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检索条件"主题词=detection algorithms"

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Influencers detection Algorithm to Combat Rumors in Social Networks

Influencers Detection Algorithm to Combat Rumors in Social N...

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International Conference on Engineering Technology and their Applications (IICETA)

作者： Ansam Ali AbdulAmeer Mahdi Abed Salman Muhammed Abaid Mahdi Dept. of Computer Science College of Science for Women University of Babylon Babylon Iraq

ISBN: (数字)9781665472159

ISBN: (纸本)9781665472166

Although online social networks can provide a potential opportunity for interchanging information among individuals, rumor is still a big challenge. It is a harmful social phenomenon that needs to be paid attention to. Therefore, several researchers have focused on this research direction. This paper is going to combat negative rumors on online social networks by involving trusted influencers. They are members of online social networks (OSNs) who have a high degree of influence than the others. They can play an important role to minimize the effect of rumors. The process of detecting influencers is the first step in this paper. The proposed model uses some criteria to achieve this task. The next step after the detection process is to employ those influencers to combat rumors. To evaluate the proposed model, we used a tweet dataset; this demonstrates that the model can identify influencers with a high influence score who disseminate anti-rumor in social networks. The results emphasize that the model has a good performance in discovering influencers as well as spreading anti-rumor compared with others.

关键词： Legged locomotion Social networking (online) Blogs Task analysis detection algorithms

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The Simulation of the Signal detection Algorithm in MIMO System Application

The Simulation of the Signal Detection Algorithm in MIMO Sys...

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International Conference on Network and Information Systems for Computers (ICNISC)

作者： Yalin Kang Jie Guo Mingyan Xiao Di Wu School of Physics and Electronic Information Yunnan Normal University Kunming China

ISBN: (纸本)9781665453523

With the increase of the antennas number in the MIMO system, the error and distortion caused by the signal interference between antennas increase. The signal detection algorithm can effectively reduce the bit error rate. In this paper, the minimum mean squared error algorithm (MMSE), zero-forced algorithm (ZF), serial interference cancellation zero-forced algorithm (ZF-SIC) and serial interference cancellation minimum mean squared error algorithm (MMSE-SIC) are compared. Furthermore, the bit error simulation and complexity analysis of these detection algorithms are performed by MATLAB software. The increase of receiving antennas can reduce the bit error rate because of the diversity gain of multiple antennas. The error rate using MMSE is lower than ZF due to the enlarged noise by ZF algorithm. SIC can improve the detection accuracy in which the decision feedback mechanism is introduced. The results show MMSE-SIC is better than MMSE by 6dB and ZF-SIC better than ZF by 5dB. MMSE-SIC algorithm has the best detection performance.

关键词： Analytical models Interference cancellation Software algorithms Bit rate Receiving antennas Modulation detection algorithms

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Synthesized speech quality measurement of an Improved Fundamental Frequency (Pitch) detection Algorithm

Synthesized speech quality measurement of an Improved Fundam...

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Silchar Subsection Conference (SILCON), IEEE

作者： Sandeep Kumar Piyush Ananda Tiwari Prabhakar Agarwal Upendra Kumar Acharya Department of Electronics and Communication Engineering National Institute of Technology Delhi India Department of Computer Science and Engineering National Institute of Technology Delhi India Department of Electronics and Communication Engineering Galgotias College of Engineering and Technology Greater Noida India

ISBN: (纸本)9781665471015

The fundamental frequency (pitch) is essential in determining speech quality. This paper proposes a modified circular average magnitude difference function (CAMDF) based algorithm for estimating the pitch of the speech signal. The performance of the proposed algorithm has been compared with autocorrelation function (ACF), average magnitude difference function (AMDF), and CAMDF based pitch detection methods by incorporating them into an analysis-synthesis system for speech signal. The proposed method has a lower gross error of 5.33%, 4.7%, and 0.83 % compared to AMDF, ACF, and CAMDF methods. Also, the synthesized speech quality of the proposed algorithm in respect of subjective and objective tests is superior with respect to other algorithms.

关键词： Frequency synthesizers Frequency measurement Classification algorithms Autocorrelation detection algorithms

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Lightweight Drowsy Driving Recognition System

Lightweight Drowsy Driving Recognition System

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International Conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Chang Jian Qiao Ge Jin Haibo School of Software Engineering Liaoning Technical University Huludao Liaoning China

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

To address the issues of excessive parameters and high computational load in fatigue driving detection models, a lightweight detection algorithm based on YOLOv8n is proposed. The AIFI module from RT-DETR is employed to enhance and optimize the SPPF layer in the backbone network, improving both speed and computational efficiency in target detection. The FasterBlock module from FasterNet is integrated to optimize the C2f module, reducing computational demands and increasing detection speed. Additionally, MPDIoU is introduced to redefine the loss function, accelerating model convergence and enhancing accuracy. Results indicate a 1.5% improvement in accuracy, a 23% reduction in computational load, a 26% decrease in parameters, and a 25% reduction in model size compared to the original YOLOv8n. This ensures accurate, real-time detection while maintaining a lightweight structure, providing robust support for vehicle-mounted systems.

关键词： Accuracy Computational modeling Switches Object detection Fatigue Real-time systems detection algorithms Autonomous vehicles Load modeling Vehicles

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Design and Implementation of a W-band Continuous Wave Cloud Radar and Its User-control Software

Design and Implementation of a W-band Continuous Wave Cloud ...

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International Conference on Electronics and Information Technology (EIT)

作者： Shuyuan Lu Shan Shi Weiwei Ji Ge Jin Feiyu Wu Shanghai Aerospace Electronic Technology Institute Shanghai China

ISBN: (数字)9798350369151

ISBN: (纸本)9798350369168

Clouds, with a global average occurrence rate exceeding 60%, represent one of the greatest sources of uncertainty regarding future climate change. Millimeter-wave radar can detect characteristic information about particles within clouds, thereby enhancing our understanding of clouds' role in the Earth’s atmosphere. Compared to the Ka-band, the W-band offers higher sensitivity, enabling the detection of a wider range of cloud types. This article presents a scanning-capable W-band continuous wave cloud radar. Additionally, it introduces a supporting user-control software designed to automatically configure radar components for various detection modes, ensuring intelligent and long-term operation of the cloud radar.

关键词： Uncertainty Sensitivity Radar measurements Clouds Radar detection Debugging Software engineering Real-time systems Software measurement Climate change detection algorithms

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Adaptive Threshold-Based Algorithm for Cloud Parameter Retrieval Using Single-Photon Lidar

Adaptive Threshold-Based Algorithm for Cloud Parameter Retri...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Wenting Ma Mingyao Ma Xilian Zhou Yihan Chen Teng Tian National and Local Joint Engineering Laboratory for Renewable Energy Access to Grid Technology Hefei University of Technology Hefei China Grid Anhui Electric Power Co. Ltd. Electric Power Research Institute of State Hefei China

ISBN: (数字)9798350360868

ISBN: (纸本)9798350360875

Clouds are crucial meteorological elements with significant implications for global climate change research, weather forecasting, renewable energy power prediction, and aviation safety. Lidar has gained widespread application in cloud detection due to its strong continuity, long-range detection, and high resolution. The differential zero-crossing method is a commonly used approach for cloud parameter retrieval based on lidar return signals; however, it is prone to interference from background noise and aerosols, leading to misjudgments and necessitating appropriate thresholds to separate noise signals. This paper proposes an adaptive thresholding method that accurately filters out cloud peak signals. By combining cloud peak signals with the differential zero-crossing method to obtain cloud layer intervals and integrating the results from both the original and distance-corrected signals, further refinement of the cloud layer intervals is achieved. Experimental validation using single-photon lidar data demonstrates that the proposed algorithm can accurately extract cloud information and effectively identify thin high-altitude cloud signals.

关键词： Climate change Laser radar Clouds Meteorology Weather forecasting Renewable energy sources Noise measurement Interference Energy resolution Filters Predictive models detection algorithms

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Where You Look Matters in Group Photos: A Demo of GARGI iOS App

Where You Look Matters in Group Photos: A Demo of GARGI iOS ...

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IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)

作者： Omkar N. Kulkarni Thomas Lloyd-Jones My Tran Gregory Vincent Vivek K. Singh Pradeep K. Atrey Albany Lab for Privacy and Security University at Albany SUNY Albany NY USA Rutgers University New Brunswick NJ USA

ISBN: (数字)9798350351422

ISBN: (纸本)9798350351439

Capturing group photos at professional or social events is a prevalent practice nowadays. In such photos, the direction in which each individual looks plays a crucial role in determining the aesthetic appeal of the image. Generally, unless instructed otherwise, it's desirable for all individuals in the photo to look in the same direction, commonly towards the camera, a concept known as gaze uniformity. However, when multiple photographers are involved in taking a group photo, there's a higher likelihood of nonuniform gazes occurring with multiple subjects. The paper introduces ‘GARGI’, an iOS app for evaluating gaze uniformity in group photos, categorizing them as GOOD, BAD, or OK based on their aesthetic value. Utilizing Apple's Vision framework and SwiftUI, GARGI em-ploys face, eye, and landmark detection algorithms to detect and analyze gaze direction. It supports camera and gallery inputs, with custom algorithms to assess gaze uniformity in live photos. Equipped with functionality to use the high-quality on-device camera, intuitive user interface, and customization options, GARGI ensures aesthetically better group photo outcomes.

关键词： Photography Data privacy Computer vision Lighting Information processing User interfaces Cameras Indexes detection algorithms Faces

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Research on Picking Decision Algorithm of Apple Picking Robot Based on Improved YOLOv8

Research on Picking Decision Algorithm of Apple Picking Robo...

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Information Systems and Computing Technology (ISCTech), International Conference on

作者： Zhou Guo Zishen Liu Qi Dong Kaihang Wang Xiang-An Yan Yaoheng Su School of Science Xi’an Polytechnic University Xi’an China

ISBN: (数字)9798350379860

ISBN: (纸本)9798350379877

The apple picking robot relies heavily on the precision and accuracy of the apple's target detection algorithm. At present, most of the apple detection algorithms are designed to identify more apples without considering the complexity of the actual picking environment. If the apple picking robot directly adopts these algorithms, recognizing too many apples will cause the apple robot to need more time to determine whether the recognized apples meet the picking conditions of the apple picking robot. Aiming at this question, in order to quickly identify apples that can be picked directly, this paper proposes a picking decision algorithm for apple picking robots based on improved YOLOv8. Firstly, the YOLOv8s backbone was reconstructed using the lightweight network MobileNetV3. Secondly, the small target detection Header in the head part and the corresponding network layer in the Neck part are deleted. Then, in the last layer of Backbone, the Spatial Pyramid Pooling Fast Cross Stage Partial Channel-Squeeze and Excitation (SPPFCSPC-SE) proposed in this paper is added to enhance the ability of model feature extraction. Finally, the Apple dataset, which only labels large targets, is built to train and test the improved model. From the experimental results, it can be observed that the F1 value of our model is improved from 92.4% of the original model to 93.0%, and the model size is 5.3 MB, which is 76.5% less than the original YOLOv8s.

关键词： Computers Target recognition Computational modeling Semantics Object detection Feature extraction Neck detection algorithms Robots Information systems

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SUDS: A Strategy for Unsupervised Drift Sampling

SUDS: A Strategy for Unsupervised Drift Sampling

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Christofer Fellicious Lorenz Wendlinger Mario Lopez Gancarski Jelena Mitrović Michael Granitzer University of Passau INSA Lyon The Institute for Artificial Intelligence Research and Development of Serbia

ISBN: (数字)9798331527235

ISBN: (纸本)9798331527242

Supervised machine learning often encounters concept drift, where the data distribution changes over time, degrading model performance. Existing drift detection methods focus on identifying these shifts but often overlook the challenge of acquiring labeled data for model retraining after a shift occurs. We present the Strategy for Drift Sampling (SUDS), a novel method that selects homogeneous samples for retraining using existing drift detection algorithms, thereby enhancing model adaptability to evolving data. SUDS seamlessly integrates with current drift detection techniques. We also introduce the Harmonized Annotated Data Accuracy Metric (HADAM), a metric that evaluates classifier performance about the quantity of annotated data required to achieve the stated performance, thereby taking into account the difficulty of acquiring labeled data. Our contributions are twofold: SUDS combines drift detection with strategic sampling to improve the retraining process, and HADAM provides a metric that balances classifier performance with the amount of labeled data, ensuring efficient resource utilization. Empirical results demonstrate the efficacy of SUDS in optimizing labeled data use in dynamic environments, significantly improving the performance of machine learning applications in real-world scenarios. Our code is open source and available at https://***/cfellicious/SUDS/

关键词： Measurement Adaptation models Machine learning algorithms Heuristic algorithms Concept drift Training data Data models Resource management detection algorithms Unsupervised learning

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Unveiling Real-World Company Hidden Communication Patterns: Comparing Informal Large-Scale Email Network and Formal Structure

Unveiling Real-World Company Hidden Communication Patterns: ...

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IEEE International Conference on Big Data

作者： Leonardo Di Perna Matteo Bianchi Matteo Matteucci Marco Brambilla DEIB Politecnico di Milano Milan Italy

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

The increasing adoption of digital communication technologies has led to the accumulation of vast amounts of unstructured data, providing new opportunities to analyze organizational dynamics. In this study, we present a comprehensive framework for capturing, processing, and analyzing large-scale digital communication data to uncover hidden informal communication patterns within a real-world corporate environment. Utilizing a unique dataset of 40.425.247 emails within a banking company, spanning a two-year period, we construct a social network highlighting the email interactions. The graph is compared with organizational data, including business unit affiliations, enabling a detailed analysis of the relationship between formal organizational structures and informal social networks. By applying state-of-the-art community detection algorithms, we extract informal communities and compare them to the formal structure using various external validation metrics. Our results reveal that while informal communities may overlap with the formal structure to some extent, they often diverge in ways that may reveal organizational dynamics, communication flow, and inter-departmental collaboration that are not apparent from the formal structure alone. This study aims to pave the way for future research by exploring how human resource management can leverage insights derived from digital communication interactions to foster positive organizational behavior and enhance management practices.

关键词： Measurement Social networking (online) Collaboration Companies Big Data Digital communication Resource management Human resource management detection algorithms Business

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