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检索条件"任意字段=3rd IEEE Intelligent Data Acquisition and Advanced Computing Systems"

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Byte-Sized Reviewers: Exploring Review Generation through LNRG-S

Byte-Sized Reviewers: Exploring Review Generation through LN...

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data Science and Computer Application (ICDSCA), ieee International Conference on

作者： Yiting Li Min Fu School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China Department of Computing Macquarie University Sydney Australia

The development of network technology has led to an explosive growth of user-generated comments. These contents are based on users' experiences after using services and products, reflecting users' evaluations. Automatically generating high-quality and controllable sentiment comments can help creators improve work quality, help service providers enhance service levels, and improve consumers' consumption experience. However, current methods have some limitations. Seq2seq models have difficulty maintaining semantic consistency and generating long texts, LSTM can maintain a memory state and generate coherent text, but is prone to overfitting, generating repetitive text, and having difficulty establishing local coherence. N-gram models text locally but cannot understand the global context. This paper proposes a hybrid architecture, called LNRG-S (LSTM N-gram Reviews Generator with Sentiments), which combines LSTM and N-gram to model both global and local dependencies, and trains separate LSTM generators for positive and negative samples to control sentiment polarity of generated comments. We computed the perplexity of comments generated by different models. LNRG-S has 43.93% and 18.89% lower perplexity than LSTM and n-gram respectively, demonstrating that LNRG-S generates more fluent and coherent text.

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Adaptive Power Control with Improved Genetic Algorithm to Enhance QoS Performance in Vehicular Communication systems

Adaptive Power Control with Improved Genetic Algorithm to En...

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Integrated Circuits and Communication systems (ICICACS), ieee International Conference on

作者： A. Aranganathan Dalia Al-Tarazi S. Bharath Durga Devi Jayant Giri S. Ravi Department of ECE Sathyabama Institute of Science and Technology Chennai India Department of Architecture Engineering Faculty of Engineering Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of ECE Rajalakshmi Institute of Technology Chennai India Department of CSE Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Division of Research and Development Lovely Professional University Phagwara India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Background: Vehicular Ad hoc Networks (VANETs) play a crucial role in intelligent transportation systems since they enable vehicles to communicate instantaneously. Elevated mobility and dynamic settings create difficulties for dependable data transmission, resulting in longer delays and packet loss. Method used: An advanced Genetic Algorithm-based Vehicular Ad hoc Routing (AIGAV) model that leverages meta-heuristic optimization to enhance routing decisions in VANETs. It utilizes GPS for live navigation and modifies routes as required. This optimization reduces congestion and distributes network load more evenly for improved packet forwarding. Results: The AIGAV model exceeds the performance in crucial efficacy aspects. It enhanced data success rates (95.25%), remarkable throughput (521.41 Kbps), and minimal routing overhead (254.18 packets), and decreased data loss rates. Conclusion: The AIGAV model improves the efficiency of VANET routing through dependable and adaptable communication in mobile settings. It employs optimization to enhance safer, more intelligent transportation systems for upcoming uses.

关键词： Adaptation models Metaheuristics Vehicular ad hoc networks Routing Throughput Reliability Integrated circuit modeling Genetic algorithms Load modeling Global Positioning System

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An intelligent and Dynamic Replication Scheme for Reliable Cloud Environment

An Intelligent and Dynamic Replication Scheme for Reliable C...

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Integrated Circuits and Communication systems (ICICACS), ieee International Conference on

作者： Damodhar M Ch. D V Subba Rao Department of Computer Science and Engineering Sri Venkateswara University College of Engineering Tirupati India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Recently, cloud computing research, especially Virtual Machine based replication schemes and their applications, were becoming prevalent. A cloud data center (DC) comprises of hosts with an enormous of virtual machines (VMs) where determining replica quantity and replication order is a tedious task to minimize cost and bandwidth. Majority of the existing dynamic replication techniques rely on budget constraints and server failure rate to guarantee customer requirements. But, there is a strong need to determine replication order of servers to retain the performance and system stability. In this paper, we suggest two different algorithms for better selection with proper and intelligent placement of replicas in the cloud region. The proposed algorithms towards dynamic replication are request based replica selection (RRS) and ant colony optimization (ACO). The foremost proposed algorithm was request based replica selection used for obtaining the best selected replicas depends upon the VM performance ranking and failure probability based on the task requirement. Conversely, the subsequent offered algorithm is ACO that is helpful to find the best location to place the designated replica depends on the least-cost distance metric, and the replicas availability in order to encompass and regulate with the entire network load and the link bottlenecks. The performance of proposed techniques using CloudSim was evaluated against various pro-active fault tolerance strategies. The simulation results gives that dynamic replica selection provides efficient task replication when compared with the existing pro-active algorithms. Additionally, ACO realizes high availability, low cost, and less bandwidth utilization.

关键词： Cloud computing Ant colony optimization Costs Heuristic algorithms Simulation Bandwidth Throughput Virtual machines Stability analysis Servers

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Research on Model Construction and Algorithm of intelligent Analysis and Fault Diagnosis of Power Equipment

Research on Model Construction and Algorithm of Intelligent ...

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data Analytics, computing and Artificial Intelligence (ICDACAI), International Conference on

作者： Longxiang Chen Lihua Li Ning Yang Yang Yang State Grid Corporation Of China Beijing China China Electric Power Research Institute Beijing China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

In this paper, a hierarchical intelligent analysis and fault diagnosis model of power equipment is proposed, which includes data acquisition layer, feature extraction layer, fault diagnosis layer and result output layer. Key parameters such as current and voltage are collected in real time by sensors, and fault-related features are extracted by signal processing technology and machine learning algorithm. In the feature extraction layer, wavelet transform (WT) and principal component analysis (PCA) are used to reduce the dimension of features, so as to fully capture the effective information in fault signals. In the fault diagnosis layer, the deep learning (DL) model is adopted, and a large number of historical fault data are trained to realize high-precision fault identification. Aiming at the shortcomings of existing algorithms, this paper proposes a hybrid fault diagnosis algorithm combining DL and physical model. The algorithm improves the diagnosis accuracy and real-time performance by combining the feature learning ability of DL model and the prior knowledge of physical model. Specifically, a dual-stream network structure is designed. One stream processes the original data, and the other stream processes the data converted by the physical model. Finally, the features of the two streams are weighted and fused at the high level of the network for classification and diagnosis. The experimental results show that the hybrid fault diagnosis algorithm proposed in this paper is superior to other algorithms in accuracy, precision, recall and F1 score, which shows its effectiveness and superiority in dealing with complex fault diagnosis problems. In the experiment with 14,000 training data, 4,000 verification data and 2,000 test data, the accuracy of the algorithm is 0.957, the precision is 0.943, the recall rate is 0.962 and the F1 score is 0.952.

关键词： Fault diagnosis Representation learning Analytical models Accuracy Signal processing algorithms Feature extraction Real-time systems data models Classification algorithms Principal component analysis

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Forecasting and analysis of online orders rejections using data mining algorithms and penalty function 3

Forecasting and analysis of online orders rejections using d...

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3rd ieee International Conference on advanced Trends in Information Theory, ATIT 2021

作者： Suprun, Oleh Klimenkova, Nina Melnyk, Maksym Lavriy, Sonya Department of intelligent Software Systems Taras Shevchenko National University of Kyiv Kyiv Ukraine intellectual Technologies Department Taras Shevchenko National University of Kyiv Kyiv Ukraine

ISBN: (纸本)9781665438452

The direction of products and food delivery from online stores and similar transportation services have become especially relevant in recent years in connection with the rapid informatization of society, and especially because of quarantine restrictions due to the worldwide pandemic. However, along with increasing demand, online stores and individual couriers often incur significant financial losses due to customer rejection of orders, disruption in preparation, or other resignation issues that are independent of the company. Therefore, the article presents a way to partially reduce financial and time losses by analyzing preliminary order statistics, identifying potential dependencies and, consequently, predicting future customer rejection based on order details. Given the large number of statistics, it is advisable to use the tools and paradigms of data mining, two different approaches are considered and compared. At the same time there is a problem of false positive conclusion of the model, due to which the system ignores the profitable orders. To reduce such losses, the use of the method of penalty functions is proposed, which allows to assess the model risks and possible benefits. The described approaches were tested on a large real sample, and the corresponding results are presented, which demonstrate the effectiveness of the approach. © 2021 ieee.

关键词： data mining

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An Online Wear State Identification Method for Axial Piston Pump Key Friction Pair based on FSANN 3

An Online Wear State Identification Method for Axial Piston ...

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3rd International Conference on Sensing, Measurement and data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Wang, Dandan Liu, Shihao Huang, Weidi Zhang, Junhui Xu, Bing Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou China

ISBN: (纸本)9781665492812

Wear state identification of the axial piston pump is of great importance to secure the modern hydraulic system. Offline intelligent fault diagnosis methods are significant to solve the wear state identification problems. Nevertheless, these methods cannot meet the demand of real-time wear state identification. In this paper, an accurate and online wear state identification method based on edge computing using feature selected artificial neural network (FSANN) is proposed for the axial piston pump key friction pair. To reduce latency, an edge end node including data collection, signal pre-processing, feature extraction, fault classification is established. To cut down the amount of calculation and transmission while retaining accuracy, features sensitive to the fault are selected. The embedding performance of one-against-all support vector machine (OAA-SVM), artificial neural network (ANN), deep belief network (DBN) is compared and ANN is chosen as the embedded diagnostic model. The experimentally verified accuracy of this method is 99.0%. The single diagnosis time(SDT) is about 0.24s. Compared to transmitting raw data to the host computer, this method cut down the amount of data by about 200 times. The proposed method could diagnose the slipper wear state accurately and quickly and provide a potential way for real-time fault diagnosis for axial piston pump. © 2022 ieee.

关键词： Artificial neural network Axial piston pump Edge computing Feature selection Wear state identification

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Design of Multi-Source data Collection Cloud Platform for Transmission Lines Integrating Technology

Design of Multi-Source Data Collection Cloud Platform for Tr...

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Mobile Networks and Wireless Communications (ICMNWC), ieee International Conference on

作者： Yifeng Zhu Lin Tian Chengyu Liu Jian Cui Ming Fang China Southern Power Grid Co. Ltd. EHV Transmission Company Guangzhou Guangdong China Guiyang Bureau of China Southern Power Grid Co. Ltd. EHV Transmission Company Guiyang Guizhou China

As power systems continue to evolve, aggregation technology has become a mainstream solution. This article aims to provide a detailed analysis and study of data acquisition issues in centralized transmission lines. In the aggregation of transmission lines, this article adopts a distributed computing service model, which is to set up a centralized municipal area network through the network to realize the monitoring and management of load demand and power supply reliability at the bureau and county level. This article combines smart terminals with traditional networks to meet users' different application functional requirements, and uses the characteristics of the Internet to provide a data collection platform with strong realtime performance, high reliability, and large maintenance and management capabilities. Finally, this article tests the performance of the platform. The test results show that the system receives data quickly and processes data quickly. The overall disk utilization index is above 0.84, and the highest index of 0.98 is infinitely close to 1, which shows that the performance of the system is efficient.

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Lightweight Intrusion Detection systems for IoT-Based Smart Agriculture: A Deep Learning Perspective

Lightweight Intrusion Detection Systems for IoT-Based Smart ...

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Wireless Africa Conference (WAC)

作者： Issah Ngomane Mthulisi Velempini Mncedisi Bembe dept. of Mathematics and Computer Science University of Limpopo Polokwane South Africa School of Computing and Mathematical Sciences University of Mpumalanga Mbombela

ISBN: (数字)9798331517588

ISBN: (纸本)9798331517595

The rapid increase of Internet of Things (IoT) devices in smart agriculture (SA) has enabled a more connected and intelligent world. IoT devices are a collection of interconnected systems that can communicate, share data and information to achieve an automated environment. SA presents a transformative approach to farming that leverages technology and data-driven solutions to address the challenges of modern agriculture, which includes the need to sustain a growing global population while minimizing environmental impact and resource depletion. However, the increase in the deployment of IoT systems has led to an increase in cyber-attacks and security challenges. Moreover, security challenges such as man-in-the-middle, denial and distributed denial of service, botnets, sinkhole, and spoofing attacks compromise the confidentiality, integrity, and availability (CIA) of SA. State of the art research shows that researchers are adopting Machine Learning (ML) and Deep Learning (DL) techniques to design effective Intrusion Detection systems (IDSs). However, integrating those systems with IoT resource constrained devices is presenting new challenges. Furthermore, SA has its known challenges of different weather conditions which includes heavy rainfall, snow, hail, extreme heat etc. This paper presents a short analysis of the current security issues in SA and state of the art deployed ML and DL models that effectively detect and mitigate the effects of cyber-attacks.

关键词： Smart agriculture Deep learning Wireless communication Botnet Biological system modeling Intrusion detection Security Internet of Things Sustainable development Cyberattack

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Searching Routing using A-Star (A*) Search Algorithm

Searching Routing using A-Star (A*) Search Algorithm

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Creative Communication and Innovative Technology (ICCIT), ieee

作者： Felix Wilsen Yodianto Harco Leslie Hendric Spits Warnars Leonel Leslie Heny Spits Warnars Arief Ramadhan Teddy Siswanto Department of Computer Science Bina Nusantara University Jakarta Indonesia Department of Architecture Bina Nusantara University Jakarta Indonesia School of Computing Telkom University Bandung Indonesia Department of Information Systems Trisakti University Jakarta Indonesia

ISBN: (数字)9798350367492

ISBN: (纸本)9798350367508

The rapid development of smart cities requires the integration of advanced technologies to maintain efficiency in urban areas. One critical aspect is the implementation of effective route-finding methods to ensure smooth traffic flow and enhance the quality of life for residents. This paper explores the application of the A-Star (A*) Search Algorithm within the context of Indonesia’s planned new capital in Kalimantan. The study employs the A-Star algorithm, incorporating various heuristic distance measures such as Euclidean, Manhattan, and Chebyshev distances. The methodology involves creating graphs and trees from input data to optimize route planning for both public and private vehicles. The study aims to design a flexible and efficient routing system that can accommodate the diverse driving styles and conditions specific to Indonesia, particularly for the proposed new capital city, which aims to be a hub of smart city innovations. The findings reveal that the algorithm’s flexibility allows it to adapt effectively to real-time traffic data and various environmental conditions. In conclusion, integrating the A-Star algorithm into intelligent city infrastructure presents a promising solution for managing traffic and transportation in Indonesia’s new capital. By leveraging IoT and real-time data, the algorithm can enhance the efficiency and safety of urban mobility, contributing to the overall success of innovative city initiatives in Indonesia. This study’s novelty lies in its adaptation of the A-Star algorithm to Indonesia’s unique cultural and environmental conditions, providing a customized solution for the country’s new capital city.

关键词： Technological innovation Smart cities Heuristic algorithms Transportation Routing Real-time systems Safety Planning Global communication Cultural differences

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Bayesian Optimization for Reverse Stress Testing

Bayesian Optimization for Reverse Stress Testing

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3rd International Conference on intelligent computing and Optimization, ICO 2020

作者： Mitic, Peter Department of Computer Science UCL Gower Street LondonWC1E 6BT United Kingdom Santander UK 2 Triton Square Regents Place LondonNW1 3AN United Kingdom

ISBN: (纸本)9783030681531

Bayesian Optimization with an underlying Gaussian Process is used as an optimization solution to a black-box optimization problem in which the function to be optimized has particular properties that result in difficulties. It can only be expressed in terms of a complicated and lengthy stochastic algorithm, with the added complication that the value returned is only required to be sufficiently near to a pre-determined ‘target’. We consider the context of financial stress testing, for which the data used has a significant noise component. Commonly-used Bayesian Optimization acquisition functions cannot analyze the ‘target’ condition in a satisfactory way, but a simple modification of the ‘Lower Confidence Bound’ acquisition function improves results markedly. A proof that the modified acquisition function is superior to the unmodified version is given. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Gaussian noise (electronic)

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