly and is widely used in the field of intelligent video monitoring, especially in the classroom scenes, where incorporating the intelligent object detection technique into the video monitoring systems for intelligent monitoring purpose has important practical significance. However, surveillance videos may encounter issues such as object occlusion, motion blur, posture changes, and video defocus, which poses great challenges to object detection in the surveillance video scenarios. therefore, in this paper, we conduct in-depth research on human detection in classroom multi camera surveillance video scenes and propose a multi frame feature fusion network based on the attention mechanism. the algorithm first adds the dilated convolution layers to the ResNet50 backbone network to expand the receptive field and retain more feature information;Secondly, a multi frame feature fusion strategy based on the attention mechanism was proposed to enhance the feature representation ability of the deep detector by fusing the feature information of adjacent frames, thereby improving the accuracy of human detection in the classroom monitoring video. To verify the effectiveness of the proposed method, we have compiled and produced a large-scale classroom monitoring video dataset, named OUC-ClassVideo. the proposed object detection network, which is developed based on the attention mechanism and multi frame feature fusion strategy, has achieved satisfactory performance on both the public dataset ImageNet VID and the OUC-ClassVideo dataset collected by ourselves. © 2023 ieee.

关键词： Object detection

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