Linear regression based bayesian predictive classification for speech recognition

Chien, Jen‐Tzung

doi:10.1109/tsa.2002.805640

Cited by 21 publications

(6 citation statements)

References 17 publications

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“…Spark can operate at a much faster speed than Hadoop, and its application scope is wider. Therefore, the application of Spark in the smart medical industry has become a wildfire, especially for the analysis and prediction of guidance data [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Smart Medical Prediction for Guidance: A Mechanism Study of Machine Learning

Wang

Dong

2021

Journal of Healthcare Engineering

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Data analysis and prediction have gradually attracted more and more attention in the smart healthcare industry. The smart medical prediction system is of great importance to the enterprise strategy and business development, and it is also of great value to provide medical advices for patients and assist patient guidance. The research theme is the use of machine learning technologies with the application in the areas of smart medical analysis. In this paper, the actual data of the smart medical industry were statistically analysed and visualized according to the features, and the most influential feature combinations were selected for the establishment of the prediction model. Based on machine learning technology, namely, random forest, the guidance prediction model is established, and the combination of features is repeatedly adjusted to improve its accuracy. The practical significance of this paper is to provide a high-precision solution for smart medical data analysis and to realize the proposed data analysis and prediction on the cloud platform based on the Spark environment.

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Section: Introductionmentioning

confidence: 99%

Smart Medical Prediction for Guidance: A Mechanism Study of Machine Learning

Wang

Dong

2021

Journal of Healthcare Engineering

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“…Tiwana et al [6] used a regression analysis as statistical learning approach to propose a new model for handover. J. T. Chien [7] proposed a novel Linear Regression based on Bayesian Predictive Classification (LRBPC) for robust speech recognition. Tiwana et al [8] used the linear regression model as statistical learning approach to troubleshoot the Key Performance Indicator (KPIs) for 3G and LTE networks and better manage the radio resource.…”

Section: Related Workmentioning

confidence: 99%

Statistical learning and multiple linear regression model for network selection using MIH

Rahil

Mbarek

Atieh

et al. 2014

The Third International Conference on E-Technologies and Networks for Development (ICeND2014)

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A key requirement to provide seamless mobility and guaranteeing Quality of Service in heterogeneous environment is to select the best destination network during handover. In this paper, we propose a new schema for network selection based on Multiple Linear Regression Model (MLRM). A thorough investigation, on a huge live data collected from GPRS/UMTS networks led to identify the Key Performance Indicators (KPIs) that play the most important role in the handover process. These KPIs are: Received Signal Code Power (RSCP), received energy per chip (Ec/No) and Available Bandwidth (ABW) of the destination network. To extract a handover model from collected data, we study the correlation among values of identified KPIs parameters, before, during and after handover, thanks to a statistical learning approach, using the predictive analytics software SPSS. For model assessment, Pearson Correlation Coefficient and determination coefficient Rsquared (R 2 ) are used. Media Independent Handover (MIH) IEEE 802.21 standard is used in this work to retrieve the lower layer information of available networks and announce the handover needs (handover initiation). The proposed model will help to select the most appropriate network between many existing ones in the vicinity of the mobile node.

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“…Classification is a very important field of research due to the advantageous nature that a classifier with high generalization ability would benefit the economical, industrial and medical fields [1]. As a result of this, extensive research has been carried out over the years and this has resulted in a large number of applications such as risk classification of loan clients [2], hand-writing recognition [3], image classification [4] and speech recognition [5].…”

Section: Introductionmentioning

confidence: 99%

“…This is done by using observations in the historical data to estimate the probability of transition [24]. In the literature we also see fuzzy logic being applied markovian jump systems [5,3,4]. Interval type-2 fuzzy logic systems can also be applied to deal with complex non-linear MJS [25].…”

Section: Introductionmentioning

confidence: 99%