ANN application in emotional speech analysis

Tuckova, Jana; Sramka, Martin

doi:10.1504/ijdats.2012.047819

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Applications of data mining techniques encompasses wide variety of domains including credit card use [6], customer relationship management [7], bankruptcy prediction [8,9], bacteriology for bacterial identification [10], MIG welding process [11], detecting blog spam [12], fault diagnosis and condition monitoring [13,14], software fault prediction [15], machining parameter optimization [16], demand forecasting [17], emotional speech analysis [18] and software engineering [19]. Data mining techniques have been used in a wide range of stock market prediction applications.…”

Section: Background Of the Studymentioning

confidence: 99%

Predicting Daily Returns of Global Stocks Indices: Neural Networks vs Support Vector Machines

Kaur

Dharni

2019

JEMT

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Uniqueness in economies and stock markets has given rise to an interesting domain of exploring data mining techniques across global indices. Previously, very few studies have attempted to compare the performance of data mining techniques in diverse markets. The current study adds to the understanding regarding the variations in performance of data mining techniques across the global stock indices. We compared the performance of Neural Networks and Support Vector Machines using accuracy measures Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) across seven major stock markets. For prediction purpose, technical analysis has been employed on selected indicators based on daily values of indices spanning a period of 12 years. We created 196 data sets spanning different time periods for model building such as 1 year, 2 years, 3 years, 4 years, 6 years and 12 years for selected seven stock indices. Based on prediction models built using Neural Networks and Support Vector Machines, the findings of the study indicate there is a significant difference, both for MAE and RMSE, across the selected global indices. Also, Mean Absolute Error and Root Mean Square Error of models built using NN were greater than Mean Absolute Error and Root Mean Square Error of models built using SVM.

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Section: Background Of the Studymentioning

confidence: 99%

Predicting Daily Returns of Global Stocks Indices: Neural Networks vs Support Vector Machines

Kaur

Dharni

2019

JEMT

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“…Data mining techniques have found applications in a range of domains, including the use of credit cards (Kumar & Ravi, 2008; Sun & Vasarhelyi, 2018), customer relationship management (Rygielski et al, 2002), bankruptcy prediction (Paramjeet & Ravi, 2011; Peat & Jones, 2012; Pendharkar, 2011; Ramu & Ravi, 2009), credit risk assessment (Lahmiri, 2016), accounting and finance problems (Coakley & Brown, 2000), financial fraud detection (Fanning & Cogger, 1998), financial risk forecasting (Sun, 2012), bacteriology for bacterial identification (Rahman et al, 2011), metal inert gas welding process (Lahoti & Pratihar, 2017), detecting blog spam (Yang & Kwok, 2017), fault diagnosis and condition monitoring (Muralidharan & Sugumaran, 2016; Saimurugan & Ramachandran, 2014), software fault prediction (Erturk & Sezer, 2016; Singhal et al, 2019), machining parameter optimization (Ahmad et al, 2014), demand forecasting (Tigas et al, 2013), emotional speech analysis (Tuckova & Sramka, 2012), and software engineering (Taylor et al, 2010). Data mining techniques have rapidly found applications in diverse fields, including stock markets.…”

Section: Introductionmentioning

confidence: 99%

Application and performance of data mining techniques in stock market: A review

Kaur

Dharni

2022

Intell Sys Acc Fin Mgmt

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Prediction and the stock market go hand in hand. Due to the inherent limitations of traditional forecasting methods and the pursuit to uncover the hidden patterns in stock market data, stock market prediction using data mining techniques has caught the fancy of academicians, researchers, and investors. Based on a systematic review of more than 143 research studies spanning 25 years, the present paper brings to light the major issues concerning forecasting of stock markets based on data mining techniques, such as usage of data mining techniques in the stock market, input data types, single versus hybrid techniques, instruments and stock markets researched, types of software and algorithms used, measures of forecast accuracy, and performance of various data mining techniques. Emerging patterns related to various dimensions have been critically analyzed by highlighting the existing limitations and suggesting future research paradigms. This analysis can be useful for academicians, researchers and investors looking for futuristic directions in a given research domain.

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Mining approximate sequential patterns with gaps

Yip

Nembhard

2015

IJDMMM

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ANN application in emotional speech analysis

Cited by 4 publications

References 7 publications

Predicting Daily Returns of Global Stocks Indices: Neural Networks vs Support Vector Machines

Predicting Daily Returns of Global Stocks Indices: Neural Networks vs Support Vector Machines

Application and performance of data mining techniques in stock market: A review

Mining approximate sequential patterns with gaps

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