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检索条件"主题词=detection Algorithms"

共 7403 条记录，以下是71-80 订阅

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A Regularized Least Squares-Based Method for Optimal Fusion of Speech Pitch detection algorithms

A Regularized Least Squares-Based Method for Optimal Fusion ...

引用

International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Ziba Imani Seyed Jahanshah Kabudian Department of Computer Engineering and Information Technology Razi University Kermanshah Iran

ISBN: (纸本)9781728111957;9781728111940

Fundamental frequency estimation is one of the most important issues in the field of speech processing. An accurate estimate of the fundamental frequency plays a key role in the field of speech and music analysis. So far, various methods have been proposed in the time- and frequency-domain. However, the main challenge is the strong noises in speech signals. In this paper, to improve the accuracy of fundamental frequency estimation, we propose a method for optimal combination of fundamental frequency estimation methods, in noisy signals. In this method, to discriminate voiced frames from unvoiced frames in a better way, the Voiced/Unvoiced (V/U) scores of four pitch detection methods are combined linearly. These methods are: Autocorrelation, Yin, YAAPT and SWIPE. After identifying the Voiced/Unvoiced label of each frame, the fundamental frequency (F 0 ) of the frame is estimated using the SWIPE method. The optimal coefficients for linear combination are determined using the regularized least squares method with Tikhonov regularization. To evaluate the proposed method, 10 speech files (5 female and 5 male voices) are selected from the PTDB-TUG standard database and the results are presented in terms of SDFPE, GPE, VDE, PTE and FFE standard error criteria. The results indicate that our proposed method relatively reduced the aforementioned criteria (averaged in various SNRs) by 27.13%, 22.14%, 17.40% and 26.74% respectively, which demonstrate the effectiveness of the proposed method in comparison to state-of-the-art methods.

关键词： Mathematical model Estimation detection algorithms Training Frequency estimation Noise measurement Standards

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A Review on Community detection algorithms in Social Networks

A Review on Community Detection Algorithms in Social Network...

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International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT)

作者： Puneet Kumar Priyanka Chawla Ajay Rana School of Computer Science & Engineering Lovely Professional University Jalandhar Punjab School of Computer Science & Engineering Amity University Noida

ISBN: (数字)9781538677063

ISBN: (纸本)9781538677070

There are many real networks that are closely formed according to a community network. Many researchers have devoted their research efforts to develop algorithms and methods that can efficiently find the hidden pattern in the network which is called community. As there are different kinds of networks having different level of complexity and each may contain different properties, each algorithm in literature holds some of these properties and has different definition of community. As per the definitions provided and algorithms used, only part of the features of real communities gets extracted. Objective of this paper is to find different community detection techniques that are already available, analyze them and find the problem in these algorithms. Objective of paper is to categorize the different community discovery methods on the basis of properties these algorithms contain. The classification is also helpful in further research in community detection.

关键词： Heuristic algorithms Social network services Clustering algorithms detection algorithms Complexity theory Image edge detection Computer science

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Performance Analysis of T-Wave-Offset detection algorithms on Patients with Cardiac Diseases

Performance Analysis of T-Wave-Offset Detection Algorithms o...

引用

Computers in Cardiology (CinC)

作者： Deborah Nairn Pietro Bonizzi Joël Karel Alfonso Aranda Medtronic Bakken Research Center Maastricht The Netherlands Department of Data Science and Knowledge Engineering Maastricht University The Netherlands

ISBN: (纸本)9781728109244

Cardiovascular diseases are the number one cause of death in the world. The application of automatic processing algorithms can provide important information about these heart diseases. However, the design of these algorithms can be challenging due to the morphological variations in ECG signals, specifically in the T-wave-offset. This study proposes a comparison of several T-offset detection algorithms on healthy subjects and patients suffering from cardiac diseases. Seven state of the art algorithms were selected for implementation and were evaluated using the same dataset and benchmark to provide a fair comparison. Although no algorithm performs with 100% accuracy for all patients, most can perform well with regards to the healthy patients, with two algorithms having a high performance, above 70% accuracy, on all patients.

关键词： Databases Electrocardiography Morphology Cardiac disease Heart Standards detection algorithms

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Evaluation and Customization of Community detection algorithms in Large Social Networks

Evaluation and Customization of Community Detection Algorith...

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International Conference on Computing Methodologies and Communication (ICCMC)

作者： Sanjay Kumar Stuti Pandey Ria Gupta Department of Computer Science and Engineering Delhi Technological University New Delhi India

Nowadays, social, natural, technological and information systems can be exhibited by complex networks having millions of nodes interconnected to each other. The extraction of comprehensive information from these massive networks call for computationally efficient methods. A promising approach to accomplish this task is to disintegrate the network into sub-units or communities and then using these identified communities to uncover relevant information. Thus, identifying communities in large scale networks plays a pivotal role in several scientific domains. In this paper, we extensively evaluate the functioning of two known algorithms and propose an improvement over one of them, in order to overcome its shortcomings to some extent, for optimal identification of community structure. We also present experimental results and evidences indicating that both the established algorithms, as well as our suggested approach, when applied to large social network datasets yields different results in terms of goodness and performance.

关键词： Partitioning algorithms detection algorithms Conferences Social network services Clustering algorithms Computer science Standards

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FraudVis: Understanding Unsupervised Fraud detection algorithms

FraudVis: Understanding Unsupervised Fraud Detection Algorit...

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Pacific (formerly Asia-Pacific APVIS) Visualization Symposium

作者： Jiao Sun Qixin Zhu Zhifei Liu Xin Liu Jihae Lee Zhigang Su Lei Shi Ling Huang Wei Xu Institute of Interdisciplinary Information Sciences Tsinghua University SKLCS Chinese Academy of Sciences ***

Discovering fraud user behaviors is vital to keeping online websites healthy. Fraudsters usually exhibit grouping behaviors, and researchers have effectively leveraged this behavior to design unsupervised algorithms to detect fraud user groups. In this work, we propose a visualization system, FraudVis, to visually analyze the unsupervised fraud detection algorithms from temporal, intra-group correlation, inter-group correlation, feature selection, and the individual user perspectives. FraudVis helps domain experts better understand the algorithm output and the detected fraud behaviors. Meanwhile, FraudVis also helps algorithm experts to fine-tune the algorithm design through the visual comparison. By using the visualization system, we solve two real-world cases of fraud detection, one for a social video website and another for an e-commerce website. The results on both cases demonstrate the effectiveness of FraudVis in understanding unsupervised fraud detection algorithms.

关键词： Feature extraction detection algorithms Data visualization Image color analysis Visualization IP networks Correlation

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Analysis of Community detection algorithms

Analysis of Community Detection Algorithms

引用

International Conference on Inventive Communication and Computational Technologies (ICICCT)

作者： R Jaya Krishna Yash Chaudhry Devi Prasad Sharma Manipal University Jaipur Rajasthan India

Community detection is starting to become an inevitable task for data science community and like-minded analysts to understand the hidden insights and trends in large complex networks. Through this paper we would like to understand and comparatively analyze the prevalent and recent community detection techniques like Minimum Cut Method, Hierarchical Clustering, Girvan Newman Algorithm, Modularity Maximization, Statistical Inference, Clique Percolation & Maximum Permanence Method based on their network relevance, adaptability and robustness to changes and finally their capacity of size and speed. Finally, we will review the realworld application of these algorithms.

关键词： Clustering algorithms Inference algorithms Conferences detection algorithms Computational modeling Facebook

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An Evaluation Framework for Inter-Domain Link Congestion detection algorithms 14

An Evaluation Framework for Inter-Domain Link Congestion Det...

引用

14th International Symposium on Pervasive Systems, algorithms and Networks (I-SPAN) / 11th International Conference on Frontier of Computer Science and Technology (FCST) / 3rd International Symposium of Creative Computing (ISCC)

作者： Phillips, Alex Zhu, Yingwu Seattle Univ Dept Comp Sci & Software Engn Seattle WA 98122 USA

ISBN: (纸本)9781538608401

Inter-domain link congestion can be caused by either under-provision or peer disputes. TSLP and M-Lab are two representative network-tomographic methods to detect congestion on inter-domain links. In this work, we propose a performance-cost framework to evaluate and compare the two methods via a simulator built atop NS-3. Via detailed simulations, we obtained some insights on the two approaches and hope they can benefit further study in this area. Our experimental results have shown that TSLP is more responsive to link congestion and consumes less measurement bandwidth while M-Lab is capable of associating congestion direction to a congested link. Moreover, M-Lab may produce false positives due to its massive measurement traffic and underpinned TCP congestion control. Our simulator source code is also available online for the research community.

关键词： Delays Bandwidth Throughput Servers Computer science detection algorithms Internet

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Pre-Coding using Different detection algorithms in MIMO System 3

Pre-Coding using Different Detection Algorithms in MIMO Syst...

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International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC)

作者： Sharma, Purnima K. Sharma, Dinesh Gupta, Akansha SVEC ECE Dept Tadepalligudem India GE Univ CSE Dept Dehra Dun India

ISBN: (纸本)9781538632437

Pre-coding is a general understanding of beam-forming to achieve the transmission diversity of the signal. Basically transmitter sends the information which is already coded according to the known knowledge about the channel conditions to the receiver. The pre-coding technique leads to achieve a good performance by eliminating the degradation effects even in the correlation environment upon the channel in the communication system. In this paper, the different detection algorithms: Zero forcing (ZF), Minimum mean square error (MMSE) and Block diagonalization (BD) are used in pre-coding. Many solutions have been proposed for the Co-Channel Interference problem. The main aim of this paper is to select the most suitable Coding techniques to adopt. The analysis is carried by assuming the channel as Rayleigh and modulation scheme is QPSK with 4x4 MIMO system.

关键词： Pre-coding detection algorithms Channel state information (CSI) Zero forcing (ZF) Minimum mean square error (MMSE) Block diagonalization (BD)

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GLFR: A Generalized LFR Benchmark for Testing Community detection algorithms 26

GLFR: A Generalized LFR Benchmark for Testing Community Dete...

引用

26th International Conference on Computer Communication and Networks (ICCCN)

作者： Le, Ba-Dung Nguyen, Hung X. Shen, Hong Falkner, Nickolas Univ Adelaide Sch Comp Sci Adelaide SA 5005 Australia Univ Adelaide Teletraff Res Ctr Adelaide SA 5005 Australia

ISBN: (纸本)9781509029914

Comparisons between community detection methods are mostly based on their accuracies in recovering the built-in community structure in artificial benchmark networks. Current community detection benchmarks assign a fixed fraction of inter-community links, referred to as the mixing fraction, for every community in the same network. We first show in this paper that the variation in community mixing fractions has different impacts on the performances of different community detection methods that could change the decision to select a particular detecting algorithm. To comprehensively compare community detection methods, we therefore need a benchmark that generates heterogeneous community mixing fractions, which is not currently available. We address this gap by generalizing the state-of-the-art Lancichinetti-Fortunato-Radicchi benchmark to generate networks with heterogeneous community mixing fractions. Using our new benchmark, we can quantify the impact of the variation in community mixing fractions on existing community detection methods and re-evaluate the performance of the detecting algorithms as a function of the heterogeneity among the mixing fractions. Furthermore, we show that the heterogeneous community mixing tests using our generalized benchmark reflect better the performance that would be expected on real networks than the homogeneous community mixing tests using the original benchmark.

关键词： Benchmark testing Stochastic processes detection algorithms Australia Electronic mail Complex networks Color

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Specification and Evaluation of Geofence Boundary Violation detection algorithms

Specification and Evaluation of Geofence Boundary Violation ...

引用

International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Stevens, Mia N. Rastgoftar, Hossein Atkins, Ella M. Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781509044948

This paper studies two methods of geofence boundary violation detection. The first method is Ray Casting, which iterates over each geofence boundary edge to determine if a given position of interest is inside the geofence. The second method, called Triangle Weight Characterization (TWC), subdivides the geofence domain into a finite number of triangles, then iterates over each triangle to determine if the given position of interest is inside the geofence. We apply the TWC and Ray Casting methods to case studies that include both keep-in and keep-out geofence boundaries.

关键词： Casting detection algorithms Aircraft Image edge detection Aerospace engineering Algorithm design and analysis Color

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