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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Mercy, Immaculate Nainwal, Ankita Balasubramani, R. Nandankar, Praful V. Parikh, Swapnil Taqui, Syed Noeman Department of Computer Applications Vallam Tamil Nadu Thanjavur613403 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Department of Information Science and Engineering Karnataka Nitte574110 India Government College of Nagpur Department of Electrical Engineering Maharashtra 441108 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Gujarat Waghodia India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331540364

Effective communication between autonomous vehicles is essential for ensuring their safe and efficient operation. This research introduces Parr-OrAGNet, a novel approach that addresses the limitations of traditional communication methods in IoT automotive networks. Parr-OrAGNet integrates Macro-Micro Depth Normalization (MaMiDN) for accurate data normalization, Orientation-Aware Graph Neural Network (OrAGNet) for enhanced neural network modeling, and Parrot Optimizer (ParrOpt) for optimization. This combined approach effectively reduces packet loss, communication overhead, and latency. The evaluation results demonstrate significant improvements in communication efficiency, with a 97% reduction in packet loss and a 95% decrease in communication overhead. Additionally, Parr-OrAGNet achieves exceptional performance metrics in terms of accuracy, precision, recall, and computational efficiency. This research contributes to the advancement of autonomous vehicle communication by establishing a new benchmark for performance and scalability. Parr-OrAGNet offers a promising solution for addressing the challenges of IoT automotive networks and paving the way for the realization of fully autonomous driving. © 2024 IEEE.

关键词： Macroinvertebrates

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Optimizing Packet Delivery in Underwater Wireless Sensor Networks: A Comparative Study of Genetic Bee Colony and Ant Colony Algorithms 9

Optimizing Packet Delivery in Underwater Wireless Sensor Net...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Kannappan, A. Kolagani, Nagesh Kandaswamy, Vijay Anand Vishnupriyan, J. Saveetha School of Engineering Simats Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Simats Department of Computer Science and Engineering Chennai India Chennai Institute of Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350377972

Physically Underwater communication adopt the bio inspired genetic bee colony formed network communication structures. Underwater environment condition Communication is established between the Transmitting node and in between several clustering relay nodes to reach receiver nodes. Transmitting nodes are placed on sea bed areas at certain depth and clustering relay nodes are placed in various underwater depth conditions dipping the sensor nodes. Also, underwater battery life is very hard and battery replacement is a very tough task. Hence, an alternate solution of optimization battery capacity utilization is used. This optimization algorithm improves Packet delivery ratio by comparing genetic bee algorithm with Ant colony algorithm. Hence, equivalence of two algorithms implemented in underwater conditions. The genetic bee colony Algorithm produces better output results more than 4 to 6 percentage compared to ant colony parameter output. The genetic bee colony algorithm offers a maximum clustering node technique compared to ant colony algorithm. Its greater support for saving energy consumption extending battery life. © 2024 IEEE.

关键词： Ant colony optimization

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Shape Organization for Enhanced Design Optimization in Generative Systems Using Clustering Technology 6

Shape Organization for Enhanced Design Optimization in Gener...

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6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Krishna, Raji Suganya, R. Yaganti, Mounika Sai Mohamed Buhari, Seyed Vellore Institute of Technology School of Electrical Engineering Chennai Campus Chennai India Vellore Institute of Technology School of Computer Science Engineering Chennai Campus Chennai India University Brunei Darussalam Department of Computer Science Gadong Brunei

ISBN: (纸本)9798350394702

The current research in design space articulation tries to solve the problem that comes up because computer generative systems are becoming more complex, and there are more design options. This study makes a contribution to the existing body of research by introducing a technique for form clustering that utilizes K-medoids, a Machine Learning approach. The suggested method uses a clustering method to group design shapes that are similar and then pick a representative body for each cluster in a 2D-Grid Based representation. The efficacy of the process has been empirically validated by applying it to a novel test case and comparing it with other clustering evaluation methods. The accuracy of the results of this evaluation is about the same as that seen in an outside study. This gives us essential information about how to judge machine learning techniques. © 2023 IEEE.

关键词： Hamming distance

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A Fast Recommendation Algorithm for Data Streams Based on Differential Privacy 11

A Fast Recommendation Algorithm for Data Streams Based on Di...

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11th International Conference on Advanced Cloud and Big Data, CBD 2023

作者： Guo, Chang Wang, Xiujun Zheng, Xiao Shao, Xun Mo, Lei Yang, Gaoming AnHui University of Technology School of Computer Science and Technology Ma'Anshan243032 China Toyohashi University of Technology Department of Electrical and Electronic Information Engineering Aichi4418580 Japan Anhui University of Science and Technology School of Computer Science and Engineering Huainan232001 China

ISBN: (纸本)9798350345346

Recommendation systems are an essential tools for people to access interesting information from vast amounts of data and have a wide range of applications. However, current recommendation algorithms encounter persistent challenges, including significant time overhead and limitations in safeguarding user privacy effectively. These challenges stem from their reliance on static clustering algorithms to compress data streams. To tackle these issues, we introduce the Fast Recommendation Algorithm based on Differential Privacy (FRDP) for data streams. FRDP leverages the growing learning vector quantization technique for clustering and compressing data streams in a single pass. Additionally, FRDP dynamically introduces noise based on clustering error to protect individuals' privacy while using differentially private clustering data for recommendation analysis. Finally, we compare the FRDP algorithm with the best existing recommendation algorithms based on two real datasets. Extensive experimental results demonstrate that, while maintaining similar recommendation accuracy under the same privacy protection constraints, the FRDP algorithm achieves substantial time savings, with an average reduction in running time ranging from 55.82% to 59.89%. © 2023 IEEE.

关键词： Recommender systems

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Biometric Fusion Approaches Based on Deep Convolutional Neural Network

Biometric Fusion Approaches Based on Deep Convolutional Neur...

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2023 Al-Sadiq International Conference on Communication and Information Technology, AICCIT 2023

作者： Hussain, Hawraa A. Abbas, Hawraa H. University of Babylon College of Sciences for Women Department of Computer Science Babel Iraq University of Kerbala College of Engineering Department of Electrical Engineering Kerbala Iraq

ISBN: (纸本)9798350341881

Spoofing attacks have increased significantly, and a lot of research is being done on biometric security systems. Researchers are more interested in multimodal biometrics to give better security using biometric applications. A complicated model structure with minimal risk of spoofing attack can be created using multimodal biometrics applications. The new multimodal human identification model presented in this paper was created by fusing the two multimodal biometrics methodologies (face, iris, and fingerprint). The Convolutional Neural Networks (CNNs) that form the system's structure extract features from the photos and use the Softmax classifier to categorize them. Five different CNN algorithms (VggNetl6, ReseNet50, MobilevNet, DenseNet, GoogleNet) are used with two fusion approaches (score and feature level). The designed automatic identification system is conducted on the SDUMLA-HMT dataset, the best-acquired accuracy results are 99.37% for score-level fusion and the accuracy is 97.55% with a feature-level fusion approach when the VggNetl6 algorithm is used. This system's accuracy results are outperforming the latest current method methods. © 2023 IEEE.

关键词： Convolutional neural networks

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A super resolution target separation and reconstruction approach for single channel sar against deceptive jamming

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Defence Technology（防务技术） 2023年第3期21卷 164-175页

作者： Shi-qi Liu Bing Li Bo Zhao Lei Huang Yue-zhou Wu Wei-min Bao Guangdong Key Laboratory of Intelligent Information Processing College of Electronic and Information EngineeringShenzhen UniversityShenzhen518060China School of Electrical Engineering Southwest Jiaotong UniversityChengdu611756China State Key Laboratory of Millimeter Waves Southeast UniversityNanjing210096China School of Computer Science Civil Aviation Flight University of ChinaGuanghan618307China China Aerospace Science and Technology Corporation Beijing100048China

The excellent remote sensing ability of synthetic aperture radar(SAR)will be misled seriously when it encounters deceptive jamming which possesses high fidelity and *** this paper,the dynamic synthetic aperture(DSA)scheme is used to extract the difference between the true and false targets.A simultaneous deceptive jamming suppression and target reconstruction method is proposed for a single channel SAR system to guarantee remote sensing *** system model is formulated as a sparse signal recovery problem with an unknown parametric dictionary to be *** iterative reweighted method is employed to jointly handle the dictionary parameter learning and target reconstruction problem in an majorization-minimization framework,where a surrogate function majorizing the Gaussian entropy in the objective function is introduced to circumvent its *** dictionary parameter learning,the grid mismatching problem in a fixed grid based method is ***,the proposed method can reap a super resolution ***,a simple yet effective DSA section scheme is developed for the SAR data excerpting,in which only two DSAs are *** results about location error and reconstruction power error reveal that the proposed method is able to achieve a good performance in deceptive jamming suppression.

关键词： channel resolution separation

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A design method of model error compensator for systems with polytopic-type uncertainty and disturbances

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SICE Journal of Control, Measurement, and System Integration 2021年第2期14卷 119-127页

作者： Yoshida, Ryuichiro Tanigawa, Yuki Okajima, Hiroshi Matsunaga, Nobutomo Department of Computer Science and Electrical Engineering Kumamoto University Kumamoto Japan

Control systems achieve the desired performance with the model-based controller if the dynamical model of the actual plant is given with sufficient accuracy. However, if there exists a difference between the actual plant and its model dynamics, the model-based controller does not work well and does not achieve the intended desired performance. A model error compensator (MEC) is proposed for overcoming the model error in our previous study. Attaching the compensator for the model error to the actual plant, the output trajectory of the actual plant is made close to that of its model. Then, from the controller, the apparent difference in the dynamics can be smaller, and performance degradation is drastically reduced. MEC is useful for various control systems such as non-linear systems and the control systems with delay, and so on. In this paper, we propose an original design method of the filter parameters in MEC for systems with polytopic-type uncertainty and disturbances. First, we show an analysis method about the robust performance of MEC for the system with the polytopic type uncertainty based on an linear matrix inequality problem. The gain parameters in MEC is designed using particle swarm optimization and the presented analysis method. The effectiveness of the design method for the system with polytopic-type uncertainty and disturbance is evaluated using numerical examples. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Linear matrix inequalities

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Assessment of processing time and measurement accuracy of different phase compensation methods in Quantitative Phase Imaging via Digital Holographic Microscopy applied to Biological Specimens Holographic Microscopy

Assessment of processing time and measurement accuracy of di...

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3D Image Acquisition and Display: Technology, Perception and Applications, 3D 2024 - Part of Optica Imaging Congress

作者： Obando-Vásquez, Sofía Doblas, Ana Trujillo, Carlos Applied optics group School of Applied Science and Engineering Universidad EAFIT Medellín Colombia Department of Electrical and Computer Engineering University of Massachusetts DartmouthMA United States

Six different methods for phase compensation in Digital Holographic Microscopy are compared using a calibrated test target and a Toxocara canis larva sample regarding processing time, measurement accuracy, and usefuln...

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Random Selection for Cloud-Based Load-Distributing in Varied P2P Networks using Enhanced Throttled Algorithm 5

Random Selection for Cloud-Based Load-Distributing in Varied...

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5th IEEE International Conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Prakash, V.S. Mary, Margaret T. Shivakumar, B.N. Gnanadurai, Jasmine Divya, K.S. Vennila, J. Kristu Jayanti College Department of Computer Science Karnataka Bangalore India Cmr Institute of Technology Department of Mathematics Karnataka Bengaluru India George Fox University Dept. Electrical and Computer Science Engineering OR United States Manipal College of Health Professions Mahe Department of Statistics Manipal India

ISBN: (纸本)9798350379884

A peer-to-peer network connection is essential to most computer users' daily activities. On heterogeneous P2P networks, maintaining cloud-based load balance is a difficult goal to achieve. Several previous research have provided load rearrangement procedures for additional load diversions, and load division on the point where the original peer arrives. The goal of cloud-based load-sharing in P2P structures is to evenly distribute the burden among net nodes, then it is not effective in efficiently removing traffic that is already happening on the P2P network's routes. By removing the traffic flow and consuming the Random Selection Load Balancing (RSLB) method and the Incredible Store Model, the behaviors in this study are characterized. Using an improved throttled method, the Incredible Store Model is examined to broaden the effects of node variety in addition to agitation toward the capacity circulation in P2P systems. Now different from the current load distribution balancing strategy, the presentation of the Random Selection Load Balancing (RSLB) technique by the Incredible Store Model decreases the network traffic flow pathways with little cloud rate of P2P. By combining the burden in the P2P net system, an investigative and observed conclusion delivers a destruction-free system with information calculated. Peer failure probability in addition to traffic control effectiveness is used to estimate the presentation of the random selection load assessment. © 2024 IEEE.

关键词： Peer to peer networks

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Cyber Attacks Detection using Deep Learning Methods for Resilient Operation in DC Shipboard Microgrids

Cyber Attacks Detection using Deep Learning Methods for Resi...

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2024 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2024

作者： Ali, Zulfiqar Hussain, Tahir Su, Chun-Lien Jurcut, Anca Delia Baloch, Shazia Sadiq, Muhammad National Kaohsiung University of Science and Technology Department of Electrical Engineering Kaohsiung city Taiwan Graduate School of Informatics and Engineering The University of Electro-Communication Department of Informatics Tokyo Japan School of Computer Science University College Dublin BelfieldDublin 4 Ireland Balochistan University of Engineering and Technology Department of Electrical Engineering Khuzdar Pakistan

ISBN: (纸本)9798350352290

Cyber resilience has become paramount as a transition of maritime systems towards digitization, particularly within DC shipboard microgrids (SMGs). Adopting innovative communication technologies can enhance the resilience of SMGs for stable operation. However, challenges like false data injection and Man-in-The-Middle attacks pose significant threats to SMG operations when integrating these technologies into ship intelligent grids. In this regard, this paper proposes a reliable deep learning (DL) method, especially an Artificial neural network (ANN) with a deep encoder-decoder architecture, for the detection of cyber-intrusions and mitigating their effects, ensuring system control and stability for resilient operation of SMG. Detecting malicious data intrusions is crucial for maintaining optimal grid conditions and preventing disruptions in load dispatch. The proposed method utilizes a fusion of current and voltage data features for comprehensive DL model training, resulting in an adequate level of detection accuracy and providing cybersecurity analysis for SMGs, addressing component-level attacks, and devising defense strategies from aspects of detection, mitigation, and prevention. Furthermore, the deep ANN is fine-tuned with optimal hyperparameters to effectively counter cyberattacks, achieving an enhanced accuracy rate of 97.51% and minimal loss of 0.101%, surpassing conventional machine learning approaches. Rigorous test scenarios are performed to validate the robustness of the proposed method, emphasizing the cyber resilience of DC SMGs for enhanced security and operational integrity. © 2024 IEEE.

关键词： Network intrusion

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