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International Conference on Computational and Characterization Techniques in engineering & Sciences (CCTES)

作者： Pabbisetty Venkata Sai Varshith V Parthipan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Big Data and Network Security Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

The purpose of this study is to determine whether the Catboost algorithm is superior to the Extreme Gradient Boosting Classifier (XGBoost) method in terms of its ability to generate predictions about the sorts of clothing based on evaluations from online retailers. This study contains 2 groups: Novel Catboost algorithm and Extreme Gradient Boosting Classifier, where each group is iterated for a sample size of 10 calculated using the ClinCalc software with 0.05 as the alpha value, and a CI of 95%. The e-commerce reviews dataset with a size of 23485 was considered from ***. The novel catboost algorithm accuracy is 92%, which appears to be superior to the XGBoost classifier's accuracy of 89%. It can be concluded that there exists a statistically significant distinction between the CatBoost algorithm and the XGBoost classifier algorithm. This is evidenced by a value of p=0.007 (Independent sample t-test p<0.05) in the SPSS statistical analysis prediction of clothing types based on e-commerce reviews.. Conclusion: In analysing various clothes types by E-commerce reviews the CatBoost algorithm results show better than the Extreme Gradient Boosting Classifier.

关键词： Accuracy Reviews Statistical analysis Software algorithms Clothing Prediction algorithms Boosting Software Classification algorithms Electronic commerce

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A Submodular Energy Function Approach to Controlled Islanding with Provable Stability

A Submodular Energy Function Approach to Controlled Islandin...

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IEEE Conference on Decision and Control

作者： Shiyu Cheng Luyao Niu Andrew Clark Radha Poovendran Department of Electrical and Systems Engineering Washington University in St. Louis St. Louis MO USA Department of Electrical and Computer Engineering Network Security Lab University of Washington Seattle WA

Cascading failures occur when failures of one or more nodes in a network lead to failures in neighboring nodes that propagate through the remainder of the network. One approach to mitigate cascading failures is through controlled islanding, in which a subset of edges is deliberately removed in order to partition the network into disjoint and stable islands. In this paper, we propose a submodular optimization algorithm for selecting edges to remove in order to create islands with provable stability. In contrast to existing approaches that optimize over stability-related metrics such as network coherence, our approach maps standard Lyapunov stability conditions to the objective function of an optimization problem. We prove that this optimization problem is equivalent to minimizing a supermodular function subject to a matroid basis constraint. We propose a local search algorithm for selecting the islands with provable optimality bounds, and discuss special cases including signed linear consensus and nonlinear synchronization dynamics. We simulate our approach using linear consensus dynamics with negative edges and find that our proposed algorithms partition the network into a stable island and an unstable island.

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Performance Analysis of Predicting LIC Stock Price using Lasso Regression Compared with Random Forest Regression

Performance Analysis of Predicting LIC Stock Price using Las...

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International Conference on Computational and Characterization Techniques in engineering & Sciences (CCTES)

作者： Chennupati Charan Kumar V Parthipan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Big Data and Network Security Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

With regard to the objective of the entire study, the goal is to improve the accuracy of predicting stock price using Lasso Regression in comparison to Random Forest Regression. The Novel K-Nearest Neighbors and Support Vector Machine Algorithm are the two groups that are the emphasis of this research activity. Each of these groups is carried out ten times. For the purpose of calculating samples, the Clincal software was utilized. The alpha value was determined to be 0.05, and the confidence interval was set at 95%. We took into consideration the stock price prediction dataset that was available on *** and had a size of 12617. With a value of p=0.000 (Independent sample t-test p<0.05), the SPSS statistical analysis prediction of stock price demonstrates that there is a statistically significant difference between the Lasso regression algorithm and Random Forest Regression. This data indicates that the Lasso regression algorithm is superior to Random Forest Regression. For the Random Forest regression technique it obtained a percentage accuracy of 84%, while in the new improved Lasso regression approach it records a greater level of accuracy of 93%. The comparison of Random Forest Regressions and Lasso Regressions in the progress of predicting the stock price analysis shows that the latter was substantially more effective. This was revealed in the accomplishment of the current research development project.

关键词： Support vector machines Accuracy Statistical analysis Software algorithms Prediction algorithms Software Performance analysis Stock markets Random forests Overfitting

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CPIMAC: MEC-assisted Message Dissemination Based on Traffic Prediction at Intersections

CPIMAC: MEC-assisted Message Dissemination Based on Traffic ...

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Smart World Congress (SWC), IEEE

作者： Bingyi Liu Xun Shao Haoxiang Zhao Enshu Wang Zhi Liu Sanya Science and Education Innovation Park Wuhan University of Technology Sanya China Department of Electrical and Electronic Information Engineering Toyohashi University of Technology Aichi Japan School of Cyber Science and Engineering Wuhan University Wuhan China Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

The Internet of Vehicles (IoV) is gaining increasing attention for its ability to improve traffic safety and efficiency by providing situational awareness and detecting potential hazards beyond the visual range. Time division multiple access (TDMA) based communication protocol effectively prevents transmission collisions in IoV. However, road intersections pose challenges for the implementation of TDMA-based MAC protocols, as vehicles converge from different directions, leading to severe communication access and merging collisions. To address this issue, we propose a distributed MAC protocol named slot collisions prediction based MAC at intersection (CPIMAC). Specifically, We combine mobile edge computing (MEC) with a CNN-BiLSTM-Attention neural network model to predict potential transmission collisions and propose corresponding collision avoidance algorithms. In addition, we design the head-tail beacon (HTB) algorithm to reduce slot collisions during vehicle waiting periods at intersections by adjusting beacon frequency. Finally, we conduct extensive experiments to evaluate the proposed protocol. The results show that transmission collisions can be reduced by $58.1 \%$ compared to the existing protocols.

关键词： Time division multiple access Machine learning algorithms Protocols Multi-access edge computing Roads Merging Media Access Control Machine learning Prediction algorithms Collision avoidance

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Performance Analysis for Prediction of Trending Footwear Classification using DensNet compared with Logistic Regression model

Performance Analysis for Prediction of Trending Footwear Cla...

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International Conference on Computational and Characterization Techniques in engineering & Sciences (CCTES)

作者： E. V. Giridhar V Parthipan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Big Data and Network Security Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

The purpose of this work is to investigate the effectiveness of DensNet and Logistic Regression in terms of accurately predicting the classification of footwear *** this study, there are two groups: the Novel DensNet model and the Logistic Regression model. Each group is being iterated for a sample size of ten, which is being generated with the ClinCalc software. The alpha value is set at 0.05, and the confidence interval is set at 95%. We took into consideration the footwear prediction dataset that was available on *** and had a size of 15854. The statistical analysis reveals a significant difference between the DensNet algorithm and the Logistic Regression model, with a value of p=0.001 (independent sample t-test p<0.05). The DensNet algorithm exhibits a significantly higher accuracy rate of 94.56% when compared to the Logistic Regression model, which records an accuracy rate of 90.62%.In comparison to the Logistic Regression model, the DensNet algorithm has a higher level of accuracy when it comes to predicting the Novel Footwear Trending situation.

关键词： Logistic regression Analytical models Accuracy Machine learning algorithms Statistical analysis Footwear Predictive models Prediction algorithms Market research Classification algorithms

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Resonant Beam Communications: A New Design Paradigm and Challenges

arXiv

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arXiv 2024年

作者： Tian, Yuanming Li, Dongxu Huang, Chuan Liu, Qingwen Zhou, Shengli The Future Network of Intelligence Institute The School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China The School of Science and Engineering The Future Network of Intelligence Institute The Chinese University of Hong Kong Shenzhen518172 China The College of Electronics and Information Engineering Tongji University Shanghai201804 China The Department of Electrical and Computer Engineering University of Connecticut StorrsCT06250 United States

Resonant beam communications (RBCom), which adopt oscillating photons between two separate retroreflectors for information transmission, exhibit potential advantages over other types of wireless optical communications (WOC). However, echo interference generated by the modulated beam reflected from the receiver affects the transmission of the desired information. To tackle this challenge, a synchronization-based point-to-point RBCom system is proposed to eliminate the echo interference, and the design for the transmitter and receiver is discussed. Subsequently, the performance of the proposed RBCom is evaluated and compared with that of visible light communications (VLC) and free space optical communications (FOC). Finally, future research directions are outlined and several implementation challenges of RBCom systems are highlighted. Copyright © 2024, The Authors. All rights reserved.

关键词： Visible light communication

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Design and Performance of Resonant Beam Communications—Part II: Mobile Scenario

arXiv

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arXiv 2024年

作者： Li, Dongxu Tian, Yuanming Huang, Chuan Liu, Qingwen Zhou, Shengli The Future Network of Intelligence Institute The School of Science and Engineering the Chinese University of Hong Kong Shenzhen518172 China The School of Science and Engineering The Future Network of Intelligence Institute the Chinese University of Hong Kong Shenzhen518172 China The College of Electronics and Information Engineering Tongji University Shanghai201804 China The Department of Electrical and Computer Engineering University of Connecticut StorrsCT06250 United States

This two-part paper focuses on the system design and performance analysis for a point-to-point resonant beam communication (RBCom) system under both the quasi-static and mobile scenarios. Part I of this paper proposes a synchronization-based information transmission scheme and derives the capacity upper and lower bounds for the quasi-static channel case. In Part II, we address the mobile scenario, where the receiver is in relative motion to the transmitter, and derive a mobile RBCom channel model that jointly considers the Doppler effect, channel variation, and echo interference. With the obtained channel model, we prove that the channel gain of the mobile RBCom decreases as the number of transmitted frames increases, and thus show that the considered mobile RBCom terminates after the transmitter sends a certain number of frames without frequency compensation. By deriving an upper bound on the number of successfully transmitted frames, we formulate the throughput maximization problem for the considered mobile RBCom system, and solve it via a sequential parametric convex approximation (SPCA) method. Finally, simulation results validate the analysis of our proposed method in some typical scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Transmitters

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A news image captioning approach based on multimodal pointer-generator network

A news image captioning approach based on multimodal pointer...

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作者： Chen, Jingqiang Zhuge, Hai School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science and Network Engineering Guangzhou University Guangzhou China Department of Computer Science School of Engineering and Applied Science Aston University Birmingham United Kingdom Key Laboratory of Intelligent Information Processing Institute of Computing Technology University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing China

News image captioning aims to generate captions or descriptions for news images automatically, serving as draft captions for creating news image captions manually. News image captions are different from generic captions as news image captions contain more detailed information such as entity names and events. Therefore, both images on news and the accompanying text are the source of generating caption of news image. Pointer-generator network is a neural method defined for text summarization. This article proposes the Multimodal pointer-generation network by incorporating visual information into the original network for news image captioning. The multimodal attention mechanism is proposed by splitting attention into visual attention paid to the image and textual attention paid to the text. The multimodal pointer mechanism is proposed by using both textual attention and visual attention to compute pointer distributions, where visual attention is first transformed into textual attention via the word-image relationships. The multimodal coverage mechanism is defined to reduce repetitions of attentions or repetitions of pointer distributions. Experiments on the DailyMail test dataset and the out-of-domain BBC test dataset show that the proposed model outperforms the original pointer-generator network, the generic image captioning method, the extractive news image captioning method, and the LDA-based method according BLEU, METEOR, and ROUGL-L evaluations. Experiments also show that the proposed multimodal coverage mechanisms can improve the model, and that transforming visual attention to pointer distributions can improve the model. © 2020 John Wiley & Sons, Ltd.

关键词： Statistical tests

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Blockchain-based Edge-assisted Knowledge Base Management for Semantic Communication in Remote Driving

Blockchain-based Edge-assisted Knowledge Base Management for...

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International Conference on network Protocols

作者： Yangfei Lin Celimuge Wu Muhammad Luqman Fikri Jiale Wu Jie Li Lei Zhong Yusheng Ji Department of Computer and Network Engineering University of Eletronic-communication Chofu Tokyo Japan Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai China Toyota Motor Corporation Tokyo Japan National Institute of Informatics (NII) Tokyo Japan

Remote driving, an emergent technology enabling remote operation of vehicles, presents a significant challenge due to the necessity of transmitting substantial volumes of image data from the vehicle to a central server. This requirement outpaces the capacity of traditional communication methods, emphasizing the need for efficient data communication. We propose a framework using semantic communication, specifically through a semantic segmentation-based method, which reduces the communication cost by transmitting meaningful semantic information rather than bit-wise data. Addressing the challenge of inconsistencies across knowledge bases in semantic communication, we present a blockchain-based, edge-assisted knowledge base management system. This system leverages edge nodes to manage multiple, geographically and contextually diverse knowledge bases while ensuring security through blockchain's tamper-resistant nature. Furthermore, blockchain sharding is employed to manage different knowledge bases for varying tasks, thereby enhancing the blockchain's throughput. Experimental results showed a great reduction in latency by sharding and an increase in model accuracy, confirming our framework's effectiveness.

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Deep Learning for Object Detection:A Survey

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computer Systems Science & engineering 2021年第8期38卷 165-182页

作者： Jun Wang Tingjuan Zhang Yong Cheng Najla Al-Nabhan Science and Technology Industry Department Nanjing University of Information Science&Technology210044China School of Computer&Software Jiangsu Engineering Center of Network MonitoringNanjing University of Information Science&Technology210044China Department Computer Science King Saud UniversityRiyadhKSA

Object detection is one of the most important and challenging branches of computer vision,which has been widely applied in people s life,such as monitoring security,autonomous driving and so on,with the purpose of locating instances of semantic objects of a certain *** the rapid development of deep learning algorithms for detection tasks,the performance of object detectors has been greatly *** order to understand the main development status of target detection,a comprehensive literature review of target detection and an overall discussion of the works closely related to it are presented in this *** paper various object detection methods,including one-stage and two-stage detectors,are systematically summarized,and the datasets and evaluation criteria used in object detection are *** addition,the development of object detection technology is ***,based on the understanding of the current development of target detection,we discuss the main research directions in the future.

关键词： Object detection convolutional neural network computer vision

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