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检索条件"主题词=Inference Algorithms"

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Speech Enhancement Using Transient Speech Components

Speech Enhancement Using Transient Speech Components

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： C. Tantibundhit J.R. Boston C.C. Li J.D. Durrant S. Shaiman K. Kovacyk A. El-Jaroudi Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh PA USA University of Pittsburgh Pittsburgh PA US Department of Communication Science and Disorders University of Pittsburgh Pittsburgh PA USA

This paper describes an algorithm to decompose speech into tonal, transient, and residual components. The algorithm uses an MDCT-based hidden Markov chain model to isolate the tonal component and a wavelet-based hidden Markov tree model to isolate the transient component. We suggest that the auditory system, like the visual system, is probably sensitive to abrupt stimulus changes and that the transient component in speech may be particularly critical to speech perception. To test this suggestion, the transient component isolated by our algorithm was selectively amplified and recombined with the original speech to generate enhanced speech, with energy adjusted to be equal to the energy of the original speech. The intelligibility of the original and enhanced speech was evaluated in eleven human subjects by the modified rhyme protocol. The word recognition rates show that the enhanced speech can provide substantial improvement in speech intelligibility at low SNR levels (8% at -15 dB, 14% at -20 dB, and 18% at -25 dB)

关键词： Speech enhancement Hidden Markov models Gaussian distribution Wavelet coefficients Wavelet transforms Auditory system Visual system inference algorithms Testing Speech analysis

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Multivariate interdependent discretization for continuous attribute

Multivariate interdependent discretization for continuous at...

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International Conference on Information Technology and Applications, ICITA

作者： Sam Chao Yiping Li Faculty of Science and Technology University of Macau Taipa Macao China

Decision tree is one of the most widely used and practical methods in the data mining and machine learning discipline. However, many discretization algorithms developed in this field focus on univariate only, which is inadequate to handle the critical problems especially owned by medical domain. In this paper, we propose a new multivariate discretization method called multivariate interdependent discretization for continuous attributes - MIDCA. Our novel algorithm can minimize the uncertainty between the interdependent attribute and the continuous-valued attribute, and at the same time to maximize their correlation. The empirical results demonstrate a comparison of performance of various decision tree algorithms on twelve real-life datasets from UCI repository.

关键词： Decision trees Data mining Machine learning algorithms Blood pressure Machine learning inference algorithms Hypertension Aging Bayesian methods Chaos

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Bayesian networks to support the management of patients with ASCUS/LSIL pap tests

Bayesian networks to support the management of patients with...

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EAI International Conference on Wireless Mobile Communication and Healthcare (Mobihealth)

作者： Panagiotis Bountris Charalampos Tsirmpas Maria Haritou Abraham Pouliakis Petros Karakitsos Dimitrios Koutsouris The Biomedical Engineering Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens Greece The Institute of Communication and Computer Systems National Technical University of Athens Athens Greece The Department of Cytopathology University of Athens Athens Greece

In the majority of cases, cervical cancer (CxCa) develops as a result of underestimated abnormalities in the Pap test. Nowadays, there are ancillary molecular biology techniques providing important information related to CxCa and the Human Papillomavirus (HPV) natural history, including HPV DNA test, HPV mRNA tests and immunocytochemistry tests. However, these techniques have their own performance, advantages and limitations, thus a combinatorial approach via computational intelligence methods could exploit the benefits of each method and produce more accurate results. In this paper we present a risk assessment model based on a Bayesian Network which, by combining the results of Pap test and ancillary tests, may identify women at true risk of developing cervical cancer and support the management of patients with ASCUS or LSIL cytology. The model, following the paradigm of other implemented systems, can be integrated into existing platforms and be available on mobile terminals for anytime/anyplace medical consultation.

关键词： Bayes methods Educational institutions DNA inference algorithms Risk management Neoplasms Accuracy

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A Sequential Gradient-Based Multiple Access for Distributed Learning over Fading Channels

A Sequential Gradient-Based Multiple Access for Distributed ...

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Annual Allerton Conference on Communication, Control, and Computing

作者： Tomer Sery Kobi Cohen School of Electrical and Computer Engineering Ben-Gurion University of the Negev Beer Sheva Israel School of Electrical and Computer Engineering Ben-Gurion University of the Negev Israel

A distributed learning problem over multiple access channel (MAC) using a large wireless network is considered. The objective function is a sum of the nodes' local loss functions. The inference decision is made by the network edge and is based on received data from distributed nodes which transmit over a noisy fading MAC. We develop a novel Gradient-Based Multiple Access (GBMA) algorithm to solve the distributed learning problem over MAC. Specifically, the nodes transmit an analog function of the local gradient using common shaping waveforms. The network edge receives a superposition of the analog transmitted signals which represents a noisy distorted gradient used for updating the estimate. We analyze the performance of GBMA theoretically, and prove that it can approach the convergence rate of the centralized gradient descent (GD) algorithm in large networks under both convex and strongly convex loss functions with Lipschitz gradient.

关键词： Fading channels Convergence Noise measurement Receivers Distortion inference algorithms Servers

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Detection of Pill Intake Associated Gestures using Smart Wearables and Machine Learning

Detection of Pill Intake Associated Gestures using Smart Wea...

引用

International Symposium on Computational Intelligence and Informatics

作者： Dacian Avramoni Roxana Virlan Lucian Prodan Alexandru Iovanovici Department of Computer and Information Technology Politehnica University Timisoara

ISBN: (纸本)9798350398830

Gesture identification represents one way of monitoring adherence to medical treatment for cognitive-impaired individuals, dementia-related conditions being severely dependent on precise medication. This paper proposes a gesture identification algorithm used to detect the pill ingestion, that runs on an inexpensive smart wearable device. We use techniques pertaining to supervised machine learning and the data set is processed with the Keras framework. Data collected is represented by acceleration values supplied by the wearable and is proceed on the wearable device itself, showing high accuracy results in identifying the pill intake gesture. The trained model is deployed in a resource constrained embedded device and the inferences is carried locally onto the device.

关键词： Training Machine learning algorithms Wearable computers Medical treatment Machine learning inference algorithms Object recognition

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An algorithm of extracting fuzzy rules directly from numerical examples by using FNN

An algorithm of extracting fuzzy rules directly from numeric...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Lili Rong Zhongtuo Wang Institute of Systems Engineering Dalian University of Technology Dalian China

ISBN: (纸本)0780332806

In this paper, a general method to obtain fuzzy rules directly from numerical data is proposed. The membership function of antecedent part and consequent part is determined by calculating the variance and expectation of the examples. The extraction of fuzzy rules is done by using a fuzzy neural network. The algorithm has two parts. The first part is to determine the optimal number of fuzzy rules. The second part is to improve the accuracy of the inference system. The structure of the FNN is different from previous methods. In the rule layer, there are three counters in each node in order to calculate the maximum degree and the probability of the rule. The output of the node and the weight connecting to the next layer depend on the contents of these counters. From these counters, we can obtain the useful fuzzy rules. The backpropagation learning method is used to improve the accuracy of the system. Finally, through a simulation example, the effectiveness of the proposed algorithm is verified.

关键词： Fuzzy neural networks Fuzzy control Data mining Counting circuits Fuzzy systems Learning systems inference algorithms Probability Joining processes Systems engineering and theory

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Influence-Driven Data Poisoning in Graph-Based Semi-Supervised Classifiers

Influence-Driven Data Poisoning in Graph-Based Semi-Supervis...

引用

AI Engineering - Software Engineering for AI (WAIN), IEEE/ACM Workshop on

作者： Adriano Franci Maxime Cordy Martin Gubri Mike Papadakis Yves Le Traon SnT Universit&#x00E9 de Luxembourg Luxembourg

ISBN: (纸本)9781665452069

Graph-based Semi-Supervised Learning (GSSL) is a practical solution to learn from a limited amount of labelled data together with a vast amount of unlabelled data. However, due to their reliance on the known labels to infer the unknown labels, these algorithms are sensitive to data quality. It is therefore essential to study the potential threats related to the labelled data, more specifically, label poisoning. In this paper, we propose a novel data poisoning method which efficiently approximates the result of label inference to identify the inputs which, if poisoned, would produce the highest number of incorrectly inferred labels. We extensively evaluate our approach on three classification problems under 24 different experimental settings each. Compared to the state of the art, our influence-driven attack produces an average increase of error rate 50% higher, while being faster by multiple orders of magnitude. Moreover, our method can inform engineers of inputs that deserve investigation (relabelling them) before training the learning model. We show that relabelling one-third of the poisoned inputs (selected based on their influence) reduces the poisoning effect by 50%. ACM Reference Format: Adriano Franci, Maxime Cordy, Martin Gubri, Mike Papadakis, and Yves Le Traon. 2022. Influence-Driven Data Poisoning in Graph-Based Semi-Supervised Classifiers. In 1st Conference on AI Engineering - Software Engineering for AI (CAIN’22), May 16–24, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 11 pages. https://***/10.1145/3522664.3528606

关键词： Measurement Training Error analysis Data integrity Semisupervised learning Approximation algorithms inference algorithms

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PM2.5 Prediction Combined ANFIS with Meta-Heuristic Optimization algorithms: A Case Study in Wuhan

PM2.5 Prediction Combined ANFIS with Meta-Heuristic Optimiza...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yapei Qin Hong Fan Mohammed A.A. Al-Qaness Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan China College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Meta-heuristic algorithms and ANFIS have extraordinarily impressive performance for predicting time-series PM2.5 air quality data. This paper explores the possibility of mainstream meta-heuristic algorithms combined with the adaptive neuro-fuzzy inference system (ANFIS) applied to PM2.5 prediction in Wuhan. It is a major difficulty to choose appropriate methods to connect the strengths of both MHA and ANFIS for data prediction. Therefore, three sorts of meta-heuristic algorithms, including ant colony optimization, particle swarm optimization, and genetic algorithm, have been constructed to update the parameters of the neural network of ANFIS. In the case study in Wuhan, modified fuzzy inference systems possess the studying capacity of neural networks and integrate the human ability for reasoning and interpretation, thereby handling the uncertainty of data prediction better.

关键词： Fuzzy logic Uncertainty Metaheuristics Neural networks Geoscience and remote sensing Prediction algorithms inference algorithms

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A distributed algorithm for adaptive regulation of image processing parameters

A distributed algorithm for adaptive regulation of image pro...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： V. Murino C.S. Regazzoni Department of Biophysical and Electronic Engineering (D.I.B.E.) University of Genova Genoa Italy

The authors describe an algorithm able to regulate and control the parameters that manage some common processing algorithms, to obtain the best data quality. The processing chain to be regulated consists of an acquisition module and two filtering processes. The system has been implemented as an expert system, which, in addition to the processing units, includes a regulation module. Each regulation module has been specialized with knowledge about the parameters regulating the related processing unit. Criteria to judge the quality of data at each level have been devised to develop methods to select and regulate the processing parameters to be adjusted. The system is described specifying the knowledge-based architecture and single units where knowledge is embedded. The functioning of each processing module is illustrated by defining the parameters used to judge data and those used to regulate the units. Results on a set of indoor images, acquired by using different camera parameters, are reported.< >

关键词： Distributed algorithms Image processing Expert systems Prototypes inference algorithms Engines Programmable control Adaptive control Data engineering Quality management

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Sequential fuzzy diagnosis method and identification method of membership function by probability and possibility theories

Sequential fuzzy diagnosis method and identification method ...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： T. Toyota Peng Chen Faculty of Computer Science and System Engineering Kyushu Institute of Technology Fukuoka Japan

When building up a fuzzy diagnosis system for machinery diagnosis, fuzzy relation between failure symptoms and failure categories must be defined for fuzzy inference. However, it is not easy to search out the failure symptoms by which all failure categories can be distinguished perfectly and automatically. In order to resolve the problem, we proposed (1) a new type of symptom parameter function called off-group type of symptom parameter (OGSP); (2) the identification method of the OGSP; (3) the identification method of the membership function of OGSP; (4) the algorithm of sequential fuzzy inference by using the OGSP and its membership function for diagnosis. The efficiency of the above methods has been verified by applying them to the ball bearing diagnosis system.

关键词： Fuzzy systems Possibility theory Machinery Density measurement Signal processing Computer science Systems engineering and theory Pattern recognition inference algorithms Probability density function

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