scholarly journals Development of the Arabic Voice Pathology Database and Its Evaluation by Using Speech Features and Machine Learning Algorithms

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Tamer A. Mesallam ◽  
Mohamed Farahat ◽  
Khalid H. Malki ◽  
Mansour Alsulaiman ◽  
Zulfiqar Ali ◽  
...  
Keyword(s):  
Machine Learning ◽  
English Language ◽  
Language Disorder ◽  
Learning Algorithms ◽  
Voice Disorders ◽  
Essential Element ◽  
Machine Learning Algorithms ◽  
Voice Disorder ◽  
Speech Features ◽  
The Voice

A voice disorder database is an essential element in doing research on automatic voice disorder detection and classification. Ethnicity affects the voice characteristics of a person, and so it is necessary to develop a database by collecting the voice samples of the targeted ethnic group. This will enhance the chances of arriving at a global solution for the accurate and reliable diagnosis of voice disorders by understanding the characteristics of a local group. Motivated by such idea, an Arabic voice pathology database (AVPD) is designed and developed in this study by recording three vowels, running speech, and isolated words. For each recorded samples, the perceptual severity is also provided which is a unique aspect of the AVPD. During the development of the AVPD, the shortcomings of different voice disorder databases were identified so that they could be avoided in the AVPD. In addition, the AVPD is evaluated by using six different types of speech features and four types of machine learning algorithms. The results of detection and classification of voice disorders obtained with the sustained vowel and the running speech are also compared with the results of an English-language disorder database, the Massachusetts Eye and Ear Infirmary (MEEI) database.

scholarly journals User interface approaches implemented with automated patient deterioration surveillance tools: protocol for a scoping review

BMJ Open ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. e055525
Author(s):  
Yik-Ki Jacob Wan ◽  
Guilherme Del Fiol ◽  
Mary M McFarland ◽  
Melanie C Wright
Keyword(s):  
Machine Learning ◽  
User Interface ◽  
Adverse Events ◽  
Scoping Review ◽  
English Language ◽  
Learning Algorithms ◽  
Sudden Cardiac Arrest ◽  
Machine Learning Algorithms ◽  
Cochrane Library ◽  
Patient Deterioration

IntroductionEarly identification of patients who may suffer from unexpected adverse events (eg, sepsis, sudden cardiac arrest) gives bedside staff valuable lead time to care for these patients appropriately. Consequently, many machine learning algorithms have been developed to predict adverse events. However, little research focuses on how these systems are implemented and how system design impacts clinicians’ decisions or patient outcomes. This protocol outlines the steps to review the designs of these tools.Methods and analysisWe will use scoping review methods to explore how tools that leverage machine learning algorithms in predicting adverse events are designed to integrate into clinical practice. We will explore the types of user interfaces deployed, what information is displayed, and how clinical workflows are supported. Electronic sources include Medline, Embase, CINAHL Complete, Cochrane Library (including CENTRAL), and IEEE Xplore from 1 January 2009 to present. We will only review primary research articles that report findings from the implementation of patient deterioration surveillance tools for hospital clinicians. The articles must also include a description of the tool’s user interface. Since our primary focus is on how the user interacts with automated tools driven by machine learning algorithms, electronic tools that do not extract data from clinical data documentation or recording systems such as an EHR or patient monitor, or otherwise require manual entry, will be excluded. Similarly, tools that do not synthesise information from more than one data variable will also be excluded. This review will be limited to English-language articles. Two reviewers will review the articles and extract the data. Findings from both researchers will be compared with minimise bias. The results will be quantified, synthesised and presented using appropriate formats.Ethics and disseminationEthics review is not required for this scoping review. Findings will be disseminated through peer-reviewed publications.

Demographic and Symptomatic Features of Voice Disorders and Their Potential Application in Classification Using Machine Learning Algorithms

2018 ◽  
Vol 70 (3-4) ◽  
pp. 174-182 ◽  
Author(s):  
Sheng-Yang Tsui ◽  
Yu Tsao ◽  
Chii-Wann Lin ◽  
Shih-Hau Fang ◽  
Feng-Chuan Lin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Potential Application ◽  
Learning Algorithms ◽  
Voice Disorders ◽  
Machine Learning Algorithms

scholarly journals Performance of Machine Learning Algorithms with Different K Values in K-fold CrossValidation

2021 ◽  
Vol 13 (6) ◽  
pp. 61-71
Author(s):  
Isaac Kofi Nti ◽  
◽  
Owusu N yarko-Boateng ◽  
Justice Aning
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Experimental Studies ◽  
Area Under The Curve ◽  
Essential Element ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Machine Learning Algorithm ◽  
K Value

The numerical value of k in a k-fold cross-validation training technique of machine learning predictive models is an essential element that impacts the model’s performance. A right choice of k results in better accuracy, while a poorly chosen value for k might affect the model’s performance. In literature, the most commonly used values of k are five (5) or ten (10), as these two values are believed to give test error rate estimates that suffer neither from extremely high bias nor very high variance. However, there is no formal rule. To the best of our knowledge, few experimental studies attempted to investigate the effect of diverse k values in training different machine learning models. This paper empirically analyses the prevalence and effect of distinct k values (3, 5, 7, 10, 15 and 20) on the validation performance of four well-known machine learning algorithms (Gradient Boosting Machine (GBM), Logistic Regression (LR), Decision Tree (DT) and K-Nearest Neighbours (KNN)). It was observed that the value of k and model validation performance differ from one machine-learning algorithm to another for the same classification task. However, our empirical suggest that k = 7 offers a slight increase in validations accuracy and area under the curve measure with lesser computational complexity than k = 10 across most MLA. We discuss in detail the study outcomes and outline some guidelines for beginners in the machine learning field in selecting the best k value and machine learning algorithm for a given task.

scholarly journals Improving the Short-term Forecast of World Trade During the Covid-19 Pandemic Using Swift Data on Letters of Credit

2020 ◽  
Vol 20 (247) ◽  
Author(s):  
Benjamin Carton ◽  
Nan Hu ◽  
Joannes Mongardini ◽  
Kei Moriya ◽  
Aneta Radzikowski
Keyword(s):  
Machine Learning ◽  
International Trade ◽  
Learning Algorithms ◽  
Essential Element ◽  
Machine Learning Algorithms ◽  
Short Term ◽  
Short Term Forecast ◽  
Term Forecast ◽  
Letters Of Credit ◽  
Linear Regressions

An essential element of the work of the Fund is to monitor and forecast international trade. This paper uses SWIFT messages on letters of credit, together with crude oil prices and new export orders of manufacturing Purchasing Managers’ Index (PMI), to improve the short-term forecast of international trade. A horse race between linear regressions and machine-learning algorithms for the world and 40 large economies shows that forecasts based on linear regressions often outperform those based on machine-learning algorithms, confirming the linear relationship between trade and its financing through letters of credit.

scholarly journals An Analysis of Automatic Voice Recognition and Speaker Identification Algorithms and its Applications

2018 ◽  
Vol 7 (2.24) ◽  
pp. 415
Author(s):  
Ram Sethuraman ◽  
J Selvin Paul Peter ◽  
Shanthan Reddy Middela ◽  
. .
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Speaker Identification ◽  
Learning Algorithms ◽  
Voice Recognition ◽  
Machine Learning Algorithms ◽  
The Voice

Voice recognition is the domain which is used to identify the speaker behind a speech through their voice. In the field of research, Voice recognition is a domain which has been widely explored by data mining experts and used for various applications. The features of the voice are extracted through methods like MFCC and then various Data Mining and Machine learning algorithms are applied for each specific application. Researchers have explored and tested the efficiencies of various algorithms for various purposes. There appears to be specific algorithms which outperform the rest in certain applications whereas they tend to perform badly for certain other applications. This paper aims to discuss the various Voice recognition techniques and its uses in various domains. The work aims in providing the characteristics and limitations of these approaches. 

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

The Unlearnable Checkerboard Pattern

2019 ◽  
Vol 1 (2) ◽  
pp. 78-80
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Checkerboard Pattern ◽  
Simple Task

Detecting some patterns is a simple task for humans, but nearly impossible for current machine learning algorithms.  Here, the "checkerboard" pattern is examined, where human prediction nears 100% and machine prediction drops significantly below 50%.

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